Miracle-dz/UniFAR-FTUs huggingface dataset:
Automation of the Timed Up and Go Test Using a Doppler Radar System for Gait and Balance Analysis in Elderly People

The timed up and go (TUG) test is a simple, valid, and reliable clinical tool that is widely used to assess mobility in elderly people. Several research studies have been conducted to automate the TUG test using wearable sensors or motion-tracking systems. Despite their promising results, the adopted technological systems present inconveniences in terms of acceptability and privacy protection. In this work, we propose to overcome these problems by using a Doppler radar system set into the backrest of a chair in order to automate the TUG test and extract additional information from its phases (i.e., transfer, walk, and turn). We intend to segment its phases and extract spatiotemporal gait parameters automatically. Our methodology is mainly based on a multiresolution analysis of radar signals. We proposed a segmentation technique based on the extraction of limbs oscillations signals through a semisupervised machine learning approach, on the one hand, and the application of the DARC algorithm on the other hand. Once the speed signals of torso and limbs oscillations were detected, we suggested estimating 14 gait parameters. All our approaches were validated by comparing outcomes to those obtained from a reference Vicon system. High correlation coefficients were obtained by comparing the speed signals of the torso (ρ=0.8), the speed signals of limbs oscillations (ρ=0.91), the initial and final indices of TUG phases (ρ=0.95), and the extracted parameters (percentage error < 4.8) obtained after radar signal processing to those obtained from the Vicon system.
Efficacy for Bone Deterioration in Mice with Osteoporosis and Pharmaceutical Properties of a Novel Compound Calcium Carbonate Granule

Osteoporosis (OP) has been a global health concern, and calcium supplements are still an effective method to relieve the disease. In this study, we investigated the pharmaceutical properties, antiosteoporosis, and adverse effects of a novel calcium supplement named compound calcium carbonate granule (CCCG). The dissolution rate, stability of vitamin D3, and drug permeability of the calcium supplements were explored. And the osteoporosis was constructed in mice with retinoic acid (100 mg/kg/d) for 21 days, and orally treated with CCCG (0.34, 0.68, and 1.36 g/kg/d) for 36 days to evaluate the efficacy against osteoporosis and the occurrence of kidney stones. The results showed that CCCG exhibited higher dissolution rate and better vitamin D3 stability than the standard calcium carbonate granule. CCCG also displayed more absorption in Caco-2 cell model. On the other hand, CCCG alleviated the deterioration in femur microstructure and oxidative stress in the liver and kidney, and increased the expression of bone metabolic markers. CCCG also exhibited lower risk of nephrolithiasis than the commercial calcium carbonate granule. In summary, from the perspectives of both pharmaceutics and efficacy, we illustrated the profiles of an intriguing calcium supplement, which could possibly be an alternative of commonly used ones. Practical Application. Calcium carbonate is widely used to supplement calcium, primarily because of its high calcium load, low cost, and good efficacy. Now, we provided a new calcium supplement named CCCG, which was prepared by inclusion and complexation techniques. Vitamin D3 in CCCG was made into vitamin D-β-cyclodextrin inclusion complex by inclusion technology, which improved the stability of vitamin D3. And calcium carbonate was converted to a water-soluble calcium citrate complex via complexation techniques to accelerate the release of calcium ions and minimize the risk of kidney stone formation. From the perspective of both pharmaceutics and efficacy, we illustrated the profiles of an intriguing calcium supplement, which could possibly be an alternative for the supplemental and preventive treatment of osteoporosis.
Identification of Genes and SNPs Related to Body Colors by TranscriptomeProfiling inLeopardCoralGrouper (Plectropomus

Guangdong Yuequn Biotechnology Co., Ltd., Guangdong Provincial Key Laboratory of Aquatic Larvae Feed, Jieyang 522000, China Development and Research Center for Biological Marine Resources, Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang 524006, China Fisheries College, Guangdong Ocean University, Zhanjiang 524088, China Guangdong Research Center on Reproductive Control and Breeding Technology of Indigenous Valuable Fish Species, Guangdong Ocean University, Zhanjiang 524088, China
Scaling Integer Arithmetic in Probabilistic Programs

Distributions on integers are ubiquitous in probabilistic modeling but remain challenging for many of today's probabilistic programming languages (PPLs). The core challenge comes from discrete structure: many of today's PPL inference strategies rely on enumeration, sampling, or differentiation in order to scale, which fail for high-dimensional complex discrete distributions involving integers. Our insight is that there is structure in arithmetic that these approaches are not using. We present a binary encoding strategy for discrete distributions that exploits the rich logical structure of integer operations like summation and comparison. We leverage this structured encoding with knowledge compilation to perform exact probabilistic inference, and show that this approach scales to much larger integer distributions with arithmetic.
The Association between 24 h Movement Guidelines and Internalising and Externalising Behaviour Problems among Chinese Preschool Children

This study examined the relationship between adherence to 24 h movement guidelines (24 h MGs) and internalising and externalising behavioural problems in Chinese children aged 3–6 years, with a specific focus on the differences between weekdays and weekends. The guidelines include recommendations for physical activity (PA), screen time (ST), and sleep duration (SD). The results indicated a stronger association between adherence to these guidelines and behavioural problems on weekends compared to weekdays. Specifically, the odds of experiencing internalising problems were 1.33 higher (95% CI: 1.05–1.69) when not satisfying all three behaviours compared to not satisfying one or two. Moreover, on weekends, when ST was not fulfilled, there was a higher likelihood of externalising behaviour problems compared to when it was fulfilled (OR, 1.18, 95% CI, 1.01–1.38), and when all three behaviours were not met, the likelihood was even higher (OR, 1.50, 95% CI, 1.04–2.18). Children who met all three guidelines had fewer internalising and externalising behavioural problems, suggesting a potential beneficial effect on mental health. The study revealed that a higher adherence to these recommendations corresponded to a lower risk of mental health problems. Additionally, higher screen time was linked to an increase in externalising behavioural issues. These findings underscore the importance of adherence to 24 h MGs for optimal mental health in children. Future interventions should consider these behavioural factors and incorporate strategies to promote adherence to these guidelines, particularly on weekends.
MoQA: Benchmarking Multi-Type Open-Domain Question Answering

Previous research on open-domain question answering (QA) mainly focuses on questions with short answers. However, information-seeking QA often requires various formats of answers depending on the nature of the questions, e.g., why/how questions typically require a long answer. In this paper, we present MoQA, a benchmark for open-domain QA that requires building one system that can provide short, medium, long, and yes/no answers to different questions accordingly. MoQA builds upon Natural Questions with multiple types of questions and additional crowdsourcing efforts to ensure high query quality. We adapt state-of-the-art models, and reveal unique findings in multi-type open-domain QA: (1) For retriever-reader models, training one retriever on all types achieves the overall best performance, but it is challenging to train one reader model to output answers of different formats, or to train a question classifier to distinguish between types; (2) An end-to-end closed-book QA model trained on multiple types struggles with the task across the board; (3) State-of-the-art large language models such as the largest GPT-3 models (Brown et al., 2020; Ouyang et al., 2022) also lag behind open-book QA models. Our benchmark and analysis call for more effort into building versatile open-domain QA models in the future.
A Wide Bandwidth, Low Power, Linear Quantized-Analog VGA in 28 nm CMOS Technology and 1.2V Supply

This work presents wide-band/wide range Variable Gain Amplifier (VGA) based on the Quantized-Analog (QA) processing. The VGA is formed by an array of sub-units that amplify different portions of the input signal. The gain variation is obtained by acting on the signal correlation among adjacent slices that are eventually recombined on an output load. The VGA has been characterized, by inserting the VGA inside an optical RF front-end which includes a TIA stage and output buffer. Designed and layouted in 28nm CMOS, the VGA shows a gain variation of about 22 dB obtained in one stage, and a -3dB bandwidth of 29 GHz. At maximum gain, the average equivalent input-referred current noise is equal to 10.3 pA/$\sqrt{Hz}$ while the Total Harmonic Distortion (THD) is below 2.3% for a maximum output swing of 500-mV peak-to-peak differential. The VGA consumes 64 mW while the complete front-end 124 mW from a 1.2V voltage supply.
Multi-Source Discriminant Subspace Alignment for Cross-Domain Speech Emotion Recognition

Cross-domain speech emotion recognition (SER) is an effective strategy to improve the generalization ability of emotion classification models, which is an important research direction in speech signal processing. However, since the speech signals are non-stationary, it is difficult to train a robust classifier from single-source emotional corpus. To solve this shortcoming, we propose a novel method named multi-source discriminant subspace alignment (MDSA) for cross-domain SER. In MDSA, we first conduct linear discriminant analysis (LDA) in the multi-source domain. Then, the instances in the multi-source discriminant subspace are used to linearly reconstruct the instances in the target subspace. At the same time, the reconstruction contribution of each source discriminant subspace is determined by adaptive weights. Furthermore, the multi-source discriminant subspace is aligned by reducing the loss between projections, which can make our model more robust. In this way, MDSA considers both the alignment of cross-domain data distribution and the structural information of cross-domain instances. Finally, extensive experiments are conducted on five standard emotional corpora, i.e., Berlin, IEMOCAP, CVE, EMOVO, and TESS, and the results demonstrate the proposed MDSA is superior to several state-of-the-art transfer learning algorithms in terms of performance.
Compressing Distributed Network Sketches With Traffic-Aware Summaries

Network measurements are important for identifying congestion, DDoS attacks, and more. To support real-time analytics, stream ingestion is performed jointly by multiple nodes, each observing part of the traffic, periodically reporting its measurements to a single centralized server that aggregates them. To avoid communication congestion, each node reports a compressed version of its collected measurements. Traditionally, nodes symmetrically report summaries of the same size computed on their data. We explain that to maximize the accuracy of the joint measurement, nodes should imply various compression ratios on their measurements based on the amount of traffic observed by each node. We illustrate the approach for three common sketches: The Count-Min sketch (CM), which estimates flow sizes as well as for the K-minimum-values (KMV) sketch and the HyperLogLog (HLL), which both estimate the number of distinct flows. For each sketch, we compute node compression ratios based on the traffic distribution. In general, this is done with a single round of communication with the central server, after which the compression ratio for each node can be computed. We perform extensive simulations for the sketches and analytically show that, under real-world scenarios, our sketches send smaller summaries than traditional ones while retaining similar error bounds.
Algorithmic Optimization of Transistors Applied to Silicon LDMOS

We propose a pioneering approach that integrates optimization algorithms and technology computer-aided design to automatically optimize laterally-diffused metal-oxide-semiconductors (LDMOS) with a field-oxide structure. We define the ratio of the square of the breakdown voltage divided by the specific on-resistance as the figure-of-merit (FOM) and the objective function of our optimization. We compare the performance of three different algorithms: Nelder-Mead, Powell, and Bayesian Optimization. We show how the LDMOS performance evolves as each of the three optimization algorithms reach their optimized structure. We show that a straightforward Nelder-Mead optimization leads to a local optimum when optimizing over six input parameters. We find that Bayesian Optimization is the most data-efficient method to find the global optimized structure in the multi-domain design space.
Self-Supervised Learning for Seismic Data: Enhancing Model Interpretability With Seismic Attributes

Deep learning (DL) has shown great potential in geosciences, such as seismic data processing and interpretation, improving decision-making and reducing analysis time. However, DL faces two main challenges. First, many DL models rely on labeled data, which can be time-consuming to obtain. Second, the predictions from these models often lack interpretability, making it difficult to use them for high-value decisions. To address these limitations, we propose a novel workflow that eliminates the need for labeled data and enables interpretation of the results, highlighting key geological features. The proposed workflow trains a vision transformer (ViT) to produce six attention maps, focusing on diverse and relevant regions, by assigning higher attention values. We first train the ViT using a modified distillation with no labels (DINO) method specifically designed for the seismic domain and monitor for overfitting. Then, to evaluate the focus of each attention head (AH), we use nine seismic attributes as predictor features for the assigned attention using a gradient boosting model. Finally, the method samples the seismic attributes in stationary regions of the attention maps and calculates Shapley additive explanations (SHAP) values to determine the most impactful attributes on the attention prediction. Each AH can concentrate on unique geological features of the input seismic image, as indicated by different relationships between SHAP values and seismic attributes. Additionally, regardless of location, each AH can detect the same geologically significant pattern based on the attributes used. The proposed workflow enables the interpretability of the model’s importance, guided by expert knowledge through seismic attributes.
Exploring Regional Innovation Dynamics Using Directed Acyclic Graphs

Biannually, the European Commission publishes the Regional Innovation Scoreboard (RIS) as a tool to benchmark the performance of 240 regions, 22 EU Member States (MS) and 4 Associated Countries (AC): Norway, Serbia, Switzerland, and the UK. Only Cyprus, Estonia, Latvia, Luxembourg, and Malta are included at the country level. The RIS consists of a synthetic index that starts from the arithmetic mean of a set of 21 indicators. This allows ranking the various territories of Europe for their innovative performance. EU Regions, MS and AC are expected to take their position on the ranking into account when designing innovation policies. Due to its importance for policymaking, the RIS is subject to recurrent assessments of the extent to which it constitutes a meaningful measure of a territory’s innovation performance. This paper contributes to the debate by proposing an approach based on algorithms to discover the causal relations among the RIS variables and visualize them as edges of Directed Acyclic Graphs (DAGs). In so doing, we shed some light on the limitations of the RIS tool to assess the dynamics and performance drivers of the EU national/regional innovation systems.
A C/V Converter for MEMS Gyroscope Readouts Featuring a Single Stage Class AB OTA

In this paper, we present a fully differential single stage charge-to-voltage (C/V) converter with a class AB output stage for gyroscope readout circuits. The proposed amplifier offers low noise and low power consumption by using a current reuse architecture in the OTA core while also providing a rail to-rail output swing for a good signal-to-noise ratio (SNR). I this work we benefit from a single stage amplifier which has the advantage of requiring no compensation capacitance, therefor (reducing the overall area. Moreover, having a class AB output stage allows us to reduce the needed quiescent current compare to a class A output stage while offering a high voltage swing at the output. In order to ensure that the proposed amplifier fulfills the sensor frontends specifications, extensive simulations have been carried out over a temperature range from (−5°C to 70°C) including process, voltage and micro electro-mechanical system (MEMS) variations. The simulated power consumption is below 37 $\mu$W from a 1.6 V supply and the input-referred noise stays below 35 nV/$\sqrt{\mathrm{H}\mathrm{z}}$ from 35 kHz to 10 MHz. The C/V is designed in a 40 nm CMOS process and offers a gain drift of 0.4% from −5°C to 70°C, a phase drift of below 3.5 mrad and a total harmonic distortion (THD) of below 1.5% for an output swing of 1.2 $\mathrm{V}_{\mathrm{p}\mathrm{p}}$ over all process corners while consuming an area of $145 \times 165\mu \mathrm{m}^{2}$.
Analysis and Design of a Low Power Double Tail Comparator with Dynamic Bias in 5nm FinFET Technology

This paper presents the analysis and design of a two-stage low power comparator suitable for high-speed applications. The proposed consists of a two-stage preamplifier and a latch. The entire structure works in two phases: a reset and an operating period, which is further divided in two time-intervals: integration and latching. This comparator, designed in a 5nm FinFET technology, will be compared to the state-of-the-art implementations in 5nm FinFET technology, showing noise and charge minimisation for a given decision time.
A 2.5 GHz, 0.6 V Body Driven Dynamic Comparator Exploiting Charge Pump Based Dynamic Biasing

In this paper a novel 0.6 V dynamic biased, body driven Strong Arm latch is presented. The proposed topology exploits a charge pump based dynamic biasing configuration that boosts the effective supply headroom. In addition, the body terminals are used to drive the input pair while the gates are driven by the clock signal to allow for the removal of the tail transistor, resulting in larger peak currents and smaller parasitic capacitances. This reduces power consumption and delay. Moreover, the body driven approach enables rail-to-rail input common mode range (ICMR). As a result, the proposed topology is capable of high-speed operation (up to 2.5 GHz) despite the low supply voltage. At 2.5 GHz the comparator shows a competitive energy-delay product (EDP) of 2.98 fJ/GHz.
Transparent Actor Model

Several programming and formal modeling languages are designed based on actors. Each language has certain policies for message delivery between actors and for handling the messages in the buffers. These policies are implicit in the semantics of each language. One can infer interesting properties of actor languages related to communication and coordination based on different policies and their interactions. We define the “Transparent Actor” model where we make policies explicit as points of possible variations. We identify an abstract network entity and define the semantics of Transparent Actors in three parts: actors, network, and composition. We define a core actor language named BABEL as a basis to describe the semantics of Transparent Actors using structural operational semantics (SOS) rules with variation points. These parametric rules make the implicit policies clear and can be used as a template to define the semantics of different actor-based languages. We evaluate the applicability of the template by examining the semantics for actor-based languages Rebeca, Lingua Franca, ABS, AKKA, and Erlang. We implement BABEL in Maude as a proof of concept, then concretize the parametric rules to implement some of the above languages. We consider a few properties, check them via a set of designated litmus test cases using our Maude implementations, and discuss the policy interactions.
A Survey on Usage of Multimedia Databases for Emotion Elicitation: A Quantitative Report on How Content Diversity Can Improve Performance

Affective picture databases provide a standardized set of images to elicit controlled and consistent emotional responses in research participants. They are a valuable tool for studying various emotion-related phenomena across several research domains. These domains include emotion perception, emotion regulation, and the neural basis of emotion. However, affective picture databases have diverse schemas, structures, and content, making them difficult to use. Searching and retrieving optimal pictures relevant to affective stimulation may be challenging and time-consuming. In this context, we surveyed domain experts about their practices and experiences working with affective multimedia databases such as IAPS, NAPS, OASIS, GAPED, and others. The survey identified a need for novel data observatory software. This finding motivates the authors’ intention to develop and validate such software platform that relies on AI. Such a platform would describe better, retrieve, and integrate various semi-structured affective multimedia datasets. The results prominently indicate the overwhelming dissatisfaction regarding stimuli content diversity and cultural bias, specifically regarding emotional and semantic context. The main driver of satisfaction from users of existing automated retrieval software is the quality of semantic descriptors available. This points to the direction AI should take in novel data observatory software. This survey follows up on a similar survey conducted ten years ago and explores the differences in researchers’ opinions and experiences during that time. The complete aggregated results are publicly available at https://github.com/mhorvat/stimdbsurvey.
Automatic Hardware Accelerators Reconfiguration through LinearUCB Algorithms on a RISC-V Processor

Reconfigurable processors are hardware architectures that allow for the dynamic configuration of processing resources to optimize performance and power consumption, using partial reconfiguration to modify a portion of the design or update it without affecting the entire system. In this work, we present an automatic reconfiguration technique that leverages machine learning (ML) algorithms to automatically select the optimal configuration of a general-purpose hardware accelerator according to the workload and reconFigure the architecture at run-time. The problem is formulated as a Contextual Bandit (CB) case using the Linear Upper Confidence Bound (LinearUCB) algorithms and verified using the RISC-V Klessydra family cores as a case of study.