Miracle-dz/UniFAR-FTUs huggingface dataset:
Validating the Representational Space of Deep Reinforcement Learning Models of Behavior with Neural Data

Deep Reinforcement Learning (Deep RL) agents have in recent years emerged as successful models of animal behavior in a variety of complex learning tasks, as exemplified by Song et al. [2017]. As agents are typically trained to mimic an animal subject, the emphasis in past studies on behavior as a means of evaluating the fitness of models to experimental data is only natural. But the true power of Deep RL agents lies in their ability to learn neural computations and codes that generate a particular behavior—factors that are also of great relevance and interest to computational neuroscience. On that basis, we believe that model evaluation should include an examination of neural representations and validation against neural recordings from animal subjects. In this paper, we introduce a procedure to test hypotheses about the relationship between internal representations of Deep RL agents and those in animal neural recordings. Taking a sequential learning task as a running example, we apply our method and show that the geometry of representations learnt by artificial agents is similar to that of the biological subjects’, and that such similarities are driven by shared information in some latent space. Our method is applicable to any Deep RL agent that learns a Markov Decision Process, and as such enables researchers to assess the suitability of more advanced Deep Learning modules, or map hierarchies of representations to different parts of a circuit in the brain, and help shed light on their function. To demonstrate that point, we conduct an ablation study to deduce that, in the sequential task under consideration, temporal information plays a key role in molding a correct representation of the task.
Atlantic salmon farms are a likely source of Tenacibaculum maritimum infection in migratory Fraser River sockeye salmon

Infectious disease from domestic hosts, held for agriculture, can impact wild species that migrate in close proximity, potentially reversing selective advantages afforded by migration. For sockeye salmon in British Columbia, Canada, juveniles migrate past numerous Atlantic salmon farms from which they may acquire a number of infectious agents. We analyse patterns of molecular detection in juvenile sockeye salmon for one bacterial pathogen, Tenacibaculum maritimum, known to cause disease in fish species around the globe and to cause mouthrot disease in farmed Atlantic salmon in BC. Our data show a clear peak in T. maritimum detections in the Discovery Islands region of BC, where sockeye migrate close to salmon farms. Using well established differential-equation models to describe sockeye migration and T. maritimum infection spread, we fit models to our detection data to assess support for multiple hypotheses describing farm- and background-origin infection. Despite a data-constrained inability to resolve certain epidemiological features of the system, such as the relative roles of post infection mortality and recovery, our models clearly support the role of Discovery-Islands salmon farms in producing the observed patterns. Our best models (with 99.8% empirical model support) describe relatively constant (background) infection pressure, except around Discovery-Islands salmon farms, where farm-origin infection pressure peaked at 12.7 (approximate 95% CI: 4.5 to 31) times background levels. Given the evidence for farm-origin transfer of T. maritimum to Fraser-River sockeye salmon, the severity of associated disease in related species, and the imperilled nature of Fraser River sockeye generally, our results suggest the need for a more precautionary approach to managing farm/wild interactions in sockeye salmon.
Theoretically Designed Li3PO4 (100)/LiFePO4 (010) Coherent Electrolyte/Cathode Interface for All Solid-State Li Ion Secondary Batteries

Controlling the electrolyte/electrode interface is of great importance to promote new-generation solid-state Li ion secondary batteries. In this paper, we report a theoretically designed electrolyte/cathode coherent interface at the density functional theory level, where γ-Li3PO4 and LiFePO4 are used as an electrolyte and a cathode, respectively. At the stoichiometric Li3PO4 (100)/LiFePO4 (010) coherent interface, there are vacant Li-sites that give the chance for Li ions to migrate. From the density functional molecular dynamics at 1500 K, it is found that this interface is stable and no impurity phase is produced, and also that Li ions in the Li3PO4 phase around the interface can diffuse with large diffusion coefficients. The dynamic behavior of these Li ions is also reflected in the layered phonon spectra of Li ions; the diffusible Li ions around the interface have the same spectrum.
A Temporal-Spatial Attention Model for Medical Image Detection

A local region model with attentive temporal-spatial pathways is proposed for automatically learning various target structures. The attentive spatial pathway highlights the salient region to generate bounding boxes and ignores irrelevant regions in an input image. The proposed attention mechanism allows efficient object localization, and the overall predictive performance is increased because there are fewer false positives for the object detection task for medical images with manual annotations. The experimental results show that proposed models consistently increase the base architecture’s predictive performance on the Medico dataset with satisfactory computational efficiency.
Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal CNN for MediaEval 2020

This work presents a method for classifying table tennis strokes using spatio-temporal convolutional neural networks. The fine-grained classification is performed on trimmed video segments recorded at 120 fps with different players performing in natural conditions. From those segments, the frames are extracted, their optical flow is computed and the pose of the player is estimated. From the optical flow amplitude, a region of interest is inferred. A three stream spatio-temporal convolutional neural network using combination of those modalities and 3D attention mechanisms is presented in order to perform classification.
Deep Conditional Adversarial Learning for Polyp Segmentation

This approach has addressed the Medico automatic polyp segmentation challenge , a part of Multimedia Evaluation 2020 ( MediaEval 2020 ). We have proposed a deep conditional adversarial learning-based network for the automatic polyp segmentation task. The network is composed of two interdependent models, namely a generator and a discriminator. The generator network is a Fully Convolutional Network (FCN) employed for the prediction of the polyp mask while the discriminator enforces the segmentation to be as similar as the real segmented mask (ground truth). Our proposed model achieved a Jaccard index of 0.713, Dice Similarity Coefficient of 0.801, recall of 0.835, precision of 0.826, accuracy of 0.944, and F2 measure of 0.812 quantitatively evaluated by the organizers on the test dataset.
HCMUS at Medico Automatic Polyp Segmentation Task 2020: PraNet and ResUnet++ for Polyps Segmentation

The Medico task, MediaEval 2020, explores the challenge of building accurate and high-performance algorithms to detect all types of polyps in endoscopic images. We proposed different approaches leveraging the advantages of either ResUnet++ or PraNet model to efficiently segment polyps in colonoscopy images, with modifications on the network structure, parameters, and training strategies to tackle various observed characteristics of the given dataset. Our methods outperform the other teams’ methods, for both accuracy and efficiency. After the evaluation, we are at top 2 for task 1 (with Jaccard index of 0.777, best Precision and Accuracy scores) and top 1 for task 2 (with 67.52 FPS and Jaccard index of 0.658).
Spatio-Temporal Based Table Tennis Hit Assessment Using LSTM Algorithm

In these working notes, we present our approach and results for Mediaeval 2020 Sports Video Classification Task [6]. We implemented a multi-stage pipeline with LSTM-based network. In the developed approach, firstly, the frames are extracted, sampled and resized. Then, considering that the stroke type has three different parts, each part is labelled and predicted separately. In order to obtain the predicted stroke type, the predictions for each part are fused together.
A Tweet Text Binary Artificial Neural Network Classifier

We present an Artificial Neural Network (ANN) text classifier to deal with the task of automatically detecting a tweet as being flood-related or not. The framework for classifying flood-related tweets consists of three basic ANN models. Each model is a different ANN type and the final output is determined by a majority rule on the individual model outputs. The overall F1 score on the test set was 0.5405, significantly lower than on the training/validation set, suggesting that we overfitted the training set.
Use Visual Features From Surrounding Scenes to Improve Personal Air Quality Data Prediction Performance

In this paper, we propose a method to predict the personal air quality index in an area by using the combination of the levels of the following pollutants: PM2.5, NO2, and O3, measured from the nearby weather stations of that area, and the photos of surrounding scenes taken at that area. Our approach uses the Inverse Distance Weighted (IDW) technique to estimate the missing air pollutant levels and then use regression to integrate visual features from taken photos to optimize the predicted values. After that, we can use those values to calculate the Air Quality Index (AQI). The results show that the proposed method may not improve the performance of the prediction in some cases.
Computationally Efficient Joint-Domain Clutter Cancellation for Waveform-Agile Radar

Waveform agility provides greater design freedom through the generation of a coherent processing interval (CPI) of nonrepeating waveforms. However, doing so introduces coupling of the range and slow-time Doppler dimensions that hinders clutter cancellation if not addressed. Non-identical multiple pulse compression (NIMPC) is a joint-domain approach that solves this problem, though the high dimensionality incurs a prohibitive computational cost. Inspired by recent work that exploits the Toeplitz structure of NIMPC, here we take that notion even further, demonstrating the processing of measured data at a cost significantly lower than direct NIMPC application.
An Adaptive Fusion Algorithm for Multistatic and Multichannel Passive Radar Detection

Passive bistatic radar is of great interest in both civilian and military applications due to its numerous advantages. However, it also faces a great challenge of detecting weak echo signals. In this paper, a multistatic and multichannel passive radar detection system is constructed, which uses digital video broadcast-satellite as illuminator of opportunity. Furthermore, an adaptive fusion combination framework for moving target detection is then proposed, which exploits the optimization criterion of the normal deflection coefficient to optimize the weight that is designed for test statistics obtained in each individual static, and then yields the global decision through weighted fusion of the all detection statistics. Simulation results illustrate that the proposed algorithm outperforms exiting works under time-varying interference.
Introducing GARMI - A Service Robotics Platform to Support the Elderly at Home: Design Philosophy, System Overview and First Results

This letter introduces GARMI, a service robotics platform conceptualized with a focus on assisting elderly at home. GARMI is designed to provide support with household tasks, as an avatar for tactile-enabled communication and as an interface for telemedicine and emergency assistance. Its unique humanoid design features a sensor-equipped multi-modal head designed for natural human-machine communication as well as a whole-body torque-control interface for safe physical human-machine interaction. GARMI's modular software architecture and distinctive whole-body control scheme allows multimodal dynamic coupling. Additionally, every system component can actively produce as well as sense forces and can thus serve as a haptic feedback interface when interacting with its environment and users. Furthermore, GARMI is the first mobile humanoid designed with specialized use-inspired avatar stations: PARTI for dual-arm-based exoskeleton-like remote-control with force-feedback and MUCKI for transparent remote doctor-patient interaction with both audiovisual and safe haptic feedback. The specialized software architecture allows for rapid prototyping and field-testing of new behaviors for telemedicine, multi-modal interaction and autonomous physical and service assistance. Our first results reveal the potential of our use-driven force-based whole-body control mobile humanoid for daily living and telemedicine scenarios in an elderly care research facility.
Design of Antenna Arrays by Using Modified Fruit Fly Optimization Algorithm

This research paper proposed the modified fruit fly optimization algorithm (MFOA) to design antenna arrays. The MFOA is used to estimate parameters of three configurations of antenna arrays. There are linear, planar and circular uniform antenna arrays. From numerical results, the MFOA is effective in determining antenna array parameters. The proposed algorithm has advantages in easy implementation, global search space and fast convergence rate.
Augmenting Experimental Data with Simulations to Improve Activity Classification in Healthcare Monitoring

Human micro-Doppler signatures in most passive WiFi radar (PWR) scenarios are captured through real-world measurements using various hardware platforms. However, gathering large volumes of high quality and diverse real radar datasets has always been an expensive and laborious task. This work presents an open-source motion capture data-driven simulation tool SimHumalator that is able to generate human micro-Doppler radar data in PWR scenarios. We qualitatively compare the micro-Doppler signatures generated through SimHumalator with the measured real signatures. Here, we present the use of SimHumalator to simulate a set of human actions. We demonstrate that augmenting a measurement database with simulated data, using SimHumalator, results in an 8% improvement in classification accuracy. Our results suggest that simulation data can be used to augment experimental datasets of limited volume to address the cold-start problem typically encountered in radar research.
The Tec21 educational model and its perception. An educational innovation for student-based learning

In this work we analyze students’ first impressions on the Tec21 educational model, a new educational proposal for undergraduate education implemented by Tecnológico de Monterrey. Perceptions were collected through a survey that measures six model components. We contrast the perceptions of students enrolled in the Tec 21 with the ones of students enrolled in the previous, but still active, educational model in this institution and whose components are those that prevail in a “traditional” educational model. The purpose of this study is to investigate if there is a statistically significant difference between students’ perceptions of these two educational models. Moreover, we compare the perception of students enrolled in STEM programs with the one of students enrolled in NON-STEM programs in the Tec21 model. Finally, we make a basic sentiment analysis from students’ general comments.
Robust Online Overhead Transmission Line Monitoring With Cost Efficiency in Smart Power Grid

In this work, we investigate a cost-efficient strategy to implement robust online overhead transmission line monitoring for stable energy transfer in smart power grid. As a first step, we first present an analytical framework to evaluate communication performances of wireless sensor networks installed for the real-time monitoring of overhead power transmission line, wherein each tower performs fountain code (FC)-based transmission for guaranteeing transmission reliability between each tower and a substation. Then, we evaluate communication performances given in terms of successful FC-based transmission probability and its end-to-end delay for each tower. Further, our analysis helps formulate an optimization problem that aims at minimizing the total number of activated wide area network (WAN) communication modules deployed on towers(i.e., minimizing the total monetary cost required to use WAN communications), while simultaneously supporting three different types of communication quality of service (QoS) requirements (e.g., reliability, delay, robustness). To find the solution of the problem, we propose the Path Decision in the order of the Farthest Towers (PDFP) algorithm, which enables to find a suboptimal-solution of the problem in real-time. The key idea of PDFT algorithm is that paths (i.e., whether to use a WAN communication module) are sequentially determined in the order of towers farthest from the substation. To identify the validity of the proposed algorithm, we conduct performance evaluations with using public topological and geometrical information of an actual national power grid in Korea. Performance evaluation results show that the proposed PDFT algorithm can find towers that need to activate their WAN communication modules for guaranteeing diverse QoS requirements in real-time, as compared to an optimal-solution algorithm without significant difference in total number of activated WAN communication modules.
When SMILES Smiles, Practicality Judgment and Yield Prediction of Chemical Reaction via Deep Chemical Language Processing

Simplified Molecular Input Line Entry System (SMILES) provides a text-based encoding method to describe the structure of chemical species and formulize general chemical reactions. Considering that chemical reactions have been represented in a language form, we present a symbol only model to generally predict the yield of organic synthesis reaction without considering complex quantum physical modeling or chemistry knowledge. Our model is the first deep neural network application that treats chemical reaction text segments as embedding representation to the most recent deep natural language processing. Experimental results show our model can effectively predict chemical reactions, which achieves a high accuracy of 99.76% on practicality judgment and the Root Mean Square Error (RMSE) is around 0.2 for yield prediction. Our work shows the great potential for automatic yield prediction for organic reactions under general conditions and further applications in synthesis path prediction with the least modeling cost.
In-Campus Work and Study: Motivation, Challenges, Benefits, Skills Developed and Impact on Studies

This paper analyzes technology major students’ experience in combining in campus work and study. Using structured interviews, it examines the factors influencing students’ choice, the challenges and benefits of the experience, the skills developed and the impact on their studies. Results suggest that students choose to work for financial reasons and for gaining work experience, but also because of lecturer encouragement. Regarding the challenges in working and studying, the main one was managing time, while benefits include gaining work experience, widening social circle/socialization and putting in practice what was learned in class. The main skills developed were time management, critical thinking and problem solving, written and verbal communication, and teamwork. Finally, the impact on studies is mostly seen as positive since, despite time pressures, students feel that work has helped them appreciate the application of theoretical knowledge gained in class and made them more organized and improved their ability to work with deadlines.
Constant Current - voltage with Maximum Efficiency Inductive Wireless EV Charging Control using Dual - sides DC Converters

This paper presents a design of a 2.0kW at 160 mm of transmitter (TX) and receiver (TR) distant CC-CV with maximum efficiency EV charging using dual side dc converters. The method was validated using MATLAB programming. In cases of variations of the input voltage, load resistance of battery state of charge, and the TX-TR offsets. The results found that the uncontrolled wireless EV charging makes over-input current, output voltage, and overload output power and unbalance of input and output currents and voltages. The CC-CV wireless charger applied the constant current then constant voltage but the input and output currents and voltages are unbalanced. Finally, the CC-CV with maximum efficiency wireless EV charging can be operated in constant current and then constant voltage with balanced the input and output currents and voltages.
Q-Learning traffic-distributing approach to managing multiple-destination traffic

In disastrous situations (e.g., Tsunami or Flash flood), people must be evacuated to safety (e.g., shelter or high ground) as fast as possible, which depends directly on the number of available evacuation routes and the traffic flow along those routes. This study proposes a Q-Learning traffic-distributing approach to managing multiple-destination traffic. The approach processes current traffic on every available route and provides possible routes for people to take to evacuate to safety. This approach uses a reinforcement-learning algorithm to dynamically find and provide the best possible routes for people evacuating from different locations and times. The algorithm was developed from existing machine learning algorithms especially for processing traffic data. Evaluation and comparison of the developed algorithm, basic A*, and Yen’s k-shortest path algorithms were conducted in terms of average travel time for various traffic and route situations. The developed algorithm provided the shortest average travel time for three tested situations. The evaluation test results may directly benefit developers and planners of evacuation programs in their effort to devise the best possible evacuation plans.
An On-board Monitoring System for Driving Fatigue and Distraction Detection

In the past few decades, it is shown in various studies that driving fatigue or distraction are the main threats of traffic accidents. Thus, the on-board monitoring for driving behaviors is becoming an important component of advanced driver assistance systems (ADAS) for intelligent vehicles. In this paper, we present the techniques to simultaneously detect the fatigue and distracted driving behaviors using vision and learning based approaches. In fatigue driving detection, we use facial features to detect the open/close of eyes, yawning and head posture. The random forest is adopted to analyze the driving conditions. In the distraction detection, the convolutional neural network (CNN) is used to classify various distracted driving behaviors. The experiments are carried out on the PC and embedded hardware platform using public and our own datasets for training and testing. Compared to the previous approaches, the proposed methods provide better results in terms of accuracy and computation time.