Miracle-dz/UniFAR-FTUs huggingface dataset:
NeuronBlocks - Building Your NLP DNN Models Like Playing Lego

When building deep neural network models for natural language processing tasks, engineers often spend a lot of efforts on coding details and debugging, instead of focusing on model architecture design and hyper-parameter tuning. In this paper, we introduce NeuronBlocks, a deep neural network toolkit for natural language processing tasks. In NeuronBlocks, a suite of neural network layers are encapsulated as building blocks, which can easily be used to build complicated deep neural network models by configuring a simple JSON file. NeuronBlocks empowers engineers to build and train various NLP models in seconds even without a single line of code. A series of experiments on real NLP datasets such as GLUE and WikiQA have been conducted, which demonstrates the effectiveness of NeuronBlocks.
Current Evidence for Immune Checkpoint Inhibition in Advanced Hepatocellular Carcinoma

The treatment of advanced unresectable HCC (aHCC) remains a clinical challenge, with limited therapeutic options and poor prognosis. The results of IMbrave150 and HIMALAYA have changed the treatment paradigm for HCC and established immune checkpoint inhibition (ICI), either combined with anti-angiogenic therapy or dual ICI, as preferred first-line therapy for eligible patients with aHCC. Numerous other combination regimens involving ICI are under investigation with the aim of improving the tumour response and survival of patients with all stages of HCC. This review will explore the current evidence for ICI in patients with advanced HCC and discuss future directions, including the unmet clinical need for predictive biomarkers to facilitate patient selection, the effects of cirrhosis aetiology on response to ICI, and the safety of its use in patients with impaired liver function.
High-Gain Omnidirectional Transmitarray Antenna

A new omnidirectional transmitarray antenna (TA) is proposed to realize the high gain and high omnidirectionality for millimeter-wave (mmW) applications. The antenna consists of a feeding probe as the source of the TA and an annular receiving/transmitting (Re/Tr) array surrounding the probe. The Re/Tr unit cell is composed of a pair of back-to-back angled dipoles and a delay line. The unit cell utilizes the rotation freedom of the direction of the Tr unit for a 0°/180° phase shift (1 bit), and an additional delay line between the Re and Tr cells for a continuous phase shift between 180°. Then, 1 bit+ Re/Tr unit cells with a continuous phase shift between 0° and 360° are built to form an omnidirectional array. By tuning the phase distribution of the longitudinal cells, the array can compress a wide conical omnidirectional beam from the probe radiator into a high-gain narrow omnidirectional beam in the azimuth plane. Measurement results show that the operating bandwidth of the antenna is 14.2% (from 26.1 to 30.1 GHz), within which the antenna can achieve a high gain of around 8.0 dBi and a stable omnidirectional beam. The design that transplants the concept of TA into a high-gain omnidirectional antenna is proposed for the first time. The proposed omnidirectional TA shows the advantages of remarkable omnidirectionality, broad bandwidth, and low cross-polarization level.
A Binary Bee Foraging Algorithm-Based Feature Selection Approach for IoT Intrusion Detection

Intrusion detection system (IDS) is a security solution to detect the anomalies of the Internet of Thing (IoT) network and raise alerts. Feature selection is one of the essential preprocess steps to build an efficient intelligent intrusion detection system (IDS), which is helpful to reduce the number of features (NF) that are used to classify attacks. The meta-heuristic algorithm-based approaches has been widely used to solve feature selection problems in the past few years. However, as the data dimension increases, the dramatically expanded search space challenges the optimization algorithms, including the recently proposed bee foraging algorithm (BFA). In this article, a binary variant of BFA (BBFA) is presented to binarize BFA. BBFA utilizes three diverse mechanisms to update the position of three different types of bees, which helps to promote the evolutionary efficiency. First, a Sigmoidal transfer function is utilized to update the positions of scout bees. Second, a V-shaped transfer function is applied to binarize the positions of forager bees. Third, a binary update strategy is proposed to make the onlooker bees efficiently search the space. The performance is evaluated using 22 well-known public machine learning data sets to compare the proposed approach with several similar variants of meta-heuristic algorithms of the literature in terms of fitness function values, classification accuracy, number of selected features, computational cost, and convergence properties. The results show that the proposed approach is able to find the subsets with high classification accuracy and few NF while also requiring less computational load.
Impact of the Combination of Probiotics and Digital Poultry System on Behavior, Welfare Parameters, and Growth Performance in Broiler Chicken

Recently, applied technology in the form of the combination of a probiotics and a digital poultry system, with the convergence of Information and Communications Technology and farm animals, has enabled a new strategy to overcome the livestock production crisis caused by climate change, while maintaining sustainable poultry farming in terms of care, feeding, and environmental management systems for poultry. The aim of this study was to investigate the biological mechanisms of animal behavioral welfare and production improvement using the combination of a probiotics and a digital poultry system in broiler chickens. A total of 400 one-day-old male broilers (ROSS 308) were randomly divided into four treatment groups, with five replicates each (20 birds/replicate pen) in a completely randomized design: control group with a conventional poultry system without probiotics (CON), conventional poultry system with 500 ppm of probiotics (CON500), digital poultry control system without probiotics (DPCS), and digital poultry system with 500 ppm of probiotics (DPS500). All experimental animals were reared for 35 days under the same standard environmental conditions. The experimental results indicated that the animal behavioral welfare, which includes drinking, eating, locomotion, grooming, and resting, in addition to foot pads, knee burns, plumage, and gait scores, as well as the growth performance of the broiler chickens, were improved by maintaining immune function and cecal microbiota balance via interaction between the combination of a probiotics and a digital poultry system. In conclusion, it was found that the combined system showed improved broiler growth performance and animal behavioral welfare. Thus, further studies of molecular biological mechanisms by the use of such a combined system to improve the nutritional composition and quality of chicken meats are recommended.
Automatic modal identification of bridges based on free vibrations and advanced signal decomposition techniques

Free vibration tests are attractive because they can be performed by means of a network consisting of few sensors temporarily installed on the structure in such a way to limit duration and cost of the experimental campaign. Additionally, free vibration tests are usually performed by introducing an initial perturbation that can induce a structural response significantly higher than the ambient excitation. This, in turn, allows to reduce the noise-to-signal ratio in the final measurements and/or to consider less stringent requirements about the technical specifications of the sensors. Since free vibration tests can provide accurate estimates of the modal parameters while being rather cheap and easy to implement, they have been performed in many applications, such as the experimental dynamic characterization of base-isolated buildings, masonry towers, ancient tie-rods, and bridges. An efficient and automatic computational framework is thus presented for the modal identification of bridges based on their free vibrations. The novel procedure proposed in the current work combines advanced signal decomposition techniques and a robust approach for damping identification. Two advanced signal decomposition techniques are considered, namely the Variational Mode Decomposition and the Empirical Fourier Decomposition. Experimental applications are then illustrated for roadway and railway bridges.
Automatic Multi-Parameter Performance Modeling of HPC Applications on a New Sunway Supercomputer

As the successor to Sunway TaihuLight, the new Sunway supercomputer has ultra-high computing capacity, but the unique heterogeneous architecture presents performance optimization challenges for High Performance Computing (HPC) applications. Performance modeling is an effective way to discover the performance bottlenecks and then improve the performance of HPC applications. Existing performance modeling techniques do not work well on large-scale HPC applications due to high overhead and low accuracy, and are not suitable for the heterogeneous architecture due to a lack of support for multi-resource parameters. To address the above challenges, we propose an automatic multi-parameter performance modeling method for HPC applications on the new Sunway supercomputer. First, a lightweight performance profiling method is proposed to achieve low overhead performance profiling. Then, performance models with multiple resource parameters based on the Fourier neural operator are built, achieving high prediction accuracy and generalization ability. Finally, the Fourier neural operator is extended on the new Sunway supercomputer to realize the performance modeling automatically. Experimental results show that the average prediction error is less than 10% and the average overhead is less than 4%, and the results are superior to the baselines.
Ehlers-Danlos Syndrome - living with chronic pain. Current knowledge of the disease

Ehlers Danlos Syndrome (EDS) is a group of the most common inherited connective tissue disorders. The main symptoms of this syndrome are excessive skin flexibility, joint hypermobility and blood vessels fragility. The diagnosis is based on clinical symptoms, but to confirm the type of EDS, it is necessary to identify the collagen-encoding gene or protein interacting with it. The group of Ehlers and Danlos diseases include fourteen types, the most common are classic, vascular, hypermobile, arthrochalasis, kyphoscoliotic and dermatosparaxis. [15] Connective tissue plays a key role in the process of movement, constituting the main component of bones, joint cartilage, tendons, ligaments and intervertebral discs. In addition, connective tissue is a part of every internal organ including the intestines and parenchymal organs and the cardiovascular system. [19] For this reason, patients with EDS show symptoms of many diseases, including respiratory, muscular, skeletal and vascular. Women suffering from EDS often struggle with complications during their pregnancies. Psychological and emotional issues are also common. [6] However, the most noticeable and debilitating symptom of EDS is pain, which is often severe and chronic. The pain occurs in approximately 90% or more of EDS patients. [8] Successful pain management in patients with EDS is a major challenge for specialists. The treatment involves non-steroidal anti-inflammatory drugs, acetaminophen, opioids and physiotherapy. Also marijuana, traditional chinese treatments, herbal remedies are considered to reduce the severity of the symptoms. [9] During the creation of this article, the authors analyzed the clinically relevant knowledge of EDS treatment and management based on research published on Pubmed.
Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

The main aim of this study is to comprehensively analyze the dynamics of land use and land cover (LULC) changes in the Bathinda region of Punjab, India, encompassing historical, current, and future trends. To forecast future LULC, the Cellular Automaton–Markov Chain (CA) based on artificial neural network (ANN) concepts was used using cartographic variables such as environmental, economic, and cultural. For segmenting LULC, the study used a combination of ML models, such as support vector machine (SVM) and Maximum Likelihood Classifier (MLC). The study is empirical in nature, and it employs quantitative analyses to shed light on LULC variations through time. The result indicates that the barren land is expected to shrink from 55.2 km2 in 1990 to 5.6 km2 in 2050, signifying better land management or increasing human activity. Vegetative expanses, on the other hand, are expected to rise from 81.3 km2 in 1990 to 205.6 km2 in 2050, reflecting a balance between urbanization and ecological conservation. Agricultural fields are expected to increase from 2597.4 km2 in 1990 to 2859.6 km2 in 2020 before stabilizing at 2898.4 km2 in 2050. Water landscapes are expected to shrink from 13.4 km2 in 1990 to 5.6 km2 in 2050, providing possible issues for water resources. Wetland regions are expected to decrease, thus complicating irrigation and groundwater reservoir sustainability. These findings are confirmed by strong statistical indices, with this study’s high kappa coefficients of Kno (0.97), Kstandard (0.95), and Klocation (0.97) indicating a reasonable level of accuracy in CA prediction. From the result of the F1 score, a significant issue was found in MLC for segmenting vegetation, and the issue was resolved in SVM classification. The findings of this study can be used to inform land use policy and plans for sustainable development in the region and beyond.
Effects of Me–Solvent Interactions on the Structure and Infrared Spectra of MeTFSI (Me = Li, Na) Solutions in Carbonate Solvents—A Test of the GFN2-xTB Approach in Molecular Dynamics Simulations

We investigated the performance of the computationally effective GFN2-xTB approach in molecular dynamics (MD) simulations of liquid electrolytes for lithium/sodium batteries. The studied systems were LiTFSI and NaTFSI solutions in ethylene carbonate or fluoroethylene carbonate and the neat solvents. We focused on the structure of the electrolytes and on the manifestations of ion–solvent interactions in the vibrational spectra. The IR spectra were calculated from MD trajectories as Fourier transforms of the dipole moment. The results were compared to the data obtained from ab initio MD. The spectral shifts of the carbonyl stretching mode calculated from the GFN2-xTB simulations were in satisfactory agreement with the ab initio MD data and the experimental results for similar systems. The performance in the region of molecular ring vibrations was significantly worse. We also found some differences in structural data, suggesting that the GFN2-xTB overestimates interactions of Me ions with TFSI anions and Na+ binding to solvent molecules. We conclude that the GFN2-xTB method is an alternative worth considering for MD simulations of liquids, but it requires testing of its applicability for new systems.
A Bilinear Petrov-Galerkin Finite Element Method for Solving Elliptic Equation with Discontinuous Coefficients

In this paper, a bilinear Petrov-Galerkin finite element method is introduced to solve the variable matrix coefficient elliptic equation with interfaces using nonbody-fitted grid. Different cases the interface cut the cell are discussed. The condition number of the large sparse linear system is studied. Numerical results demonstrate that the method is nearly second order accurate in the L∞ norm and L2 norm, and is first order accurate in the H1 norm. AMS subject classifications: 65N30
Explainable Machine Learning Models for Identification of Food-Related Lifestyle Factors in Chicken Meat Consumption Case in Northern Greece

A consumer’s decision-making process regarding the purchase of chicken meat is a multifaceted one, influenced by various food-related, personal, and environmental factors that interact with one another. The mediating effect of food lifestyle that bridges the gap between consumer food values and the environment, further shapes consumer behavior towards meat purchase and consumption. This research introduces the concept of Food-Related Lifestyle (FRL) and aims to identify and explain the factors associated with chicken meat consumption in Northern Greece using a machine learning pipeline. To achieve this, the Boruta algorithm and four widely recognized classifiers were employed, achieving a binary classification accuracy of up to 78.26%. The study primarily focuses on determining the items from the FRL tool that carry significant weight in the classification output, thereby providing valuable insights. Additionally, the research aims to interpret the significance of these selected factors in the decision-making process using the SHAP model. Specifically, it turns out that the freshness, safety, and nutritional value of chicken meat are essential considerations for consumers in their eating habits. Additionally, external factors like health crises and price fluctuations can have a significant impact on consumer choices related to chicken meat consumption. The findings contribute to a more nuanced understanding of consumer preferences, enabling the food industry to align its offerings and marketing efforts with consumer needs and desires. Ultimately, this work demonstrates the potential of AI in shaping the future of the food industry and informs strategies for effective decision-making.
Hybridization-induced resonances with high-quality factor in a plasmonic chipscale ring-disk nanocavity

ABSTRACT Plasmonic resonators have drawn more attention due to the ability to confine light into subwavelength scale. However, they always suffer from a low-quality (Q) factor owing to the intrinsic loss of metal. Here, we numerically propose a plasmonic resonator with ultra-high Q factor based on plasmonic metal–insulator-metal (MIM) waveguide structures. The resonator consists of a disk cavity surrounded by a concentric ring cavity, possessing an ultra-small volume. Arising from the plasmon hybridization between plasmon modes in the disk and ring cavity, the induced bonding hybridized modes have an ultra-narrow full width at half maximum (FWHM) as well as ultra-high Q factors. The FWHM can be nearly 1 nm and Q factor can be more than 400. Furthermore, such a device can act as a refractive index sensor with an ultra-high figure of merit (FOM). This work provides a novel approach to design plasmonic high-Q-factor resonators and has potential on-chip applications such as filters, multi-spectral sensors and nanolasers.
Lymphoma in Border Collies: Genome-Wide Association and Pedigree Analysis

Simple Summary Lymphoma is a common cancer in dogs, with large variation between breeds in both the incidence and frequency of immunophenotypes (B-cell or T-cell lymphoma). Very few studies have explored the genetic underpinnings of lymphoma in dogs, and it is not known whether various breeds share common risk genes considering the disparities in disease occurrence and immunophenotype distribution. In this study, our aim was to investigate lymphoma in a population of Border Collies, a breed at increased risk of lymphoma (often B-cell type). To our knowledge, this is the first genetic investigation of lymphoma risk in Border Collies. We examined pedigree data for possible inheritance patterns, and conducted a genetic investigation that incorporated the pedigree information to uncover possible genetic predispositions for lymphoma. We identified regions on chromosomes 18 and 27 that harbour cancer-related genes as prime candidates for lymphoma susceptibility, warranting further investigation. Abstract There has been considerable interest in studying cancer in dogs and its potential as a model system for humans. One area of research has been the search for genetic risk variants in canine lymphoma, which is amongst the most common canine cancers. Previous studies have focused on a limited number of breeds, but none have included Border Collies. The aims of this study were to identify relationships between Border Collie lymphoma cases through an extensive pedigree investigation and to utilise relationship information to conduct genome-wide association study (GWAS) analyses to identify risk regions associated with lymphoma. The expanded pedigree analysis included 83,000 Border Collies, with 71 identified lymphoma cases. The analysis identified affected close relatives, and a common ancestor was identified for 54 cases. For the genomic study, a GWAS was designed to incorporate lymphoma cases, putative “carriers”, and controls. A case-control GWAS was also conducted as a comparison. Both analyses showed significant SNPs in regions on chromosomes 18 and 27. Putative top candidate genes from these regions included DLA-79, WNT10B, LMBR1L, KMT2D, and CCNT1.
Studying perceptual bias in favor of the from-above Necker cube perspective in a goal-directed behavior

When viewing a completely ambiguous image, different interpretations can switch involuntarily due to internal top-down processing. In the case of the Necker cube, an entirely ambiguous stimulus, observers often display a bias in perceptual switching between two interpretations based on their perspectives: one with a from-above perspective (FA) and the other with a from-below perspective (FB). Typically, observers exhibit a priori top-down bias in favor of the FA interpretation, which may stem from a statistical tendency in everyday life where we more frequently observe objects from above. However, it remains unclear whether this perceptual bias persists when individuals voluntarily decide on the Necker cube's interpretation in goal-directed behavior, and the impact of ambiguity in this context is not well-understood. In our study, we instructed observers to voluntarily identify the orientation of a Necker cube while manipulating its ambiguity from low (LA) to high (HA). Our investigation aimed to test two hypotheses: (i) whether the perspective (FA or FB) would result in a bias in response time, and (ii) whether this bias would depend on the level of stimulus ambiguity. Additionally, we analyzed electroencephalogram (EEG) signals to identify potential biomarkers that could explain the observed perceptual bias. The behavioral results confirmed a perceptual bias in favor of the from-above perspective, as indicated by shorter response times. However, this bias diminished for stimuli with high ambiguity. For the LA stimuli, the occipital theta-band power consistently exceeded the frontal theta-band power throughout most of the decision-making time. In contrast, for the HA stimuli, the frontal theta-band power started to exceed the occipital theta-band power during the 0.3-s period preceding the decision. We propose that occipital theta-band power reflects evidence accumulation, while frontal theta-band power reflects its evaluation and decision-making processes. For the FB perspective, occipital theta-band power exhibited higher values and dominated over a longer duration, leading to an overall increase in response time. These results suggest that more information and more time are needed to encode stimuli with a FB perspective, as this template is less common for the observers compared to the template for a cube with a FA perspective.