Miracle-dz/UniFAR-FTUs huggingface dataset:
Capacity Planning in E-Commerce Logistics Using a Hybrid Machine Learning Model

Due to the increase in e-commerce demand, the escalating exponential growth of congestion within transportation systems has reached a critical juncture, significantly impinging upon the punctual delivery of routine parcels and groceries. A crucial challenge to be resolved is that drivers operate under time constraints within a specific number of deliveries and have a restricted daily capacity that requires more comprehensive and effective capacity planning. In this paper, an integrated approach composed of clustering and stages of regressions is developed in which the delivery information of the cross-docks in a cluster is utilized to predict the daily delivery capacity of a fleet that starts its routes from a cross-dock depot in a specific time slot. Each prediction specifies the amount of delivery in total and for a given cross-dock, within a certain time slot of the day by the drivers. Our results show that for most of the clusters, the proposed GPR model outperforms other state-of-the-art regression methods. Also, the model is daily updated using data from shipments delivered on the same day. This ensures adaptability to unforeseen events and factors like special occasions (e.g., Black Friday or Christmas) in the logistics domain.
Ensemble Masked Autoencoder and Gaussian Mixture Models for Cardinality Estimation

In recent years, deep learning models have been employed for cardinality estimation in databases. These models can be categorized into query-driven and data-driven approaches. Query-driven methods rely on training queries and lack generalization ability, while data-driven methods infer cardinalities by learning the joint distribution of data in the database, independent of specific queries. Some data-driven methods utilize deep autoregressive models, but they suffer from fixed attribute order during training and the issue of requiring large embedding matrices for continuous attributes. In order to overcome these challenges, we propose MAGMM, which combines masked autoencoder and Gaussian mixture models and is data-driven. Experimental results demonstrate that MAGMM performs well in terms of estimation accuracy and inference time, providing a robust solution for cardinality estimation in databases.
Design of Dual-band Substrate Integrated Waveguide Dielectric Resonator Antenna in D-band

This work proposes a dual-band dielectric resonator antenna (DRA) for D-band millimeter wave frequency applications. The proposed design is fed from a planar substrate integrated waveguide (SIW). The antenna is designed to operate between 126.95 GHz – 136.5 GHz and 139.67 GHz – 149.48 GHz frequency bands. The proposed antenna achieves a bandwidth performance of 7.2% and 6.78% in the lower and upper-frequency bands respectively. The proposed antenna achieves a simulated gain performance of 10.69 dBi and a peak efficiency of 87% with stable broadside radiation patterns in both bands.
Increased Intrauterine Pressure (IUP) Magnitude during Labor at 2g Revealed By Telemetry in Freely Moving Pregnant Rats

Past studies of pregnant rat dams flown onboard the NASA space shuttle from mid-gestation until 48-72 hrs prior to birth reported two times the number of labor contractions relative to ground controls. Conversely, pregnant rats exposed to 1.5 or 2g centrifugation during the same gestational period showed significantly fewer labor contractions relative to 1g controls. Here we test the hypothesis that the down-regulation of labor contractions at 2g is associated with intrauterine pressures of greater magnitude than those measured in 1g controls. Intrauterine pressure (IUP) associated with labor contractions was analyzed using telemetry in freely moving female rats exposed to 2g centrifugation. Female rats were bred at 1g and 2g (n=9 per condition). On gestation day 19 (G19) a pressure telemeter was implanted into one of that dams’ paired uterine horns to measure IUP before and during parturition. Dam behavior was simultaneously video recorded to quantify frequency of contractions and time of birth. Our results indicate that sustained gravitational loading during the latter half of pregnancy increases IUP during labor. These ground-based findings contribute to our overall understanding of how the complex process of mammalian reproduction may be altered in space.
AGF-Net: Adaptive Road Extraction Network with Global Feature Fusion

Remote sensing image road extraction is of great significance for vehicle navigation and emergency insurance. However, due to the limited presence of roads in remote sensing images, they are often obstructed by shadows from trees or buildings along the road, leading to fragmented extraction results. Moreover, the variation in road scales within remote sensing images makes it challenging to accurately extract small-sized roads. Therefore, automatic road extraction from remote sensing images poses a significant challenge. To address these issues, this paper proposes an adaptive road extraction method based on global feature fusion. The proposed method employs an encoder-decoder structure for end-to-end road extraction. In the middle module, a dilated convolution strip attention module is designed to enhance the continuity of roads. The dilated convolution module expands the receptive field, while the strip attention module adaptively emphasizes the relevant features along the road direction. Multiple global feature fusion modules in the skip connection are designed to fuse the features of different stages. Meanwhile, a multi-scale strip convolution module is employed to compensate for the discrepancies in feature maps across different stages. Extensive experiments reveal that AGF-Net outperforms existing methods on different road datasets (Massachusetts Road dataset and CHN6-CUG Road dataset).
A Deep Learning Framework for Hyperspectral Image Classification using PCA and Spectral LSTM Networks

Deep learning techniques, like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, have demonstrated remarkable performance in hyperspectral image (HSI) classification in recent years. This paper proposes a novel framework for the classification of hyperspectral images using a Long Short-Term Memory Networks (LSTM) and Principal Component Analysis (PCA). PCA is basically used to minimize the high-dimensional image data, and then it send to LSTM for classification. LSTM is a RNN that is designed to perform better on sequential analysis of hyperspectral images. This proposed research and analysis of HIS data was performed on the standardized dataset of Pavia University and Kennedy Space Center (KSC). The proposed research framework effectively classifies the HIS images and shows its efficency in comparison to existing state-of-the-art classifiers. Overall, the proposed framework presents a promising approach to the classification of hyperspectral images by combining the strengths of LSTM and PCA. The paper contributes to the literature on hyperspectral image classification by demonstrating the effectiveness of deep learning models and the importance of considering the sequential nature of hyperspectral data.
Convex Combination of Compressed Sensing Algorithms

In this paper, we propose a new framework for compressed sensing (CS) based on data fusion principles, where several CS algorithms work in parallel to recover a $K$-sparse signal, and their outputs are combined convexly using a set of combiner coefficients drawn randomly, followed by a 2K level hard-thresholding, a pursuit step and final pruning by another $K$ level hard-thresholding. A rigorous convergence analysis is presented and sufficient conditions for convergence are derived. Unlike other existing algorithms which use data fusion concepts but do not employ convex combination of estimates generated by the participating algorithms and instead, use a union of support of individual estimates, the proposed method is free from the restrictions on the number of participating algorithms. Empirically, it is observed that the proposed framework allows us to recover the sparse signal of interest using fewer measurements than required by any individual algorithm. It is further seen that even when some of the participating algorithms do not perform well, the proposed framework is still able to recover the $K$-sparse signal maintaining the same level of performance. Simulations also reveal that performance of the proposed framework is either at par or better than that of existing algorithms that apply data fusion.
An FPGA based Real-Time Flat Field Correction for Infrared Focal Plane Arrays

Correction of fixed pattern noise (FPN) plays a vital role in fully exploiting the potential of the infrared focal plane arrays (IRFPA). The paper presents FPGA based hardware architecture for flat field correction (FFC) for thermal camera with an external shutter. The proposed hardware architecture is built using a high-level synthesis (HLS) approach to generate Verilog or VHDL codes from C++ code. HLS Video Direct Memory Access (VDMA) generated have 256-bit width interconnected with First In First Out (FIFO) that reduces Double Data Rate (DDR) traffic. The design is tested on Efinix quantum technology that delivers substantial power, performance, area, and advantages over traditional FPGA products. The design achieves a maximum frequency of 400 MHz and one pixel per clock. Long Wavelength Infrared (LWIR) thermal camera with a resolution of $640\times 512$ and 12um pitch is used as the source for the raw video. The design achieves 60 frames per second (fps).
Transactivation via RAR / RXR-Sp 1 Interaction : Characterization of Binding Between Sp 1 and GC Box Motif

Laboratory of Molecular Cell Sciences, Tsukuba Institute, RIKEN, Koyadai, Tsukuba, Ibaraki 3050074, Japan (J.S., Y.S., S.K.); Institute of Applied Biochemistry, University of Tsukuba, Tennoudai, Tsukuba, Ibaraki 305-0006, Japan (J.S., Y.S., P.-C.W., M.M.); and Laboratory of Cell Regulation and Carcinogenesis, Division of Basic Science, National Cancer Institute, National Institute of Health, Bethesda, Maryland 20892 (S.-J.K.)
Lightweight Stream Cipher for Health Care IoT

Recently, the exponential expansion of Internet of Things (IoT) devices and an increasing need for secure communication have presented novel challenges for cryptographic algorithms. Lightweight cryptography has emerged as a promising solution for dealing with the constraints of resource-constrained devices while maintaining security. Presently, a variety of lightweight cryptographic algorithms offer several advantages during implementation. Nevertheless, the extensive range of choices can challenge determining the appropriate IoT device, context, and timing for ensuring security. This paper present a comprehensive study on the design and applications of stream cipher based on nonlinear feedback shift registers (NLFSRs) in lightweight cryptography. NLFSRs are simple shift register-based structures with low computational overhead and minimal hardware requirements. The performance analysis of the proposed method for random and pseudorandom number generators includes a statistical test called the NIST test. The evaluation of the outcomes demonstrates that the scheme is both highly secure and lightweight.
Discovering Novel Pharmaceutical Molecules with Generative Adversarial Networks

The purpose of drug design is to construct a chemical component that can fit into a specific cavity on a protein target both physically and chemically. Traditional drug design procedures are both costly and time demanding. The era of computer-aided drug design (CADD), provided a quantitative relationship between structure and biological activity. However, due to time constraints and a shortage of trained and knowledgeable pharmaceutical personnel, it fell short of precision. With the increasing popularity of AI-based methods, the pharmaceutical industry also gained acceleration and streamlined the drug development pipeline. Generative Adversarial Networks(GAN) offered very good results in the creation of new drugs. This paper discusses the design and implementation of GAN with experiments on the QM9 dataset. The molecules produced are evaluated with various performance metrics and also compared with benchmark models such as MolGAN and ORGAN.
ChatBot Integrated with Sentiment Anlaysis: Application to E-Learning

Online learning has become a popular choice for many schools and universities as well as for people with work or family obligations. However, many factors can lead the student to quit learning such as the lack of quality of the content, and the difficulty of use, the unavailability of support. These factors lead the student to become bored, demotivated, and drop out of the course. In this paper, an architecture of a chatbot integrated with sentiment analysis will be presented to determine the emotional state of the user based on the feedback collected from several e-learning platforms. In our case, a LSTM (Long Short-Term Memory) network is used with an accuracy of 91%. Integrating sentiment analysis with the chatbot in e-learning enables a more engaging and personalized learning experience for users. By understanding the emotional state of users, the chatbot can adapt its responses to provide more appropriate and effective support. In addition, by providing personalized feedback and support, the chatbot can help users identify and overcome obstacles they may encounter in their learning process.
Machine Learning-based Fast Circuit Simulation for Analog Circuit Array

Fast circuit simulation offers quick verification and exploration of a wide design space for analog circuit arrays (e.g., display panel). However, as the size of analog circuit arrays increases, the simulation complexity of traditional methods like SPICE analysis becomes excessively challenging. In this work, we introduce a machine learning-based circuit simulation to reduce the complexity without accuracy loss. We propose a novel machine learning model, Initial Hidden state Embedded Convolution Long Short-Term Memory (IHE-CLSTM) to effectively process sequential data and input design parameters at once. In addition, the model can completely decompose the circuit array into individual components and enable complete parallel simulations of individual components. Our experimental results demonstrate that our method achieves average runtime improvements of 66.01% and 79.11% compared to the golden SPICE simulation results for various input parameters and circuit sizes, respectively. Moreover, it effectively performs interpolation and extrapolation for unseen parameters and sizes of circuit arrays, and has a linear time complexity with respect to the size of circuit arrays.
ConVERTS: Contrastively Learning Structurally InVariant Netlist Representations

Graph neural network (GNN)-based representations of hardware designs are used in electronic design automation (EDA) tasks like logic synthesis, verification, and hardware security. While promising, state-of-the-art methods are supervised and require target labels and/or need different behavioral register transfer level (RTL) codes of the same function as training data to generalize. We propose ConVERTS, a self-supervised netlist contrastive learning method that generalizes well using one-shot RTL of a design. We demonstrate the effectiveness of ConVERTS on two use-cases: (1) netlist classification, and (2) Recovering functionality of obfuscated designs.
On the Design of Robust Differential Beamformers with Uniform Circular Microphone Arrays

While circular microphone arrays (CMAs) have been used in a wide range of applications, e.g., smart speakers and conferencing phones, how to design robust beamformers that can obtain the highest possible directivity with such arrays remains a challenging issue. In this paper, we study this problem and present a method to design robust differential beamformers based on uniform CMAs (UCMAs). The approach casts the differential beamforming problem into one of optimization, which attempts to maximize the directivity factor (DF) subject to some constraints on symmetric nulls in the beampattern and the white noise gain (WNG). The robust beamformers are obtained by transforming the optimization problem into a quadratic eigenvalue problem (QEP). Simulation results show that the deduced beamformers can obtain high DFs and consistent beampatterns, regardless of the steering angle, and the WNG is effectively controlled to be larger than a pre-specified threshold.
Enhancing Fake News Detection through Clustering with Convolutional Neural Networks

In this study, a deep learning-based clustering algorithm was applied for the detection of fake news. In the initial stage, true and fake news articles were collected from three different news websites using web scraping method. Subsequently, these collected news data were merged with two publicly available datasets. This resulted in the creation of a dataset encompassing approximately 130206 text samples of true and fake news, providing a wide range of topics. A Convolutional Neural Network (CNN) architecture based on Bidirectional Encoder Representations from Transformers (BERT) was developed to obtain the deep representations of the dataset. Then, clustering using the K-Means algorithm was performed on the deep features of the data to detect fake news. This study presented a higher accuracy rate while reducing the cost and computational process by utilizing fewer features. Additionally, this work contributes to the field as it introduces a new dataset and addresses the scarcity of studies on fake news detection using the deep learning-based clustering approach, thereby serving as a valuable resource for future research in this area.
Novel split-well resonant-phonon terahertz quantum cascade laser supporting clean four-level system.

We present a novel terahertz quantum cascade laser (THz QCL) scheme supporting a clean four-level system, ‘four’ being the number of the active laser states, as verified by the negative differential resistance (NDR) observed all the way up to room temperature. In this study, we analyze and discuss the temperature performance of this new design. Experimental as well as theoretical work was performed to analyze the effects of the doping density as well as the impact of the different scattering mechanisms.
An LR-FHSS Receiver for a Massive IoT Connectivity

Long Range-Frequency Hopping Spread Spectrum (LR-FHSS) is an extension of the LoRa physical (PHY) layer that has been recently announced by Semtech and will be supported on their chips. This new PHY layer is designed to address extremely long-range and large-scale communication scenarios, such as satellite Internet of Things (IoT). LR-FHSS features high interference resistance that alleviates packet collisions by increasing the spectral efficiency with an uplink modulation based on the frequency hopping spread spectrum (FHSS). Subsequently, each data packet being sent from an LR-FHSS device is split into small pieces, which are randomly spread over a defined frequency bandwidth. In this paper, we propose several interference blanking techniques allowing increasing the number of simultaneously received LR-FHSS signals, that are accurately decoded, over the same hopping bandwidth. This processing would increase the capacity of LR-FHSS receivers and make this technology more suited to be deployed in satellite IoT applications, which entails the connectivity of a massive number of devices.
A Robust Model and its EM Algorithm for the Estimation of the Multifractality Parameter

Multifractal analysis is a powerful modeling and estimation framework for the characterization of data dynamics via the fluctuations of its pointwise regularity in time or space. Though successfully applied in a large number of applications in very different contexts, the estimation of parameters related to the data multifractality is an intricate issue for discrete realworld data in non-standard situations, e.g., for small sample size or in the presence of data corruptions. Building upon a recently introduced generic statistical model for log-leaders, specific multiresolution quantities designed for multifractal analysis, the present work proposes a novel robust model and estimator for the multifractality parameter that quantifies the degree of multifractality in data. Our model explicitly accounts for certain data corruptions as outliers in the spectral log-leader domain. Moreover, we propose an original expectation-maximization algorithm for estimating the parameters associated with the model. Several Monte Carlo simulations have been conducted to evaluate the performance of the proposed estimator, which confirm its good performance when compared to standard linear regression in the presence of different additive or multiplicative data perturbations.
Energy Management of Reconfigurable Manufacturing Systems by using Symbolic Control

We present a systematical framework for achieving the energy-aware configuration manager of reconfigurable manufacturing systems. Our framework considers minimizing energy consumption and the mutual exclusion on shared resources by means of the symbolic discrete controller synthesis technique. In our study, the control objectives are divided into safety objectives and optimization objectives. Safety objectives focus on maintaining mutually shared resource constraints. On the other hand, optimization objectives aim to enhance system performance, particularly in terms of energy consumption. We construct symbolic models of reconfigurable manufacturing systems with desired specifications in order for the automatic computation of reconfiguration controllers; and then we use this controller as a manager. We experimentally evaluate our approach in a case study; and our results validate the advantages of employing symbolic optimal discrete controller synthesis in achieving the energy-efficient configuration manager of reconfigurable manufacturing systems.
Granger Causality Pattern Learning Equipped with Noise Invalidation Soft Thresholding

This paper concentrates on pattern learning of Granger causality. In this context, the entities of the Granger causality matrix estimation derived from the state-space model indicate directional dependencies of the observations. Existing methods propose different forms of thresholding to eliminate insignificant entities from this matrix. These approaches do not exploit the difference in statistical characteristics of insignificant entities compared to the Granger causality itself. In this work, Noise Invalidation Soft Thresholding is chosen as the thresholding method to discard null relations in the Granger causality matrix pattern learning. Unlike existing approaches, the proposed method benefits from the statistical properties of insignificant entities. The simulation results demonstrate the advantages and superiority of the proposed method in the sense of accuracy and robustness for both randomly generated datasets with causal footprints as well as simulated electroencephalogram datasets.
Statistical Characteristics of Rainstorms Derived from Weather Radar Images

The hydrologic response of urban catchments is sensitive to small scale space-time rainfall variations. A stochastic rainfall model must reproduce important statistics at these scales. Using an extensive database of weather radar images for the Sydney region, a detailed study of the variation of important statistics within and between storms is undertaken. The statistics include wet area ratio, correlation length scales, and transformation parameters to render the rainfield approximately Gaussian. It was found that large variation occurred within and between storms and that uni- and bivariate AR(1) models could adequately describe within-storm variation. The results suggest that key statistics describing the stochastic evolution of rainfields at 10-minute, 1 km{2} resolution exhibit strong time dependence. This has significant implications for the development of stochastic rainfall models at this scale.