Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECT OF TRANSCUTANEOUS ELECTRICAL NERVE STIMULATION (TENS) ON THE PATIENTS WITH CHRONIC LOW BACK PAIN : SYSTEMATIC REVIEW

Background: The ailment known as low back pain (LBP) is quite common and intricate. It is frequently advised that people receive pharmaceutical therapy in order to reduce the burden of LBP on their everyday life Aims : This systematic review is to review the effect and efficacy of transcutaneous electrical nerve stimulation in the treatment of chronic low back pain. Methods: By comparing itself to the standards set by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. So, the experts were able to make sure that the study was as up-to-date as it was possible to be. For this search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed, ScienceDirect and SagePub, were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Result: In the PubMed database, the results of our search brought up 240 articles, whereas the results of our search on SAGEPUB brought up 1.683 articles, our search on SCIENCE DIRECT brought up 1.647 articles. The results of the search conducted for the last year of 2014 yielded a total 124 articles for PubMed, 610 articles for SAGEPUB and 429 articles for SCIENCE DIRECT. In the end, we compiled a total of 7 papers, 4 of which came from PubMed, 1 of which came from SAGEPUB and 2 of which came from SCIENCE DIRECT. We included seven research that met the criteria. Conclusion: In summary, although the use of transcutaneous electrical nerve stimulation (TENS) still controversial in medical to treat low back pain, the studies mostly showed that TENS can reduce the pain of chronic low back pain (CLBP), and also can decreased the function of the back.
Improved Lower Bounds for Property B

If an $n$-uniform hypergraph can be 2-colored, then it is said to have property B. Erd\H{o}s (1963) was the first to give lower and upper bounds for the minimal size $m(n)$ of an $n$-uniform hypergraph without property B. His asymptotic upper bound $O(n^22^n)$ still is the best we know, his lower bound $2^{n-1}$ has seen a number of improvements, with the current best $\Omega$ $(2^n\sqrt{n/\log(n)})$ established by Radhakrishnan and Srinivasan (2000). Cherkashin and Kozik (2014) provided a simplified proof of this result, using Pluh\'ar's (2009) idea of a random greedy coloring. In the present paper, we use a refined version of this argument to obtain improved lower bounds on $m(n)$ for small values of $n$. We also study $m(n,v)$, the size of the smallest $n$-hypergraph without property B having $v$ vertices.
Smart-Infinity: Fast Large Language Model Training using Near-Storage Processing on a Real System

The recent huge advance of Large Language Models (LLMs) is mainly driven by the increase in the number of parameters. This has led to substantial memory capacity requirements, necessitating the use of dozens of GPUs just to meet the capacity. One popular solution to this is storage-offloaded training, which uses host memory and storage as an extended memory hierarchy. However, this obviously comes at the cost of storage bandwidth bottleneck because storage devices have orders of magnitude lower bandwidth compared to that of GPU device memories. Our work, Smart-Infinity, addresses the storage bandwidth bottleneck of storage-offloaded LLM training using near-storage processing devices on a real system. The main component of Smart-Infinity is SmartUpdate, which performs parameter updates on custom near-storage accelerators. We identify that moving parameter updates to the storage side removes most of the storage traffic. In addition, we propose an efficient data transfer handler structure to address the system integration issues for Smart-Infinity. The handler allows overlapping data transfers with fixed memory consumption by reusing the device buffer. Lastly, we propose accelerator-assisted gradient compression/decompression to enhance the scalability of Smart-Infinity. When scaling to multiple near-storage processing devices, the write traffic on the shared channel becomes the bottleneck. To alleviate this, we compress the gradients on the GPU and decompress them on the accelerators. It provides further acceleration from reduced traffic. As a result, Smart-Infinity achieves a significant speedup compared to the baseline. Notably, Smart-Infinity is a ready-to-use approach that is fully integrated into PyTorch on a real system. We will open-source Smart-Infinity to facilitate its use.
Lactic Acid Production by Enterococcus durans Is Improved by Cell Recycling and pH Control

Lactic acid bacteria are widely used because they produce lactic acid naturally, are resistant to acidic pH and a wide temperature range, and frequently produce lactic acid as a primary metabolite. In this study, Enterococcus durans isolated from buffalo milk was employed in lactic acid fermentation with the primary goal of obtaining fermentation parameters for an effective process enabling the use of lactose as an alternative carbon source. Fermentative parameters such as initial concentration of carbon source, dissolved oxygen concentration, cell recycling, and batch with pulse operation mode were studied to find the best conditions for L-(+)-lactic acid production. The association of 20 g·L−1 of lactose with 10 g·L−1 of glucose enabled the best bioconversion to lactic acid. Anaerobiosis did not contribute to increasing lactic acid production. Batch fermentation with cell recycling was the strategy that enhanced lactic acid production and lactose consumption, reaching 26.07 g·L−1, 0.36 g·L−1·h−1 of productivity and yielding about 0.86 g·g−1. It is fundamental to evaluate the parameters of lactic acid fermentation and provide efficient and sustainable production methods.
Bayesian Random Semantic Data Augmentation for Medical Image Classification

Data augmentation is a critical regularization technique for deep neural networks, particularly in medical image classification. Popular data augmentation approaches include image transformation-based methods, generative data augmentation, and automatic data augmentation. However, these approaches encounter notable limitations: image transformation-based and automated data augmentation techniques cannot implement semantic transformations, leading to a constrained variety of augmented samples, and generative data augmentation methods are computationally expensive. In response to these challenges, we proposed Bayesian Random Semantic Data Augmentation (BRSDA), a novel, efficient, and plug-and-play semantic data augmentation method. BRSDA is motivated by a simple translation in the feature space along specific directions that can effectuate semantic transformations. When given a feature, we define its augmentable semantic magnitude as a random variable and estimate its distribution using variational Bayesian, then sample semantic magnitude and add to the randomly selected semantic direction to achieve semantic data augmentation. We demonstrate the effectiveness of BRSDA on five 2D and six 3D medical image datasets covering nine modalities. We also test BRSDA with mainstream neural network architectures, showcasing its robustness. Furthermore, combining BRSDA with other leading data augmentation methods achieves superior performance. Code is available online at \url{https://github.com/YaoyaoZhu19/BRSDA}.
Born to Run, Programmed to Play: Mapping the Extended Reality Exergames Landscape

Many people struggle to exercise regularly, raising the risk of serious health-related issues. Extended reality (XR) exergames address these hurdles by combining physical exercises with enjoyable, immersive gameplay. While a growing body of research explores XR exergames, no previous review has structured this rapidly expanding research landscape. We conducted a scoping review of the current state of XR exergame research to (i) provide a structured overview, (ii) highlight trends, and (iii) uncover knowledge gaps. After identifying 1318 papers in human-computer interaction and medical databases, we ultimately included 186 papers in our analysis. We provide a quantitative and qualitative summary of XR exergame research, showing current trends and potential future considerations. Finally, we provide a taxonomy of XR exergames to help future design and methodological investigation and reporting.
An Image-based Typology for Visualization

We present and discuss the results of a qualitative analysis of visual representations from images. We labeled each image's essential stimuli, the removal of which would render a visualization uninterpretable. As a result, we derive a typology of 10 visualization types of defined groups. We describe the typology derivation process in which we engaged. The resulting typology and image analysis can serve a number of purposes: enabling researchers to study the evolution of the community and its research output over time, facilitating the categorization of visualization images for the purpose of research and teaching, allowing researchers and practitioners to identify visual design styles to further align the quantification of any visual information processor, be that a person or an algorithm observer, and it facilitates a discussion of standardization in visualization. In addition to the visualization typology from images, we provide a dataset of 6,833 tagged images and an online tool that can be used to explore and analyze the large set of labeled images. The tool and data set enable scholars to closely examine the diverse visual designs used and how they are published and communicated in our community. A pre-registration, a free copy of this paper, and all supplemental materials are available via osf.io/dxjwt.
Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object Detection

Developing high-performance, real-time architectures for LiDAR-based 3D object detectors is essential for the successful commercialization of autonomous vehicles. Pillar-based methods stand out as a practical choice for onboard deployment due to their computational efficiency. However, despite their efficiency, these methods can sometimes underperform compared to alternative point encoding techniques such as Voxel-encoding or PointNet++. We argue that current pillar-based methods have not sufficiently captured the fine-grained distributions of LiDAR points within each pillar structure. Consequently, there exists considerable room for improvement in pillar feature encoding. In this paper, we introduce a novel pillar encoding architecture referred to as Fine-Grained Pillar Feature Encoding (FG-PFE). FG-PFE utilizes Spatio-Temporal Virtual (STV) grids to capture the distribution of point clouds within each pillar across vertical, temporal, and horizontal dimensions. Through STV grids, points within each pillar are individually encoded using Vertical PFE (V-PFE), Temporal PFE (T-PFE), and Horizontal PFE (H-PFE). These encoded features are then aggregated through an Attentive Pillar Aggregation method. Our experiments conducted on the nuScenes dataset demonstrate that FG-PFE achieves significant performance improvements over baseline models such as PointPillar, CenterPoint-Pillar, and PillarNet, with only a minor increase in computational overhead.
Augmented maize seed germination and seedling growth under water stress using Trichoderma harzianum from semi-arid soils

The present study sought to assess the effect of Trichoderma harzianum from semiarid soils on maize seed germination and seedling growth under water stress. Trichoderma harzianum from semiarid soils was isolated and identified using macroand micro-morphological characteristics. A three-factor factorial (2×3×4) design was employed, arranged in a completely randomized design with three replications. Maize seeds treated with T. harzianum had higher germination than untreated seeds. Maximum germination (96%) was recorded in both treated and untreated maize seeds when grown under water stress free condition (0MPa). However, seeds treated with T. harzianum showed a significant higher germination than untreated seeds grown under -0.3, -0.6 and -0.9 MPa among three varieties of maize. Under extreme water stress (-0.9 MPa), shoot dry weight of maize seedlings increased significantly from 0.05–0.06 mg/seedling in control to 0.17–0.19 mg/seedling in treated seedlings. Similarly, root dry weight increased significantly from 0.05–0.06 mg/seedling in control to 0.39–0.42 mg/seedling in treated maize seedlings at -0.9MPa. Under normal conditions (0MPa), T. harzianum did not enhance either maize seed germination or seedling growth. Taken together, the study recommends that for enhanced maize seed germination and seedling growth, 10 spores/ml of T. harzianum isolated from semiarid soils should be used as seed treatment regardless of the maize variety.
Temporal-Mapping Photography for Event Cameras

Event cameras, or Dynamic Vision Sensors (DVS) are novel neuromorphic sensors that capture brightness changes as a continuous stream of ``events'' rather than traditional intensity frames. Converting sparse events to dense intensity frames faithfully has long been an ill-posed problem. Previous methods have primarily focused on converting events to video in dynamic scenes or with a moving camera. In this paper, for the first time, we realize events to dense intensity image conversion using a stationary event camera in static scenes. Different from traditional methods that mainly rely on event integration, the proposed Event-Based Temporal Mapping Photography (EvTemMap) measures the time of event emitting for each pixel. Then, the resulting Temporal Matrix is converted to an intensity frame with a temporal mapping neural network. At the hardware level, the proposed EvTemMap is implemented by combining a transmittance adjustment device with a DVS, named Adjustable Transmittance Dynamic Vision Sensor. Additionally, we collected TemMat dataset under various conditions including low-light and high dynamic range scenes. The experimental results showcase the high dynamic range, fine-grained details, and high-grayscale-resolution of the proposed EvTemMap, as well as the enhanced performance on downstream computer vision tasks compared to other methods. The code and TemMat dataset will be made publicly available.
Dependence of the surface ozone concentration on the air temperature and conditions of atmospheric circulation in Western Siberia in the warm season (May-September)

The relationship between the surface ozone concentration and the air temperature (O3-T) is rather strong. It is more pronounced for day-to-day variations of O3 and T for every particular month in comparison with year-to-year variations of monthly average values. The O3-T relationship is variable from one year to another. The correlation coefficient can be both positive (achieving about 0.8) and negative. The analysis of some cases revealed that the magnitude of O3-T relationship depends on the character of atmospheric circulation. For the analyzed situations, the O3-Т correlation was stronger at the well-developed advection processes and dynamic alternation of air masses. We have found that the increase of the surface ozone concentration and the air temperature at the measurement site for the cases of threefold and higher excess of the maximum permissible diurnal average ozone concentration (MPCda) occurs synchronously with the alternation of air masses. The analysis of the geopotential height gradient (GHGS) [1] and the corresponding behavior of the potential temperature at the level of dynamic tropopause has demonstrated that, in general, GHGS well reflects the dynamics of air mass alternation, at least, for the most of analyzed cases of heat and cold waves. The use of the rigorous blocking criterions (GHGS>0) yielded no positive results. In addition, no one case of threefold and higher excess of MPCda of ozone was observed for the conditions of “actual” blocking with a duration of five and more days.
Disturbance ecology of the greater bilby (Macrotis lagotis)

Understanding how species respond to disturbance, both natural and anthropogenic, can be crucial to designing effective and targeted management strategies. The greater bilby is a medium-sized Australian marsupial that has suffered a dramatic decline in distribution since European settlement, largely due to introduced predators (foxes and feral cats), agriculture, and changed fire regimes. I conducted a series of natural experiments to investigate the habitat selection, predator-prey interactions, and ecosystem engineering of the bilby in the grazed rangelands of the West Kimberley; I also explored the impacts of fire and clearing of seismic lines (for petroleum exploration) on these processes. Firstly, I used remotely sensed and field sourced habitat characteristics, selected based on previous studies, to identify and predict the presence of bilbies. All variables used had little power to explain the presence of bilbies. Secondly, I investigated the effect of disturbance at two scales; at a fine scale, by investigating the use of seismic lines by a range of species, and at a broad scale, by recording changes in the occupancy of a range of species within disturbed area. I found that seismic lines are used by bilbies, cats, and dingoes (a naturalised predator), increasing the spatial and temporal overlap between bilbies and potential predators. The diet of a species is an important ‘bottom-up’ regulator, often influencing the habitat preference of a species. Therefore, thirdly, I tested the efficacy of contemporary methodologies for identifying bilby diet, determining that DNA barcoding can improve identification of dietary items from scats. Lastly, I identified a diverse range of species that use bilby burrows for shelter, indicating that bilbies are important ecosystem engineers. My research faced significant challenges associated with the rare and cryptic nature of bilbies and their predators, especially feral cats, and I provide suggestions for useful and practical approaches for future research. Anthropogenic disturbance can influence predator-prey interactions, which may increase the predation pressure on an already vulnerable species, adding to existing management concerns for bilbies in the West Kimberley associated with vegetation clearing, unmanaged and frequent broad-scale fires, and cattle grazing. Land managers should work in concert to reduce pressures on bilbies, by using of coordinated and targeted cat control, and managing fires to increase fire-age heterogeneity.
A Microstructural Cam Clay Model for Hydro-Mechanical Behavior of Unsaturated Soils

In this paper, two new state variables are included in a conventional elastoplastic model, Modified Cam-clay, to incorporate the influence of pore size distribution changes in the mechanical behavior of unsaturated soils. The first state variable allows capturing the influence of large pore changes in the mechanical behavior, and is controlled by an evolution law that incorporates plastic volumetric strain. The second state variable is added to reproduce suction effects and the evolution of this variable is also controlled by plastic volumetric strain. The new approach is validated against a variety of experimental data of high porosity soils.
Enhancing Expressiveness in Dance Generation Via Integrating Frequency and Music Style Information

Dance generation, as a branch of human motion generation, has attracted increasing attention. Recently, a few works attempt to enhance dance expressiveness, which includes genre matching, beat alignment, and dance dynamics, from certain aspects. However, the enhancement is quite limited as they lack comprehensive consideration of the aforementioned three factors. In this paper, we propose ExpressiveBailando, a novel dance generation method designed to generate expressive dances, concurrently taking all three factors into account. Specifically, we mitigate the issue of speed homogenization by incorporating frequency information into VQ-VAE, thus improving dance dynamics. Additionally, we integrate music style information by extracting genre- and beat-related features with a pre-trained music model, hence achieving improvements in the other two factors. Extensive experimental results demonstrate that our proposed method can generate dances with high expressiveness and outperforms existing methods both qualitatively and quantitatively.
Poly Kernel Inception Network for Remote Sensing Detection

Object detection in remote sensing images (RSIs) often suffers from several increasing challenges, including the large variation in object scales and the diverse-ranging context. Prior methods tried to address these challenges by expanding the spatial receptive field of the backbone, either through large-kernel convolution or dilated convolution. However, the former typically introduces considerable background noise, while the latter risks generating overly sparse feature representations. In this paper, we introduce the Poly Kernel Inception Network (PKINet) to handle the above challenges. PKINet employs multi-scale convolution kernels without dilation to extract object features of varying scales and capture local context. In addition, a Context Anchor Attention (CAA) module is introduced in parallel to capture long-range contextual information. These two components work jointly to advance the performance of PKINet on four challenging remote sensing detection benchmarks, namely DOTA-v1.0, DOTA-v1.5, HRSC2016, and DIOR-R.
Microglia-neuron interactions in schizophrenia

Multiple lines of evidence implicate increased neuroinflammation mediated by glial cells to play a key role in neurodevelopmental disorders such as schizophrenia. Microglia, which are the primary innate immune cells of the brain, are crucial for the refinement of the synaptic circuitry during early brain development by synaptic pruning and the regulation of synaptic plasticity during adulthood. Schizophrenia risk factors as genetics or environmental influences may further be linked to increased activation of microglia, an increase of pro-inflammatory cytokine levels and activation of the inflammasome resulting in an overall elevated neuroinflammatory state in patients. Synaptic loss, one of the central pathological hallmarks of schizophrenia, is believed to be due to excess removal of synapses by activated microglia, primarily affecting glutamatergic neurons. Therefore, it is crucial to investigate microglia-neuron interactions, which has been done by multiple studies focusing on post-mortem brain tissues, brain imaging, animal models and patient iPSC-derived 2D culture systems. In this review, we summarize the major findings in patients and in vivo and in vitro models in the context of neuron-microglia interactions in schizophrenia and secondly discuss the potential of anti-inflammatory treatments for the alleviation of positive, negative, and cognitive symptoms.