Miracle-dz/UniFAR-FTUs huggingface dataset:
Genomic insights into ESBL-producing Escherichia coli isolated from non-human primates in the Peruvian Amazon

Introduction Extended-spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae are on the WHO priority pathogens list because they are associated with high mortality, health-care burden, and antimicrobial resistance (AMR), a serious problem that threatens global public health and should be addressed through the One Health approach. Non-human primates (NHP) have a high risk of acquiring these antibiotic-resistant bacteria due to their close phylogenetic relationship with humans and increased anthropogenic activities in their natural environments. This study aimed to detect and analyze the genomes of ESBL-producing Escherichia coli (ESBL-producing E. coli) in NHP from the Peruvian Amazon. Materials and methods We collected a total of 119 fecal samples from semi-captive Saguinus labiatus, Saguinus mystax, and Saimiri boliviensis, and captive Ateles chamek, Cebus unicolor, Lagothrix lagothricha, and Sapajus apella in the Loreto and Ucayali regions, respectively. Subsequently, we isolated and identified E. coli strains by microbiological methods, detected ESBL-producing E. coli through antimicrobial susceptibility tests following CLSI guidelines, and analyzed their genomes using previously described genomic methods. Results We detected that 7.07% (7/99) of E. coli strains: 5.45% (3/55) from Loreto and 9.09% (4/44) from Ucayali, expressed ESBL phenotype. Genomic analysis revealed the presence of high-risk pandemic clones, such as ST10 and ST117, carrying a broad resistome to relevant antibiotics, including three blaCTX-M variants: blaCTX-M-15, blaCTX-M-55, and blaCTX-M-65. Phylogenomic analysis confirmed the clonal relatedness of high-risk lineages circulating at the human-NHP interface. Additionally, two ESBL-producing E. coli strains were identified as EPEC (eae) and ExPEC according to their virulence profiles, and one more presented a hypermucoviscous phenotype. Discussion We report the detection and genomic analysis of seven ESBL-producing E. coli strains carrying broad resistome and virulence factors in NHP from two regions of the Peruvian Amazon. Some of these strains are closely related to high-risk pandemic lineages previously reported in humans and domestic animals, highlighting the negative impact of anthropogenic activities on Amazonian wildlife. To our knowledge, this is the first documentation of ESBL-producing E. coli in NHP from the Amazon, underscoring the importance of adopting the One Health approach to AMR surveillance and minimizing the potential transmission risk of antibiotic-resistant bacteria at the human-NHP interface.
Convolutional Neural Network Algorithms for Car Accident Classification

Road accident is one of the top contributors to the number of death tolls around the globe. Consequently, much effort has been put in to minimize road accidents, and not often are many lives lost due to delayed response from emergency medical teams, notably in secluded or congested areas in the event of road accidents. The implementation of numerous deep learning algorithms has been widely used in many research fields; road accident detection is no exception. Therefore, this paper highlights and compares 3 different classification models from convolutional neural networks (CNN), namely MobileNetV3-Large, EfficientNetV2L, and ResNet-50 trained with a dataset of Closed-Circuit Television (CCTV) scenes obtained from Kaggle. The evaluation metrics consist of four categories for each proposed model, such as accuracy, precision, recall, and f1-score from two distinct classes, which are accident and non-accident events. Based on our study, it is evidently proven that ResNet-50 gained the overall highest f1-score of 95.13% followed by EfficientNetV2L and MobileNetV3-Large with 83.24% and 75.56% respectively. Additionally, ResNet-50's result validates the highest point with an average of 95.19% in terms of the remaining evaluation metrics, then 83.49% and 75.62% for EfficientNetV2L and MobileNetV3-Large correspondingly. Ultimately, ResNet-50 serves as the most appropriate and reliable convolutional neural network model in detecting a road accident effectively, followed with the lowest risk of detection inaccuracies.
SmokeAway: An Early Smoke Detection IoT Architecture for Outdoors

The lack of available smoke detectors outdoors constitutes a significant factor contributing to the rapid spread of fires before effective containment can be achieved. The integration of outdoor smoke detectors helps tremendously with reducing fire and smoke hazards, consequently fostering a safe environment. With the emergence of smart cities and the revolutionary shift in relying on technologies such as the Internet of Things (IoT) and artificial intelligence (AI) tools, the current stage requires developing systems that integrate these tools and leverage them. To address that, this paper presents "SmokeAway," a reliable system to promptly and accurately detect outdoor smoke. In order to do so, we propose a layered IoT architecture that relies on computer vision and a deep learning model. The efficacy of our model is assessed through two key performance metrics: accuracy and categorical cross-entropy loss. Compared to other models, our model has achieved notably low loss values, and accuracy rates of up to 96%, outperforming alternate models.
Social Performance Rating During Social Skills Training in Adults with Autism Spectrum Disorder and Schizophrenia

Social communication skills are crucial factors influencing human social life. Quantifying the degree of social communication difficulties is necessary to understand developmental and neurological disorders and to create systems for automatic symptom screening and early intervention methods such as social skills training. Social skills training by human trainers is well established. Some automated social skills training systems have been proposed in the past. The previous social skills training system used a revised roleplay test to evaluate human social communication skills. However, this scale was evaluated by specialists such as psychiatrists and was not based on a standard internationally used scale. In this paper, we propose applying a social performance rating scale to social skills training data to measure social communication skills. We made a Japanese version of the social performance rating scale that can be rated without special qualifications and has English and French versions. In a previous study, we collected videos of interactions between trainers and adults with autism spectrum disorder, schizophrenia, and control participants during social skills training sessions. Two raters used the scale to annotate the collected data. The results found social performance rating scale showed good psychometric properties for assessing social communication skills. We found significantly greater gaze scores in adults with autism spectrum disorder than in adults with schizophrenia. There were differences between the ratings of different tasks in the adults with schizophrenia and control participants. These results suggest that the social performance rating scale can be a useful tool to assess social communication skills in different cultures and different pathologies. Possible future directions include using the social performance rating scale for assessing social behavior during interaction with a virtual agent.
Statistical Characterization of ReRAM Arrays for Analog In-Memory Computing

A key challenge in developing new memories for analog in memory computing is being able to rapidly characterize statistics across a large number of analog devices. In this paper, we introduce a unique application specific integrated circuit (ASIC) designed for characterizing ReRAM statistics in a crossbar array architecture. Using this platform, a routine is developed to eliminate stuck bits and provide a 100% yield for our TaOx ReRAM memory cell. The platform allows us to characterize the noise and drift across multiple devices. We see that over five minutes the median value of the weights is highly stable changing by an average of 0.8%. Nevertheless, the standard deviation of the weights typically increases more than the median drift in the weights. Using the measured weight drift and standard deviation, we simulate the accuracy of an analog accelerator on the CIFAR-10 dataset and show that a near numeric accuracy of 91.2% (ideal numeric is 91.5%) can be achieved at t=0, but that it decreases to 88.6% by 300 seconds.
CardioDiverse: A Balanced ECG Dataset for Improved Association of Heart Diseases with Relevant Leads

This study introduces CardioDiverse, a new dataset that has been constructed to address the scarcity of balanced data in existing electrocardiogram (ECG) datasets. CardioDiverse is a combination of multiple datasets integrated to provide rich information to support the association between the different ECG leads and CVD subclasses. The dataset is available to the public through GitHub. Our findings illustrate that particular leads provide clear manifestation to certain CVD subclasses. For instance, leads II, AVF, V3, V4, V5, and V6 were predominantly associated with ST-T wave abnormality, whereas leads II, III, AVF, V1, V5, and V6 were more affiliated with hypertrophy. Furthermore, by extending the analysis to include specific subclasses within the PTB-XL dataset, our study shows promising results in categorizing these subclasses using single-lead ECGs. CardioDiverse is an invaluable resource for researchers, as it provides a more balanced dataset and includes essential data analysis tools within the repository. The data and insights obtained through this study pave the way for future research to investigate the use of these leads for the early detection and diagnosis of cardiovascular diseases.
Time Synchronization Sensitivity Analysis of Power System Phasor Measurement Applications

Insufficient resiliency of time synchronization of elements spread over wide areas is the main obstacle in transition from mission supportive WAM systems to mission critical WAMPAC systems. This paper presets an overview of time synchronization elements and principles currently implemented in Croatian Transmission System Operator-HOPS. Analysis of time synchronization error impacts to WAMPAC revealed that more robust but less accurate synchronization techniques such as NTP can be used in all elements except PMUs. GPS receivers that are in use exclusively for time synchronization in PMUs nowadays can easily fail to provide exact timing due to low reception signal level under negative influences of natural phenomena or man-made signals. A proposal of several potential alternatives to GPS time synchronization is given paper.
Wireless Power Transfer for Smart Power Outlet

Wireless power transmission, based on electromagnetic principles, involves delivering electrical energy from a power source to an electrical load without physical conductors. This technology is precious when wires are impractical, unsafe, or unfeasible. In wireless power transmission, the paramount consideration is efficiency. Ensuring that a substantial portion of the energy generated reaches the intended receiver(s) is crucial for optimizing economic viability and minimizing power loss during transmission. Conventional power outlet sockets, often serving as extension points for multiple devices, are standard fixtures in small offices and homes. However, their wired nature limits distance introduces clutter, and raises safety concerns. Addressing these drawbacks, this paper presents a wireless power transfer solution—an intelligent power outlet. This innovative outlet is powered via wireless transmission, utilizing primary and secondary coils. Furthermore, the intelligent power outlet can be conveniently toggled ON/OFF using a remote control, enhancing its functionality and practicality.
Mitigating Test-Induced Yield-Loss by IR-Drop-Aware X-Filling

Scan shift causes a lot of switching activity in the combinational logic that in turn can lead to high IR-drop. Excessive IR-drop may corrupt test vectors or responses during shifting, leading to test-induced yield-loss. Power-aware X-filling is a widely used method to reduce the overall switching activity during shifting without modifying the scan infrastructure. While such untargeted approaches help reduce the average IR-drop during shifting, some hot-spots of worst-case IR-drop still remain. Through an innovative in-depth IR-drop analysis of high-risk shift cycles, we discovered that these hot-spots are caused by a few High-Impact scan-cell Transitions (HITs). In this paper, we propose to reduce the peak IR-drop during scan shift by using a novel X-filling strategy that (1) identifies HITs through GPU-accelerated IR-drop estimation, and (2) adjusts the X-filling of test vectors to mitigate the remaining worst-case IR-drop hotspots. The results on ITC’99 benchmark circuits demonstrate a 26% peak IR-drop reduction on average without impact on fault coverage or test vector count.
Hollow Microneedle Fabrication and Characterization for Interstitial Fluid Extraction in Minimally Invasive Sensors

Interstitial fluid (ISF) is a rich source of relevant biomarkers, which surrounds the cells and tissues of the body and its composition is very similar to blood. Whereas in contrast to blood it does not clot and thus is an ideal matrix for the continuous monitoring of biomarkers. However, due to a number of technological challenges in its application it is still a largely unexplored niche in the context of wearable sensors. The main challenge is hereby the ISF collection, its transport to the sensor and its sensing in real time. Due to the direct accessibility of the ISF through the human skin by passing the stratum corneum and reaching the viable epidermis, hollow microneedles (MN) with lengths of about 1 mm are a highly promising approach for ISF sampling. In this contribution, we propose the fabrication of hollow MNs by using hot embossing of polymer foils into Laser generated master patterns on Borofloat® glass wafers. With this cost effective approach MNs can be fabricated on a flexible substrate, which make them ideal for the integration into flexible patch like sensor devices. The MNs are characterized by means of penetration and liquid extraction and transportation on a laboratory scale towards its application in a sensor patch in a real world scenario.
A Near-Memory Dynamically Programmable Many-Core Overlay

Improving computational efficiency has remained a persistent challenge over the years, particularly in light of the increasing utilization of machine learning and deep learning applications in daily routines. Researchers have proposed diverse methodologies to improve processor speed; however, memory technologies have not kept pace with developments of processors. A potential solution to this disparity is found in leveraging High Bandwidth Memory (HBM), which improves memory efficiency by seamlessly integrating memory with logic on a single chip, thereby facilitating high bandwidth data transfers.In this paper, we introduce a novel architecture for a many-core processor. Through the HBM’s fully independent channels, the processing elements (PEs) can execute memory transactions concurrently. The main PE in our design employs an open-source RISC-V core, which is replicated to enable parallel computation. Our proposed system, incorporating HBM, demonstrates a remarkable speed enhancement of 16x compared to a baseline configuration utilizing DDR4 memory. The effectiveness of our design is evaluated using matrix multiplication and 2D convolution benchmarks. Experimental results are generated using an Alveo U280 data accelerator card.
Convolutional Neural Networks Inference Accelerator Design using Selective Convolutional Layer

Convolutional Neural Networks (CNNs) often require a huge amount of multiplication. The current approach of multiplication reduction requires data preprocessing, which is power-hungry and time-consuming. The paper proposed an Image-wised Selective Processing Engine (SPE-I) for accelerating CNN processing by eliminating unessential operations through algorithm-hardware co-designs. The SPE-I compares the similarity of two input images and identifies any redundant calculations that can be skipped. A modified LeNet-5 network, LeNet3x3 was designed to validate the performance improvement of SPE-I using the MNIST dataset. LeNet3x3 with and without SPE-I were implemented in TSMC 90-nm CMOS technology at 87.5 MHz operating frequency. Compared to the network without SPE-I, the network with SPE-I only has 0.12% - 1.79% accuracy drop, achieving 43.1% power saving due to 73% - 81% multiplication reduction. Regarding timing, SPE-I takes 20% of total clock cycles to provide convolutional data compared to the convolutional layer using preprocessing.
High-Speed Compilation of Large-Scale Stochastic Circuits

Stochastic computing (SC) is executed on bitstreams which encode probabilities into the ratio between the number of one bits and the length of the stream. SC is successfully applied, for example, to image processing and beliefpropagation (BP) of low-density parity-check (LDPC) codes. With increased interest into SC, there is a gap with respect to the available scalable design automation tools needed for the fast prototyping of SC circuits. We bridge this gap by starting from the BitSAD.jl library, and implement data structures and methods to achieve orders of magnitude speed-ups for the compilation of SC circuits. We demonstrate the speedups by compiling BP decoders for LDPC codes. Our improvements enable the compilation of circuits which were previously out of reach for the tools. Our methods pave the way towards scalable design automation of SC.
Efficient LiDAR Point Cloud Oversegmentation Network

Point cloud oversegmentation is a challenging task since it needs to produce perceptually meaningful partitions (i.e., superpoints) of a point cloud. Most existing oversegmentation methods cannot efficiently generate superpoints from large-scale LiDAR point clouds due to complex and inefficient procedures. In this paper, we propose a simple yet efficient end-to-end LiDAR oversegmentation network, which segments superpoints from the LiDAR point cloud by grouping points based on low-level point embeddings. Specifically, we first learn the similarity of points from the constructed local neighborhoods to obtain low-level point embeddings through the local discriminative loss. Then, to generate homogeneous superpoints from the sparse LiDAR point cloud, we propose a LiDAR point grouping algorithm that simultaneously considers the similarity of point embeddings and the Euclidean distance of points in 3D space. Finally, we design a superpoint refinement module for accurately assigning the hard boundary points to the corresponding superpoints. Extensive results on two large-scale outdoor datasets, SemanticKITTI and nuScenes, show that our method achieves a new state-of-the-art in LiDAR oversegmentation. Notably, the inference time of our method is 100× faster than that of other methods. Furthermore, we apply the learned superpoints to the LiDAR semantic segmentation task and the results show that using superpoints can significantly improve the LiDAR semantic segmentation of the baseline network. Code is available at https://github.com/fpthink/SuperLiDAR.
Enhancing Deep Reinforcement Learning with Compressed Sensing-based State Estimation

In various real-world applications, sensor data collected for adaptive control using Reinforcement Learning (RL) often suffer from missing information due to sensor failures, data transmission errors, or other sources of noise. Such missing data can significantly hinder the agent’s ability to make informed decisions and degrade performance. In this paper, we propose a novel approach to address this challenge by leveraging Compressed Sensing (CS) techniques to recover missing information from the sensor data and reconstruct the state observation. The reconstructed state is then fed to the RL agents. As a result, they exhibit enhanced robustness and intelligence, surpassing the performance achievable when solely presented with noisy data as state input.
Towards the Evaluation of the Role of Embodiment in Emotions Elicitation

In the human-robot interaction (HRI) literature, there is a focus on the ability of a robotic system to display emotional expressions to make them easily recognizable by the users. However, only few studies addressed how the multimodal behaviours of a robot may cause a specific emotional reaction in a user. In this work, we deploy a late fusion model for emotion recognition using facial and bio-signal inputs, and we use such a model in a user study aiming at evaluating the role of embodied interaction in emotion elicitation. To this extent, we evaluate the ability of a robot to change the elicited emotion with respect to the one elicited by a standardized stimulus (e.g., a video). Initial results show that the robot’s behaviour is effective in changing the valence of the perceived emotion, but not the arousal that is lowered.
Simulation Study of p-GaN Gate HEMTs With Dielectric Interlayer

In this work, we present a p-GaN gate HEMT with a thin dielectric interlayer and investigate the impacts of interlayer on the performances of p-GaN gate HEMTs using Silvaco TCAD. The transfer characteristics and gate forward breakdown characteristics are simulated, and the results show that devices with interlayers exhibit a smaller gate leakage and a much higher gate forward breakdown voltage (V<inf>G-BD</inf>) compared to the conventional p-GaN gate HEMT. The V<inf>G-BD</inf> of devices with 3 nm Al<inf>2</inf>O<inf>3</inf>, HfO<inf>2</inf> and SiN interlayers represent 145%, 79% and 97% increase (@ I<inf>G</inf> = 1μA/mm), respectively.
Enhancing Anonymity in NoC Communication to Counter Traffic Profiling by Hardware Trojans

Over time, interconnect systems have progressed from basic buses to more scalable networks-on-chip (NoC) IP. Since NoC is what drives SoC’s performance, researchers pay close attention to its security needs. NoC can be used as a significant platform by adversaries from unreliable third-party IP providers to implant malicious circuits like hardware trojans (HT). Since it is directly related to the chip’s functional specifications and floor plan design, any security breaches would malfunction the whole SoC itself. When packets are exchanged anonymously, it is more challenging for attackers to determine which nodes are communicating. This prevents decoding the nature and purpose of applications running on the cores. Only encryption cannot stop HTs from identifying communicating nodes in a tiny medium like NoC. We report the limitations of traditional anonymous communication in the presence of HTs that can predict the source node of NoC packets using lightweight traffic analysis. The HTs can even target certain nodes and leak packets from NoCs built with existing anonymous systems. In order to strengthen anonymity in NoC, both packet encryption and a routing method that explores path diversity are to be considered equally. We propose extended DyXY (E-DyXY) routing that introduces path diversity between nodes to reduce the source prediction accuracy of HTs at the expense of a marginal increase in packet latency. Experimental results show that we can reduce the rate of targeted packet leakage by 90% in anonymous systems embedded with the proposed E-DyXY algorithm when compared with baseline anonymous systems employing traditional XY routing.