Miracle-dz/UniFAR-FTUs huggingface dataset:
Appropriate Graph-Algorithm Selection for Edge Devices Using Machine Learning

As a new information technology, edge computing has attracted much attention in recent years. Edge computing collects, stores, and processes data on the edge devices, such as smartphones and sensors. Edge devices can process data in real time by reducing communication time with central servers. Therefore, various algorithms for preprocessing should be executed on edge devices to provide services rapidly. Graph algorithms are one of such candidates, because graph data play an important role in representing a variety of information around us such as maps, SNS, and web structures, which is one of data collected and processed on edge devices. However, since execution time of graph algorithms varies greatly depending on the graph data, the preprocessing on edge devices may take a long time. Therefore, it is necessary to select an appropriate algorithm and process data for edge devices as fast as possible. This paper proposes a method to select the appropriate graph algorithm on edge devices. By using machine learning that takes features of graph data as input, the performances of graph algorithms, such as the execution times, are predicted. Then, the proposed method selects a suitable algorithm for requests of edge users. The evaluation result demonstrates that the proposed method can select the appropriate algorithm from the several algorithms depending on the characteristics of graph data.
Predicting Stress and Providing Counterfactual Explanations: A Pilot Study on Caregivers

Caregiving for spouses with Alzheimer’s disease or related dementias (ADRD) is one of the most stressful experiences. Evidence-based treatments for caregivers who have a high risk of mental health issues are needed. In this study, we designed models for predicting changes in perceived stress scale (PSS) (increase/not increase) in one week and generated some examples of counterfactual (’what-if’) explanations to change the stress state for helping manage stress. Using self-report (positive and negative affect and sleep quality) and sensor data (heart rate, sleep, and steps) collected in 132 week-long study sessions from 57 participants, we compared explainable PSS change prediction models (Random Forest, XGBoost, LightGBM, EBM, and Neural Network) along with ’what-if’ explanations. First, we developed machine learning models for classifying the change in PSS scores before and after the session period. Second, we identified feature importance using our explainable models. Our results showed that XGBoost performed the best with an accuracy of 0.79 and an F1 score of 0.78 for predicting changes in perceived stress. Our results also showed that minimum heart rate, mean steps per day, and negative affect are the most predictive features. Our preliminary counterfactual examples about sleep parameters would be able to provide suggestions for improving one’s health. We discussed our ideas to provide better suggestions using DiCE.
SpinCam: High-Speed Imaging via a Rotating Point-Spread Function

High-speed cameras are an indispensable tool used for the slow-motion analysis of scenes. However, the fixed bandwidth of any imaging system quickly becomes a bottleneck, resulting in a fundamental trade-off between the camera’s spatial and temporal resolutions. In recent years, compressive high-speed imaging systems have been proposed to circumvent these issues by optically encoding the signal and using a reconstruction procedure to recover a video. Our work proposes a novel approach for compressive high-speed imaging based on temporally coding the camera’s point-spread function (PSF). By mechanically spinning a diffraction grating in front of a camera, the sensor integrates an image blurred by a PSF that continuously rotates over time. We also propose a deconvolution-based reconstruction algorithm to reconstruct videos from these measurements. Our method achieves superior light efficiency and handles a wider scene class than prior methods. Also, our mechanical design yields flexible temporal resolution that can be easily increased, potentially allowing capture at 192 kHz—far higher than prior works. We demonstrate a prototype for various applications, including motion capture and particle image velocimetry (PIV).
Pt–Se Hybrid Nanozymes with Potent Catalytic Activities to Scavenge ROS/RONS and Regulate Macrophage Polarization for Osteoarthritis Therapy

The activation of pro-inflammatory M1-type macrophages by overexpression of reactive oxygen species (ROS) and reactive nitrogen species (RONS) in synovial membranes contributes to osteoarthritis (OA) progression and cartilage matrix degradation. Here, combing Pt and Se with potent catalytic activities, we developed a hybrid Pt–Se nanozymes as ROS and RONS scavengers to exert synergistic effects for OA therapy. As a result, Pt–Se nanozymes exhibited efficient scavenging effect on ROS and RONS levels, leading to repolarization of M1-type macrophages. Furthermore, the polarization of synovial macrophages to the M2 phenotype inhibited the expression of pro-inflammatory factors and salvaged mitochondrial function in arthritic chondrocytes. In vivo results also suggest that Pt–Se nanozymes effectively suppress the early progression of OA with an Osteoarthritis Research International Association score reduction of 68.21% and 82.66% for 4 and 8 weeks, respectively. In conclusion, this study provides a promising strategy to regulate inflammatory responses by macrophage repolarization processes for OA therapeutic.
Classification of Acute Bilirubin Encephalopathy Based on Individual and Fused Attention Network

Hyperbilirubinemia is a common disease caused by abnormal metabolism of bilirubin. As a serious complication of hyperbilirubinemia, acute bilirubin encephalopathy (ABE) in newborns, is caused by abnormal bilirubin metabolism leading to high levels of bilirubin in the blood. In severe cases, it can threaten the life of patients. Recently, medical image-assisted diagnosis technology based on convolutional neural networks has been widely used to assist radiologists in analyzing and diagnosing case samples of brain diseases. However, the existed methods are not good at representing the crucial information inside each modal and between modals. In this paper, a novel attention network based on individual attention module (IAM) and fused attention module (FAM) is proposed for acute bilirubin encephalopathy classification. The IAM is used to gather internal correlations and key information within each modality while the FAM is exploited to fuse the guided features from different modalities to enhance their connectivity. Experimental results indicate that the proposed network performs better in the medical image classification and diagnosis of ABE.
Auditing Machine Behaviors: Does a diverse crowd result in different social norms?

Many intelligent systems depend on black-box services for achieving goals. For example, Decision Support Systems (DSS) incorporate image tagging services for translating the content of an image into useful information that serves the purpose of the system under development (e.g., facilitating hiring decisions). Auditing the results of DSS for fairness can thus become a cumbersome process as they are correlated with the output of the black-box image tagger. In this work, we assume that tags generated by an image tagger are part of the DSS and in terms of fairness can be considered socially acceptable or unacceptable conforming to the varying goals of a DSS. For example, the tag "good-looking" can be considered appropriate in a dating context but inappropriate in a hiring context. We propose a methodology for leveraging the use of crowdsourcing as an approach for auditing what is socially acceptable and unacceptable for tags generated by black-box image taggers. As a first step, we attempt to understand how the crowdworkers perceive (un)acceptable tags in two scenarios: (i) a hiring DSS, and (ii) a dating DSS. Through our analysis, we investigate how answers (in our case tags) received by crowdworkers representing different demographic groups (i.e., country of residence and gender) impact the auditing process. Results indicate that varying the DSS scenario, and the demographics of the crowdworkers, yields a different fairness perspective concurrent to the "acceptable" and "unacceptable" social norms.
Ordinal Label Distribution Learning

Label distribution learning (LDL) is a recent hot topic, in which ambiguity is modeled via description degrees of the labels. However, in common LDL tasks, e.g., age estimation, labels are in an intrinsic order. The conventional LDL paradigm adopts a per-label manner for optimization, neglecting the internal sequential patterns of labels. Therefore, we propose a new paradigm, termed ordinal label distribution learning (OLDL). We model the sequential patterns of labels from aspects of spatial, semantic, and temporal order relationships. The spatial order depicts the relative position between arbitrary labels. We build cross-label transformation between distributions, which is determined by the spatial margin in labels. Labels naturally yield different semantics, so the semantic order is represented by constructing semantic correlations between arbitrary labels. The temporal order describes that the presence of labels is determined by their order, i.e. five after four. The value of a particular label contains information about previous labels, and we adopt cumulative distribution to construct this relationship. Based on these characteristics of ordinal labels, we propose the learning objectives and evaluation metrics for OLDL, namely CAD, QFD, and CJS. Comprehensive experiments conducted on four tasks demonstrate the superiority of OLDL against other existing LDL methods in both traditional and newly proposed metrics. Our project page can be found at https://downdric23.github. io/.
CAD2X - A Complete, End-to-End Solution for Training Deep Learning Networks for Industrial Applications

With the growing demand for automation in the manufacturing industry, computer vision-based systems have become a popular tool for tasks such as object detection, object picking, and quality control. One of the main challenges in developing such systems is obtaining enough high-quality training data. In this paper, we present a suite of tools that create artificial training data and applies it to solve industrial problems. CAD2Render is a tool for generating synthetic images, starting from a 3D CAD model, which can be used to create large datasets with a large spectrum of controlled variations. CAD2Detect uses these synthetic images to train object detection models. CAD2Pose focuses on estimating the 6 degree of freedom pose of objects in images. Finally, CAD2Defect uses anomaly detection to identify defects in manufactured parts. Overall, the CAD2X suite provides a comprehensive set of tools for training computer vision models for manufacturing applications, while minimizing the need for large amounts of real training data. We demonstrate the effectiveness of our approach on a range of industrial use cases.
Characterization, Analysis, and Failure Investigation of SiC Materials and Power Devices

SiC materials and devices are highly regarded in the power semiconductor industry for their exceptional electrical, thermal, and chemical properties. They meet the demanding requirements of high temperature, voltage, frequency, power, and radiation resistance applications. With the increasing demand for new energy vehicles, 5G communications, rail transit, and ultra-high voltage transmission, SiC power devices are rapidly expanding. However, the quality of SiC wafers still needs improvement compared to well-established Si-based semiconductor technology. The maturity of SiC wafer manufacturing directly impacts device performance. This comprehensive review focuses on the thorough characterization and analysis of SiC wafers. It includes quantitative and qualitative assessments of material composition, trace elements, microscopic and lattice structures, binding energy, chemical bonds, and electrical properties. SiC wafers and epitaxial layers have defect densities several orders of magnitude higher than Si wafers, significantly affecting the electrical performance and reliability of semiconductor devices. The study analyzes surface defects in SiC wafers and calculates yield using a Poisson Model based on defect density, providing insights for SiC manufacturing cost analysis. Additionally, the review conducts a series of systematic failure investigations on a SiC power trench MOSFET through failure mode analysis. Thermally-induced voltage variation technology identifies the failure location. Electrical performance simulation analysis utilizes physical parameters extracted from a matured SiC MOSFET product chip. This report offers a comprehensive overview of SiC material and device characterization, analysis, and failure investigation for power electronics engineering. The findings enhance the understanding of SiC technology and its potential for further advancements in the power semiconductor industry.
Structural Generation of Virtual Prototypes for Smart Sensor Development in SystemC-AMS from Simulink Models

We present a flow to reuse system-level analog/mixed-signal (AMS) models developed in MATLAB/Simulink for the extension of virtual prototypes in SystemC. To prevent co-simulation, our flow translates the Simulink model into an equivalent SystemC-AMS model. Translation is supported either by wrapping code generated by MATLAB's Embedded Coder or by instantiating previously generated models. Thus, a one-to-one mapping of the model's hierarchy is possible which allows deep insights into the architecture and good traceability. The conducted case study on an accelerometer model shows the applicability of our approach. The generated hierarchical model is half as fast as a generated monolithic (black box) version but allows better observability and traceability of the system. It is tens of times faster than simulation in Simulink, thus especially faster than co-simulation. The extended virtual prototype aims to support software engineers during development and validation of firmware in smart sensors.
Three Remarks On Asset Pricing

We make three remarks to the main CAPM equation presented in the well-known textbook by John Cochrane (2001). First, we believe that any economic averaging procedure implies aggregation of corresponding time series during certain time interval Δ and explain the necessity to use math expectation for both sides of the main CAPM equation. Second, the first-order condition of utility max used to derive main CAPM equation should be complemented by the second one that requires negative utility second derivative. Both define the amount of assets ξ max that delivers max to utility. Expansions of the utility in a Taylor series by price and payoff variations give approximations for ξ max and uncover equations on price, payoff, volatility, skewness, their covariance ’ s and etc. We discuss why market price-volume positive correlations may prohibit existence of ξ max and main CAPM equation. Third, we argue that the economic sense of the conventional frequency-based price probability may be poor. To overcome this trouble we propose new price probability measure based on widely used volume weighted average price (VWAP). To forecast price volatility one should predict evolution of squares of the value and the volume of market trades aggregated during averaging interval Δ . The forecast of the new price probability measure may be the main tough puzzle for CAPM and finance. However investors are free to chose any probability measure they prefer as ground for their investment strategies but should be ready for unexpected losses due to possible distinctions with real market trade price dynamics.
Understanding the concerns and choices of public when using large language models for healthcare

Large language models (LLMs) have shown their potential in biomedical fields. However, how the public uses them for healthcare purposes such as medical Q\&A, self-diagnosis, and daily healthcare information seeking is under-investigated. In this paper, we adopt a mixed-methods approach, including surveys (N=167) and interviews (N=17) to investigate how and why the public uses LLMs for healthcare. LLMs as a healthcare tool have gained popularity, and are often used in combination with other information channels such as search engines and online health communities to optimize information quality. LLMs provide more accurate information and a more convenient interaction/service model compared to traditional channels. LLMs also do a better job of reducing misinformation, especially in daily healthcare questions. Doctors using LLMs for diagnosis is less acceptable than for auxiliary work such as writing medical records. Based on the findings, we reflect on the ethical and effective use of LLMs for healthcare and propose future research directions.
High-Quality Mesh Blendshape Generation from Face Videos via Neural Inverse Rendering

Readily editable mesh blendshapes have been widely used in animation pipelines, while recent advancements in neural geometry and appearance representations have enabled high-quality inverse rendering. Building upon these observations, we introduce a novel technique that reconstructs mesh-based blendshape rigs from single or sparse multi-view videos, leveraging state-of-the-art neural inverse rendering. We begin by constructing a deformation representation that parameterizes vertex displacements into differential coordinates with tetrahedral connections, allowing for high-quality vertex deformation on high-resolution meshes. By constructing a set of semantic regulations in this representation, we achieve joint optimization of blendshapes and expression coefficients. Furthermore, to enable a user-friendly multi-view setup with unsynchronized cameras, we propose a neural regressor to model time-varying motion parameters. This approach implicitly considers the time difference across multiple cameras, enhancing the accuracy of motion modeling. Experiments demonstrate that, with the flexible input of single or sparse multi-view videos, we reconstruct personalized high-fidelity blendshapes. These blendshapes are both geometrically and semantically accurate, and they are compatible with industrial animation pipelines. Code and data will be released.
Methodological observation in sport: Current situation and challenges for the next future

Observational methods applied to sport are scientific procedures that reveal the occurrence of perceptible motor behaviours, allowing them to be formally recorded and quantified. They alsoallow the analysis of the relations between these behaviours, such as sequentiality, association, and covariation. In many situations observational methods are the best strategy, or even the only strategy possible. Assessment in natural sportive contexts through observation is unquestionably complex. In all settings we find a range of behaviours which form a pyramid structure. Starting from the top of the pyramid, we can break down daily sportive life into different situations such as school, leisure, competition, etc., revealing a tree structure with a hierarchical subdivision of situations in which behaviours that tend towards molarity interact with their natural contexts. Towards the base of the pyramid, the perceptible motor behaviours are increasingly molecular.
EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis

Sentiment analysis and emotion detection are important research topics in natural language processing (NLP) and benefit many downstream tasks. With the widespread application of LLMs, researchers have started exploring the application of LLMs based on instruction-tuning in the field of sentiment analysis. However, these models only focus on single aspects of affective classification tasks (e.g. sentimental polarity or categorical emotions), and overlook the regression tasks (e.g. sentiment strength or emotion intensity), which leads to poor performance in downstream tasks. The main reason is the lack of comprehensive affective instruction tuning datasets and evaluation benchmarks, which cover various affective classification and regression tasks. Moreover, although emotional information is useful for downstream tasks, existing downstream datasets lack high-quality and comprehensive affective annotations. In this paper, we propose EmoLLMs, the first series of open-sourced instruction-following LLMs for comprehensive affective analysis based on fine-tuning various LLMs with instruction data, the first multi-task affective analysis instruction dataset (AAID) with 234K data samples based on various classification and regression tasks to support LLM instruction tuning, and a comprehensive affective evaluation benchmark (AEB) with 14 tasks from various sources and domains to test the generalization ability of LLMs. We propose a series of EmoLLMs by fine-tuning LLMs with AAID to solve various affective instruction tasks. We compare our model with a variety of LLMs on AEB, where our models outperform all other open-sourced LLMs, and surpass ChatGPT and GPT-4 in most tasks, which shows that the series of EmoLLMs achieve the ChatGPT-level and GPT-4-level generalization capabilities on affective analysis tasks, and demonstrates our models can be used as affective annotation tools.
Digital quantum simulation of gravitational optomechanics with IBM quantum computers

We showcase the digital quantum simulation of the action of a Hamiltonian that governs the interaction between a quantum mechanical oscillator and an optical field, generating quantum entanglement between them via gravitational effects. This is achieved by making use of a boson-qubit mapping protocol and a digital gate decomposition that allow us to run the simulations in the quantum computers available in the IBM Quantum platform. We present the obtained results for the fidelity of the experiment in two different quantum computers, after applying error mitigation and post-selection techniques. The achieved results correspond to fidelities over 90%, which indicates that entanglement was indeed generated through the simulation of a quantum gravitational field.
Can Synthetic Data Boost the Training of Deep Acoustic Vehicle Counting Networks?

In the design of traffic monitoring solutions for optimizing the urban mobility infrastructure, acoustic vehicle counting models have received attention due to their cost effectiveness and energy efficiency. Although deep learning has proven effective for visual traffic monitoring, its use has not been thoroughly investigated in the audio domain, likely due to real-world data scarcity. In this work, we propose a novel approach to acoustic vehicle counting by developing: i) a traffic noise simulation framework to synthesize realistic vehicle pass-by events; ii) a strategy to mix synthetic and real data to train a deep-learning model for traffic counting. The proposed system is capable of simultaneously counting cars and commercial vehicles driving on a two-lane road, and identifying their direction of travel under moderate traffic density conditions. With only 24 hours of labeled real-world traffic noise, we are able to improve counting accuracy on real-world data from $63\%$ to $88\%$ for cars and from $86\%$ to $94\%$ for commercial vehicles.