Miracle-dz/UniFAR-FTUs huggingface dataset:
PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies

The proliferation of physiological sensors opens new opportunities to explore interactions, conduct experiments and evaluate the user experience with continuous monitoring of bodily functions. Commercial devices, however, can be costly or limit access to raw waveform data, while low-cost sensors are efforts-intensive to setup. To address these challenges, we introduce PhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKit provides a one-stop pipeline consisting of (i) a sensing and data acquisition layer that can be configured in a modular manner per research needs, and (ii) a software application layer that enables data acquisition, real-time visualization and machine learning (ML)-enabled signal quality assessment. This also supports basic visual biofeedback configurations and synchronized acquisition for co-located or remote multi-user settings. In a validation study with 16 participants, PhysioKit shows strong agreement with research-grade sensors on measuring heart rate and heart rate variability metrics data. Furthermore, we report usability survey results from 10 small-project teams (44 individual members in total) who used PhysioKit for 4–6 weeks, providing insights into its use cases and research benefits. Lastly, we discuss the extensibility and potential impact of the toolkit on the research community.
Epidemic spreading under game-based self-quarantine behaviors guided by local and global infection information

During the outbreak of an epidemic, individuals may modify their behaviors in response to external (including local and global) infection-related information. However, the difference between local and global information in influencing the spread of diseases remains inadequately explored. Here we study a simple epidemic model that incorporates the game-based self-quarantine behavior of individuals, taking into account the influence of local infection status, global disease prevalence and node heterogeneity (i.e., non-uniform node degrees). Our findings reveal that local information can effectively contain an epidemic, even with only a small proportion of individuals opting for self-quarantine. On the other hand, global information can induce oscillations in infection evolution curves during the declining phase of an epidemic, owing to the synchronous release of nodes with the same degree from the quarantined state. In contrast, the releasing pattern under the local information appears to be more random. This oscillation phenomenon can be observed in various types of networks associated with different characteristics. Significantly, our model is essentially different from conventional epidemic models in that the network heterogeneity plays a negative role in the spread of epidemics, which is contrary to the previous findings.
MeTACAST: Target- and Context-Aware Spatial Selection in VR

We propose three novel spatial data selection techniques for particle data in VR visualization environments. They are designed to be target- and context-aware and be suitable for a wide range of data features and complex scenarios. Each technique is designed to be adjusted to particular selection intents: the selection of consecutive dense regions, the selection of filament-like structures, and the selection of clusters—with all of them facilitating post-selection threshold adjustment. These techniques allow users to precisely select those regions of space for further exploration—with simple and approximate 3D pointing, brushing, or drawing input—using flexible point- or path-based input and without being limited by 3D occlusions, non-homogeneous feature density, or complex data shapes. These new techniques are evaluated in a controlled experiment and compared with the Baseline method, a region-based 3D painting selection. Our results indicate that our techniques are effective in handling a wide range of scenarios and allow users to select data based on their comprehension of crucial features. Furthermore, we analyze the attributes, requirements, and strategies of our spatial selection methods and compare them with existing state-of-the-art selection methods to handle diverse data features and situations. Based on this analysis we provide guidelines for choosing the most suitable 3D spatial selection techniques based on the interaction environment, the given data characteristics, or the need for interactive post-selection threshold adjustment.
Motor rehabilitation after stroke: European Stroke Organisation (ESO) consensus-based definition and guiding framework

Purpose: To propose a consensus-based definition and framework for motor rehabilitation after stroke. Methods: An expert European working group reviewed the literature, attaining internal consensus after external feedback. Findings: Motor rehabilitation is defined as a process that engages people with stroke to benefit their motor function, activity capacity and performance in daily life. It is necessary for people with residual motor disability whose goal is to enhance their functioning, independence and participation. Motor rehabilitation operates through learning- and use-dependent mechanisms. The trajectory of motor recovery varies across patients and stages of recovery. Early behavioral restitution of motor function depends on spontaneous biological mechanisms. Further improvements in activities of daily living are achieved by compensations. Motor rehabilitation is guided by regular assessment of motor function and activity using consensus-based measures, including patient-reported outcomes. Results are discussed with the patient and their carers to set personal goals. During motor rehabilitation patients learn to optimize and adapt their motor, sensory and cognitive functioning through appropriately dosed repetitive, goal-oriented, progressive, task- and context-specific training. Motor rehabilitation supports people with stroke to maximize health, well-being and quality of life. The framework describes the International Classification of Functioning, Disability and Health in the context of stroke, describes neurobiological mechanisms of behavioral restitution and compensation, and summarizes recommendations for clinical assessment, prediction tools, and motor interventions with strong recommendations from clinical practice guidelines (2016–2022). Conclusions: This definition and framework may guide clinical educators, inform clinicians on current recommendations and guidelines, and identify gaps in the evidence base.
WASMixer: Binary Obfuscation for WebAssembly

WebAssembly (Wasm) is an emerging binary format that draws great attention from our community. However, Wasm binaries are weakly protected, as they can be read, edited, and manipulated by adversaries using either the officially provided readable text format (i.e., wat) or some advanced binary analysis tools. Reverse engineering of Wasm binaries is often used for nefarious intentions, e.g., identifying and exploiting both classic vulnerabilities and Wasm specific vulnerabilities exposed in the binaries. However, no Wasm-specific obfuscator is available in our community to secure the Wasm binaries. To fill the gap, in this paper, we present WASMixer, the first general-purpose Wasm binary obfuscator, enforcing data-level (string literals and function names) and code-level (control flow and instructions) obfuscation for Wasm binaries. We propose a series of key techniques to overcome challenges during Wasm binary rewriting, including an on-demand decryption method to minimize the impact brought by decrypting the data in memory area, and code splitting/reconstructing algorithms to handle structured control flow in Wasm. Extensive experiments demonstrate the correctness, effectiveness and efficiency of WASMixer. Our research has shed light on the promising direction of Wasm binary research, including Wasm code protection, Wasm binary diversification, and the attack-defense arm race of Wasm binaries.
Beyond the classical strong maximum principle: Forcing changing sign near the boundary and flat solutions

We show that the classical strong maximum principle, concerning positive supersolutions of linear elliptic equations vanishing on the boundary of the domain $\Omega $ can be extended, under suitable conditions, to the case in which the forcing term $f(x)$ is changing sign. In addition, in the case of solutions, the normal derivative on the boundary may also vanish on the boundary (definition of flat solutions). This leads to examples in which the unique continuation property fails. As a first application, we show the existence of positive solutions for a sublinear semilinear elliptic problem of indefinite sign. A second application, concerning the positivity of solutions of the linear heat equation, for some large values of time, with forcing and/or initial datum changing sign is also given.
Anticancer Potential of the Plant‐Derived Saponin Gracillin: A Comprehensive Review of Mechanistic Approaches

With the increasing prevalence of cancer and the toxic side effects of synthetic drugs, natural products are being developed as promising therapeutic approaches. Gracillin is a naturally occurring triterpenoid steroidal saponin with several therapeutic activities. It is obtained as a major compound from different Dioscorea species. This review was designated to summarize the research progress on the anti‐cancer activities of gracillin focusing on the underlying cellular and molecular mechanisms, as well as its pharmacokinetic features. The data were collected (up to date as of May 1, 2023) from various reliable and authentic literatures comprising PubMed, Springer Link, Scopus, Wiley Online, Web of Science, ScienceDirect, and Google Scholar. The findings demonstrated that gracillin displays promising anticancer effects through various molecular mechanisms, including anti‐inflammatory effects, apoptotic cell death, induction of oxidative stress, cytotoxicity, induction of genotoxicity, cell cycle arrest, anti‐proliferative effect, autophagy, inhibition of glycolysis, and blocking of cancer cell migration. Additionally, this review highlighted the pharmacokinetic features of gracillin, indicating its lower oral bioavailability. As a conclusion, it can be proposed that gracillin could serve as a hopeful chemotherapeutic agent. However, further extensive clinical research is recommended to establish its safety, efficacy, and therapeutic potential in cancer treatment.
Reconstructing Lyman-$\alpha$ Fields from Low-Resolution Hydrodynamical Simulations with Deep Learning

Hydrodynamical cosmological simulations are a powerful tool for accurately predicting the properties of the intergalactic medium (IGM) and for producing mock skies that can be compared against observational data. However, the need to resolve density fluctuation in the IGM puts a stringent requirement on the resolution of such simulations which in turn limits the volumes which can be modelled, even on most powerful supercomputers. In this work, we present a novel modeling method which combines physics-driven simulations with data-driven generative neural networks to produce outputs that are qualitatively and statistically close to the outputs of hydrodynamical simulations employing 8 times higher resolution. We show that the Ly-$\alpha$ flux field, as well as the underlying hydrodynamic fields, have greatly improved statistical fidelity over a low-resolution simulation. Importantly, the design of our neural network allows for sampling multiple realizations from a given input, enabling us to quantify the model uncertainty. Using test data, we demonstrate that this model uncertainty correlates well with the true error of the Ly-$\alpha$ flux prediction. Ultimately, our approach allows for training on small simulation volumes and applying it to much larger ones, opening the door to producing accurate Ly-$\alpha$ mock skies in volumes of Hubble size, as will be probed with DESI and future spectroscopic sky surveys.
Negative Lexical Constraints in Neural Machine Translation

This paper explores negative lexical constraining in English to Czech neural machine translation. Negative lexical constraining is used to prohibit certain words or expressions in the translation produced by the NMT model. We compared various methods based on modifying either the decoding process or the training data. The comparison was performed on two tasks: paraphrasing and feedback-based translation refinement. We also studied how the methods “evade” the constraints, meaning that the disallowed expression is still present in the output, but in a changed form, most interestingly the case where a different surface form (for example different inflection) is produced. We propose a way to mitigate the issue through training with stemmed negative constraints, so that the ability of the model to induce different forms of a word might be used to prohibit the usage of all possible forms of the constraint. This helps to some extent, but the problem still persists in many cases.
RIP-based Performance Guarantee for Low Rank Matrix Recovery via $L_{*-F}$ Minimization

In the undetermined linear system $\bm{b}=\mathcal{A}(\bm{X})+\bm{s}$, vector $\bm{b}$ and operator $\mathcal{A}$ are the known measurements and $\bm{s}$ is the unknown noise. In this paper, we investigate sufficient conditions for exactly reconstructing desired matrix $\bm{X}$ being low-rank or approximately low-rank. We use the difference of nuclear norm and Frobenius norm ($L_{*-F}$) as a surrogate for rank function and establish a new nonconvex relaxation of such low rank matrix recovery, called the $L_{*-F}$ minimization, in order to approximate the rank function closer. For such nonconvex and nonsmooth constrained $L_{*-F}$ minimization problems, based on whether the noise level is $0$, we give the upper bound estimation of the recovery error respectively. Particularly, in the noise-free case, one sufficient condition for exact recovery is presented. If linear operator $\mathcal{A}$ satisfies the restricted isometry property with $\delta_{4r}<\frac{\sqrt{2r}-1}{\sqrt{2r}-1+\sqrt{2}(\sqrt{2r}+1)}$, then $r$-\textbf{rank} matrix $\bm{X}$ can be exactly recovered without other assumptions. In addition, we also take insights into the regularized $L_{*-F}$ minimization model since such regularized model is more widely used in algorithm design. We provide the recovery error estimation of this regularized $L_{*-F}$ minimization model via RIP tool. To our knowledge, this is the first result on exact reconstruction of low rank matrix via regularized $L_{*-F}$ minimization.
An Affine Invariant Region Detector Using the 4th Differential Invariant

A novel affine invariant region detector based on the 4th differential invariant (DI4) is proposed in this paper. The detector combines scale-space theory with an autocorrelation matrix. Since it is proved that DI4 is a scale- space selection function, feature points and their characteristic scales are first detected by the local maxima of the normalized DI4 over scale-space. Then, the auto-correlation matrices, which are used to describe the affine shapes, are estimated on the characteristic scales of the feature points. The ellipse regions given by the auto-correlation matrices are affine invariant. In order to verify the affine invariance, we build up a simulation experiment to test affine invariance using two single- parameter transforms. The experimental results show the detected regions are invariant to rotation, scale and affine transforms as well as robust to illumination changes.
Customizing Textile and Tactile Skins for Interactive Industrial Robots

Textiles are a promising material for building tactile skins because they are lightweight, flexible, and can conform to complex surfaces. They enhance robot-human interaction by localizing contact points and measuring contact forces. This paper presents a solution for rapidly fabricating, calibrating, and deploying these skins on industrial robot arms. The novel automated skin calibration procedure maps skin locations to robot geometry and calibrates contact force. Through experiments on a FANUC LR Mate 200id/7L industrial robot, we demonstrate that tactile skins made from textiles can be effectively used for human-robot interaction in industrial environments. We demonstrated the following: 1) modifying trajectories using human contact signals and 2) improving force interactions with humans using tactile skins This work presents a comprehensive solution for rapidly fabricating, calibrating, and deploying textile and tactile skins for interactive industrial robots. Our contributions are as follows: 1) we introduce a method for determining skin parameters based on robot geometry; 2) we propose an automated skin calibration process that calibrates both the position and force reading from the skin to the robot; and 3) we demonstrate how the skins can be integrated into robot control and learning to improve the safety and interactivity of industrial robots through experiments on a FANUC LR Mate 200id/7L industrial robot arm.
Hominin technological behavior during the later middle Pleistocene in the Gademotta formation, main Ethiopian rift

The evidence now strongly supports an African origin of the first Homo sapiens. Currently, the best-known fossil evidence for the earliest H. sapiens derives from the Omo Kibish and Herto sites in Ethiopia, and dates to ca. 200150 thousand years ago (ka). However, very few archaeological data spanning the critical period across our species’ evolutionary origin are known from securely dated contexts on the continent. Through renewed excavations and analysis of newly-recovered as well as previously excavated lithic assemblages from the Gademotta Formation, this dissertation investigates the technological behavior and broader evolutionary context across the time period when the first H. sapiens emerged in the wider rift valley in Ethiopia. Results from new geochronological analyses establish the age of the lowermost cultural horizon at Gademotta as 279 ± 2 ka, making it the oldest precisely dated Middle Stone Age (MSA) site. Lithic analysis reveals that Middle Pleistocene hominin populations at Gademotta incorporated into their technological repertoire the earliest stone-tipped projectile weapons as early as >279 ka. Comparisons of technological behavior of later Middle Pleistocene with earlier Upper Pleistocene hominin populations in the Ethiopian rift valley indicate that they possessed substantially comparable capacities for behavioral variability. Data from the present study strongly support the existence of relatively stable adaptations across the period when H. sapiens emerged in northeast Africa. Behavioral patterns considered as indications of “modernity” have much older roots than previously realized. Un ive rsi ty of Ca pe To wn ii TABLE OF CONTENTS ABSTRACT i LIST OF FIGURES v LIST OF TABLES ix ACKNOWLEDGMENTS xi LIST OF FIGURES v LIST OF TABLES ix ACKNOWLEDGMENTS x CHAPTER ONE
Personalization of Stress Mobile Sensing using Self-Supervised Learning

Stress is widely recognized as a major contributor to a variety of health issues. Stress prediction using biosignal data recorded by wearables is a key area of study in mobile sensing research because real-time stress prediction can enable digital interventions to immediately react at the onset of stress, helping to avoid many psychological and physiological symptoms such as heart rhythm irregularities. Electrodermal activity (EDA) is often used to measure stress. However, major challenges with the prediction of stress using machine learning include the subjectivity and sparseness of the labels, a large feature space, relatively few labels, and a complex nonlinear and subjective relationship between the features and outcomes. To tackle these issues, we examine the use of model personalization: training a separate stress prediction model for each user. To allow the neural network to learn the temporal dynamics of each individual's baseline biosignal patterns, thus enabling personalization with very few labels, we pre-train a 1-dimensional convolutional neural network (CNN) using self-supervised learning (SSL). We evaluate our method using the Wearable Stress and Affect prediction (WESAD) dataset. We fine-tune the pre-trained networks to the stress prediction task and compare against equivalent models without any self-supervised pre-training. We discover that embeddings learned using our pre-training method outperform supervised baselines with significantly fewer labeled data points: the models trained with SSL require less than 30% of the labels to reach equivalent performance without personalized SSL. This personalized learning method can enable precision health systems which are tailored to each subject and require few annotations by the end user, thus allowing for the mobile sensing of increasingly complex, heterogeneous, and subjective outcomes such as stress.
From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion

Deep generative models can generate high-fidelity audio conditioned on various types of representations (e.g., mel-spectrograms, Mel-frequency Cepstral Coefficients (MFCC)). Recently, such models have been used to synthesize audio waveforms conditioned on highly compressed representations. Although such methods produce impressive results, they are prone to generate audible artifacts when the conditioning is flawed or imperfect. An alternative modeling approach is to use diffusion models. However, these have mainly been used as speech vocoders (i.e., conditioned on mel-spectrograms) or generating relatively low sampling rate signals. In this work, we propose a high-fidelity multi-band diffusion-based framework that generates any type of audio modality (e.g., speech, music, environmental sounds) from low-bitrate discrete representations. At equal bit rate, the proposed approach outperforms state-of-the-art generative techniques in terms of perceptual quality. Training and, evaluation code, along with audio samples, are available on the facebookresearch/audiocraft Github page.
Resource Management for GPT-based Model Deployed on Clouds: Challenges, Solutions, and Future Directions

The widespread adoption of the large language model (LLM), e.g. Generative Pre-trained Transformer (GPT), deployed on cloud computing environment (e.g. Azure) has led to a huge increased demand for resources. This surge in demand poses significant challenges to resource management in clouds. This paper aims to highlight these challenges by first identifying the unique characteristics of resource management for the GPT-based model. Building upon this understanding, we analyze the specific challenges faced by resource management in the context of GPT-based model deployed on clouds, and propose corresponding potential solutions. To facilitate effective resource management, we introduce a comprehensive resource management framework and present resource scheduling algorithms specifically designed for the GPT-based model. Furthermore, we delve into the future directions for resource management in the GPT-based model, highlighting potential areas for further exploration and improvement. Through this study, we aim to provide valuable insights into resource management for GPT-based models deployed in clouds and promote their sustainable development for GPT-based models and applications.