Miracle-dz/UniFAR-FTUs huggingface dataset:
Gossypium hirsutum gene of unknown function, Gohir.A02G044702.1, encodes a potential B3 Transcription Factor of the REM subfamily

A gene of unknown function, Gohir.A02G044702. 1, identified in Gossypium hirsutum was studied using sequence and structure bioinformatic tools. The encoded protein (UniProt A0A1U8MGX4) was predicted to localize to the nucleus, was found to retain the B3 transcription factor domain with conserved DNA-binding residues and to most closely cluster with REM subfamily members of B3-domain containing proteins.
An Interdisciplinary Agent-based Multimodal Wildfire Evacuation Model : Critical Decisions and Life Safety

This article applies an interdisciplinary agent-based model (ABM) of wildfire evacuation to the devastating 2018 wildfire in Mati, Greece, where the second-deadliest wildfire of the 21st century took place. This model integrates the natural hazard system (wildfire propagation) with the sociotechnical response system comprising social (population response) and engineered (transportation network and shelter location) components. The research objective is to investigate the effects on wildfire casualties of the risk area population’s decisions about (1) whether to leave and how long to wait (i.e., departure time); (2) what transportation mode to use (e.g., walking or driving); and (3) how fast to travel. Analysis of several evacuation scenarios shows that the absence of children, multi-modal travel, staged evacuation, and increased shelter capacity lead to a more successful wildfire evacuation. These analyses can help emergency managers improve the effectiveness of their communities’ wildfire evacuation plans.
Levinson and fast Choleski algorithms for Toeplitz and almost Toeplitz matrices

In this paper, we review Levinson and fast Choleski algorithms for solving sets of linear equations involving Toeplitz or almost Toeplitz matrices. The Levinson-Trench-Zohar algorithm is first presented for solving problems involving exactly Toeplitz matrices. A fast Choleski algorithm is derived by a simple linear transformation. The almost Toeplitz problem is then considered and a Levinson-style algorithm is proposed for solving it. A set of linear transformations converts the algorithm into a fast Choleski method. Symmetric and band diagonal applications are considered. Formulas for the inverse of an almost Toeplitz matrix are derived. The relationship between the fast Choleski algorithms and a Euclidian algorithm is exploited in order to derive accelerated "doubling" algorithms for inverting the matrix. Finally, strategies for removing the strongly nonsingular constraint of Levinson recursion are considered. We conclude by applying the techniques to several applications, including covariance methods of linear prediction, rational Toeplitz matrices, and optimal finite interval ARMA smoothing filters.
Field assessment of BinaxNOW antigen tests as COVID-19 treatment entry point at a community testing site in San Francisco during evolving omicron surges

Background. COVID-19 oral treatments require initiation within 5 days of symptom onset. Although antigen tests are less sensitive than RT-PCR, rapid results could facilitate entry to treatment. As SARS-CoV-2 variants and host immunity evolve, it is important to characterize the use case for rapid antigen tests for treatment entry. Methods. We collected anterior nasal swabs for BinaxNOW and RT-PCR testing and clinical data at a walk-up, community site in San Francisco, California between January and June 2022. SARS-CoV-2 genomic sequences were generated from positive samples and classified according to subtype and variant. Monte Carlo simulations were conducted to estimate the expected proportion of SARS-CoV-2 infected persons who would have been diagnosed within 5 days of symptom onset using RT-PCR versus BinaxNOW testing. Results. Among 25,309 persons tested with BinaxNOW, 2,952 had concomitant RT-PCR. 1321/2952 (44.7%) were SARS-CoV-2 RT-PCR positive. We identified waves of predominant omicron BA.1, BA.2, BA.2. 12, BA.4, and BA.5 among 720 sequenced samples. Among 1,321 RT-PCR positive samples, 938/1321 (71%) were detected by BinaxNOW; 95% (774/817) of those with Ct value <30 were detected by BinaxNOW. BinaxNOW detection was consistent over lineages. In analyses to evaluate entry to treatment, BinaxNOW detected 82.7% (410/496, 95% CI: 79-86%) of persons with COVID-19 within 5 days of symptom onset. In comparison, RT-PCR (24-hour turnaround) detected 83.1% (412/496 95% CI: 79-86%) and RT-PCR (48-hour turnaround) detected 66.3% (329/496 95% CI: 62-70%) of persons with COVID-19 within 5 days of symptom onset. Conclusions. BinaxNOW detected high viral load from anterior nasal swabs consistently across omicron sublineages emerging between January and June of 2022. Simulations support BinaxNOW as an entry point for COVID-19 treatment in a community field setting.
A Hybrid Machine Learning Model for Estimation of Obesity Levels

Obesity has always been a problem which has plagued humans for many generations, which, since the 1975, almost doubled to turn into a global epidemic. The current human dependence on technology has contributed to the problem even more, with the effects visibly pronounced in late teenagers and early adults. Researchers till date, have tried numerous ways to determine the factors that cause obesity in early adults. On that frontier, our hybrid machine-learning model uses the help of some supervised and unsupervised data mining methods like Extremely Randomized Trees, Multilayer Perceptron and XGBoost using Python to detect and predict obesity levels and help healthcare professionals to combat this phenomenon. Our dataset is a publicly available dataset in the UCI Machine Learning Repository, containing the data for the estimation of obesity levels in individuals from the countries of Mexico, Peru, and Colombia, based on their eating habits and physical condition. The proposed model heavily utilizes feature engineering methods and introduces the concept of a hybrid model. This work has shown improved results over prior works and extensive studies have been undertaken to preserve the robustness of this model.
The challenges of implementing green factors in urban greening schemes in Indonesia

Recently, new planning tools have emerged to support urban landscape planners in achieving sustainability through optimizing the urban greening scheme. Some cities have approved the green factor tool to increase the quantity and quality of urban green spaces. This greening scheme is considered suitable for a relatively dense city where challenging to provide adequate urban green areas. This short communication challenges the usefulness of the green factor tool and how it relates to the existing urban green space planning procedures through a qualitative case study in Jakarta, Indonesia. The result shows that while the green factor tool performs well, improvements could be made primarily in convincing stakeholders regarding the new paradigm in calculating green areas in relatively dense urban areas. Furthermore, trials in actual cases need to be carried out to examine the formula’s effectiveness. Regarding the usefulness of this tool, we conclude that while increasing the number of green areas, improving its quality is more necessary to build resilient infrastructures.
Servo Motor Controller using PID and Graphical User Interface on Raspberry Pi for Robotic Arm

The usage of the robotic arm in industries has been growing as the robotic arm can provide many advantages to the industries. However, the usage of the robotic arm has many challenges faced by the industries. One of the problems is the error of the angle position caused by the movement of the robotic arm’s joint which affects the precision of the desired position of the end-effector. Therefore, this paper aims to study the solution of the PID controller to increase the accuracy of the position of the end-effector in the X-Y plane using forward kinematic. It describes the mechanical system, design concept, and the prototype of a 6 DOF robotic arm. Initially, the angle error of the joint for 2D plane movement is captured manually using the trigonometric calculation. Then, to reduce this error, the coefficient of the PID controller, k p , k i , and k d are tuned automatically using Simulink and MATLAB. Then, the controller is programmed on a Raspberry Pi with a servo driver, PCA9685. There are six servo motors used as the joints of the robotic arm, namely MG996R. Each servo motor can be controlled by using a Graphical User Interface (GUI) scripted in Python Programming Language. The end position of the end-effector will be displayed on this GUI. It is found that the angle position error of the 6 DOF robotic arm for X-Y plane movement is reduced from 4.7° to 0° by using the PID controller.
Health risk-based prioritization approaches of pharmaceuticals in the Upper Citarum River Basin

Two methods were developed to prioritize human health risks of pharmaceuticals based on defined daily doses (DDDs) obtained from the WHO and acute toxicity data on pharmaceuticals (TDLo) obtained from the PubChem database. A major advantage of these methods is that it requires no acceptable daily intake (ADI), reference dose (RfD), or tolerable daily intake (TDI) values, which are often unavailable or difficult to obtain for pharmaceuticals group. Both methods were applied to prioritize 16 pharmaceuticals detected by passive samplers in the water source of Ciwalengke Village, a peri-urban area in the Upper Citarum River Basin. Ten out of 16 pharmaceuticals could be prioritized by using both methods. The risk indicator values (RIhum) showed good agreement between the two approaches, especially for Caffeine and Trimethoprim. The priority ranking of Caffeine and Lidocaine were always in the top 3 highest human health risks. The ranking comparison showed considerable consistency, indicating that both prioritization methods are reasonably in line with each other. The pharmaceutical intake through the exposure of contaminated river water was expected to pose negligible human health risks (i.e. RIhum < 1) but can be refined more by conducting supplementary effects studies for other individual pharmaceuticals or pharmaceuticals mixtures.
Cyber Attack Detection Dataset: A Review

As cyber attack become more complicated, it becomes more difficult to identify breaches successfully. The inability to identify intrusions might jeopardize security services’ confidence, compromising data confidentiality, integrity, and availability. Cyber attacks like, Ping of Death, Botnets, also IP spoofing, as well as Social Engineering attacks, are becoming more common. A number of Intrusion Detection System (IDS) approaches developed to encounter cyber security intrusion. In order to discover attack patterns, the IDS performance was evaluated by employing dataset of IDS made up of network traffic properties. Intrusion detection is a classification problem in which different Artificial Intelligence techniques have been utilized to classify between legitimate also malicious network traffic. The multiple IDS datasets used to evaluate the IDS model are listed in this publication. These are new attack categories and recent datasets containing network attack features. This paper presents several IDS dataset with many existing evaluation techniques in model of IDS. Hopefully the outcome can be used in designing efficient and effective systems employing the benchmark and new IDS datasets.
Target Selection and Validation of DESI Quasars

The Dark Energy Spectroscopic Instrument (DESI) survey will measure large-scale structures using quasars as direct tracers of dark matter in the redshift range 0.9 < z < 2.1 and using Lyα forests in quasar spectra at z > 2.1. We present several methods to select candidate quasars for DESI, using input photometric imaging in three optical bands (g, r, z) from the DESI Legacy Imaging Surveys and two infrared bands (W1, W2) from the Wide-field Infrared Survey Explorer. These methods were extensively tested during the Survey Validation of DESI. In this paper, we report on the results obtained with the different methods and present the selection we optimized for the DESI main survey. The final quasar target selection is based on a random forest algorithm and selects quasars in the magnitude range of 16.5 < r < 23. Visual selection of ultra-deep observations indicates that the main selection consists of 71% quasars, 16% galaxies, 6% stars, and 7% inconclusive spectra. Using the spectra based on this selection, we build an automated quasar catalog that achieves a fraction of true QSOs higher than 99% for a nominal effective exposure time of ∼1000 s. With a 310 deg−2 target density, the main selection allows DESI to select more than 200 deg−2 quasars (including 60 deg−2 quasars with z > 2.1), exceeding the project requirements by 20%. The redshift distribution of the selected quasars is in excellent agreement with quasar luminosity function predictions.
Termination Dependence of the Surface States in Pb2Pd

Topological properties of systems lead to the emergence of surface states which can be observed experimentally within the angle-resolved photoemission spectroscopy (ARPES) measurements. Recently, the topological properties of Pb$_{2}$Pd were reported. In this paper, we discuss the role of the surface termination on the realized surface states. We discuss the termination dependence of the surface state for (001) and (110) surface. We demonstrate that the Pd terminated (001) surface allow realization of the Dirac cone-like surface state. In the case (110) we observe well visible surface states with parabolic-like dispersion relation in close vicinity of the Fermi level.
Visual Explanation of Deep Q-Network for Robot Navigation by Fine-tuning Attention Branch

—Robot navigation with deep reinforcement learn- ing (RL) achieves higher performance and performs well under complex environment. Meanwhile, the interpretation of the decision-making of deep RL models becomes a critical problem for more safety and reliability of autonomous robots. In this paper, we propose a visual explanation method based on an attention branch for deep RL models. We connect attention branch with pre-trained deep RL model and the attention branch is trained by using the selected action by the trained deep RL model as a correct label in a supervised learning manner. Because the attention branch is trained to output the same result as the deep RL model, the obtained attention maps are corresponding to the agent action with higher interpretability. Experimental results with robot navigation task show that the proposed method can generate interpretable attention maps for a visual explanation.
The 8-Point Algorithm as an Inductive Bias for Relative Pose Prediction by ViTs

We present a simple baseline for directly estimating the relative pose (rotation and translation, including scale) between two images. Deep methods have recently shown strong progress but often require complex or multi-stage architectures. We show that a handful of modifications can be applied to a Vision Transformer (ViT) to bring its computations close to the Eight-Point Algorithm. This inductive bias enables a simple method to be competitive in multiple settings, often substantially improving over the state of the art with strong performance gains in limited data regimes.
An intertwined neural network model for EEG classification in brain-computer interfaces

The brain computer interface (BCI) is a nonstimulatory direct and occasionally bidirectional communication link between the brain and a computer or an external device. Classically, EEG-based BCI algorithms have relied on models such as support vector machines and linear discriminant analysis or multiclass common spatial patterns. During the last decade, however, more sophisticated machine learning architectures, such as convolutional neural networks (40, 41), recurrent neural networks, long short-term memory networks and gated recurrent unit networks, have been extensively used to enhance discriminability in multiclass BCI tasks. Additionally, preprocessing and denoising of EEG signals has always been key in the successful decoding of brain activity, and the determination of an optimal and standardized EEG preprocessing activity is an active area of research. In this paper, we present a deep neural network architecture specifically engineered to a) provide state-of-the-art performance in multiclass motor imagery classification and b) remain robust to preprocessing to enable real-time processing of raw data as it streams from EEG and BCI equipment. It is based on the intertwined use of time-distributed fully connected (tdFC) and space-distributed 1D temporal convolutional layers (sdConv) and explicitly addresses the possibility that interaction of spatial and temporal features of the EEG signal occurs at all levels of complexity. Numerical experiments demonstrate that our architecture provides superior performance compared baselines based on a combination of 3D convolutions and recurrent neural networks in a six-class motor imagery network, with a subjectwise accuracy that reaches 99%. Importantly, these results remain unchanged when minimal or extensive preprocessing is applied, possibly paving the way for a more transversal and real-time use of deep learning architectures in EEG classification. We present a novel DL architecture for EEG classification that is agnostic to electrode positioning and outperforms state-of-the-art deep architectures based on combinations of convolutional and recurrent neural network blocks. The classification results on a recently published, high-quality 6-class motor imagery dataset are insensitive to preprocessing, possibly bypassing a major source of arbitrariness and paving the way for bedside implementations of EEG-based BCI applications.
Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment

A basic premise in graph signal processing (GSP) is that a graph encoding pairwise (anti-)correlations of the targeted signal as edge weights is exploited for graph filtering. However, existing fast graph sampling schemes are designed and tested only for positive graphs describing positive correlations. In this paper, we show that for datasets with strong inherent anti-correlations, a suitable graph contains both positive and negative edge weights. In response, we propose a linear-time signed graph sampling method centered on the concept of balanced signed graphs. Specifically, given an empirical covariance data matrix $\bar{\bf{C}}$, we first learn a sparse inverse matrix (graph Laplacian) $\mathcal{L}$ corresponding to a signed graph $\mathcal{G}$. We define the eigenvectors of Laplacian $\mathcal{L}_B$ for a balanced signed graph $\mathcal{G}_B$ -- approximating $\mathcal{G}$ via edge weight augmentation -- as graph frequency components. Next, we choose samples to minimize the low-pass filter reconstruction error in two steps. We first align all Gershgorin disc left-ends of Laplacian $\mathcal{L}_B$ at smallest eigenvalue $\lambda_{\min}(\mathcal{L}_B)$ via similarity transform $\mathcal{L}_p = \S \mathcal{L}_B \S^{-1}$, leveraging a recent linear algebra theorem called Gershgorin disc perfect alignment (GDPA). We then perform sampling on $\mathcal{L}_p$ using a previous fast Gershgorin disc alignment sampling (GDAS) scheme. Experimental results show that our signed graph sampling method outperformed existing fast sampling schemes noticeably on various datasets.
Banishing Burnout: Six Strategies for Improving Your Relationship with Work

Acknowledgments 1. Your Job and You 2. What Is My Relationship with Work? 3. Making a Plan of Action 4. Solving Workload Problems 5. Solving Control Problems 6. Solving Reward Problems 7. Solving Community Problems 8. Solving Fairness Problems 9. Solving Values Problems 10. Changing Your Relationship Checking Up Web Site Information About the Authors Index
Modeling Two-Way Selection Preference for Person-Job Fit

Person-job fit is the core technique of online recruitment platforms, which can improve the efficiency of recruitment by accurately matching the job positions with the job seekers. Existing works mainly focus on modeling the unidirectional process or overall matching. However, recruitment is a two-way selection process, which means that both candidate and employer involved in the interaction should meet the expectation of each other, instead of unilateral satisfaction. In this paper, we propose a dual-perspective graph representation learning approach to model directed interactions between candidates and jobs. To model the two-way selection preference from the dual-perspective of job seekers and employers, we incorporate two different nodes for each candidate (or job) and characterize both successful matching and failed matching via a unified dual-perspective interaction graph. To learn dual-perspective node representations effectively, we design an effective optimization algorithm, which involves a quadruple-based loss and a dual-perspective contrastive learning loss. Extensive experiments on three large real-world recruitment datasets have shown the effectiveness of our approach. Our code is available at https://github.com/RUCAIBox/DPGNN .
Exploiting Sentiment and Common Sense for Zero-shot Stance Detection

The stance detection task aims to classify the stance toward given documents and topics. Since the topics can be implicit in documents and unseen in training data for zero-shot settings, we propose to boost the transferability of the stance detection model by using sentiment and commonsense knowledge, which are seldom considered in previous studies. Our model includes a graph autoencoder module to obtain commonsense knowledge and a stance detection module with sentiment and commonsense. Experimental results show that our model outperforms the state-of-the-art methods on the zero-shot and few-shot benchmark dataset–VAST. Meanwhile, ablation studies prove the significance of each module in our model. Analysis of the relations between sentiment, common sense, and stance indicates the effectiveness of sentiment and common sense.
Musika! Fast Infinite Waveform Music Generation

Fast and user-controllable music generation could enable novel ways of composing or performing music. However, state-of-the-art music generation systems require large amounts of data and computational resources for training, and are slow at inference. This makes them impractical for real-time interactive use. In this work, we introduce Musika, a music generation system that can be trained on hundreds of hours of music using a single consumer GPU, and that allows for much faster than real-time generation of music of arbitrary length on a consumer CPU. We achieve this by first learning a compact invertible representation of spectrogram magnitudes and phases with adversarial autoencoders, then training a Generative Adversarial Network (GAN) on this representation for a particular music domain. A latent coordinate system enables generating arbitrarily long sequences of excerpts in parallel, while a global context vector allows the music to remain stylistically coherent through time. We perform quantitative evaluations to assess the quality of the generated samples and showcase options for user control in piano and techno music generation. We release the source code and pretrained autoencoder weights at github.com/marcoppasini/musika, such that a GAN can be trained on a new music domain with a single GPU in a matter of hours.