Recent ics 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,...

Modeling Local Field Potentials with Regularized Matrix Data Clustering

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

In this paper, we propose a novel regularized mixture model for clustering matrix-valued image data. The new framework introduces a sparsity structure (e.g., low rank, spatial sparsity) and separable covariance structure motivated by scientific interpretability. We formulate the problem as a fi-nite mixture model of matrix-normal distributions with regularization terms, and then develop an Expectation-Maximization-type of algorithm for efficient computation. Simulation results and analysis on brain signals show the excellent performance of the proposed method in terms of a better prediction accuracy than the competitors and the scientific interpretability of the solution.

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

Shaping HCI Research for Children's Care Ecosystem Involvement

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

Recent HCI research emphasizes the importance of considering children’s care ecosystems in the design of technologies, extending the focus beyond families to include teachers, peers, therapists, and institutions. While this ecosystem perspective opens opportunities for more inclusive and collaborative technologies, it also introduces challenges such as recruitment, power dynamics, reconciling diverse perspectives, and complex ethical considerations. This CHI 2026 workshop builds on prior community efforts at IDC 2023, CHI 2024, and IDC 2025. Its primary focus is on children’s care ecosystems, but we also welcome researchers working with other populations who wish to apply an ecosystem lens. The workshop will bring together researchers and practitioners to discuss opportunities, challenges, and methods, and to collaboratively articulate a research agenda for care ecosystem-centered HCI. Participants will engage in mapping and synthesis activities that produce care ecosystem maps...

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...

Collaboration and Assistive Technology: Facilitating Joint Awareness for Noise Sensitivity

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

Existing research has explored various methods to support people with noise sensitivity (PWNS), from desensitization therapies to technological solutions. However, there is a gap in systems that identify and monitor characteristics of noise sensitivity experiences to help PWNS and their companions better understand their condition and make informed management decisions. To fill this gap, we developed AudioBuddy, an app with sensing and tracking features designed to promote awareness between PWNS and their companions. We tested AudioBuddy as a technological probe over a two-week field deployment. Our results show that AudioBuddy can support awareness of how sounds and environments influence the psychophysiological states of PWNS, aiding in understanding noise sensitivity experiences. Nonetheless, technical limitations impacted the depth of awareness participants could attain. We discuss challenges and opportunities for future systems to facilitate awareness among PWNS and their...

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...

Understanding the Perspectives of Autistic Gamers through an Online Autistic Community and a Survey

Wed, 22 Apr 2026 00:00:00 +0000

Autistic people often have an interest in and spend a substantial amount of time engaged with video games. Games can be supportive of their mental health and social needs and have been widely used for behavioral interventions among autistic people. However, the gaming experiences and preferences of autistic people themselves have not been thoroughly studied. To explore these experiences, we used a multi-method approach, analyzing game-related posts from a large autism-related subreddit and conducting a survey with 145 autistic people. The survey allowed us to further understand preferences around accessibility and sensory experiences, representation, and social experiences in communities that emerged in the Reddit posts. We found that games offering a sense of freedom, control, and creativity might be particularly appealing to autistic gamers. Discussions also emerged around what types of audio and visual sensory input were considered soothing and appropriate. Moreover, both the...

DermaVision

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

Approximately 10 million people in the United States suffer from domestic violence annually, with 4 out of 10 cases affecting people of color. Traditional coloration guides remain the primary forensic strategy to evaluate bruise injuries, which are highly subjective and inaccurate for monitoring bruises. Additionally, this approach fails to consider bruise pigmentation in darker skin tones, and the results of this qualitative method vary by the medical professional conducting the inspection. There is a need for reliable, quantitative bruise information across all skin tones that can be utilized in both medicine and justice. DermaVision aims to address this need by designing a portable multi-spectral camera to quantitatively analyze bruises in diverse skin tones. By correlating the reflective spectra of a bruise with its age and healing progression, our camera will provide an accurate timeline for when bruises occur irrespective of patients’ skin color. This technology will assist...

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...

The effectiveness of participatory near-peer digital media literacy interventions

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

The impacts of digital media literacy (DML) interventions are mixed, perhaps due to adult-driven curricula and misalignment with youth’s needs. We tested how student-led, developmentally-informed DML education could leverage youth’s expertise. Using Youth Participatory Action Research and near-peer mentoring, high school students (n = 31) designed and taught digital literacy interventions in two areas (1) digital communication (i.e. group-chats) and (2) persuasive design (e.g. features such as infinite scroll), resulting in significant increases in knowledge of digital communication conflict prevention and remediation strategies, along with identification and knowledge of persuasive design for 79 middle and 31 high school students. High schoolers also reported more feelings of agency and research knowledge. These findings indicate that making DML interventions developmentally aligned might be key for supporting youth in the digital age.

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...

Video Games and Learning

Mon, 30 Mar 2026 00:00:00 +0000

The interdisciplinary field of the learning sciences encompasses educational psychology, cognitive science, computer science, and anthropology, among other disciplines. The Cambridge Handbook of the Learning Sciences, first published in 2006, is the definitive introduction to this innovative approach to teaching, learning, and educational technology. In this significantly revised third edition, leading scholars incorporate the latest research to provide seminal overviews of the field. This research is essential in developing effective innovations that enhance student learning - including how to write textbooks, design educational software, prepare effective teachers, and organize classrooms. The chapters illustrate the importance of creating productive learning environments both inside and outside school, including after school clubs, libraries, and museums. The Handbook has proven to be an essential resource for graduate students, researchers, consultants, software designers,...

Situating Big Data

Mon, 30 Mar 2026 00:00:00 +0000

Lacking a digital crystal ball, we cannot predict the future of education or the precise instructional role games will have going forward. Yet we can safely say that games will play some role in the future of K-12 and higher education, and members of the games community will have to choose between being passive observers or active, progressive contributors to the complex and often political process of weaving together pedagogy, technology, and culture. This will involve agreeing that games—or, more specifically, game mechanics and the engagement in joyful learning that they engender—are not only critical for shaping online and classroom instruction but also the evolution of schooling as a whole. Likewise, it will involve a hard push beyond questions like “Are video games ‘good’ or ‘bad’ for education?” and “Are games ‘better’ for all students than traditional face-to-face teaching” to unpack how game experiences vary with individual learner goals as an interaction with the parameters...

Editorial: Extremism in games

Mon, 30 Mar 2026 00:00:00 +0000

Editorial: Extremism in games

PIKACHU: (est. 1996) Franchise: Pokémon Developer: Game Freak

Mon, 30 Mar 2026 00:00:00 +0000

PIKACHU: (est. 1996) Franchise: Pokémon Developer: Game Freak

How much we express love predicts how much we feel loved in daily life