Miracle-dz/UniFAR-FTUs huggingface dataset:
Aortic elasticity and the influence of valve morphology in children with bicuspid aortic valve

Abstract Aim We investigated dimensions and elasticity of whole aorta in patients with bicuspid aortic valve and influence of valve phenotype. Method The study group included 44 patients and 42 controls. Patients were divided into groups according to the type of valve opening as horizontal – fusion between right and left coronary cusps – and vertical – fusion between right–non-coronary cusps; according to age they were divided into younger (5–10 years) and older patients (11–16 years). Our study did not include valve phenotype with fusion between left and non-coronary cusps. Systolic–diastolic diameters of aortic annulus, sinus valsalva, sinutubular junction, arcus, and ascending–descending and abdominal aorta were measured and z-scores were obtained. Aortic strain, distensibility, and stiffness index were calculated. Flow-mediated dilatation of brachial artery was studied. Results z-Scores at annulus, sinus valsalva, sinutubular junction, and ascending aorta were higher in study patients (p=0.001, p=0.0001, p=0.0001, p=0.0001, respectively). z-Scores of sinus valsalva and sinotubular junction were higher in the horizontal group than in the vertical group (p=0.006, p=0.023, respectively). z-Score was over +2 in 51% of patients with horizontal morphology and 33% of patients with vertical morphology (p=0.0001). Ascending aorta was more distensible and less stiff in the study group (11.3±5.63 versus 7.91±4.5, p=0.002; 4.76±3.60 versus 6.19±3.44 cm2.dyn−1.10−6, p=0.033, respectively). Stiffness index of ascending, arcus, and abdominal aorta were higher in the horizontal group (p=0.004, p=0.038, p=0.006, respectively). Ascending aorta was more distensible and less stiff in the younger group (p=0.007, p=0.027, respectively) but did not differ in the older group compared with the control group. Conclusion Aortic dimensions are enlarged in patients with bicuspid aortic valve starting from childhood, suggesting the presence of generalised aortopathy. Aortic elasticity is increased at young age and decreased with age.
MisconfDoctor: Diagnosing Misconfiguration via Log-Based Configuration Testing

As software configurations continue to grow in complexity, misconfiguration has become one of major causes of software failure. Software configuration errors can have catastrophic consequences, seriously affecting the normal use of software and quality of service. And misconfiguration diagnosis faces many challenges, such as path-explosion problems and incomplete statistical data. Our study of the log that is generated in response to misconfigurations by six widely used pieces of software highlights some interesting characteristics. These observations have influenced the design of MisconfDoctor, a misconfiguration diagnosis tool via log-based configuration testing. Through comprehensive misconfiguration testing, MisconfDoctor first extracts log features for every misconfiguration and builds a feature database. When a system misconfiguration occurs, MisconfDoctor suggests potential misconfigurations by calculating the similarity of the new exception log to the feature database. We use manual and real-world error cases from Httpd, MySQL and PostgreSQL in order to evaluate the effectiveness of the tool. Experimental results demonstrate that the tool's accuracy reaches 85% when applied to manual-error cases, and 78% for real-world cases.
Intra-Cluster Coalescing to Reduce GPU NoC Pressure

GPUs continue to increase the number of streaming multiprocessors (SMs) to provide increasingly higher compute capabilities. To construct a scalable crossbar network-on-chip (NoC) that connects the SMs to the memory controllers, a cluster structure is introduced in modern GPUs in which several SMs are grouped together to share a network port. Because of network port sharing, clustered GPUs face severe NoC congestion, which creates a critical performance bottleneck. In this paper, we target redundant network traffic to mitigate GPU NoC congestion. In particular, we observe that in many GPU-compute applications, different SMs in a cluster access shared data. Issuing redundant requests to access the same memory location wastes valuable NoC bandwidth — we find on average 19.4% (and up to 48%) of the requests to be redundant. To reduce redundant NoC traffic, we propose intracluster coalescing (ICC) to merge memory requests from different SMs in a cluster. Our evaluation results show that ICC achieves an average performance improvement of 9.7% (and up to 33%) over a conventional design.
Design of a Compact Wideband Butler Matrix Using Vertically Installed Planar Structure

In this paper, a novel <inline-formula> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> Butler matrix with wide bandwidth and compact size is presented. The existing coupled-line-based quadrature coupler cannot be used to construct a Butler matrix due to the location of output ports. To solve this problem, a swap is introduced in a vertically installed planar structure to implement the quadrature coupler and a crossover required by a Butler matrix. For demonstration, the proposed coupler and crossover and the corresponding Butler matrix operating at 1.8 GHz are designed, fabricated, and measured. The implemented Butler matrix exhibits a wide bandwidth of 56.4% defined by 10-dB return loss and 1-dB insertion loss variation, by occupying a circuit size of <inline-formula> <tex-math notation="LaTeX">$0.25\lambda _{g}\times 0.74\lambda _{g}$ </tex-math></inline-formula>. Simulated and experimental results are in good agreement.
Nuclear Architecture in the Nervous System: Development, Function, and Neurodevelopmental Diseases

Decades of study have shown that epigenetic regulation plays an important role in neural development and function. Several layers of epigenetic mechanisms control functions of the eukaryotic cell nucleus, a well-organized subcellular organelle with distinct compartments: chromatin, its related architectural proteins, and nuclear bodies. As these components function together in the epigenetic regulation of cellular development and functions, they are collectively termed nuclear architecture. In the nervous system, dynamic rearrangement of nuclear architecture correlates with alteration of transcription programs. During maturation and upon depolarization, neurons undergo a reorganization of nuclear architecture that alters gene expression programs. As such changes allow for specialized functions, including learning and memory, nuclear architecture is distinct among cell types. Studying nuclear architecture of neurons may uncover cell-division-independent mechanisms of global and local changes to nuclear architecture. We herein review recent research concerning nuclear architecture in the nervous system and will discuss its importance to the development, maturation, function, and diseases of the nervous system.
KSM: Online Application-Level Performance Slowdown Prediction for Spatial Multitasking GPGPU

Colocating multiple applications on the same spatial multitasking GPGPU improves the system-wide throughput. However, the colocated applications are slowed down differently due to the contention on streaming multiprocessors (SMs), L2 cache and global memory bandwidth. The ability to precisely predict application slowdowns online is useful in many scenarios, e.g., ensuring fair pricing in multi-tenant Cloud systems. Prior work on predicting application slowdown is either inaccurate, due to the ignoring of contention on SMs, or inefficient, due to the expensive sequential profiling of concurrent applications via runtime environment switching. To solve the above problem, we propose KSM that enables precise and efficient application-level slowdown prediction without priori application knowledge. KSM is proposed based on the observation that hardware event statistics caused by the colocated applications are strongly correlated with their slowdowns. In more detail, KSM builds a slowdown model based on the hardware event statistics using machine learning techniques offline. At runtime, KSM collects the hardware event statistics, and predicts the slowdowns of all the colocated applications based on the model. Our experimental results show that KSM has negligible runtime overhead and precisely predicts the application-level slowdowns with the prediction error smaller than 9.7 percent.
Electrical Modeling and Characterization of Silicon-Core Coaxial Through-Silicon Vias in 3-D Integration

Based on the extracted resistance, inductance, capacitance, and conductance parameters, this paper introduces the distributed transmission line model of silicon-core coaxial through-silicon vias (CTSVs), in which the vertical interconnect is made of a Cu-coated silicon pole. The proposed model is validated against a commercial electromagnetic simulation tool, showing it is highly accurate up to 100 GHz. Using the proposed model, the impact of various material properties and physical parameters on the electrical performance of the silicon-core CTSVs is investigated. It is observed that the thickness of the plated Cu and isolation dielectric are two important parameters that determine the electrical performance of silicon-core CTSVs. Performance comparison shows that the proposed silicon-core CTSVs provide a comparable performance to standard Cu-based CTSVs and better performance to the conventional signal-ground TSV pairs. The results in this paper would provide some design guides for silicon-core CTSVs in 3-D integration.
Evaluation of MD5Hash Kernel on OpenCL FPGA Platform

Field-Programmable Gate Array (FPGA) is a promising choice as a heterogeneous computing component for energy-aware high-performance computing applications. The emerging high-level synthesis (HLS) tools such as Intel OpenCL SDK provide a streamlined design flow to facilitate the use of FPGAs. In this paper, we are focused on the evaluation of the MD5 hashing kernel in the scalable heterogeneous computing (SHOC) bench suite. We explain the kernel and then evaluate the resource usage, performance, and performance per watt of the kernel implementations on the FPGA. The experimental results show that the optimized implementation using kernel duplication can achieve a factor of 6.1 X speedup over the naïve code on the Nallatech 385A FPGA card that features an Arria 10 FPGA chip. For the performance per watt, we achieve 43 million hashes per watt on an Intel Arria 10 GX1150 FPGA, which is 39.7X, 15.6X, and 1.07X improvement over a dual-socket Intel Xeon E5-2687W processor, an Intel Xeon Phi Knights Landing 7210 processor, and an Nvidia K80 GPU, respectively.
A Shape-Reconfigurable Modularized Wireless Power Transfer Array System for Multipurpose Wireless Charging Applications

We present a shape-reconfigurable modularized magnetically coupled resonance wireless power transfer (MCR-WPT) array system for multipurpose wireless charging applications. In it, three interconnected resonant modules are employed for the transmitter and two interconnected resonant modules for the receiver. Such a modularized configuration allows both the transmitter and receiver to be reconfigurable in shapes and positions conformal to the devices to be charged; it also provides multiple power flow paths that improve system stability and reliability. Unlike the conventional systems, the proposed system achieves shape reconfigurability by different positional combinations of resonant modules and structures without any control circuits or changes of any resonant and matching capacitances. A prototype was developed, simulated, and tested. It has the power transfer efficiencies above 78% in various positions. Thus, the proposed system presents a good and simple system for practical WPT applications.
Agnostic Domain Generalization

The ability to generalize across visual domains is crucial for the robustness of visual recognition systems in the wild. Several works have been dedicated to close the gap between a single labeled source domain and a target domain with transductive access to its data. In this paper we focus on the wider domain generalization task involving multiple sources and seamlessly extending to unsupervised domain adaptation when unlabeled target samples are available at training time. We propose a hybrid architecture that we name ADAGE: it gracefully maps different source data towards an agnostic visual domain through pixel-adaptation based on a novel incremental architecture, and closes the remaining domain gap through feature adaptation. Both the adaptive processes are guided by adversarial learning. Extensive experiments show remarkable improvements compared to the state of the art.
Streaming Tiles: Flexible Implementation of Convolution Neural Networks Inference on Manycore Architectures

Convolution neural networks (CNN) are extensively used for deep learning applications such as image recognition and computer vision. The convolution module of these networks is highly compute-intensive. Having an efficient implementation of the convolution module enables realizing the inference part of the neural network on embedded platforms. Low precision parameters require less memory, less computation time, and less power consumption while achieving high classification accuracy. Furthermore, streaming the data over parallelized processing units saves a considerable amount of memory, which is a key concern in memory constrained embedded platforms. In this paper, we explore the design space for streamed CNN on Epiphany manycore architecture using varying precisions for weights (ranging from binary to 32-bit). Both AlexNet and GoogleNet are explored for two different memory sizes of Epiphany cores. We are able to achieve competitive performance for both Alexnet and GoogleNet with respect to emerging manycores. Furthermore, the effects of different design choices in terms of precision, memory size, and the number of cores are evaluated by applying the proposed method.
Phosphorus Nutrition Affects Temperature Response of Soybean Growth and Canopy Photosynthesis

In nature, crops such as soybean are concurrently exposed to temperature (T) stress and phosphorus (P) deficiency. However, there is a lack of reports regarding soybean response to T × P interaction. To fill in this knowledge-gap, soybean was grown at four daily mean T of 22, 26, 30, and 34°C (moderately low, optimum, moderately high, and high temperature, respectively) each under sufficient (0.5 mM) and deficient (0.08 mM) P nutrition for the entire season. Phosphorus deficiency exacerbated the low temperature stress, with further restrictions on growth and net photosynthesis. For P deficient soybean at above optimum temperature (OT) regimes, growth, and photosynthesis was maintained at levels close to those of P sufficient plants, despite a lower tissue P concentration. P deficiency consistently decreased plant tissue P concentration ≈55% across temperatures while increasing intrinsic P utilization efficiency of canopy photosynthesis up to 147%, indicating a better utilization of tissue P. Warmer than OTs delayed the time to anthesis by 8–14 days and pod development similarly across P levels. However, biomass partitioning to pods was greater under P deficiency. There were significant T × P interactions for traits such as plant growth rates, total leaf area, biomass partitioning, and dry matter production, which resulted a distinct T response of soybean growth between sufficient and deficient P nutrition. Under sufficient P level, both lower and higher than optimum T tended to decrease total dry matter production and canopy photosynthesis. However, under P-deficient condition, this decrease was primarily observed at the low T. Thus, warmer than optimum T of this study appeared to compensate for decreases in soybean canopy photosynthesis and dry matter accumulation resulting from P deficiency. However, warmer than OT appeared to adversely affect reproductive structures, such as pod development, across P fertilization. This occurred despite adaptations, especially the increased P utilization efficiency and biomass partitioning to pods, shown by soybean under P deficiency.
Advancing Patient Safety and Access to Concentrated Insulin (U-500 Regular Insulin) in the Veterans Health Administration: A Clinician Education Program in the Primary Care Setting

IN BRIEF The national epidemic of diabetes and the exposure of Vietnam veterans to Agent Orange has led to insulin resistance requiring concentrated insulin (U-500 regular [U-500R] insulin) for glycemic control. Initiation of U-500R insulin is limited to endocrinology expertise housed at “hub” Veterans Health Administration locations hours away from smaller “spoke” facilities. To overcome potential health care disparities and improve patient safety, a program was developed ensuring that all clinicians could co-manage U-500R insulin. This program evaluation was undertaken to improve patient safety and access to U-500R insulin by improving spoke clinicians’ knowledge of safe delivery and management of U-500R insulin.
Does Socio-Technical Congruence Have an Effect on Continuous Integration Build Failures? An Empirical Study on 10 GitHub Projects

Coordination is important in software development. Socio-Technical Congruence (STC) is proposed to measure the match between coordination requirements and actual coordination activities, and has been proved to have impact on software failures in commercial projects. Continuous defect prediction is aimed to predict defects just in time, which is more meaningful than traditional defect prediction in practice. In this paper, we compute the build-level STC and investigate its usefulness in continuous defect prediction based on 10 GitHub projects. We find that adding STC metrics into logistic regression models can significantly improve both the explanatory power and the predictive power when predicting build failures. Furthermore, we compare the performance of STC and MDL from the aspects of regression and prediction. MDL is short for Missing Developer Links, a deviation of the STC metric. We find that MDL usually performs better than STC. Our work is promising to help detect coordination issues during real time process of software development.
Pair-Activity Analysis From Video Using Qualitative Trajectory Calculus

The automated analysis of interacting objects or people from video has many uses, including the recognition of activities, and the identification of prototypical or unusual behaviors. Existing techniques generally use temporal sequences of quantifiable real-valued features, such as object position or orientation; however, more recently, qualitative representations have been proposed. In this paper, we present a novel and robust qualitative method, which can be used for both the classification and the clustering of pair-activities. We use qualitative trajectory calculus (<inline-formula> <tex-math notation="LaTeX">$QTC$ </tex-math></inline-formula>) to represent the relative motion between two objects and encode their interactions as a trajectory of <inline-formula> <tex-math notation="LaTeX">$QTC$ </tex-math></inline-formula> states. A key element is a general and robust means of determining the sequence similarity, which we term <italic>Normalized Weighted Sequence Alignment</italic>; we show that this is an effective metric for both recognition and clustering problems. We have evaluated our method across three different data sets, and have shown that it outperforms the state-of-the-art quantitative methods, achieving an error rate of no more than 4.1% for recognition, and cluster purities higher than 90%. Our motivation originates from an interest in automated analysis of animal behaviors, and we present a comprehensive video data set of fish behaviors (<italic>Gasterosteus aculeatus</italic>), collected from lab-based experiments.
WarpDrive: Massively Parallel Hashing on Multi-GPU Nodes

Hash maps are among the most versatile data structures in computer science because of their compact data layout and expected constant time complexity for insertion and querying. However, associated memory access patterns during the probing phase are highly irregular resulting in strongly memory-bound implementations. Massively parallel accelerators such as CUDA-enabled GPUs may overcome this limitation by virtue of their fast video memory featuring almost one TB/s bandwidth in comparison to main memory modules of state-of-the-art CPUs with less than 100 GB/s. Unfortunately, the size of hash maps supported by existing single-GPU hashing implementations is restricted by the limited amount of available video RAM. Hence, hash map construction and querying that scales across multiple GPUs is urgently needed in order to support structured storage of bigger datasets at high speeds. In this paper, we introduce WarpDrive – a scalable, distributed single-node multi-GPU implementation for the construction and querying of billions of key-value pairs. We propose a novel subwarp-based probing scheme featuring coalesced memory access over consecutive memory regions in order to mitigate the high latency of irregular access patterns. Our implementation achieves 1.4 billion insertions per second in single-GPU mode for a load factor of 0.95 thereby outperforming the GPU-cuckoo implementation of the CUDPP library by a factor of 2.8 on a P100. Furthermore, we present transparent scaling to multiple GPUs within the same node with up to 4.3 billion operations per second for high load factors on four P100 GPUs connected by NVLink technology. WarpDrive is free software and can be downloaded at https://github.com/sleeepyjack/warpdrive.
Recent Advance in Colon Capsule Endoscopy: What’s New?

Colon capsule endoscopy (CCE) is a relatively new diagnostic procedure for patients with suspected colonic diseases. This convenient, noninvasive method enables the physician to explore the entire colon without significant discomfort to the patient. However, while CCE can be performed painlessly without bowel air insufflation, the need for vigorous bowel preparation and other technical limitations exist. Due to such limitations, CCE has not replaced conventional colonoscopy. In this review, we discuss historical and recent advances in CCE including technical issues, ideal bowel preparation, indications and contraindications and highlight further technical advancements and clinical studies which are needed to develop CCE as a potential diagnostic tool.
A Wirelessly Powered CMOS Electrochemical Sensing Interface With Power-Aware RF-DC Power Management

A wirelessly powered electrochemical sensing chip with high-efficiency adaptive power management for a wide RF-powering range and a low-noise chopper-stabilization potentiostat for high-resolution electrochemical current detection is presented. The chip is fabricated using a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. A novel, power-aware, multiple-path, RF-energy harvesting front end extends the high-efficiency (>20%) RF-powering range to 8.5 dB. In the sensor-readout interface, a chopper-stabilization potentiostat with an oscillator-based current readout achieves a 3.3-pA current resolution in the current range of 800nA and an <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> linearity of 0.9987 while consuming only <inline-formula> <tex-math notation="LaTeX">$4.4\mu \text{W}$ </tex-math></inline-formula>. The power efficiency of the electrochemical readout interface is 0.18, and the dynamic detection range is 107 dB.