Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-Port Converter With Leakage Inductance Energy Recycling for High Step-Down Applications

An emerging revolution in electric vehicles (EVs) is rapidly taking over the traditional fossil fuel vehicle market. Given its safety features and user-friendliness, the dual battery system (DBS) has been adopted in various EVs. A step-down three-port converter (TPC) is essential for the DBS to supply low-voltage auxiliary power in EVs. In this article, a novel high step-down TPC is proposed by integrating the tapped-inductor technique and a buck converter for fulfilling the requirements of high step-down power conversion from sources to load with a reasonable duty cycle. It can also achieve independent power transfer among three ports. In addition, the energy stored in the leakage inductance of the coupled inductor is recycled, with voltage spikes reduced across the switches. Finally, an experimental prototype is built to verify the theoretical analysis, with a main battery of 300 V, auxiliary battery of 48 V, load voltage of 24 V, and rated power of 300 W. The highest efficiency reached is 96.6%.
Hidden Markov Model with Machine Learning-Based Black hole Attack Identification in Wireless Sensor Networks

Wireless Sensor Network (WSN) is lying to several attacks because the Black hole is problematic to distinguish and protect. Impostor internments and re-programs block the packets rather than transmitting and obtaining them at the base station (BS) in a black hole attack. Consequently, any data that arrives in the black hole area is taken and cannot reach the recipient, triggering high packet losses and decreasing network performance. To overcome this problem, a hidden Markov model with machine learning (HMML)-based black hole attack identification in WSN is introduced. This approach uses the Hidden Markov Model (HMM) to detect the blackhole node in the WSN. This model examines the shortest routes between the sender and the recipient to discover the black hole route. This model contains states as well as the observation sequence. Forward and received count and reply of non-adjacent nodes are used for verifying the shortest routes. Here, the supervised and unsupervised training machine learning methods decide for detecting black hole nodes.
High Extinction Ratio Dual-Racetrack Modulator for DAC-less PAM4 Modulation

A parallel-coupled dual racetrack modulator is experimentally demonstrated. At 50 Gb/s, the extinction ratio (ER) is over 9 dB; and 40~50% modulation depth enhancement is obtained compared to a single resonator modulator with a peak-to-peak driving voltage of 2.3 V. A procedure for configuring four-level pulse-amplitude modulation (PAM-4) states is theoretically developed to tackle the complex multi-variable transfer function of this modulator. We demonstrate 100 Gb/s PAM-4 modulation without an external digital-to-analog converter (DAC), with electro-optic modulation power consumption < 40 fJ/bit for this device.
Probabilistic Modelling of Fracture Toughness of Composites with Discontinuous Reinforcement

The results presented in the paper are related to the prediction of the effective fracture toughness of particulate composites (KICeff). KICeff was determined using a probabilistic model supported by a cumulative probability function qualitatively following the Weibull distribution. Using this approach, it was possible to model two-phase composites with an arbitrarily defined volume fraction of each phase. The predicted value of the effective fracture toughness of the composite was determined based on the mechanical parameter of the reinforcement (fracture toughness), matrix (fracture toughness, Young’s modulus, yield stress), and composite (Young’s modulus, yield stress). The proposed method was validated: the determined fracture toughness of the selected composites was in accordance with the experimental data (the authors’ tests and literature data). In addition, the obtained results were compared with data captured by means of the rule of mixtures (ROM). It was found that the prediction of KICeff using the ROM was subject to a significant error. Moreover, a study of the effect of averaging the elastic–plastic parameters of the composite, on KICeff, was performed. The results showed that if the yield stress of the composite increased, a decrease in its fracture toughness was noticed, which is in line with the literature reports. Furthermore, it was noted that an increase in the Young’s modulus of the composite affected KICeff in the same way as a change in its yield stress.
Model Transmission-Based Online Updating Approach for Massive MIMO CSI Feedback

Deep learning has been widely applied in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems to achieve the accurate and effective channel state information (CSI) feedback. The challenges of the network models applied in real-world systems has also obtained more attention, especially the problem of generalization. In this letter, a model transmission-based online updating approach is proposed to achieve the real-time adaptation of the model and solve the problem of model mismatch when unseen data occurs. The feedback model will be updated using the real-time data with limited overhead, and the updating procedure is designed considering the feedback accuracy, requirement on training data, and storage usage. Experimental results indicate that the proposed approach can achieve quick model adjustment in changing scenarios and achieve comprehensive accuracy with limited training cost and storage usage, contributing to the feasibility of AI-based schemes.
Antiretrovirals Promote Metabolic Syndrome through Mitochondrial Stress and Dysfunction: An In Vitro Study

Simple Summary Antiretrovirals have several side effects. Recently, the use of antiretrovirals has been associated with metabolic syndrome. Metabolic syndrome arises through several abnormalities including mitochondrial dysfunction. The current study aimed to ascertain the effects of singular and combinational usage of current ARVs on mitochondrial dysfunction and its linkage to metabolic syndrome in liver (HepG2) cells. Cells were treated for a period of 120 h using Tenofovir disoproxil fumarate, Lamivudine, and Dolutegravir in singular and combinational doses. Results indicated that ARVs induce mitochondrial stress and dysfunction, which may be closely associated with the progression of insulin resistance. The evidence can be used to develop therapies with reduced side effects related to metabolic syndrome. Abstract The prevalence of metabolic syndrome MetS in HIV-infected patients on chronic antiretroviral (ARV) therapy continues to rise rapidly, with an estimated 21% experiencing insulin resistance. The progression of insulin resistance is strongly related to mitochondrial stress and dysfunction. This study aimed to draw links between the singular and combinational use of Tenofovir disoproxil fumarate (TDF), Lamivudine (3TC), and Dolutegravir (DTG) on mitochondrial stress and dysfunction as an underlying mechanism for insulin resistance following a 120 h treatment period using an in vitro system of human liver cells (HepG2). The relative protein expressions of pNrf2, SOD2, CAT, PINK1, p62, SIRT3, and UCP2, were determined using Western blot. Transcript levels of PINK1 and p62 were assessed using quantitative PCR (qPCR). ATP concentrations were quantified using luminometry, and oxidative damage (malondialdehyde (MDA) concentration) was measured using spectrophotometry. The findings suggest that despite the activation of antioxidant responses (pNrf2, SOD2, CAT) and mitochondrial maintenance systems (PINK1 and p62) in selected singular and combinational treatments with ARVs, oxidative damage and reduced ATP production persisted. This was attributed to a significant suppression in mitochondrial stress responses SIRT3 and UCP2 for all treatments. Notable results were observed for combinational treatments with significant increases in pNrf2 (p = 0.0090), SOD2 (p = 0.0005), CAT (p = 0.0002), PINK1 (p = 0.0064), and p62 (p = 0.0228); followed by significant decreases in SIRT3 (p = 0.0003) and UCP2 (p = 0.0119) protein expression. Overall there were elevated levels of MDA (p = 0.0066) and decreased ATP production (p = 0.0017). In conclusion, ARVs induce mitochondrial stress and dysfunction, which may be closely associated with the progression of insulin resistance.
A simulation-based method to inform serosurvey designs for estimating dengue force of infection using existing blood samples

The extent to which dengue virus has been circulating in Africa is largely unknown. Testing available blood samples from previous cross-sectional serological surveys offers a convenient strategy to investigate past dengue infections, as such serosurveys provide the ideal data to reconstruct the age-dependent immunity profile of the population and to estimate the average per-capita annual risk of infection; the force of infection (FOI), which is a fundamental measure of transmission intensity. In this study, we present a novel methodological approach to inform the size and age distribution of blood samples to test when samples are acquired from previous surveys. The method was used to inform a dengue seroprevalence survey which is currently being conducted in Ghana by the Drug for Neglected disease initiative, utilizing samples previously collected fora SARS-CoV-2 serosurvey. The method described in this paper can be employed to determine sample sizes and testing strategies for different diseases and transmission settings.
Win, Lose, or Draw: A Markov Chain Analysis of Overtime in the National Football League

Michael Jones (jonesm@mail.montclair.edu) received his Ph.D. in game theory from Northwestern University in 1994 under the supervision of Donald Saari. Some of his friends believe that his game theory training has helped him develop prognosticator prowess, as evidenced by his winning a season-long football pool three of the last four years. Although now tenured at Montclair State, he is still waiting for ESPN to hire him as resident mathematician.
Zoom (cid:221) is what you need: An empirical study of the power of zoom and spatial biases in image classiﬁcation

Image classiﬁers are information-discarding machines, by design. Yet, how these models discard information remains mysterious. We hypothesize that one way for image classiﬁers to reach high accuracy is to ﬁrst zoom to the most discriminative region in the image and then extract features from there to predict image labels. We study six popular networks ranging from AlexNet to CLIP and ﬁnd that proper framing of the input image can lead to the correct classiﬁcation of 98.91% of ImageNet images. Furthermore, we explore the potential and limits of zoom transforms in image classiﬁcation and uncover positional biases in various datasets, especially a strong center bias in two popular datasets: ImageNet-A and ObjectNet. Finally, leveraging our insights into the potential of zoom, we propose a state-of-the-art test-time augmentation (TTA) technique that improves classiﬁcation accuracy by forcing models to explicitly perform zoom-in operations before making predictions. Our method is more interpretable, accurate, and faster than MEMO, a state-of-the-art TTA method. Additionally, we propose ImageNet-Hard, a new benchmark where zooming in alone often does not help state-of-the-art models better label images.
GDP nowcasting with artificial neural networks: How much does long-term memory matter?

In our study, we apply different statistical models to nowcast quarterly GDP growth for the US economy. Using the monthly FRED-MD database, we compare the nowcasting performance of the dynamic factor model (DFM) and four artificial neural networks (ANNs): the multilayer perceptron (MLP), the one-dimensional convolutional neural network (1D CNN), the long short-term memory network (LSTM), and the gated recurrent unit (GRU). The empirical analysis presents the results from two distinctively different evaluation periods. The first (2010:Q1 -- 2019:Q4) is characterized by balanced economic growth, while the second (2010:Q1 -- 2022:Q3) also includes periods of the COVID-19 recession. According to our results, longer input sequences result in more accurate nowcasts in periods of balanced economic growth. However, this effect ceases above a relatively low threshold value of around six quarters (eighteen months). During periods of economic turbulence (e.g., during the COVID-19 recession), longer training sequences do not help the models' predictive performance; instead, they seem to weaken their generalization capability. Our results show that 1D CNN, with the same parameters, generates accurate nowcasts in both of our evaluation periods. Consequently, first in the literature, we propose the use of this specific neural network architecture for economic nowcasting.
MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) methods often suffer from high sample complexity, limiting their use in real-world problems where data is sparse or expensive to collect. Although latent-variable world models have been employed to address this issue by generating abundant synthetic data for MARL training, most of these models cannot encode vital global information available during training into their latent states, which hampers learning efficiency. The few exceptions that incorporate global information assume centralized execution of their learned policies, which is impractical in many applications with partial observability. We propose a novel model-based MARL algorithm, MABL (Multi-Agent Bi-Level world model), that learns a bi-level latent-variable world model from high-dimensional inputs. Unlike existing models, MABL is capable of encoding essential global information into the latent states during training while guaranteeing the decentralized execution of learned policies. For each agent, MABL learns a global latent state at the upper level, which is used to inform the learning of an agent latent state at the lower level. During execution, agents exclusively use lower-level latent states and act independently. Crucially, MABL can be combined with any model-free MARL algorithm for policy learning. In our empirical evaluation with complex discrete and continuous multi-agent tasks including SMAC, Flatland, and MAMuJoCo, MABL surpasses SOTA multi-agent latent-variable world models in both sample efficiency and overall performance.
Detection of Pseudo Crucial Events

We study a growth process using the logistic equation in the presence of multiplicative ﬂuctuations. It is well known that this model has a noise-induced phase transition that has attracted the attention of many researchers in the last 40 years. We study this problem and show that it generates physical eﬀects characterized by the emergence of an inverse power law with power-law index µ = 32 , found more recently in complex phenomena, for instance in the physics of blinking quantum dots. We investigate whether this index is a manifestation of complex dynamics, such as µ = 53 , generated by the Kardar-Parisi-Zhang dynamics, or whether it is a manifestation of an ordinary diﬀusion process. In the ﬁrst case, the anomalous diﬀusion is a manifestation of crucial events, in the second case it is generated by pseudo-crucial events. To realize this goal we adopt the technique of Diﬀusion Entropy Analysis (DEA) in a form used in the past to analyze physiological processes, for instance the heartbeats of patients, to establish the risk of heart attack. We ﬁnd that noise-induced phase transitions generate pseudo-crucial events. These results were obtained using a coordinate transformation replacing the multiplicative ﬂuctuations with additive ﬂuctuations and by the adoption of the ﬁrst-passage-time technique.
User Adaptive Language Learning Chatbots with a Curriculum

Along with the development of systems for natural language understanding and generation, dialog systems have been widely adopted for language learning and practicing. Many current educational dialog systems perform chitchat, where the generated content and vocabulary are not constrained. However, for learners in a school setting, practice through dialog is more effective if it aligns with students' curriculum and focuses on textbook vocabulary. Therefore, we adapt lexically constrained decoding to a dialog system, which urges the dialog system to include curriculum-aligned words and phrases in its generated utterances. We adopt a generative dialog system, BlenderBot3, as our backbone model and evaluate our curriculum-based dialog system with middle school students learning English as their second language. The constrained words and phrases are derived from their textbooks, suggested by their English teachers. The evaluation result demonstrates that the dialog system with curriculum infusion improves students' understanding of target words and increases their interest in practicing English.
Multi-agent Policy Reciprocity with Theoretical Guarantee

Modern multi-agent reinforcement learning (RL) algorithms hold great potential for solving a variety of real-world problems. However, they do not fully exploit cross-agent knowledge to reduce sample complexity and improve performance. Although transfer RL supports knowledge sharing, it is hyperparameter sensitive and complex. To solve this problem, we propose a novel multi-agent policy reciprocity (PR) framework, where each agent can fully exploit cross-agent policies even in mismatched states. We then define an adjacency space for mismatched states and design a plug-and-play module for value iteration, which enables agents to infer more precise returns. To improve the scalability of PR, deep PR is proposed for continuous control tasks. Moreover, theoretical analysis shows that agents can asymptotically reach consensus through individual perceived rewards and converge to an optimal value function, which implies the stability and effectiveness of PR, respectively. Experimental results on discrete and continuous environments demonstrate that PR outperforms various existing RL and transfer RL methods.
GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot Learning

In the realm of 3D-computer vision applications, point cloud few-shot learning plays a critical role. However, it poses an arduous challenge due to the sparsity, irregularity, and unordered nature of the data. Current methods rely on complex local geometric extraction techniques such as convolution, graph, and attention mechanisms, along with extensive data-driven pre-training tasks. These approaches contradict the fundamental goal of few-shot learning, which is to facilitate efficient learning. To address this issue, we propose GPr-Net (Geometric Prototypical Network), a lightweight and computationally efficient geometric prototypical network that captures the intrinsic topology of point clouds and achieves superior performance. Our proposed method, IGI++ (Intrinsic Geometry Interpreter++) employs vector-based hand-crafted intrinsic geometry interpreters and Laplace vectors to extract and evaluate point cloud morphology, resulting in improved representations for FSL (Few-Shot Learning). Additionally, Laplace vectors enable the extraction of valuable features from point clouds with fewer points. To tackle the distribution drift challenge in few-shot metric learning, we leverage hyperbolic space and demonstrate that our approach handles intra and inter-class variance better than existing point cloud few-shot learning methods. Experimental results on the ModelNet40 dataset show that GPr-Net outperforms state-of-the-art methods in few-shot learning on point clouds, achieving utmost computational efficiency that is 170× better than all existing works. The code is publicly available at https://github.com/TejasAnvekar/GPr-Net.
Optimizing Sensor Allocation Against Attackers With Uncertain Intentions: A Worst-Case Regret Minimization Approach

This letter focuses on the optimal allocation of multi-stage attacks with the uncertainty in attacker’s intention. We model the attack planning problem using a Markov decision process and characterize the uncertainty in the attacker’s intention using a finite set of reward functions—each reward represents a type of attacker. Based on this modeling, we employ the paradigm of the worst-case absolute regret minimization from robust game theory and develop mixed-integer linear program (MILP) formulations for solving the worst-case regret minimizing sensor allocation strategies for two classes of attack-defend interactions: one where the defender and attacker engage in a zero-sum game and another where they engage in a non-zero-sum game. We demonstrate the effectiveness of our algorithm using a stochastic gridworld example.
A Persistent-Excitation-Free Method for System Disturbance Estimation Using Concurrent Learning

Observer-based methods are widely used to estimate the disturbances of different dynamic systems. However, a drawback of the conventional disturbance observers is that they all assume persistent excitation (PE) of the systems. As a result, they may lead to poor estimation precision when PE is not ensured, for instance, when the disturbance gain of the system is close to the singularity. In this paper, we propose a novel disturbance observer based on concurrent learning (CL) with time-variant history stacks, which ensures high estimation precision even in PE-free cases. The disturbance observer is designed in both continuous and discrete time. The estimation errors of the proposed method are proved to converge to a bounded set using the Lyapunov method. A history-sample-selection procedure is proposed to reduce the estimation error caused by the accumulation of old history samples. A simulation study on epidemic control shows that the proposed method produces higher estimation precision than the conventional disturbance observer when PE is not satisfied. This justifies the correctness of the proposed CL-based disturbance observer and verifies its applicability to solving practical problems.
Optimal Interpretability-Performance Trade-off of Classification Trees with Black-Box Reinforcement Learning

Interpretability of AI models allows for user safety checks to build trust in these models. In particular, decision trees (DTs) provide a global view on the learned model and clearly outlines the role of the features that are critical to classify a given data. However, interpretability is hindered if the DT is too large. To learn compact trees, a Reinforcement Learning (RL) framework has been recently proposed to explore the space of DTs. A given supervised classification task is modeled as a Markov decision problem (MDP) and then augmented with additional actions that gather information about the features, equivalent to building a DT. By appropriately penalizing these actions, the RL agent learns to optimally trade-off size and performance of a DT. However, to do so, this RL agent has to solve a partially observable MDP. The main contribution of this paper is to prove that it is sufficient to solve a fully observable problem to learn a DT optimizing the interpretability-performance trade-off. As such any planning or RL algorithm can be used. We demonstrate the effectiveness of this approach on a set of classical supervised classification datasets and compare our approach with other interpretability-performance optimizing methods.