Miracle-dz/UniFAR-FTUs huggingface dataset:
The immediate effect of sham laser and three different spinal manipulative protocols on kicking speed in soccer players

Background: In terms of kicking speed the instep kicking technique is the most effective and commonly used method. Immobilization or restricted motion within a joint segment, results in adverse changes in the surrounding ligaments, tendons and muscular tissue. To improve joint mobility and thus improving flexibility, this study focused on spinal manipulation. Objectives: The main purpose of this study was to determine the immediate effectiveness of lumbar spine and sacroiliac manipulation on the range of motion (ROM) of the lumbar spine and sacroiliac joints as well as the kicking speed of the subjects. Methods: Forty asymptomatic soccer players, playing for the regional Premier League team or higher, were divided into four groups of 10 each. Group 1 received lumbar spine manipulation, Group 2 received sacroiliac (SI) joint manipulation, Group 3 received combined lumbar spine and SI joint manipulation and Group 4 received the sham laser intervention (placebo/control). Pre and post warm-up and post intervention lumbar and SI joint ROM were measured using a digital inclinometer. Kicking speed was measured post warm-up and post intervention with a Speed Trac™ Speed Sport Radar. The subjects’ perception of a change in kicking speed post intervention was also recorded. SPSS version 15.0 was used to analyse the data. Results: Pre and post outcome measurements were compared using a p value of < 0.05 to indicate statistical significance. Statistically significant ROM increases were noticed in left and right lumbar rotation motions post lumbar manipulative intervention. Lumbar extension, left and right lumbar rotation and SI joint extension ROM increased post combination of lumbar spine and SI joint manipulation. There VI was a significant increase in kicking speed post intervention for all three manipulative intervention groups. A significant correlation was seen between subjects’ perception of change in kicking speed post intervention and the objective results obtained. Conclusions: Lumbar spine and SI joint manipulation is an effective intervention for short-term increase in kicking speed.
Atrial Fibrillation in Children

Atrial fibrillation is a relatively uncommon arrhythmia in childhood which is seen in a variety of pediatric clinical settings and continues to challenge health care providers.Atrial fibrillation may be seen at any age during childhood. Recent advances have allowed for prenatal diagnosis and treatment. Perinatal atrial fibrillation has been described, often in the absence of obvious structural or electrophysiological cardiac abnormality. In childhood, atrial fibrillation may be seen occasionally in association with cardiac tumors, myocarditis or cardiomyopathy. Certain medications and drug exposures may precipitate atrial fibrillation in children. Several congenital defects may be associated with atrial fibrillation in early adulthood, if left unrepaired or repaired or palliated with surgical or catheter intervention. The clinical significance of atrial fibrillation may range from trivial to life threatening, depending on the clinical setting and underlying electrophysiological or structural substrate. Recent research into the origins of atrial fibrillation, new drug therapy, interventional catheter and surgical procedures offer new treatment options for children or young adults at risk. This chapter reviews the recent advances in genetic, structural, electrophysiological, pharmacological, medical and surgical observations and the impact on understanding and management of this arrhythmia in children.
Absorbing Diagonal Algorithm for Enhancing Randomized SVD Algorithms in Collaborative Filtering✱

Collaborative filtering is a main-stream technique to alleviate information overload. Singular Value Decomposition (SVD) has become very popular in the field of collaborative filtering. For computation of collaborative filtering, traditional SVD algorithms are too slow, randomized SVD algorithms using sampling techniques are more practical than them. The approximation accuracy of these randomized SVD algorithms, however, comes with a high price at their sampling. The enlargement of sample-set picked by the sampling will dramatically increase the computational complexity. The algorithm proposed in this paper aims to give a more efficient precision-improvement to these randomized SVD algorithms under some assumptions of collaborative filtering. The idea of our algorithm is two-fold. Firstly, the a priori error estimate of these randomized SVD algorithms is replaced by an efficient a posteriori estimate to control the amount of further sampling adaptively. Secondly, since SVD is actually a factorization to diagonalize a matrix with orthogonal transforms, our algorithm iteratively improve the precision with two basic operations: a diagonal-attraction operation to transform the matrix according to further sampling for better concentration of residuals near the diagonal, and a diagonal-absorption operation to absorb a large portion of these concentrated residuals into the diagonal. Experiments have shown that our algorithm can efficiently improve the precision of a randomized SVD in collaborative filtering.
MEDAL: Scalable DIMM based Near Data Processing Accelerator for DNA Seeding Algorithm

Computational genomics has proven its great potential to support precise and customized health care. However, with the wide adoption of the Next Generation Sequencing (NGS) technology, 'DNA Alignment', as the crucial step in computational genomics, is becoming more and more challenging due to the booming bio-data. Consequently, various hardware approaches have been explored to accelerate DNA seeding - the core and most time consuming step in DNA alignment. Most previous hardware approaches leverage multi-core, GPU, and FPGA to accelerate DNA seeding. However, DNA seeding is bounded by memory and above hardware approaches focus on computation. For this reason, Near Data Processing (NDP) is a better solution for DNA seeding. Unfortunately, existing NDP accelerators for DNA seeding face two grand challenges, i.e., fine-grained random memory access and scalability demand for booming bio-data. To address those challenges, we propose a practical, energy efficient, Dual-Inline Memory Module (DIMM) based, NDP Accelerator for DNA Seeding Algorithm (MEDAL), which is based on off-the-shelf DRAM components. For small databases that can be fitted within a single DRAM rank, we propose the intra-rank design, together with an algorithm-specific address mapping, bandwidth-aware data mapping, and Individual Chip Select (ICS) to address the challenge of fine-grained random memory access, improving parallelism and bandwidth utilization. Furthermore, to tackle the challenge of scalability for large databases, we propose three inter-rank designs (polling-based communication, interrupt-based communication, and Non-Volatile DIMM (NVDIMM)-based solution). In addition, we propose an algorithm-specific data compression technique to reduce memory footprint, introduce more space for the data mapping, and reduce the communication overhead. Experimental results show that for three proposed designs, on average, MEDAL can achieve 30.50x/8.37x/3.43x speedup and 289.91x/6.47x/2.89x energy reduction when compared with a 16-thread CPU baseline and two state-of-the-art NDP accelerators, respectively.
SparTen: A Sparse Tensor Accelerator for Convolutional Neural Networks

Convolutional neural networks (CNNs) are emerging as powerful tools for image processing. Recent machine learning work has reduced CNNs' compute and data volumes by exploiting the naturally-occurring and actively-transformed zeros in the feature maps and filters. While previous semi-sparse architectures exploit one-sided sparsity either in the feature maps or the filters, but not both, a recent fully-sparse architecture, called Sparse CNN (SCNN), exploits two-sided sparsity to improve performance and energy over dense architectures. However, sparse vector-vector dot product, a key primitive in sparse CNNs, would be inefficient using the representation adopted by SCNN. The dot product requires finding and accessing non-zero elements in matching positions in the two sparse vectors -- an inner join using the position as the key with a single value field. SCNN avoids the inner join by performing a Cartesian product capturing the relevant multiplications. However, SCNN's approach incurs several considerable overheads and is not applicable to non-unit-stride convolutions. Further, exploiting reuse in sparse CNNs fundamentally causes systematic load imbalance not addressed by SCNN. We propose SparTen which achieves efficient inner join by providing support for native two-sided sparse execution and memory storage. To tackle load imbalance, SparTen employs a software scheme, called greedy balancing, which groups filters by density via two variants, a software-only one which uses whole-filter density and a software-hardware hybrid which uses finer-grain density. Our simulations show that, on average, SparTen performs 4.7x, 1.8x, and 3x better than a dense architecture, one-sided sparse architecture, and SCNN, respectively. An FPGA implementation shows that SparTen performs 4.3x and 1.9x better than a dense architecture and a one-sided sparse architecture, respectively.
A study to identify and analyze adjustment problems experienced by foreign non-European graduate students enrolled in selected universities in the state of North Carolina

The purpose of this study was to identify the adjustment problems experienced by foreign non-European graduate students enrolled in selected universities in the State of North Carolina and to investigate the relationships between the adjustment problems and the selected variables of educational and home backgrounds, the nature of financial support, the types of academic programs pursued, the age upon entering the United States, the length of residence in the United States, the campus of enrollment, and the usefulness of student personnel services available to foreign graduate students. Further, the study sought to discover any discernible differences in the adjustment pattern of foreign non-European graduate students from selected geographical regions. Finally, the study had as its objective to suggest measures that could contribute to the alleviation of the adjustment problems of foreign non-European graduate students.
WARDuino: a dynamic WebAssembly virtual machine for programming microcontrollers

It is extremely hard and time-consuming to make correct and efficient programs for microcontrollers. Usually microcontrollers are programmed in a low level programming language such as C which makes them hard to debug and maintain. To raise the abstraction level, many high level programming languages have provided support for programming microcontrollers. Examples include Python, Lua, C# and JavaScript. Using these languages has the downside that they are orders of magnitude slower than the low-level languages. Moreover, they often provide no remote debugging support. In this paper we investigate the feasibility of using WebAssembly to program Arduino compatible microcontrollers. Our experiments lead to extending the standard WebAssembly VM with: 1) safe live code updates for functions and data 2) remote debugging support at the VM level 3) programmer configurable (Arduino) modules in order to keep the virtual machine’s footprint as small as possible. The resulting WARDuino VM enables the programmer to have better performance than an interpreted approach while simultaneously increasing the ease of development. To evaluate our approach, we implemented a simple breakout game and conducted micro benchmarks which show that the VM runs approximately 5 times faster than Espruino, a popular JavaScript interpreter for the ESP32 microcontroller.
SWAP: Synchronized Weaving of Adjacent Packets for Network Deadlock Resolution

An interconnection network forms the communication backbone in both on-chip and off-chip systems. In networks, congestion causes packets to be blocked. Indefinite blocking can occur if cyclic dependencies exist, leading to deadlock. All modern networks devote resources to either avoid deadlock by eliminating cyclic dependences or to detect and recover from it. Conventional buffered flow control does not allow a blocked packet to move forward unless the buffer at the next hop is guaranteed to be free. We introduce SWAP, a novel mechanism for enabling a blocked packet to perform an in-place swap with a buffered packet at the next hop. We prove that in-place swaps are sufficient to break any deadlock and are agnostic to the underlying topology or routing algorithm. This makes SWAP applicable across homogeneous or heterogeneous on-chip and off-chip topologies. We present a lightweight implementation of SWAP that reuses conventional router resources with minor additions to enable these swaps. The additional path diversity provided by SWAP provides 20-80% higher throughput with synthetic traffic patterns across regular and irregular topologies compared to baseline escape VC based solutions, and consumes 2-8× lower network energy compared to deflection and global-synchronization based solutions.
Evaluation of LoRa Performance in a City-wide Testbed: Experimentation Insights and Findings

In this paper, we present a LoRa based city-scale testbed that employs several sensing devices scattered across the urban area to characterize air quality and weather conditions in real-time. The installation is augmented through a custom link quality evaluation framework that continuously monitors the Packet Delivery Ratio versus RSSI relation to characterize the performance of LoRa standard under realistic conditions. Experimental results, collected over a period of 2 months, efficiently analyze LoRa's performance across a wide range of protocol configurations. Finally, we also present in-lab experiments that characterize the efficiency of LoRa modules in terms of power and energy efficiency per bit, along with valuable insights aimed at the development of energy efficient protocol improvements.
Aspect Aware Learning for Aspect Category Sentiment Analysis

Aspect category sentiment analysis (ACSA) is an underexploited subtask in aspect level sentiment analysis. It aims to identify the sentiment of predefined aspect categories. The main challenge in ACSA comes from the fact that the aspect category may not occur in the sentence in most of the cases. For example, the review “they have delicious sandwiches” positively talks about the aspect category “food” in an implicit manner. In this article, we propose a novel aspect aware learning (AAL) framework for ACSA tasks. Our key idea is to exploit the interaction between the aspect category and the contents under the guidance of both sentiment polarity and predefined categories. To this end, we design a two-way memory network for integrating AAL into the framework of sentiment classification. We further present two algorithms to incorporate the potential impacts of aspect categories. One is to capture the correlations between aspect terms and the aspect category like “sandwiches” and “food. ” The other is to recognize the aspect category for sentiment representations like “food” for “delicious. ” We conduct extensive experiments on four SemEval datasets. The results reveal the essential role of AAL in ACSA by achieving the state-of-the-art performance.
Patients' Acceptance of Information Published by Physicians in Online Health Communities: An Empirical Study

By means of obtaining health-related information published by physicians in online health communities (OHCs), patients are able to diagnose some simple diseases by themselves and then save queueing time, which can help optimize the allocation of medial resources and alleviate hospitals' congestion to a certain extent, However, some patients may be hesitant to adopt this information because of its shortcomings. This study established a research model based on the unified theory of acceptance and use of technology to examine patients' acceptance of information published by physicians in OHCs. An online survey involving 453 Chinese participants was conducted to collect data, and 378 (83.4%) were valid. Structural equation modelling and partial least squares were adopted to analyze data and test hypotheses Results reveal that performance expectancy, social influence and attitude toward using technology positively influence their behavioral intention and ultimately influence usage behavior of adopting information in OHCs. Our findings suggest that OHCs should inspire the intention of users to use information in OHCs, enhance the management of information, strengthen OHCs' reputation to increase social influence, and improve the service level of OHCs.
AlleAlle: bounded relational model finding with unbounded data

Relational model finding is a successful technique which has been used in a wide range of problems during the last decade. This success is partly due to the fact that many problems contain relational structures which can be explored using relational model finders. Although these model finders allow for the exploration of such structures they often struggle with incorporating the non-relational elements. In this paper we introduce AlleAlle, a method and language that integrates reasoning on both relational structure and non-relational elements -the data- of a problem. By combining first order logic with Codd's relational algebra, transitive closure, and optimization criteria, we obtain a rich input language for expressing constraints on both relational and scalar values. We present the semantics of AlleAlle and the translation of AlleAlle specifications to SMT constraints, and use the off-the-shelf SMT solver Z3 to find solutions. We evaluate AlleAlle by comparing its performance with Kodkod, a state-of-the-art relational model finder, and by encoding a solution to the optimal package resolution problem. Initial benchmarking show that although the translation times of AlleAlle can be improved, the resulting SMT constraints can efficiently be solved by the underlying solver.
Linking Scalar Elastodynamics and Non-Hermitian Quantum Mechanics

Recent years have seen a fascinating pollination of ideas from quantum theories to elastodynamics---a theory that phenomenologically describes the time-dependent macroscopic response of materials. Here, we open route to transfer additional tools from non-Hermitian quantum mechanics. We begin by identifying the differences and similarities between the one-dimensional elastodynamics equation and the time-independent Schrodinger equation, and finding the condition under which the two are equivalent. Subsequently, we demonstrate the application of the non-Hermitian perturbation theory to determine the response of elastic systems; calculation of leaky modes and energy decay rate in heterogenous solids with open boundaries using a quantum mechanics approach; and construction of degeneracies in the spectrum of these assemblies. The latter result is of technological importance, as it introduces an approach to harness extraordinary wave phenomena associated with non-Hermitian degeneracies for practical devices, by designing them in simple elastic systems. As an example of such application, we demonstrate how an assembly of elastic slabs that is designed with two degenerate shear states according to our scheme, can be used for mass sensing with enhanced sensitivity by exploiting the unique topology near the exceptional point of degeneracy.
AFDP: An Automated Function Description Prediction Approach to Improve Accuracy of Protein Function Predictions

With the rapid growth in high-throughput biological sequencing technologies and subsequently the amount of produced omics data, it is essential to develop automated methods to annotate the functionality of unknown genes and proteins. There are developed tools such as AHRD applying known proteins characterization to annotate unknown ones. Some other algorithms such as eggNOG apply orthologous groups of proteins to detect the most probable function. However, while the available tools focus on the detection of the most similar characterization, they are not able to generalize and integrate information from multiple homologs while maintaining accuracy. Here, we devise AFDP, an integrated approach for protein function prediction which benefits from the combination of two available tools, AHRD and eggNOG, to predict the functionality of novel proteins and produce more precise human readable descriptions by applying our stCFExt algorithm. StCFExt creates function descriptions applying available manually curated descriptions in swiss-prot. Using a benchmark dataset we show that the annotations predicted by our approach are more accurate than eggNOG and AHRD annotations.
Lead2Gold: Towards Exploiting the Full Potential of Noisy Transcriptions for Speech Recognition

The transcriptions used to train an Automatic Speech Recognition (ASR) system may contain errors. Usually, either a quality control stage discards transcriptions with too many errors, or the noisy transcriptions are used as is. We introduce Lead2Gold, a method to train an ASR system that exploits the full potential of noisy transcriptions. Based on a noise model of transcription errors, Lead2Gold searches for better transcriptions of the training data with a beam search that takes this noise model into account. The beam search is differentiable and does not require a forced alignment step, thus the whole system is trained end-to-end. Lead2Gold can be viewed as a new loss function that can be used on top of any sequence-to-sequence deep neural network. We conduct proof-of-concept experiments on noisy transcriptions generated from letter corruptions with different noise levels. We show that Lead2Gold obtains a better ASR accuracy than a competitive baseline which does not account for the (artificially-introduced) transcription noise.
Partial Exponential Stability Analysis of Slow–Fast Systems via Periodic Averaging

This article presents some new criteria for the partial exponential stability of a slow–fast nonlinear system with a fast scalar variable using periodic averaging methods. Unlike classical averaging techniques, we construct an averaged system by averaging over this fast scalar variable instead of the time variable. We show that the partial exponential stability of the averaged system implies that of the original one. We then apply the obtained criteria to the study of remote synchronization of Kuramoto–Sakaguchi oscillators coupled by a star network with two peripheral nodes. We show that detuning the natural frequency of the central mediating oscillator increases the robustness of the remote synchronization against phase shifts. This article appears to be the first-known attempt to analytically study the phase-unlocked remote synchronization.