Recent ics_cs items

Genetic Variation and Stroke Recovery: The STRONG Study

Tue, 12 May 2026 00:00:00 +0000

BACKGROUND: Genetic association studies can reveal biology and treatment targets but have received limited attention for stroke recovery. STRONG (Stroke, Stress, Rehabilitation, and Genetics) was a prospective, longitudinal (1-year), genetic study in adults with stroke at 28 US stroke centers. The primary aim was to examine the association that candidate genetic variants have with (1) motor/functional outcomes and (2) stress-related outcomes. METHODS: For motor/functional end points, 3 candidate gene variants (ApoE ε4, BDNF [brain-derived neurotrophic factor], and a dopamine polygenic score) were analyzed for associations with change in grip strength (3 months-baseline), function (3-month Stroke Impact Scale-Activities of Daily Living), mood (3-month Patient Health Questionnaire-8), and cognition (12-month telephone-Montreal Cognitive Assessment). For stress-related outcomes, 7 variants (serotonin transporter gene-linked promoter region, ACE [angiotensin-converting enzyme], oxytocin...

Neurodatascience: Past, Present, and Future

Thu, 7 May 2026 00:00:00 +0000

The study of the brain is a compelling example of the power of convergent science. Over the last few decades, advances in neuroscience techniques and experimentation, as well as in data science tools to analyze the resulting data, have dramatically furthered our understanding of fundamental brain functions. Historically, it has been common for analytical approaches to have a considerable lag in development following the availability of new neuroscience techniques. However, this relationship has not simply been unidirectional, as there have been examples in which analytical developments have directly led to new scientific questions and experiments. Here we review how this interplay between neuroscience and data science advances has unfolded in the past and into the present, with a focus on electrophysiology and calcium imaging. Applying lessons learned from the past and present, we then discuss expected developments, challenges, and opportunities in the future. We end by providing...

Hippocampal ensembles represent sequential relationships among discrete nonspatial events

Thu, 7 May 2026 00:00:00 +0000

ABSTRACT The hippocampus is critical to the temporal organization of our experiences, including the ability to remember past event sequences and predict future ones. Although this fundamental capacity is conserved across modalities and species, its underlying neuronal mechanisms remain poorly understood. Here we recorded hippocampal ensemble activity as rats remembered a sequence of nonspatial events (5 odor presentations unfolding over several seconds), using a task with established parallels in humans. Using novel statistical methods and deep learning techniques, we then identified new forms of sequential organization in hippocampal activity linked with task performance. We discovered that sequential firing fields (“time cells”) provided temporal information within and across events in the sequence, and that distinct types of task-critical information (stimulus identity, temporal order, and trial outcome) were also sequentially differentiated within event presentations. Finally,...

Unity by Diversity: Improved Representation Learning for Multimodal VAEs

Thu, 7 May 2026 00:00:00 +0000

Variational Autoencoders for multimodal data hold promise for many tasks in data analysis, such as representation learning, conditional generation, and imputation. Current architectures either share the encoder output, decoder input, or both across modalities to learn a shared representation. Such architectures impose hard constraints on the model. In this work, we show that a better latent representation can be obtained by replacing these hard constraints with a soft constraint. We propose a new mixture-of-experts prior, softly guiding each modality's latent representation towards a shared aggregate posterior. This approach results in a superior latent representation and allows each encoding to preserve information better from its uncompressed original features. In extensive experiments on multiple benchmark datasets and two challenging real-world datasets, we show improved learned latent representations and imputation of missing data modalities compared to existing methods.

A Model-Agnostic Graph Neural Network for Integrating Local and Global Information

Thu, 7 May 2026 00:00:00 +0000

Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, however, existing GNNs suffer from two significant limitations: a lack of interpretability in their results due to their black-box nature, and an inability to learn representations of varying orders. To tackle these issues, we propose a novel Model-agnostic Graph Neural Network (MaGNet) framework, which is able to effectively integrate information of various orders, extract knowledge from high-order neighbors, and provide meaningful and interpretable results by identifying influential compact graph structures. In particular, MaGNet consists of two components: an estimation model for the latent representation of complex relationships under graph topology, and an interpretation model that identifies influential nodes, edges, and node features. Theoretically, we establish the generalization error bound for MaGNet via empirical Rademacher...

A scalable reinforcement learning framework inspired by hippocampal memory mechanisms for efficient contextual and sequential decision making

Thu, 7 May 2026 00:00:00 +0000

Efficient decision-making in context-dependent, sequential tasks remains a fundamental challenge in reinforcement learning (RL). Inspired by the function of the brain’s hippocampal system, we introduce Hippocampal-Augmented Memory Integration (HAMI), a biologically inspired memory-based RL framework that leverages symbolic indexing, hierarchical memory refinement, and structured episodic retrieval to enhance both learning efficiency and adaptability. We also propose Hierarchical Contextual Sequences (HiCoS), a structured RL environment grounded in neuroscience studies on episodic and sequence memory and context-driven decision-making, which serves as a controlled testbed for evaluating biologically inspired memory-based decision-making systems. Our experimental results demonstrate that HAMI achieves high decision accuracy and improved sample efficiency while maintaining low memory utilization. HAMI’s architecture exhibits significantly lower inference latency than baseline memory-based...

Optimal Transport based Cross-Domain Integration for Heterogeneous Data

Thu, 7 May 2026 00:00:00 +0000

Detecting dynamic patterns shared across heterogeneous datasets is a critical yet challenging task in many scientific domains, particularly within the biomedical sciences. Systematic heterogeneity inherent in diverse data sources can significantly hinder the effectiveness of existing machine learning methods in uncovering shared underlying dynamics. Additionally, practical and technical constraints in real-world experimental designs often limit data collection to only a small number of subjects, even when rich, time-dependent measurements are available for each individual. These limited sample sizes further diminish the power to detect common dynamic patterns across subjects. In this article, we propose a novel heterogeneous data integration framework based on optimal transport to extract shared patterns in the conditional mean dynamics of target responses. The key advantage of the proposed method is its ability to enhance discriminative power by reducing heterogeneity unrelated...

STABLE-MATCHING VORONOI DIAGRAMS: COMBINATORIAL COMPLEXITY AND ALGORITHMS

Wed, 6 May 2026 00:00:00 +0000

We study algorithms and combinatorial complexity bounds for stable-matching Voronoi diagrams, where a set, S, of n point sites in the plane determines a stable matching between the points in R² and the sites in S such that (i) the points prefer sites closer to them and sites prefer points closer to them, and (ii) each site has a quota or "appetite" indicating the area of the set of points that can be matched to it. Thus, a stable-matching Voronoi diagram is a solution to the well-known post office problem with the added (realistic) constraint that each post office has a limit on the size of its jurisdiction. Previous work on the stable-matching Voronoi diagram provided existence and uniqueness proofs, but did not analyze its combinatorial or algorithmic complexity. In this paper, we show that a stable-matching Voronoi diagram of n point sites has O(n^2+ε) faces and edges, for any ε > 0, and show that this bound is almost tight by giving a family of diagrams...

Folding a paper strip to minimize thickness

Wed, 6 May 2026 00:00:00 +0000

In this paper, we study how to fold a specified origami crease pattern in order to minimize the impact of paper thickness. Specifically, origami designs are often expressed by a mountain–valley pattern (plane graph of creases with relative fold orientations), but in general this specification is consistent with exponentially many possible folded states. We analyze the complexity of finding the best consistent folded state according to two metrics: minimizing the total number of layers in the folded state (so that a “flat folding” is indeed close to flat), and minimizing the total amount of paper required to execute the folding (where “thicker” creases consume more paper). We prove both problems strongly NP-complete even for 1D folding. On the other hand, we prove both problems fixed-parameter tractable in 1D with respect to the number of layers.

NON-EUCLIDEAN ERDŐS-ANNING THEOREMS

Wed, 6 May 2026 00:00:00 +0000

NON-EUCLIDEAN ERDŐS-ANNING THEOREMS

FamilyBloom: Examining Ecologies of Collaboration in Family-Centered Health Tracking

Wed, 6 May 2026 00:00:00 +0000

Family health informatics tools can help support well-being with shared data tracking. Prior work typically focused on shared data review, but often in specific moments, like bedtime, or centered on caregiving of children or elderly members. To investigate how tracking can support mutual health collaboration between family members pervasively across daily contexts, we designed and deployed FamilyBloom, a glanceable smartwatch and home display system for mood and goal tracking. Twelve families with both neurotypical and ADHD members used FamilyBloom for three months on average. Our findings reveal how family-centered tracking created collaboration opportunities and tensions across multiple ecological systems: individual self-regulation, collaborations within family dynamics, involvement of care networks with varying trust levels, institutional school constraints and cultural stigma, and temporality of regular routines and crisis periods. We discuss an ecosystem-aware approach to...

Efficacy of Full-Packet Encryption in Mitigating Protocol Detection for Evasive VPNs

Wed, 6 May 2026 00:00:00 +0000

Full-packet encryption is a technique used by modern evasive Virtual Private Networks (VPNs) to avoid protocol-based flagging from censorship models by disguising their traffic as random noise on the network. Traditional methods for censoring full-packet-encryption based VPN protocols requires assuming a substantial amount of collateral damage, as other non-VPN network traffic that appears random will be blocked. We tested several machine learning-based classification models against the Aggressive Circumvention of Censorship (ACC) protocol, a fully-encrypted evasive VPN protocol which merges strategies from a wide variety of currently in-use evasive VPN protocols. Our testing found that while ACC was able to survive our models when compared to random noise, it was easily detectable with minimal collateral damage using several different machine learning models when within a stream of regular network traffic. While resistant to the current techniques deployed by nation-state censors,...

Towards Verifying Crash Consistency

Thu, 23 Apr 2026 00:00:00 +0000

Compute Express Link (CXL) memory sharing, persistent memory, and other related technologies allow data to survive crash events. A key challenge is ensuring that data is consistent after crashes such that it can be safely accessed. While there has been much work on bug-finding tools for persistent memory programs, these tools cannot guarantee that a program is crash-consistent. In this paper, we present a language, CrashLang, and its type system, that together guarantee that well-typed data structure implementations written in CrashLang are crash-consistent. CrashLang leverages the well-known commit-store pattern in which a single store logically commits an entire data structure operation. In this paper, we prove that well-typed CrashLang programs are crash-consistent, and provide a prototype implementation of the CrashLang compiler. We have evaluated CrashLang on five benchmarks: the Harris linked list, the Treiber stack, the Michael–Scott queue, a Read-Copy-Update binary search...

Automated Insertion of Flushes and Fences for Persistency

Thu, 23 Apr 2026 00:00:00 +0000

CXL shared memory and persistent memory allow the contents of memory to persist beyond crashes. Stores to persistent or CXL memory are typically not immediately made persistent; developers must manually flush the corresponding cache lines to force the data to be written to the underlying storage. Correctly using flush and fence operations is known to be challenging. While state-of-the-art tools can find missing flush instructions, they often require bug-revealing test cases. No existing tools can ensure the absence of missing flush bugs. In this paper, we present PMRobust, a compiler that automatically inserts flush and fence operations to ensure that code using persistent memory is free from missing flush and fence bugs. PMRobust employs a novel static analysis with optimizations that target newly allocated objects. We have evaluated PMRobust on persistent memory libraries and several persistent memory data structures and measured a geometric mean overhead of 0.26% relative to...

HyperXite 9

Wed, 15 Apr 2026 00:00:00 +0000

The overall objective for HyperXite 9 was to design and build a more robust, and reliable pod, capable of proving the feasibility of a high-speed transportation system. We are working to improve a linear induction motor as the pod's propulsion system. We are also designing and implementing a thermal cooling system to actively dissipate the heat generated by this propulsion system. Our team is comprised of the following 7 subteams: Static Structures, Braking & Pneumatics, Dynamic Structures, Propulsion, Power Systems, Control Systems, and Outreach.

Biometric Advanced Driver Assistance System (ADAS)

Wed, 15 Apr 2026 00:00:00 +0000

This project focuses on the development of a human-aware advanced driver assistance system (ADAS) that helps promote safe driving based on a driver's biometrics and driving behavior. The system uses biometric signals from the driver using BioHarness Belt and Galvanic Skin Response (GSR) sensors to monitor the driver's heart rate, breathing rate, ECG and GSR signals. These sensory information are then analyzed by machine learning models to determine whether the driver is stressed or drowsy. The project extended the current state-of-the-art driving simulator CARLA to not only include the driver's brake intensity, speed, throttle and steering but also the driver's biometric state. The vehicle controls and biometrics are then plotted and analyzed for correlations that could imply a driver is driving aggressively, assertively, or defensively. Based on these correlations, the driver will be displayed a warning on the simulation which advises them to drive carefully. The output from...

2025 UCI CanSat Annual Design Review Poster

Wed, 15 Apr 2026 00:00:00 +0000

The UCI CanSat team is a senior design team that competes in the international CanSat competition, an annual design-build-launch competition held by the American Astronautical Society focused on space-type systems. Each year, a ten-person team designs a “CanSat,” following competition requirements. This year, the CanSat is required to operate in 4 main phases: ascent, apogee, descent, and landing. During ascent, the CanSat must act as the nose cone of the rocket. At apogee, an ejection charge releases the CanSat from the rocket, and the CanSat must activate a parachute to begin a safe descent. At three-quarters of the peak altitude, the CanSat must deploy its payload. The payload consists of two cameras, one to film payload deployment, while the other is spin-stabilized to film the north side of the CanSat. In addition, while the container descends with a parachute, the payload must descend with an auto-gyro system. The auto-gyro system must significantly decrease the descent...

Summary the Savior: Harmful Keyword and Query-based Summarization for LLM Jailbreak Defense

Wed, 8 Apr 2026 00:00:00 +0000

Summary the Savior: Harmful Keyword and Query-based Summarization for LLM Jailbreak Defense

Evaluating the potential of acupuncture for Alzheimer’s disease treatment: A meta-analysis and systematic review of mouse model studies

Wed, 8 Apr 2026 00:00:00 +0000

Acupuncture is an ancient practice that was developed within the framework of traditional Chinese medicine. While acupuncture has been recently proposed as a therapy for Alzheimer’s disease (AD), acupuncture effects are not well understood in terms of neural mechanisms. Here, we review and examine the studies that used AD mouse models and analyze the experiments where researchers administered electroacupuncture (EA) to AD mice to assess the potential therapeutic impact of acupuncture on disease pathology and cognitive function in controlled laboratory settings. We analyzed 29 relevant PubMed articles published between January 2014 and July 2025. Our results reveal that EA significantly reduces both amyloid-beta (Aβ) and phosphorylated tau (p-tau) levels and neuroinflammatory biomarkers, including molecular signatures for activated microglia and astrocytes in the brain. EA also enhances cognitive functions. While no study directly compared acupoint strategies, the indirect comparisons...

An Expert Guide to Planning Experimental Tasks For Evidence-Accumulation Modeling

Wed, 25 Mar 2026 00:00:00 +0000

Evidence-accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behavior. EAMs have generated significant theoretical advances in psychology, behavioral economics, and cognitive neuroscience and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues and inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, relating experimental manipulations to EAM parameters, planning appropriate sample sizes, and preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the...

Computational Complexities of Folding

Wed, 25 Mar 2026 00:00:00 +0000

We prove several hardness results on folding origami crease patterns. Flat-folding finite crease patterns is fixed-parameter tractable in the ply of the folded pattern (how many layers overlap at any point) and the treewidth of an associated cell adjacency graph. Under the exponential time hypothesis, the singly-exponential dependence of our algorithm on treewidth is necessary, even for bounded ply. Improving the dependence on ply would require progress on the unsolved map folding problem. Finding the shape of a polyhedron folded from a net with triangular faces and integer edge lengths is not possible in algebraic computation tree models of computation that at each tree node allow either the computation of arbitrary integer roots of real numbers, or the extraction of roots of polynomials with bounded degree and integer coefficients. For a model of reconfigurable origami with origami squares are attached at one edge by a hinge to a rigid surface, moving from one flat-folded state...

String Graph Obstacles of High Girth and of Bounded Degree

Wed, 25 Mar 2026 00:00:00 +0000

A string graph is the intersection graph of curves in the plane. Kratochvíl previously showed the existence of infinitely many obstacles: graphs that are not string graphs but for which any edge contraction or vertex deletion produces a string graph. Kratochvíl's obstacles contain arbitrarily large cliques, so they have girth three and unbounded degree. We extend this line of working by studying obstacles among graphs of restricted girth and/or degree. We construct an infinite family of obstacles of girth four; in addition, our construction is K2,3-subgraph-free and near-planar (planar plus one edge). Furthermore, we prove that there is a subcubic obstacle of girth three, and that there are no subcubic obstacles of high girth. We characterize the subcubic string graphs as having a matching whose contraction yields a planar graph, and based on this characterization we find a linear-time algorithm for recognizing subcubic string graphs of bounded treewidth.

The Complexity of Iterated Reversible Computation

Wed, 25 Mar 2026 00:00:00 +0000

We study a class of functional problems reducible to computing $f^{(n)}(x)$ for inputs $n$ and $x$, where $f$ is a polynomial-time bijection. As we prove, the definition is robust against variations in the type of reduction used in its definition, and in whether we require $f$ to have a polynomial-time inverse or to be computible by a reversible logic circuit. These problems are characterized by the complexity class $\mathsf{FP}^{\mathsf{PSPACE}}$, and include natural $\mathsf{FP}^{\mathsf{PSPACE}}$-complete problems in circuit complexity, cellular automata, graph algorithms, and the dynamical systems described by piecewise-linear transformations.

Distributed Construction of Lightweight Spanners for Unit Ball Graphs

Wed, 25 Mar 2026 00:00:00 +0000

Resolving an open question from 2006 [14], we prove the existence of light-weight bounded-degree spanners for unit ball graphs in the metrics of bounded doubling dimension, and we design a simple O(log^∗ n)-round distributed algorithm in the LOCAL model of computation, that given a unit ball graph G with n vertices and a positive constant ϵ < 1 finds a (1+ϵ)-spanner with constant bounds on its maximum degree and its lightness using only 2-hop neighborhood information. This immediately improves the best prior lightness bound, the algorithm of Damian, Pandit, and Pemmaraju [13], which runs in O(log^∗ n) rounds in the LOCAL model, but has a O(log ∆) bound on its lightness, where ∆ is the ratio of the length of the longest edge to the length of the shortest edge in the unit ball graph. Next, we adjust our algorithm to work in the CONGEST model, without changing its round complexity, hence proposing the first spanner construction for unit ball graphs in the CONGEST...

Brief Announcement: Distributed Lightweight Spanner Construction for Unit Ball Graphs in Doubling Metrics

Wed, 25 Mar 2026 00:00:00 +0000

Resolving an open question from 2006, we prove the existence of light-weight bounded-degree (1+ε)-spanners for unit ball graphs in the metrics of bounded doubling dimension, and we design a simple O(log*n)-round distributed algorithm in the LOCAL model for finding such spanners using only 2-hop neighborhood information. We further study the problem in the two dimensional Euclidean plane and we propose a construction with similar properties that has a low-intersection property as well. Lastly, we provide experimental results that confirm the performance of our algorithms.

Visualizing Treewidth

Wed, 25 Mar 2026 00:00:00 +0000

A witness drawing of a graph is a visualization that clearly shows a given property of a graph. We study and implement various drawing paradigms for witness drawings to clearly show that graphs have bounded pathwidth or treewidth. Our approach draws the tree decomposition or path decomposition as a tree of bags, with induced subgraphs shown in each bag, and with “tracks” for each graph vertex connecting its copies in multiple bags. Within bags, we optimize the vertex layout to avoid crossings of edges and tracks. We implement a visualization prototype for crossing minimization using dynamic programming for graphs of small width and heuristic approaches for graphs of larger width. We introduce a taxonomy of drawing styles, which render the subgraph for each bag as an arc diagram with one or two pages or as a circular layout with straight-line edges, and we render tracks either with straight lines or with orbital-radial paths.

A Stronger Lower Bound on Parametric Minimum Spanning Trees

Wed, 25 Mar 2026 00:00:00 +0000

We prove that, for an undirected graph with n vertices and m edges, each labeled with a linear function of a parameter λ$$\lambda $$, the number of different minimum spanning trees obtained as the parameter varies can be Ω(mlogn)$$\Omega (m\log n)$$.

On the treewidth of Hanoi graphs

Wed, 25 Mar 2026 00:00:00 +0000

The objective of the well-known Tower of Hanoi puzzle is to move a set of discs one at a time from one of a set of pegs to another, while keeping the discs sorted on each peg. We propose an adversarial variation in which the first player forbids a set of states in the puzzle, and the second player must then convert one randomly-selected state to another without passing through forbidden states. Analyzing this version raises the question of the treewidth of Hanoi graphs. We find this number exactly for three-peg puzzles and provide nearly-tight asymptotic bounds for larger numbers of pegs.

Stabbing Faces by a Convex Curve

Wed, 25 Mar 2026 00:00:00 +0000

We prove that, for every plane graph G and every smooth convex curve C not on a single line, there exists a straight-line drawing of G for which every face is crossed by C.

What is... Treewidth?

Wed, 25 Mar 2026 00:00:00 +0000

What is... Treewidth?

On the complexity of embedding in graph products

Wed, 25 Mar 2026 00:00:00 +0000

Graph embedding, especially as a subgraph of a grid, is an old topic in VLSI design and graph drawing. In this paper, we investigate related questions concerning the complexity of embedding a graph G in a host graph that is the strong product of a path P with a graph H that satisfies some properties, such as having small treewidth, pathwidth or treedepth. We show that this is NP-hard, even under numerous restrictions on both G and H. In particular, computing the row pathwidth and the row treedepth is NP-hard even for a tree of small pathwidth, while computing the row treewidth is NP-hard even for series-parallel graphs.

Spatial and temporal evaluations of the liquid argon purity in ProtoDUNE-SP

Mon, 16 Mar 2026 00:00:00 +0000

Liquid argon time projection chambers (LArTPCs) rely on highly pure argon to ensure that ionization electrons produced by charged particles reach readout arrays. ProtoDUNE Single-Phase (ProtoDUNE-SP) was an approximately 700-ton liquid argon detector intended to prototype the Deep Underground Neutrino Experiment (DUNE) Far Detector Horizontal Drift module. It contains two drift volumes bisected by the cathode plane assembly, which is biased to create an almost uniform electric field in both volumes. The DUNE Far Detector modules must have robust cryogenic systems capable of filtering argon and supplying the TPC with clean liquid. This paper will explore comparisons of the argon purity measured by the purity monitors with those measured using muons in the TPC from October 2018 to November 2018. A new method is introduced to measure the liquid argon purity in the TPC using muons crossing both drift volumes of ProtoDUNE-SP. For extended periods on the timescale of weeks, the drift...

Radiative, Hydrologic, and Circulation Responses to Warming in Cess‐Potter Simulations Using the Global 3.25‐km SCREAM

Tue, 10 Mar 2026 00:00:00 +0000

Abstract Using the global 3.25‐km Simple Cloud Resolving E3SM Atmosphere Model (SCREAM 3 km), a pair of 13‐month Cess‐Potter simulations are performed to quantify the radiative feedbacks and the hydrologic and circulation responses to warming. Large‐scale aspects of SCREAM 3 km's top‐of‐atmosphere radiative fluxes, precipitation rates, and circulations are in good agreement with observations and reanalysis, with notable differences, including a drier lower free‐troposphere in the Tropics, reduced precipitation and humidity over the Tropical West Pacific, and poleward shifted Southern Hemisphere midlatitude jet. In response to warming, SCREAM 3 km predicts a total radiative feedback within the top 15% of the CMIP5 and CMIP6 models, which puts it substantially higher than the feedback reported by other kilometer‐scale models. SCREAM 3 km's high radiative feedback stems from a strongly positive shortwave cloud feedback, most prominent over the mid‐ and high‐latitudes....

Revisiting Reconfigurable Acceleration of Vision Transformer with Patch Pruning

Wed, 25 Feb 2026 00:00:00 +0000

Vision Transformers (ViTs) have become the backbone of numerous cutting-edge vision applications. The attention modules within ViTs play a crucial role in modeling spatial relationships between pixels. Although these attention modules enhance the accuracy of ViT models, they also increase computational demands, limiting the deployment of ViTs in edge computing environments. To address this issue, prior research has focused on optimizing ViTs from both software and hardware perspectives. A notable software optimization technique is reducing the image patches involved in attention computations. Two common methods to achieve this are window attention and patch pruning. However, they introduce new challenges for existing hardware platforms regarding attention computation. Therefore, it is essential to develop new hardware modules to simultaneously support pruned attention computations and efficient window shifts. In this study, we introduce an FPGA-based token reduction vision transformer...

NUDGING: Inference-time Alignment of LLMs via Guided Decoding

Wed, 11 Feb 2026 00:00:00 +0000

Large language models (LLMs) require alignment to effectively and safely follow user instructions. This process necessitates training an aligned version for every base model, resulting in significant computational overhead. In this work, we propose NUDGING, a simple, training-free algorithm that aligns any base model at inference time using a small aligned model. NUDGING is motivated by recent findings that alignment primarily alters the model's behavior on a small subset of stylistic tokens (e.g., discourse markers). We find that base models are significantly more uncertain when generating these tokens. Building on this insight, NUDGING employs a small aligned model to generate nudging tokens to guide the base model's output during decoding when the base model's uncertainty is high, with only a minor additional inference overhead. We evaluate NUDGING across 3 model families on a diverse range of open-instruction tasks. Without any training, nudging a large base model with a 7×-14×...

Episodic-like memory in a simulation of cuttlefish behavior.

Sat, 31 Jan 2026 00:00:00 +0000

Episodic memory involves remembering the what, when, and where components of an event. It has been observed in humans, other vertebrates, and the invertebrate cuttlefish. In clever behavioral experiments, cuttlefish have been shown to have episodic-like memory, where they demonstrate the ability to remember when and where a preferred food source will appear. The present work replicates this behavior with a parsimonious model of episodic memory. To further test this model and explore episodic-like memory, we introduce a predator-prey scenario in which the agent must remember what creatures (e.g. predator, desirable prey, or less desirable prey) appear at a given time and region of the model environment. This simulates similar situations that cuttlefish face in the wild. They will typically hide when predators are in the area, and hunt for prey when available. When the memory model is queried for an action (e.g., hunt or hide), the cuttlefish agent hunts for preferred food, like...

A New Online Fault Detection Mechanism for Neural Network Applications

Thu, 29 Jan 2026 00:00:00 +0000

A New Online Fault Detection Mechanism for Neural Network Applications

Enhancing the Reliability of Split Computing Deep Neural Networks

Thu, 29 Jan 2026 00:00:00 +0000

Artificial intelligence is becoming increasingly popular for IoT applications in safety-critical fields (e.g., autonomous systems and biomedical, robots). Unfortunately, the inference’s workload process alone increases as the model size grows. To meet the computational power limitations of mobile devices running IoT applications, modern services sometimes resort to the Split Computing paradigm. Split Computing divides the inference process of a Neural Network into Head and Tail for their execution in a mobile device and a server, respectively, which also allows the reduction of the overall IoT device’s computational cost. Nonetheless, Split Computing can be used in safety-critical fields where reliability is crucial, especially when mobile devices have computational and cost restrictions. This paper introduces hardening techniques acting on the software to mitigate the effects of hardware faults on Split Computing models. The proposed hardening techniques consist of i) a bounded...

AI-Based Classification of Adversarial Attacks vs. Hardware Fault Corruptions in the Split Computing Context

Thu, 29 Jan 2026 00:00:00 +0000

Split Computing has emerged as a promising paradigm for deploying Deep Neural Networks in Edge and Inter-net of Things systems, enabling inference tasks to be distributed between resource-constrained edge devices and cloud servers. This approach is particularly attractive for autonomous systems, where security and reliability may be critical. However, interme-diate feature maps transmitted between devices are vulnerable to corruption, which may result from intentional adversarial attacks or unintentional hardware faults. Distinguishing whether corruption originates from an external adversary or an inherent system fault is crucial for implementing appropriate counter-measures-reinforcing security mechanisms against attacks or improving system reliability to mitigate the effects of hardware-related faults. To the best of our knowledge, this work is the first to propose a machine learning-based classification mechanism capable of differentiating adversarial attacks from hardware...

Analysis of yeast's ORF upstream regions by parallel processing, microarrays, and computational methods.

Wed, 28 Jan 2026 00:00:00 +0000

We use a network of workstations to compute all pairwise alignments of the 500 bp upstream regions of 6,225 yeast ORFs (Open Reading Frames). We correlate the alignments with DNA microarray expression data from budding yeast cells over an oxidative stress time course. We confirm on a genomic scale that, in general, genes with extremely similar upstream regions have similar activity levels, even when located on different chromosomes. As the difference in upstream regions increases, the correlation rapidly drops towards zero. Divergent ORFs with overlapping upstream regions do not seem to be correlated in any way. The pairwise alignments coupled with the expression data, together with other computational techniques, suggest a few new putative regulatory binding sites that can be tested experimentally. Finally, we investigate the inherent symmetry present in the two strands of the yeast genome. We show that it extends at least all the way up to 9-mers and is likely to result from...

Lower Bounds for Non-adaptive Shortest Path Relaxation

Wed, 19 Nov 2025 00:00:00 +0000

We consider single-source shortest path algorithms that perform a sequence of relaxation steps whose ordering depends only on the input graph structure and not on its weights or the results of prior steps. Each step examines one edge of the graph, and replaces the tentative distance to the endpoint of the edge by its minimum with the tentative distance to the start of the edge, plus the edge length. As we prove, among such algorithms, the Bellman-Ford algorithm has optimal complexity for dense graphs and near-optimal complexity for sparse graphs, as a function of the number of edges and vertices in the given graph. Our analysis holds both for deterministic algorithms and for randomized algorithms that find shortest path distances with high probability.

Stack-Number is Not Bounded by Queue-Number

Wed, 19 Nov 2025 00:00:00 +0000

We describe a family of graphs with queue-number at most 4 but unbounded stack-number. This resolves open problems of Heath, Leighton and Rosenberg (1992) and Blankenship and Oporowski (1999).

Orthogonal Dissection into Few Rectangles

Wed, 19 Nov 2025 00:00:00 +0000

We describe a polynomial time algorithm that takes as input a polygon with axis-parallel sides but irrational vertex coordinates, and outputs a set of as few rectangles as possible into which it can be dissected by axis-parallel cuts and translations. The number of rectangles is the rank of the Dehn invariant of the polygon.

Non-Euclidean Erdos-Anning Theorems

Wed, 19 Nov 2025 00:00:00 +0000

The Erdos-Anning theorem states that every point set in the Euclidean plane with integer distances must be either collinear or finite. More strongly, for any (non-degenerate) triangle of diameter δ, at most O(δ2) points can have integer distances from all three triangle vertices. We prove the same results for any strictly convex distance function on the plane, and analogous results for every two-dimensional complete Riemannian manifold of bounded genus and for geodesic distance on the boundary of every three-dimensional Euclidean convex set. As a consequence, we resolve a 1983 question of Richard Guy on the equilateral dimension of Riemannian manifolds. Our proofs are based on the properties of additively weighted Voronoi diagrams of these distances.

Manipulating Weights to Improve Stress-Graph Drawings of 3-Connected Planar Graphs

Wed, 19 Nov 2025 00:00:00 +0000

We study methods to manipulate weights in stress-graph embeddings to improve convex straight-line planar drawings of 3-connected planar graphs. Stress-graph embeddings are weighted versions of Tutte embeddings, where solving a linear system places vertices at a minimum-energy configuration for a system of springs. A major drawback of the unweighted Tutte embedding is that it often results in drawings with exponential area. We present a number of approaches for choosing better weights. One approach constructs weights (in linear time) that uniformly spread all vertices in a chosen direction, such as parallel to the x- or y-axis. A second approach morphs x- and y-spread drawings to produce a more aesthetically pleasing and uncluttered drawing. We further explore a “kaleidoscope” paradigm for this xy-morph approach, where we rotate the coordinate axes so as to find the best spreads and morphs. A third approach chooses the weight of each edge according to its depth in a spanning tree...

Locked and Unlocked Smooth Embeddings of Surfaces

Wed, 19 Nov 2025 00:00:00 +0000

We study the continuous motion of smooth isometric embeddings of a planar surface in three-dimensional Euclidean space, and two related discrete analogues of these embeddings, polygonal embeddings and flat foldings without interior vertices, under continuous changes of the embedding or folding. We show that every starshaped or spiral-shaped domain is unlocked: a continuous motion unfolds it to a flat embedding. However, disks with two holes can have locked embeddings that are topologically equivalent to a flat embedding but cannot reach a flat embedding by continuous motion.

Noncrossing Longest Paths and Cycles

Wed, 19 Nov 2025 00:00:00 +0000

Edge crossings in geometric graphs are sometimes undesirable as they could lead to unwanted situations such as collisions in motion planning and inconsistency in VLSI layout. Short geometric structures such as shortest perfect matchings, shortest spanning trees, shortest spanning paths, and shortest spanning cycles on a given point set are inherently noncrossing. However, the longest such structures need not be noncrossing. In fact, it is intuitive to expect many edge crossings in various geometric graphs that are longest.Recently, Álvarez-Rebollar, Cravioto-Lagos, Marín, Solé-Pi, and Urrutia (Graphs and Combinatorics, 2024) constructed a set of points for which the longest perfect matching is noncrossing. They raised several challenging questions in this direction. In particular, they asked whether the longest spanning path, on every finite set of points in the plane, must have a pair of crossing edges. They also conjectured that the longest spanning cycle must have a pair of...

On the Biplanarity of Blowups

Wed, 19 Nov 2025 00:00:00 +0000

The 2-blowup of a graph is obtained by replacing each vertex with two non-adjacent copies; a graph is biplanar if it is the union of two planar graphs. We disprove a conjecture of Gethner that 2-blowups of planar graphs are biplanar: iterated Kleetopes are counterexamples. Additionally, we construct biplanar drawings of 2-blowups of planar graphs whose duals have two-path induced path partitions, and drawings with split thickness two of 2-blowups of 3-chromatic planar graphs, and of graphs that can be decomposed into a Hamiltonian path and a dual Hamiltonian path.

Quasipolynomiality of the Smallest Missing Induced Subgraph

Wed, 19 Nov 2025 00:00:00 +0000

We study the problem of finding the smallest graph that does not occur as an induced subgraph of a given graph. This missing induced subgraph has at most logarithmic size and can be found by a brute-force search, in an $n$-vertex graph, in time $n^{O(\log n)}$. We show that under the Exponential Time Hypothesis this quasipolynomial time bound is optimal. We also consider variations of the problem in which either the missing subgraph or the given graph comes from a restricted graph family; for instance, we prove that the smallest missing planar induced subgraph of a given planar graph can be found in polynomial time.

Bandwidth vs BFS Width in Matrix Reordering, Graph Reconstruction, and Graph Drawing

Wed, 19 Nov 2025 00:00:00 +0000

We provide the first approximation quality guarantees for the Cuthull-McKee heuristic for reordering symmetric matrices to have low bandwidth, and we provide an algorithm for reconstructing bounded-bandwidth graphs from distance oracles with near-linear query complexity. To prove these results we introduce a new width parameter, BFS width, and we prove polylogarithmic upper and lower bounds on the BFS width of graphs of bounded bandwidth. Unlike other width parameters, such as bandwidth, pathwidth, and treewidth, BFS width can easily be computed in polynomial time. Bounded BFS width implies bounded bandwidth, pathwidth, and treewidth, which in turn imply fixed-parameter tractable algorithms for many problems that are NP-hard for general graphs. In addition to their applications to matrix ordering, we also provide applications of BFS width to graph reconstruction, to reconstruct graphs from distance queries, and graph drawing, to construct arc diagrams of small height.

Computational Geometry with Probabilistically Noisy Primitive Operations

Wed, 19 Nov 2025 00:00:00 +0000

Much prior work has been done on designing computational geometry algorithms that handle input degeneracies, data imprecision, and arithmetic round-off errors. We take a new approach, inspired by the noisy sorting literature, and study computational geometry algorithms subject to noisy Boolean primitive operations in which, e.g., the comparison “is point q above line ℓ?” returns the wrong answer with some fixed probability. We propose a novel technique called path-guided pushdown random walks that generalizes the results of noisy sorting. We apply this technique to solve point-location, plane-sweep, convex hulls in 2D and 3D, and Delaunay triangulations for noisy primitives in optimal time with high probability.

On Polyhedral Realization with Isosceles Triangles

Wed, 19 Nov 2025 00:00:00 +0000

Answering a question posed by Joseph Malkevitch, we prove that there exists a polyhedral graph, with triangular faces, such that every realization of it as the graph of a convex polyhedron includes at least one face that is a scalene triangle. Our construction is based on Kleetopes, and shows that there exists an integer i such that all convex i-iterated Kleetopes have a scalene face. However, we also show that all Kleetopes of triangulated polyhedral graphs have non-convex non-self-crossing realizations in which all faces are isosceles. We answer another question of Malkevitch by observing that a spherical tiling of Dawson (Renaissance Banff, Bridges Conference, pp. 489–496, 2005) leads to a fourth infinite family of convex polyhedra in which all faces are congruent isosceles triangles, adding one to the three families previously known to Malkevitch. We prove that the graphs of convex polyhedra with congruent isosceles faces have bounded diameter and have dominating sets of bounded...

Data-rate-aware FPGA-based Acceleration Framework for Streaming Applications

Sat, 8 Nov 2025 00:00:00 +0000

In heterogeneous architectures, FPGAs are not only expected to provide higher performance, but also to provide a more energy efficient solution for computationally intensive tasks. While parallelism and pipelining enhance performance on FPGA platforms, the data transfer rate from/to off-chip memory can cause performance degradation. We propose an automated high-level synthesis framework for FPGA-based acceleration of nested loops on large multidimensional input data sets. Given the high-level of parallelism in such applications, our proposed data prefetching algorithm determines the data rate for each parallel datapath. The empirical results on a case study in scientific computing show that FPGA mapping of such nested loops accelerates the application compared to traditional mapping on multicores. The FPGA-accelerated computation results in 3x speedup in runtime and 27x energy-delay-product savings compared to multicore computation.

Understanding How Personal Activities Are Shared In Short-form Videos

Wed, 22 Oct 2025 00:00:00 +0000

Sharing activities that people do in everyday life, such as physical activity, health management, or hobbies, help people receive benefits like social support and positive self-presentation. Short-form videos present new opportunities for activity-sharing, which has traditionally been studied in static contexts like text- and image-sharing. We therefore aim to understand what information people incorporate into short-form activity videos, and how. We qualitatively analyzed 420 short-form activity videos on TikTok across three domains: running, studying, and sketching. We found people often present information before, during, and after activities, developing strategies for qualitatively and quantitatively incorporating activity-relevant information in each. We also uncover practices for aligning the sharing of activity-relevant information with the nature of short-form videos, such as modifying broader-scale goals into video-scale goals. We further discuss design opportunities...

SNICAR-ADv3: a community tool for modeling spectral snow albedo

Wed, 8 Oct 2025 00:00:00 +0000

The Snow, Ice, and Aerosol Radiative (SNICAR) model has been used in various capacities over the last 15 years to model the spectral albedo of snow with light-absorbing constituents (LACs). Recent studies have extended the model to include an adding-doubling two-stream solver and representations of non-spherical ice particles; carbon dioxide snow; snow algae; and new types of mineral dust, volcanic ash, and brown carbon. New options also exist for ice refractive indices and solar-zenith-angle-dependent surface spectral irradiances used to derive broadband albedo. The model spectral range was also extended deeper into the ultraviolet for studies of extraterrestrial and high-altitude cryospheric surfaces. Until now, however, these improvements and capabilities have not been merged into a unified code base. Here, we document the formulation and evaluation of the publicly available SNICAR-ADv3 source code, web-based model, and accompanying library of constituent optical properties....

A Framework for Variational Inference and Data Assimilation of Soil Biogeochemical Models Using Normalizing Flows

Thu, 2 Oct 2025 00:00:00 +0000

Soil biogeochemical models (SBMs) represent soil variables and their responses to global change. Data assimilation approaches help determine whether SBMs accurately represent soil processes consistent with soil pool and flux measurements. Bayesian inference is commonly used in data assimilation procedures that estimate posterior parameter distributions with Markov chain Monte Carlo (MCMC) methods. The ability to account for data and parameter uncertainty is a strength of MCMC inference, but the computational inefficiency of MCMC methods remains a barrier to their wider application, especially with large data sets. Given the limitations of MCMC approaches, we developed an alternative variational inference framework that uses a method called normalizing flows from the field of machine learning. Normalizing flows rely on deep learning to map probability distributions and approximate SBMs that have been discretized into state space models. As a test of our method, we fit approximated...

Single-cell spatial transcriptomics reveals distinct patterns of dysregulation in non-neuronal and neuronal cells induced by the Trem2R47H Alzheimer’s risk gene mutation

Thu, 11 Sep 2025 00:00:00 +0000

The R47H missense mutation of the TREM2 gene is a known risk factor for development of Alzheimer’s Disease. In this study, we analyze the impact of the Trem2R47H mutation on specific cell types in multiple cortical and subcortical brain regions in the context of wild-type and 5xFAD mouse background. We profile 19 mouse brain sections consisting of wild-type, Trem2R47H, 5xFAD and Trem2R47H; 5xFAD genotypes using MERFISH spatial transcriptomics, a technique that enables subcellular profiling of spatial gene expression. Spatial transcriptomics and neuropathology data are analyzed using our custom pipeline to identify plaque and Trem2R47H-induced transcriptomic dysregulation. We initially analyze cell type-specific transcriptomic alterations induced by plaque proximity. Next, we analyze spatial distributions of disease associated microglia and astrocytes, and how they vary between 5xFAD and Trem2R47H; 5xFAD mouse models. Finally, we analyze the impact of the Trem2R47H mutation on...

An AAV capsid proposed as microglia-targeting directs genetic expression in forebrain excitatory neurons

Wed, 10 Sep 2025 00:00:00 +0000

A newly developed capsid AAV-MG1.2 was reported to mediate specific microglial transduction. However, we find that AAV-MG1.2 actually enables specific genetic access to excitatory neurons in forebrain regions including hippocampal formation and visual cortex but does not confer expression in microglia or astrocytes in vivo. Furthermore, we find that AAV-MG1.2 specifically labels the deep layer of the CA1 pyramidal layer in a titer-dependent manner. We show that AAV-MG1.2-Cre can be used to genetically target excitatory neurons for cell-type-specific neural circuit mapping studies. We also find that AAV-MG1.2 conserves specificity for excitatory neurons in rat hippocampus. Thus, the AAV-MG1.2 presents a useful viral-genetic tool for targeting excitatory neurons in the forebrain across different species.

Supernova pointing capabilities of DUNE

Fri, 15 Aug 2025 00:00:00 +0000

The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electron-neutrino charged-current absorption on and elastic scattering of neutrinos on electrons. Procedures to reconstruct individual interactions, including a newly developed technique called “brems flipping,” as well as the burst direction from an ensemble of interactions are described. Performance of the burst direction reconstruction is evaluated for supernovae happening at a distance of 10 kpc for a specific supernova burst flux model. The pointing resolution is found to be 3.4 degrees at 68% coverage for a perfect interaction-channel classification and a fiducial mass of 40 kton,...

FOVDA: A Federated Architecture for Overcoming Data Silos in Water Domain [Vision]

Wed, 30 Jul 2025 00:00:00 +0000

Effective water management relies on integrating data from diverse sources, including both static and dynamic datasets. However, the challenge of data silos, especially in cities and agencies with disparate systems, has hindered progress in this domain. To address this issue, we introduce FOVDA (Federated Ontology View Data Access), an ontology-driven federated system designed to overcome data silos in the water domain. FOVDA enables seamless data integration and querying across heterogeneous data stores by leveraging a federated architecture and a domain-specific ontology. This system supports both local and global data interoperability, allowing agencies to exchange critical water-related data while maintaining data sovereignty. FOVDA’s federated query engine facilitates complex queries across distributed datasets, enabling decision-makers to access comprehensive insights for tasks such as water resource management, infrastructure resilience analysis, and disaster response....

The track-length extension fitting algorithm for energy measurement of interacting particles in liquid argon TPCs and its performance with ProtoDUNE-SP data

Fri, 25 Jul 2025 00:00:00 +0000

This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe the impact of the dE/dx model on...

A Bayesian Time-Varying Psychophysiological Interaction Model

Thu, 3 Jul 2025 00:00:00 +0000

A Bayesian Time-Varying Psychophysiological Interaction Model

Applying machine learning to assist in the morphometric assessment of brain arteriolosclerosis through automation

Wed, 2 Jul 2025 00:00:00 +0000

Objective quantification of brain arteriolosclerosis remains an area of ongoing refinement in neuropathology, with current methods primarily utilizing semi-quantitative scales completed through manual histological examination. These approaches offer modest inter-rater reliability and do not provide precise quantitative metrics. To address this gap, we present a prototype end-to-end machine learning (ML)-based algorithm, Arteriolosclerosis Segmentation (ArtSeg), followed by Vascular Morphometry (VasMorph) - to assist persons in the morphometric analysis of arteriolosclerotic vessels on whole slide images (WSIs). We digitized hematoxylin and eosin-stained glass slides (13 participants, total 42 WSIs) of human brain frontal or occipital lobe cortical and/or periventricular white matter collected from three brain banks (University of California, Davis, Irvine, and Los Angeles Alzheimer's Disease Research Centers). ArtSeg comprises three ML models for blood vessel detection, arteriolosclerosis...

Identification of the velum interpositum as a meningeal-CNS route for myeloid cell trafficking into the brain

Thu, 19 Jun 2025 00:00:00 +0000

The borders of the central nervous system (CNS) host a repertoire of immune cells and mediate critical neuroimmune interactions, including the infiltration of peripheral myeloid cells into the CNS. Despite the fundamental role of leukocyte infiltration under physiological and pathological conditions, the neuroanatomical route of cell entry into the brain remains unclear. Here, we describe a specialized structure underneath the hippocampus, the velum interpositum (VI), that serves as a site for myeloid cell entry into the CNS. The VI functions as an extra-parenchymal leptomeningeal extension containing distinct myeloid cells subsets. Fate-mapping studies confirm meningeal and peripheral myeloid cell occupancy within the VI. Additionally, we highlight the distinct use of this route in the developing, irradiated, and demyelinating disease brain, indicating that myeloid cell trafficking through the VI could have important clinical implications for neurological disease.

Miniaturized head-mount Doppler optical coherence tomography scope for freely moving mouse

Wed, 18 Jun 2025 00:00:00 +0000

Optical brain imaging has several advantages over other imaging techniques and was used to visualize both the structural and functional aspects of the brain, providing a more complete picture of brain activity. One of the promising techniques is optical coherence tomography (OCT), which uses low-coherence interferometry to obtain three-dimensional depth-resolved imaging of structures. In this research, we present a miniaturized head-mount Doppler OCT system tailored for high-resolution brain imaging in freely moving mice, providing an advanced non-invasive imaging tool in neuroscience research. With a maximum 4×4 mm field of view, 7.4 µm axial resolution, the system offers reliable imaging capabilities. Its compact design and comprehensive imaging capabilities make it well-suited for studying various brain regions and dynamic processes, contributing significantly to our understanding of brain function and pathology.

Label-Free Prediction of Fluorescently Labeled Fibrin Networks

Wed, 18 Jun 2025 00:00:00 +0000

While fluorescent labeling has been the standard for visualizing fibers within fibrillar scaffold models of the extracellular matrix (ECM), the use of fluorescent dyes can compromise cell viability and photobleach prematurely. The intricate fibrillar composition of ECM is crucial for its viscoelastic properties, which regulate intracellular signaling and provide structural support for cells. Naturally derived biomaterials such as fibrin and collagen replicate these fibrillar structures, but longitudinal confocal imaging of fibers using fluorescent dyes may impact cell function and photobleach the sample long before termination of the experiment. An alternative technique is reflection confocal microscopy (RCM) that provides high-resolution images of fibers. However, RCM is sensitive to fiber orientation relative to the optical axis, and consequently, many fibers are not detected. We aim to recover these fibers. Here, we propose a deep learning tool for predicting fluorescently...

Full event particle-level unfolding with variable-length latent variational diffusion

Thu, 22 May 2025 00:00:00 +0000

SciPost Journals Publication Detail SciPost Phys. 18, 117 (2025) Full event particle-level unfolding with variable-length latent variational diffusion

Computational Complexity of the Hylland–Zeckhauser Mechanism for One-Sided Matching Markets

Wed, 21 May 2025 00:00:00 +0000

Computational Complexity of the Hylland–Zeckhauser Mechanism for One-Sided Matching Markets

A Theory of Alternating Paths and Blossoms from the Perspective of Minimum Length

Wed, 21 May 2025 00:00:00 +0000

The Micali–Vazirani (MV) algorithm for finding a maximum cardinality matching in general graphs, which was published in 1980, remains to this day the most efficient known algorithm for the problem. The current paper gives the first complete and correct proof of this algorithm. The MV algorithm resorts to finding minimum-length augmenting paths. However, such paths fail to satisfy an elementary property, called breadth first search honesty in this paper. In the absence of this property, an exponential time algorithm appears to be called for—just for finding one such path. On the other hand, the MV algorithm accomplishes this and additional tasks in linear time. The saving grace is the various “footholds” offered by the underlying structure, which the algorithm uses in order to perform its key tasks efficiently. The theory expounded in this paper elucidates this rich structure and yields a proof of correctness of the algorithm. It may also be of independent interest as a set of...

Immunogenicity and safety of NVSI-06-07 as a heterologous booster after priming with BBIBP-CorV: a phase 2 trial