Miracle-dz/UniFAR-FTUs huggingface dataset:
Resistance is Not Futile: Haptic Damping Forces Mitigate Effects of Motor Noise During Reaching

Understanding how users adapt their motor behavior to damping forces can improve assistive haptic shared control strategies, for instance in heavy robot-assisted lifting applications. In previous experiments we showed that subjects reaching in constant and position-dependent longitudinal damping fields were able to reduce their movement time and increase end-point accuracy. The movement time versus movement distance and prescribed end-point accuracy agreed with Fitts' Law. However, why subjects were able to have shorter movement time while subjected to impeding damping forces is not explained by Fitts' Law. Based on the minimal variance principle we propose that humans exploit the noise-filtering behavior of constant or position-dependent damping forces. These damping forces attenuate mechanical effects of activation-dependent motor noise. This allows for higher motor activation and shorter movement time without losing end-point accuracy. Consequently, higher allowed motor activation allows for higher accelerations that lead to higher peak velocities, resulting in shorter movement times. Linear and non-linear stochastic optimal feedback control and optimal estimation models with multiplicative noise corroborate measurement data, supporting our hypothesis.
Quasi-Static Modelling of a Redundant Knee Prosthesis

Developing mechanical devices to restore natural locomotion for transfemoral amputees still raises many challenges. One of them is the development of an efficient control strategy for the prosthesis active joints, with the objective of making it flexible and intuitive to use. This paper reports the modelling of the knee joint of the transfemoral CYBERLEGs Beta-Prosthesis, an actuated elastic ankle-knee prosthesis designed with the objective to minimize its total power consumption. More precisely, we report the static model of the prosthetic knee that was used to build its low-level controller, converting the desired torque into a reference position for the joint actuator. Bench testing of the prosthetic knee was performed to show that this static model is able to accurately predict the actual behaviour of the device.
Star-Graft Quarterpolymer-Based Polymersomes as Nanocarriers for Co-Delivery of Hydrophilic/Hydrophobic Chemotherapeutic Agents

We report the fabrication of polymersomes, using as building blocks star-graft quarterpolymers, composed of hydrophobic polystyrene and pH-sensitive poly(2-vinylpyridine)-b-poly(acrylic acid) (P2VP-b-PAA) arms, emanated from a common nodule, enriched by thermosensitive poly(N-isopropylacrylamide) grafts covalently bonded on the PAA block-arms. These multicompartmental polymersomes were evaluated as nanocarriers for the encapsulation and controlled co-delivery of doxorubicin (hydrophilic) and paclitaxel (hydrophobic) chemotherapeutic agents. The polymersomes can load these drugs in different compartments and can efficiently be internalized in the human lung adenocarcinoma epithelial cells, delivering their cargo and inducing high cell apoptosis. The release kinetics of both anticancer agents was controlled differently by the environmental conditions (pH and temperature). Enhanced release was observed at the acidic pH 6.0 and under physiological temperature (37 °C). At the same total drug level, co-delivery of these drugs with the polymersomes caused enhanced cytotoxicity and induced significantly higher cell apoptosis in the cancer cell line compared to the polymersomes loaded with either of the two drugs.
Human Vicarious Trial and Error Is Predictive of Spatial Navigation Performance

When learning new environments, rats often pause at decision points and look back and forth over their possible trajectories as if they were imagining the future outcome of their actions, a behavior termed “Vicarious trial and error” (VTE). As the animal learns the environmental configuration, rats change from deliberative to habitual behavior, and VTE tends to disappear, suggesting a functional relevance in the early stages of learning. Despite the extensive research on spatial navigation, learning and VTE in the rat model, fewer studies have focused on humans. Here, we tested whether head-scanning behaviors that humans typically exhibit during spatial navigation are as predictive of spatial learning as in the rat. Subjects performed a goal-oriented virtual navigation task in a symmetric environment. Spatial learning was assessed through the analysis of trajectories, timings, and head orientations, under habitual and deliberative spatial navigation conditions. As expected, we found that trajectory length and duration decreased with the trial number, implying that subjects learned the spatial configuration of the environment over trials. Interestingly, IdPhi (a standard metric of VTE) also decreased with the trial number, suggesting that humans benefit from the same head-orientation scanning behavior as rats at spatial decision-points. Moreover, IdPhi captured exclusively at the first decision-point of each trial, was correlated with trial trajectory duration and length. Our findings demonstrate that in VTE is a signature of the stage of spatial learning in humans, and can be used to predict performance in navigation tasks with high accuracy.
Neutral to Joyous Happy Emotion Conversion

A method to convert neutrally synthesised speech to joyous happy emotion by prosody modification and laughter synthesis is presented. Modified zero frequency filtering (ZFF) method is proposed to extract the excitation source features from dynamically varying happy emotive, synthesised speech and laughter sequence. Prosody source features of verbal (spoken), source and system features of non-verbal (laughter) segments of elicited happy emotion are analysed and incorporated in the neutral speech. Pitch contours of verbal segments of the happy emotion are divided into three components and analysed separately for incorporation. Laughter sequence is synthesised by modifying the Line spectral frequencies (LSF), pitch period and strength of excitation (SoE) contours of a synthesised vowel utterance. The effectiveness of the synthesized happy emotive speech is confirmed by the subjective evaluation.
Artificial bandwidth extension using deep neural network-based spectral envelope estimation and enhanced excitation estimation

The authors propose a robust artificial bandwidth extension (ABE) technique to improve narrowband (NB) speech signal quality using an enhanced spectrum envelope and excitation estimation. For envelope estimation, they propose an enhanced envelope estimation method using a deep neural network with multiple layers. For excitation estimation, they use a whitened NB excitation signal that is generated by passing the excitation signal through a whitening filter. An adaptive spectral double shifting method is introduced to obtain an enhanced wideband (WB) excitation signal. The proposed ABE system is applied to the decoded output of an adaptive multi-rate (AMR) codec at 12.2 kbps. They evaluate its performance using log spectral distortion, a WB perceptual evaluation of speech quality, and a formal listening test. The objective and subjective evaluations confirm that the proposed ABE system provides better speech quality than AMR at the same bit rate.
A High-Efficient, Multi-Phase Charge Pump Scheme

In this paper, a high-efficiency solution to multi-phase stepup charge pump is proposed. Aiming at traditional multi-phase charge pump with full-coverage integer voltage conversion ratios (VCRs) for good efficiency, we further improve its efficiency at some particular VCRs. By automatically bypassing some unnecessary converter stages, dynamic power loss is reduced. Based on a 0.25 μm process, an example of three-stage charge pump with VCRs of 1∼8 is conducted. It shows that converter with proposed control strategy has improved perofrmance at VCR<5. Compared with traditional one, the efficiency is improved by 57% at most.
Electrooculography by Wearable Graphene Textiles

Study of eye movements and measurement of the resulting biopotentials, referred to as electrooculography (EOG), may find increasing use in applications within the domain of personalized medicine provided that the limitations of conventional “wet” electrodes are addressed. To overcome the limitations of conventional electrodes, in this paper, we report for the first time the use and characterization of graphene-coated electroconductive textile electrodes for EOG acquisition. The feasibility of the developed gel-free, flexible, dry textile electrodes was experimentally authenticated through side-by-side comparison with pre-gelled, wet, silver/silver chloride (Ag/AgCl) electrodes, where the simultaneously recorded signals displayed correlation of up to ~87% over durations reaching hundred seconds and repeated on eight participants. This outstanding performance demonstrates the potential of graphene textiles in wearable devices based on sensing and processing of electrooculograms.
The Clinical Imperative for Inclusivity: Race, Ethnicity, and Ancestry (REA) in Genomics

The Clinical Genome Resource (ClinGen) Ancestry and Diversity Working Group highlights the need to develop guidance on race, ethnicity, and ancestry (REA) data collection and use in clinical genomics. We present quantitative and qualitative evidence to characterize: 1) acquisition of REA data via clinical laboratory requisition forms, and 2) information disparity across populations in the Genome Aggregation Database (gnomAD) at clinically relevant sites as determined by variants in ClinVar. Our requisition form analysis showed substantial heterogeneity in clinical laboratory ascertainment of REA, as well as marked incongruity among terms used to define REA categories. There was also striking disparity across REA populations in the amount of information available about variants at clinically relevant sites in gnomAD. European ancestral populations constituted the majority of observations (55.8%), allele counts (59.7%), and private alleles (56.1%) in gnomAD at 550 loci with “pathogenic” and “likely pathogenic” expert-reviewed variants in ClinVar. Our findings highlight the importance of implementing and supporting programs to increase diversity in genome sequencing and clinical genomics, as well as measuring uncertainty around population-level datasets that are used in variant interpretation. Finally, we suggest the need for a standardized REA data collection framework to be developed and adopted across clinical genomics.
High performance WTe2-MoS2in-plane heterojunction Tunnel Field Effect Transistors

We study in-plane heterojunction tunnel field effect transistors based on monolayer transition metal dichalcogenides by means of self-consistent non-equilibrium Green's functions simulations and an atomistic tight-binding Hamiltonian. We first compare several heterojunctions before focusing on the most promising one, i.e. $\mathrm{\pmb{WTe}_{2}}{-\mathrm{\pmb{MoS}}_{2}}$. We investigate the scalability of this device as a function of the channel length, and analyze the influence of source and drain backgate voltage. Our results show that, with an appropriate design, this device can yield extremely low sub-threshold swings and $\pmb{I}_{ON}/\pmb{I}_{OFF}$ ratios higher than $10^{7}$ at a supply voltage of 0.3V.
Acousticcardiogram: Monitoring Heartbeats using Acoustic Signals on Smart Devices

Vital signs such as heart rate and heartbeat interval are currently measured by electrocardiograms (ECG) or wearable physiological monitors. These techniques either require contact with the patient's skin or are usually uncomfortable to wear, rendering them too expensive and user-unfriendly for daily monitoring. In this paper, we propose a new noninvasive technology to generate an Acousticcardiogram (ACG) that precisely monitors heartbeats using inaudible acoustic signals. ACG uses only commodity microphones and speakers commonly equipped on ubiquitous off-the-shelf devices, such as smartphones and laptops. By transmitting an acoustic signal and analyzing its reflections off human body, ACG is capable of recognizing the heart rate as well as heartbeat rhythm. We employ frequency-modulated sound signals to separate reflection of heart from that of background motions and breath, and continuously track the phase changes of the acoustic data. To translate these acoustic data into heart and breath rates, we leverage the dual microphone design on COTS mobile devices to suppress direct echo from speaker to microphones, identify heart rate in frequency domain, and adopt an advanced algorithm to extract individual heartbeats. We implement ACG on commercial devices and validate its performance in real environments. Experimental results demonstrate ACG monitors user's heartbeat accurately, with median heart rate estimation error of 0.6 beat per minute (bpm), and median heartbeat interval estimation error of 19 ms.
Transmission Line Model for a Series fed Log-periodic Microstrip Antenna array

In this paper, the authors propose a transmission line model for estimation of the input impedance of a series-fed log periodic microstrip antenna. The model considers each element as a section of transmission line. These sections load the main feeding line log-periodically. The result from the proposed model closely follows those from commercial EM code CST Microwave Studio®.
Multimodal Affective Computing to Enhance the User Experience of Educational Software Applications

Affective computing is becoming more and more important as it enables to extend the possibilities of computing technologies by incorporating emotions. In fact, the detection of users’ emotions has become one of the most important aspects regarding Affective Computing. In this paper, we present an educational software application that incorporates affective computing by detecting the users’ emotional states to adapt its behaviour to the emotions sensed. This way, we aim at increasing users’ engagement to keep them motivated for longer periods of time, thus improving their learning progress. To prove this, the application has been assessed with real users. The performance of a set of users using the proposed system has been compared with a control group that used the same system without implementing emotion detection. The outcomes of this evaluation have shown that our proposed system, incorporating affective computing, produced better results than the one used by the control group.
Opportunistic Prefetching of Cellular Internet of Things (cIoT) Device Contexts

The number of IoT devices is expected to be between 32-99 Billion by 2025, many of which will use the cellular wireless data network for communications. This presents a unique challenge to the operator while allocating resources, namely how to optimally balance CPU and memory usage in virtualized and physical hosts while simultaneously handling millions of IoT devices without affecting the quality of experience of normal mobile users. Due to the sheer number of the IoT devices, it is not feasible to store their session context in memory. In this work, we present a machine learning model that predicts the network usage pattern of five broad classes of cIoT devices. The prediction model trained on a Multilayer Perceptron allows the network operator to opportunistically prefetch cIoT context from secondary storage before it is required. Further, we propose a new metric -- Value of Perfect Information -- to assess our approach. We evaluate our approach across two fronts: First, we study the efficacy of replacement algorithms such as LRU, MRU, FIFO and random replacement; we also assess the impact of varying memory slots. Finally, we evaluate our models against the default (no prefetching) model and an on-time prefetching model to demonstrate the value of our pre-fetching approach.
Fuzzy-Genetic Approach to Solving Clustering Problem

Clustering is a task of grouping a set of objects in such a way that objects in the same group (called a cluster) are similar to each other and dissimilar to objects belonging to other groups (clusters). The article presents the idea of the hybrid Fuzzy Logic-Genetic Algorithm (FLGA) system that supports solving clustering problems. The Genetic Algorithm (GA) realizes the process of multi-objective optimization - it aims at optimal distribution of clusters and correctly assigns each object to a cluster. The Fuzzy Logic Controller (FLC) is used for setting the number of clusters. The FLC uses additional fuzzy logic criteria obtained from experts. Experiments show that the proposed algorithm is an efficient tool for the clustering problem. The algorithm can be also used for solving similar optimization problems, for example, to predict cardiovascular diseases (CVD), based on anthropometric predictors like Blood Pressure (BP) or Body Mass Index (BMI).
VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking

In this paper, we present a novel system that separates the voice of a target speaker from multi-speaker signals, by making use of a reference signal from the target speaker. We achieve this by training two separate neural networks: (1) A speaker recognition network that produces speaker-discriminative embeddings; (2) A spectrogram masking network that takes both noisy spectrogram and speaker embedding as input, and produces a mask. Our system significantly reduces the speech recognition WER on multi-speaker signals, with minimal WER degradation on single-speaker signals.
Regularized Contextual Bandits

We consider the stochastic contextual bandit problem with additional regularization. The motivation comes from problems where the policy of the agent must be close to some baseline policy which is known to perform well on the task. To tackle this problem we use a nonparametric model and propose an algorithm splitting the context space into bins, and solving simultaneously --- and independently --- regularized multi-armed bandit instances on each bin. We derive slow and fast rates of convergence, depending on the unknown complexity of the problem. We also consider a new relevant margin condition to get problem-independent convergence rates, ending up in intermediate convergence rates interpolating between the aforementioned slow and fast rates.
Multiple Input Converter for Standalone Photovoltaic Applications

Power electronic converters have become an integral part of renewable energy systems, especially solar photovoltaic system for generating electrical energy. There are various converter topologies for extracting power from the solar panels. Most of them employ maximum power point tracking (MPPT) through a single dc-dc converter for a string of panels. This make the panels operate at an operating point, which lowers the efficiency of the panel. This paper presents a Multi Input Converter (MIC) topology for a standalone solar photovoltaic (PV) system. The presented converter has a dedicated MPPT switch for each panel. This ensures MPPT of each panel even in case of partial shading without any computational efforts. A battery with bidirectional dc/dc converter has also been interfaced to maintain uninterruptable supply. A complete standalone system is studied analytically and simulated using MATLAB/Simulink.
Analysis of Hybrid Geographic/Delay-Tolerant Routing Protocols for Wireless Mobile Networks

We study a mobile wireless network in which nodes travel on an infinite plane along trajectories comprised of linear segments; a single packet is traveling towards a destination located at an infinite distance, employing both wireless transmissions and sojourns on node buffers. In this setting, we calculate analytically the long-term averages of (i) the speed with which the packet travels towards its destination and (ii) the rate with which the wireless transmission cost accumulates with distance traveled. Due to the problem's complexity, we use two approximations; simulations show that the resulting errors are modest.
VSL#3 can prevent ulcerative colitis-associated carcinogenesis in mice

AIM To investigate the effects of VSL#3 on tumor formation, and fecal and intestinal mucosal microbiota in azoxymethane/dextran sulfate sodium (AOM/DSS) induced mice model. METHODS C57BL/6 mice were administered AOM/DSS to develop the ulcerative colitis (UC) carcinogenesis model. Mice were treated with 5-ASA (75 mg/kg/d), VSL#3 (1.5 × 109 CFU/d), or 5-ASA combined with VSL#3 by gavage from the day of AOM injection for three months (five days/week). The tumor load was compared in each group, and tumor necrosis factor (TNF-α) and interleukin (IL)-6 levels were evaluated in colon tissue. The stool and intestinal mucosa samples were collected to analyze the differences in the intestinal microbiota by 16s rDNA sequencing method. RESULTS VSL#3 significantly reduced the tumor load in AOM/DSS-induced mice model and decreased the level of TNF-α and IL-6 in colon tissue. The model group had a lower level of Lactobacillus and higher level of Oscillibacter and Lachnoclostridium in fecal microbiota than the control group. After the intervention with 5-ASA and VSL#3, Bacillus and Lactococcus were increased, while Lachnoclostridium and Oscillibacter were reduced. 5-ASA combined with VSL#3 increased the Lactobacillus and decreased the Oscillibacter. The intestinal mucosal microbiota analysis showed a lower level of Bifidobacterium and Ruminococcaceae_UCG-014 and higher level of Alloprevotella in the model group as compared to the control group. After supplementation with VSL#3, Bifidobacterium was increased. 5-ASA combined with VSL#3 increased the level of both Lachnoclostridium and Bifidobacterium. CONCLUSION VSL#3 can prevent UC-associated carcinogenesis in mice, reduce the colonic mucosal inflammation levels, and rebalance the fecal and mucosal intestinal microbiota.
Platelet volume indices for the prognosis of acute ischemic stroke patients with intravenous thrombolysis

Abstract Objective: We aimed to investigate whether platelet volume indices (PVIs) were associated with an unfavorable clinical outcome in acute ischemic stroke (AIS) patients undergoing intravenous thrombolysis (IVT). Methods: We defined a modified Rankin Scale (mRS) score of 3–6 at 90 days as an unfavorable outcome. Logistic regression analysis was performed to find out whether mean platelet volume (MPV), platelet distribution width (PDW), MPV/platelet count (PC) ratio and PDW/PC ratio were associated with poor prognosis. A Spearman correlation test was carried out to assess the relationship between variables. Results: Overall, 183 patients were included in this study. Multivariate logistic regression analysis revealed that MPV (adjusted odds ratio [OR] 1.52, 95% confidence interval [CI]: 1.01–2.29, p = 0.044) and PDW-sd (adjusted OR 1.30, 95% CI: 1.06–1.59, p = 0.011) were independent predictors of the poor outcome. There was a trend of incremental OR when compared higher tertile of MPV with lower ones (second tertile, adjusted OR 2.52,95% CI：1.02–6.21, p = 0.045； third tertile, adjusted OR 2.61, 95% CI: 1.12–6.09, p = 0.027). Besides, we found a significant positive correlation between MPV and PDW-sd (or =0.874, p < 0.001). Conclusion: MPV and PDW-sd were independent predictors for 90-day outcomes in stroke patients receiving thrombolysis.