Miracle-dz/UniFAR-FTUs huggingface dataset:
From RNA sequencing measurements to the final results: A practical guide to navigating the choices and uncertainties of gene set analysis

Gene set analysis (GSA), a popular approach for analyzing high‐throughput gene expression data, aims to identify sets of related genes that show significantly enriched or depleted expression patterns between different conditions. In the last years, a multitude of methods have been developed for this task. However, clear guidance is lacking: choosing the right method is the first hurdle a researcher is confronted with. No less challenging than overcoming this so‐called method uncertainty is the procedure of preprocessing, from knowing which steps are required to selecting a corresponding approach from the plethora of valid options to create the accepted input object (data preprocessing uncertainty), with clear guidance again being scarce. Here, we provide a practical guide through all steps required to conduct GSA, beginning with a concise overview of a selection of established methods, including Gene Set Enrichment Analysis and Database for Annotation, Visualization, and Integrated Discovery (DAVID). We thereby lay a special focus on reviewing and explaining the necessary preprocessing steps for each method under consideration (e.g., the necessity of a transformation of the RNA sequencing data)—an essential aspect that is typically paid only limited attention to in both existing reviews and applications. To raise awareness of the spectrum of uncertainties, our review is accompanied by an extensive overview of the literature on valid approaches for each step and illustrative R code demonstrating the complex analysis pipelines. It ends with a discussion and recommendations to both users and developers to ensure that the results of GSA are, despite the above‐mentioned uncertainties, replicable and transparent.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data Data: Types and Structure > Data Preparation and Processing Applications of Computational Statistics > Genomics/Proteomics/Genetics
STEC: See-Through Transformer-based Encoder for CTR Prediction

Click-Through Rate (CTR) prediction holds a pivotal place in online advertising and recommender systems since CTR prediction performance directly influences the overall satisfaction of the users and the revenue generated by companies. Even so, CTR prediction is still an active area of research since it involves accurately modelling the preferences of users based on sparse and high-dimensional features where the higher-order interactions of multiple features can lead to different outcomes. Most CTR prediction models have relied on a single fusion and interaction learning strategy. The few CTR prediction models that have utilized multiple interaction modelling strategies have treated each interaction to be self-contained. In this paper, we propose a novel model named STEC that reaps the benefits of multiple interaction learning approaches in a single unified architecture. Additionally, our model introduces residual connections from different orders of interactions which boosts the performance by allowing lower level interactions to directly affect the predictions. Through extensive experiments on four real-world datasets, we demonstrate that STEC outperforms existing state-of-the-art approaches for CTR prediction thanks to its greater expressive capabilities.
FurChat: An Embodied Conversational Agent using LLMs, Combining Open and Closed-Domain Dialogue with Facial Expressions

We demonstrate an embodied conversational agent that can function as a receptionist and generate a mixture of open and closed-domain dialogue along with facial expressions, by using a large language model (LLM) to develop an engaging conversation. We deployed the system onto a Furhat robot, which is highly expressive and capable of using both verbal and nonverbal cues during interaction. The system was designed specifically for the National Robotarium to interact with visitors through natural conversations, providing them with information about the facilities, research, news, upcoming events, etc. The system utilises the state-of-the-art GPT-3.5 model to generate such information along with domain-general conversations and facial expressions based on prompt engineering.
CLIPTrans: Transferring Visual Knowledge with Pre-trained Models for Multimodal Machine Translation

There has been a growing interest in developing multimodal machine translation (MMT) systems that enhance neural machine translation (NMT) with visual knowledge. This problem setup involves using images as auxiliary information during training, and more recently, eliminating their use during inference. Towards this end, previous works face a challenge in training powerful MMT models from scratch due to the scarcity of annotated multilingual vision-language data, especially for low-resource languages. Simultaneously, there has been an influx of multilingual pretrained models for NMT and multimodal pre-trained models for vision-language tasks, primarily in English, which have shown exceptional generalisation ability. However, these are not directly applicable to MMT since they do not provide aligned multimodal multilingual features for generative tasks. To alleviate this issue, instead of designing complex modules for MMT, we propose CLIPTrans, which simply adapts the independently pre-trained multimodal M-CLIP and the multilingual mBART. In order to align their embedding spaces, mBART is conditioned on the M-CLIP features by a prefix sequence generated through a lightweight mapping network. We train this in a two-stage pipeline which warms up the model with image captioning before the actual translation task. Through experiments, we demonstrate the merits of this framework and consequently push forward the state-of-the-art across standard benchmarks by an average of +2.67 BLEU. The code can be found at www.github. com/devaansh100/CLIPTrans.
Evaluation of Key Spatiotemporal Learners for Print Track Anomaly Classification Using Melt Pool Image Streams

Recent applications of machine learning in metal additive manufacturing (MAM) have demonstrated significant potential in addressing critical barriers to the widespread adoption of MAM technology. Recent research in this field emphasizes the importance of utilizing melt pool signatures for real-time defect prediction. While high-quality melt pool image data holds the promise of enabling precise predictions, there has been limited exploration into the utilization of cutting-edge spatiotemporal models that can harness the inherent transient and sequential characteristics of the additive manufacturing process. This research introduces and puts into practice some of the leading deep spatiotemporal learning models that can be adapted for the classification of melt pool image streams originating from various materials, systems, and applications. Specifically, it investigates two-stream networks comprising spatial and temporal streams, a recurrent spatial network, and a factorized 3D convolutional neural network. The capacity of these models to generalize when exposed to perturbations in melt pool image data is examined using data perturbation techniques grounded in real-world process scenarios. The implemented architectures demonstrate the ability to capture the spatiotemporal features of melt pool image sequences. However, among these models, only the Kinetics400 pre-trained SlowFast network, categorized as a two-stream network, exhibits robust generalization capabilities in the presence of data perturbations.
Environmental association analyses identify candidates for abiotic stress tolerance in 1 Glycine soja , the wild progenitor of cultivated soybeans 2

Environmental association analyses identify candidates for abiotic stress tolerance in 1 Glycine soja, the wild progenitor of cultivated soybeans 2 Justin E. Anderson *, , Thomas J. Y. Kono , Robert M. Stupar, Michael B. Kantar §, , Peter 3 L. Morrell ‡ 4 Department of Agronomy and Plant Genetics, University of Minnesota, 411 Borlaug Hall, 1991 5 Upper Buford Circle, St. Paul, MN 55108 6 Biodiversity Research Centre and Department of Botany, University of British Columbia, 35297 6270 University Boulevard, Vancouver, British Columbia V6T 1Z4, Canada 8 Contributed equally to this work 9 To whom correspondence should be addressed 10
LLM-Based Human-Robot Collaboration Framework for Manipulation Tasks

This paper presents a novel approach to enhance autonomous robotic manipulation using the Large Language Model (LLM) for logical inference, converting high-level language commands into sequences of executable motion functions. The proposed system combines the advantage of LLM with YOLO-based environmental perception to enable robots to autonomously make reasonable decisions and task planning based on the given commands. Additionally, to address the potential inaccuracies or illogical actions arising from LLM, a combination of teleoperation and Dynamic Movement Primitives (DMP) is employed for action correction. This integration aims to improve the practicality and generalizability of the LLM-based human-robot collaboration system.
On-Device Learning with Binary Neural Networks

Existing Continual Learning (CL) solutions only partially address the constraints on power, memory and computation of the deep learning models when deployed on low-power embedded CPUs. In this paper, we propose a CL solution that embraces the recent advancements in CL field and the efficiency of the Binary Neural Networks (BNN), that use 1-bit for weights and activations to efficiently execute deep learning models. We propose a hybrid quantization of CWR* (an effective CL approach) that considers differently forward and backward pass in order to retain more precision during gradient update step and at the same time minimizing the latency overhead. The choice of a binary network as backbone is essential to meet the constraints of low power devices and, to the best of authors' knowledge, this is the first attempt to prove on-device learning with BNN. The experimental validation carried out confirms the validity and the suitability of the proposed method.
Shared Lexical Items as Triggers of Code Switching

Abstract Why do bilingual speakers code-switch (mix their two languages)? Among the several theories that attempt to explain this natural and ubiquitous phenomenon, the triggering hypothesis relates code-switching to the presence of lexical triggers, specifically cognates and proper names, adjacent to the switch point. We provide a fuller, more nuanced and refined exploration of the triggering hypothesis, based on five large datasets in three language pairs, reflecting both spoken and written bilingual interactions. Our results show that words that are assumed to reside in a mental lexicon shared by both languages indeed trigger code-switching, that the tendency to switch depends on the distance of the trigger from the switch point and on whether the trigger precedes or succeeds the switch, but not on the etymology of the trigger words. We thus provide strong, robust, evidence-based confirmation to several hypotheses on the relationships between lexical triggers and code-switching.
Is visual explanation with Grad-CAM more reliable for deeper neural networks? a case study with automatic pneumothorax diagnosis

While deep learning techniques have provided the state-of-the-art performance in various clinical tasks, explainability regarding their decision-making process can greatly enhance the credence of these methods for safer and quicker clinical adoption. With high flexibility, Gradient-weighted Class Activation Mapping (Grad-CAM) has been widely adopted to offer intuitive visual interpretation of various deep learning models' reasoning processes in computer-assisted diagnosis. However, despite the popularity of the technique, there is still a lack of systematic study on Grad-CAM's performance on different deep learning architectures. In this study, we investigate its robustness and effectiveness across different popular deep learning models, with a focus on the impact of the networks' depths and architecture types, by using a case study of automatic pneumothorax diagnosis in X-ray scans. Our results show that deeper neural networks do not necessarily contribute to a strong improvement of pneumothorax diagnosis accuracy, and the effectiveness of GradCAM also varies among different network architectures.
BIT: Bi-Level Temporal Modeling for Efficient Supervised Action Segmentation

We address the task of supervised action segmentation which aims to partition a video into non-overlapping segments, each representing a different action. Recent works apply transformers to perform temporal modeling at the frame-level, which suffer from high computational cost and cannot well capture action dependencies over long temporal horizons. To address these issues, we propose an efficient BI-level Temporal modeling (BIT) framework that learns explicit action tokens to represent action segments, in parallel performs temporal modeling on frame and action levels, while maintaining a low computational cost. Our model contains (i) a frame branch that uses convolution to learn frame-level relationships, (ii) an action branch that uses transformer to learn action-level dependencies with a small set of action tokens and (iii) cross-attentions to allow communication between the two branches. We apply and extend a set-prediction objective to allow each action token to represent one or multiple action segments, thus can avoid learning a large number of tokens over long videos with many segments. Thanks to the design of our action branch, we can also seamlessly leverage textual transcripts of videos (when available) to help action segmentation by using them to initialize the action tokens. We evaluate our model on four video datasets (two egocentric and two third-person) for action segmentation with and without transcripts, showing that BIT significantly improves the state-of-the-art accuracy with much lower computational cost (30 times faster) compared to existing transformer-based methods.
Bornil: An open-source sign language data crowdsourcing platform for AI enabled dialect-agnostic communication

The absence of annotated sign language datasets has hindered the development of sign language recognition and translation technologies. In this paper, we introduce Bornil; a crowdsource-friendly, multilingual sign language data collection, annotation, and validation platform. Bornil allows users to record sign language gestures and lets annotators perform sentence and gloss-level annotation. It also allows validators to make sure of the quality of both the recorded videos and the annotations through manual validation to develop high-quality datasets for deep learning-based Automatic Sign Language Recognition. To demonstrate the system's efficacy; we collected the largest sign language dataset for Bangladeshi Sign Language dialect, perform deep learning based Sign Language Recognition modeling, and report the benchmark performance. The Bornil platform, BornilDB v1.0 Dataset, and the codebases are available on https://bornil.bengali. ai
Cognitive behavioral therapy in the treatment of social phobia

Cognitive behavior therapy is probably the most well-known and the most practiced form of modern psychotherapy and has been integrated into highly structured package for the treatment of patients suffering from social phobia. The present case study is an attempt to provide therapeutic intervention program to a 27-year-old, unmarried Christian man suffering from social phobia. The patient was treated by using cognitive behavioral techniques. After 17 sessions of therapeutic intervention program, significant improvement was found. He was under follow-up for a period of 6 months and recovered to the premorbid level of functioning.
Abdominal Multi-Organ Segmentation Based on Feature Pyramid Network and Spatial Recurrent Neural Network

As recent advances in AI are causing the decline of conventional diagnostic methods, the realization of end-to-end diagnosis is fast approaching. Ultrasound image segmentation is an important step in the diagnostic process. An accurate and robust segmentation model accelerates the process and reduces the burden of sonographers. In contrast to previous research, we take two inherent features of ultrasound images into consideration: (1) different organs and tissues vary in spatial sizes, (2) the anatomical structures inside human body form a relatively constant spatial relationship. Based on those two ideas, we propose a new image segmentation model combining Feature Pyramid Network (FPN) and Spatial Recurrent Neural Network (SRNN). We discuss why we use FPN to extract anatomical structures of different scales and how SRNN is implemented to extract the spatial context features in abdominal ultrasound images.
Motivations and Gratifications for Selecting a Niche Television Channel: BYU Television

MOTIVATIONS AND GRATIFICATIONS FOR SELECTING A NICHE TELEVISION CHANNEL: BYU TELEVISION Diena L. Simmons Department of Communication Master of Arts The growth of direct broadcast satellite television distribution to the home as a viable competitor to cable and terrestrial broadcast has fostered the availability of special interest or niche channels and therefore provided greater choice to the viewer. This study, based on uses and gratifications theory, examined the relationships among ritual and instrumental viewing motivations and satisfactions, viewer religiosity, and viewing attentiveness as they related to the selection of a niche television channel, Brigham Young University Television. The uses and gratification approach provides an appropriate framework for studying "media consumption, the interrelated nature of television user motives, and the relationships among viewing motives and viewing patterns" (Abelman, 1989, p. 57). Data was gathered by way of an online survey of non-random, self-selected BYU Television viewers. Participants answered 67 questions about their motives for choosing to view BYU Television and the gratifications they received from their viewing. The 596 valid responses to the survey were analyzed. The study results are in harmony with previous uses and gratifications studies examining ritual and instrumental viewing patterns. The data found positive relationships between instrumental viewing motives and instrumental viewing satisfactions, as well as instrumental viewing motives and viewing selectivity. There was no support for those hypotheses that dealt with the level of viewing attention as it related to religiosity or instrumental viewing motives. Future topics of study are suggested including the opportunity an expanded media universe provides to increase the depth and breadth of uses and gratification theory, as well as to study the role of niche television services in community building.
Dephasing in a crystal-phase defined double quantum dot charge qubit strongly coupled to a high-impedance resonator

Dephasing of a charge qubit is usually credited to charge noise in the environment. Here we show that charge noise may not be the limiting factor for the qubit coherence. To this end, we study coherence properties of a crystal-phase defined semiconductor nanowire double quantum dot (DQD) charge qubit strongly coupled to a high-impedance resonator using radio-frequency (RF) reflectometry. Response of this hybrid system is measured both at a charge noise sensitive operation point (with finite DQD detuning) and at an insensitive point (so-called sweet spot with zero detuning). A theoretical model based on Jaynes-Cummings Hamiltonian matches the experimental results well and yields only a 10 % difference in dephasing rates between the two cases, despite that the sensitivity to detuning charge noise differs by a factor of 5. Therefore the charge noise is not the limiting factor for the coherence in this type of semiconducting nanowire qubits.
Inhibition of Phosphoinositide 3-Kinase Ameliorates Dextran Sodium Sulfate-Induced Colitis in Mice

The critical role of phosphoinositide 3-kinase γ (PI3K activation and recruitment makes it an attractive target for immunomodulatory therapy. AS605240, a potent PI3K γ inhibitor, has been reported to ameliorate chronic inflammatory disorders including rheumatoid arthritis, systemic lupus erythematosus and atherosclerosis. However, its in vivo effect on intestinal inflammation remains unknown. Here we evaluated the protective and therapeutic potentials of AS605240 on mice with DSS-induced acute and chronic colitis. Our results showed that AS605240 improved survival rate, disease activity index (DAI) and histological damage score in mice administered DSS in both the preventive and therapeutic studies. AS605240 treatment also significantly inhibited the increase of myeloperoxidase (MPO) levels, macrophage infiltration and CD4 + T cell number in the colon of DSS-fed mice. The DSS-induced overproductions of colonic proinflammatory cytokines including interleukin-1 necrosis factor-α and interferon-γ γ were significantly suppressed in mice undergoing down-regulations of level western blotting. These findings demonstrate that AS605240 may represent a promising novel agent for the treatment of inflammatory bowel disease by suppressing leukocyte infiltration as well as by immunoregulating the unbalance between proinflammatory and anti-inflammatory cytokines.
Inertial enhancement of the polymer diffusive instability

Beneitez et al. (Phys. Rev. Fluids, vol. 8, 2023, L101901) have recently discovered a new linear ‘polymer diffusive instability’ (PDI) in inertialess rectilinear viscoelastic shear flow using the finitely extensible nonlinear elastic constitutive model of Peterlin (FENE-P) when polymer stress diffusion is present. Here, we examine the impact of inertia on the PDI for both plane Couette and plane Poiseuille flows under varying Weissenberg number ${W}$ , polymer stress diffusivity $\varepsilon$ , solvent-to-total viscosity ratio $\beta$ and Reynolds number ${Re}$ , considering the FENE-P and simpler Oldroyd-B constitutive relations. Both the prevalence of the instability in parameter space and the associated growth rates are found to significantly increase with ${Re}$ . For instance, as $Re$ increases with $\beta$ fixed, the instability emerges at progressively lower values of $W$ and $\varepsilon$ than in the inertialess limit, and the associated growth rates increase linearly with $Re$ when all other parameters are fixed. For finite $Re$ , it is also demonstrated that the Schmidt number $Sc=1/(\varepsilon Re)$ collapses curves of neutral stability obtained across various $Re$ and $\varepsilon$ . The observed strengthening of PDI with inertia and the fact that stress diffusion is always present in time-stepping algorithms, either implicitly as part of the scheme or explicitly as a stabilizer, implies that the instability is likely operative in computational work using the popular Oldroyd-B and FENE-P constitutive models. The fundamental question now is whether PDI is physical and observable in experiments, or is instead an artifact of the constitutive models that must be suppressed.
Measurements of the associated production of a Z boson and b jets in pp collisions at √ s = 8 TeV

Measurements of the associated production of a Z boson with at least one jet originating from a b quark in proton–proton collisions at √ s = 8 TeV are presented. Differential cross sections are measured with data collected by the CMS experiment corresponding to an integrated luminosity of 19.8 fb−1. Z bosons are reconstructed through their decays to electrons and muons. Cross sections are measured as a function of observables characterizing the kinematics of the b jet and the Z boson. Ratios of differential cross sections for the associated production with at least one b jet to the associated production with any jet are also presented. The production of a Z boson with at least two b jets is investigated, and differential cross sections are measured for the dijet system. Results are compared to theoretical predictions, testing two different flavour schemes for the choice of initial-state partons.