Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Parallel Algorithm for Sinkhorn Word-Movers Distance and Its Performance on PIUMA and Xeon CPU

The Word Movers Distance (WMD) measures the semantic dissimilarity between two text documents by computing the cost of optimally moving all words of a source/query document to the most similar words of a target document. Computing WMD between two documents is costly because it requires solving an optimization problem that costs 𝑂 ( 𝑉 3 𝑙𝑜𝑔 ( 𝑉 )) where 𝑉 is the number of unique words in the document. Fortunately, WMD can be framed as an Earth Mover’s Distance (EMD) for which the algorithmic complexity can be reduced to 𝑂 ( 𝑉 2 ) by adding an entropy penalty to the optimization problem and solving it using the Sinkhorn-Knopp algorithm. Additionally, the computation can be made highly parallel by computing the WMD of a single query document against multiple target documents at once, for example by finding whether a given tweet is similar to any other tweets of a given day. In this paper, we first present a shared-memory parallel Sinkhorn-Knopp algorithm to compute the WMD of one document against many other documents by adopting the 𝑂 ( 𝑉 2 ) EMD algorithm. We then algorithmically transform the original 𝑂 ( 𝑉 2 ) dense compute-heavy version into an equivalent sparse one which is mapped onto the new Intel Programmable Integrated Unified Memory Architecture (PIUMA) system. The WMD parallel implementation achieves 67 × speedup on 96 cores across 4 NUMA sockets of an Intel Cascade Lake system. We also show that PIUMA cores are around 1.2 — 2 . 6 × faster than Xeon cores on Sinkhorn-WMD and also provide better strong scaling.
Hysteresis compensation technique applied to polymer optical fiber curvature sensor for lower limb exoskeletons

Polymer optical fiber (POF) curvature sensors present some advantages over conventional techniques for angle measurements, such as their light weight, compactness and immunity to electromagnetic fields. However, high hysteresis can occur in POF curvature sensors due to the polymer viscoelastic response. In order to overcome this limitation, this paper shows how the hysteresis sensor can be compensated by a calibration equation relating the measured output signal to the sensor’s angular velocity. The proposed method is validated using an exoskeleton with an active joint on the knee for flexion and extension rehabilitation exercises. The results show a decrease in sensor hysteresis and a decrease by more than two times in the error between the POF sensor and the potentiometer, which is employed for the angle measurement of the exoskeleton knee joint.
Neutron spin polarization and spin analysis by triplet DNP spin filter

It has recently been demonstrated that the triplet dynamic nuclear polarization (DNP) can be used to construct an efficient neutron spin filter based on polarized protons. This motivated to drive the development of this type of spin filter to create a broadband neutron beam polarizer and a spin polarization analyzer for magnetic small-angle neutron scattering. The proton spin filter has been implemented for a first time in a neutron spin analysis experiment, which allowed investigation of the magnetization of CuNiFe alloy and distribution of the precipitates within. The polarized protons represent the only alternative to the existing 3He analyzing cells in experiments that require an opaque spin filter for neutron spin analysis. Even though the application of triplet DNP to spin filter neutrons is still under development, high neutron analyzing powers can be achieved. Further development will significantly improve the triplet DNP filter perfomance and allow various applications in future.
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

Training details. We use ResNet-18 [20] as the base encoder network fθ and 2-layer multi-layer perceptron with 128 embedding dimension as the projection head gφ. All models are trained by minimizing the final loss LCSI (5) with a temperature of τ = 0.5. We follow the same optimization step of SimCLR [5]. For optimization, we train CSI with 1,000 epoch under LARS optimizer [72] with weight decay of 1e−6 and momentum with 0.9. For the learning rate scheduling, we use linear warmup [16] for early 10 epochs until learning rate of 1.0 and decay with cosine decay schedule without a restart [41]. We use batch size of 512 for both vanilla SimCLR and ours: where the batch is given by B for vanilla SimCLR and the aggregated one ⋃ S∈S BS for ours. Furthermore, we use global batch normalization (BN) [27], which shares the BN parameters (mean and variance) over the GPUs in distributed training.
RBFNN‐Based Identification and Compensation Mechanism for Disturbance‐Like Parametric Friction with Application to Tractor‐Trailer Vehicles

This paper develops an effective identification and compensation mechanism for the disturbance‐like parametric friction of a typical underactuated tractor‐trailer vehicle system. To begin with, a parametric friction model is proposed to describe various friction effects associated with the system velocity, and then a disturbance‐like parametric friction concept is introduced by considering the motion characteristics of tractor‐trailer vehicle. Next, the radial basis function neural network (RBFNN) is employed to identify the friction due to its high convergence rate, superior approximation precision and local‐minima avoidance ability. Afterwards, a sliding mode control (SMC) is utilized to compensate the identified friction due to its numerous merits, such as strong robustness and fast convergence. On the basis of the effective combination of identification and compensation mechanism, a favorable transient performance can be achieved during the desired velocity tracking process. Lastly, the simulation results confirm that the RBFNN‐based disturbance‐like parametric friction identification and compensation mechanism can effectively improve the trajectory tracking performance of tractor‐trailer vehicle.
On the Clebsch-Gordan problem in prime characteristic

We study the tensor product of an induced module and Weyl module, for the special linear group of order two over an algebraically closed field of positive characteristic. We are primarily concerned with the decomposition of such modules into indecomposable summands, and construct a method which allows one to give the decomposition for any such module in any characteristic. We also develop a variety of other techniques to give the decomposition in particular cases. Furthermore, we give a complete account of exactly when such a module is a tilting module for all primes, and give a number of results in a more general setting.
Optical vortex microscope with the simple phase object: theoretical model and its experimental verification

In this work we consider a microscopic optical system in which the beam with an optical vortex illuminates the sample. The sample modifies the geometry of the vortex beam wavefront and the information about it is transferred into the detection plane. It is shown that the beam at the detection plane can be represented by two parts: non-disturbed vortex part and sample part. We propose and test a scheme for recovering the phase changes caused by sample inserted into the vortex beam. The numerical simulations are supported by the experimental work.
Improved Snake Model Using Subsection Functions and Helmholtz Decomposition

The snake model in segmentation of real-world images has some problems because the object in the image has complex textures and weak edges and the snake model has a drawback between noise smoothing and weak edge protection. To solve this problem, we adopt the snake model based on generalized gradient vector flow (GGVF) which has higher segmentation accuracy. We also adopt a new type of coefficient setting in the form of subsection function to improve the ability of protecting weak edges while smoothing noises. Moreover, we optimize the method by using Helmholtz decomposition. Experimental results and comparisons against other methods indicate that our proposed snake model demonstrates higher ability than that of other snake models in terms of real-world image segmentation
A multiple-control system for nonlinear discrete object under uncertainty

ABSTRACT The algorithm for the analytical synthesis of a control system for a nonlinear object with random uncertainty in a description is proposed. The idea of the algorithm goes back to the method of analytical design of aggregated regulators, developed only for deterministic objects. It is shown that both algorithms can demonstrate different quality of control in different areas of the phase space. Therefore, the algorithm is proposed for constructing a multiple control system for a nonlinear object without preliminary linearization. The algorithm is based on an algebraic approach to the synthesis of correct algorithms by a set of basic algorithms. In this case, aposteriori information on the quality of control in the state space of an object is used.
Reinforcement Learning Based Cooperative P2P Energy Trading between DC Nanogrid Clusters with Wind and PV Energy Resources

— In order to replace fossil fuels with the use of renewable energy resources, unbalanced resource production of intermittent wind and photovoltaic (PV) power is a critical issue for peer-to-peer (P2P) power trading. To resolve this problem, a reinforcement learning (RL) technique is introduced in this paper. For RL, graph convolutional network (GCN) and bi-directional long short-term memory (Bi-LSTM) network are jointly applied to P2P power trading between nanogrid clusters based on cooperative game theory. The flexible and reliable DC nanogrid is suitable to integrate renewable energy for distribution system. Each local nanogrid cluster takes the position of prosumer, focusing on power production and consumption simultaneously. For the power management of nanogrid clusters, multi-objective optimization is applied to each local nanogrid cluster with the Internet of Things (IoT) technology. Charging/discharging of electric vehicle (EV) is performed considering the intermittent characteristics of wind and PV power production. RL algorithms, such as deep Q-learning network (DQN), deep recurrent Q-learning network (DRQN), Bi-DRQN, proximal policy optimization (PPO), GCN-DQN, GCN-DRQN, GCN-Bi-DRQN, and GCN-PPO, are used for simulations. Consequently, the cooperative P2P power trading system maximizes the profit utilizing the time of use (ToU) tariff-based electricity cost and system marginal price (SMP), and minimizes the amount of grid power consumption. Power management of nanogrid clusters with P2P power trading is simulated on the distribution test feeder in real-time and proposed GCN-PPO technique reduces the electricity cost of nanogrid clusters by 36.7%.
E3: Accelerating Fully Homomorphic Encryption by Bridging Modular and Bit-Level Arithmetic

The dramatic increase of data breaches in modern computing platforms has emphasized that access control is not sufficient to protect sensitive user data. Even in the case of honest parties, unknown software/hardware vulnerabilities and side-channels can enable data leakage, leading to the conclusion that as long as data exists in a decrypted form, it can be leaked. Recent advances on cryptographic homomorphic schemes allow end-to-end processing of encrypted data without any need for decryption. Such schemes, however, still incur impractical overheads and they are difficult to use by non-crypto-savvy users, inhibiting their applicability. In this work we propose a framework which allows optimal, in terms of performance, execution of standard C++ code with encrypted variables. The framework automatically generates protected types so the programmer can remain oblivious to the underlying encryption scheme. C++ protected classes redefine operators according to the encryption scheme effectively making the introduction of a new API unnecessary. The performance of encrypted computation is enhanced by our novel methodology, dubbed bridging, that blends faster and restricted modular computation with slower and comprehensive bit-level computation. Experimental results show that bridging computation can lead to a performance improvement of more than two orders of magnitude.
Discharge outcomes as predictors of social participation in the community after a stroke: a cohort study

The mapping of possible predictors of restrictions in the social participation of people after stroke in the community can be an essential tool to support the development of rehabilitation strategies even in the hospital environment. This study aimed to identify whether mobility, functional balance and dependence on functionality at hospital discharge can predict restrictions on social participation 1 year after stroke in the community. This is a hospital-based cohort study, with individuals over 18 years old admitted with a diagnosis of acute stroke included. People with dementia, previous functional limitations and cancer patients were omitted. Mobility, balance and functional independence were the predictor variables at hospital discharge, and the outcome of interest was social participation assessed 1 year after a stroke in the community. Forty-eight patients were included after a 1-year follow-up. The degree of functional independence at hospital discharge (β = 0.813; P < 0.01) was the independent predictor of social participation, specifically the locomotion (β = 0.452; P < 0.001) and social cognition (β = 0.462; P < 0.001) related to functional independence. Mobility (β = 0.040; P = 0.777) and functional balance (β = 0.060; P = 0.652) did not show an independent association. Cognitive functional independence was a predictor of daily activities (β = 0.786; P < 0.001), social roles (β = 0.390; P = 0.014) and satisfaction (β = 0.564; P < 0.001) of social participation. The degree of functional independence of people after a stroke at hospital discharge was able to predict the level of social involvement in the community one year after the stroke.
A qualitative exploration of the continuum of help-seeking among Asian American breast cancer survivors

ABSTRACT Breast cancer rates among Asian American women are increasing. Despite this, there are limited studies on help-seeking among this population. Through a qualitative exploration, this paper examines the help-seeking experiences of Asian American breast cancer survivors. Asian American women (n = 52) with early-stage breast cancer were interviewed. Findings illustrate a continuum of experiences including: keeping diagnosis personal and not asking for help, keeping diagnosis personal but asking for limited help, and sharing diagnosis and seeking support. Results indicate that seeking support is a complex process for Asian Americans with breast cancer, with implications for survivorship and quality of life.
The effect of endoscopic gastric plication on portosystemic pressure gradient in patients with nonalcoholic fatty liver disease and compensated advanced chronic liver disease

BACKGROUND AND AIMS Goals of therapy for patients with nonalcoholic fatty liver disease (NAFLD) and compensated advanced chronic liver disease (cACLD) include weight loss and reduction of the portosystemic pressure gradient (PPG) to decrease the risk of hepatic decompensation. Endoscopic gastric plication (EGP) is an effective endoscopic weight loss procedure. This study aims to assess the effect of EGP on PPG. PATIENTS AND METHODS In this prospective pilot study, patients with NAFLD and cACLD underwent endoscopic ultrasound-guided PPG determination prior to and at 6 months following EGP. Primary outcomes were the change in PPG and proportion of patients experiencing ≥20% reduction in PPG at 6 months. Secondary outcomes included weight loss and changes in non-invasive tests (NITs) of fibrosis. RESULTS 20 subjects were included. Baseline BMI and liver stiffness measurement were 40.2 [30.1-56.7] kg/m2 and 14.7 [8.2-36] kPa. At 6 months, PPG decreased from 5.4 [0.7-19.6] mmHg to 1.8 [0.4-17.6] mmHg (p=0.002), with 79% (11 out of 14) experiencing ≥20% reduction. Subjects experienced 12.5 [6.5-26.1]% TWL (p = 0.0001) at 6 months, with 89% (17 out of 19) achieving ≥7% and 68% (13 out of 19) achieving ≥10% TWL. There were significant improvements in NITs of fibrosis. CONCLUSION Endoscopic gastric plication appears effective at reducing PPG in patients with NAFLD and cACLD.
RisGraph: A Real-Time Streaming System for Evolving Graphs

Graphs in the real world are constantly changing and of large scale. In processing these evolving graphs, the combination of update workloads (updating vertices and edges in a streaming manner) and analytical (performing graph algorithms incrementally) workloads is ubiquitous. Throughput, latency, and granularity are three key requirements in processing evolving graphs with such combined workloads. Although there are several streaming systems proposed for evolving graphs to improve latency. They usually use batch-update model to improve throughput but hurt granularity. It is still challenging to fulfill all the requirements simultaneously, especially for power-law graphs because they are difficult to be partitioned. We analyze the computational cost on synthesized power-law graphs and realistic evolving graphs from public datasets. We find that the affected areas are usually small for each update, and there are scheduling opportunities for combined workloads. Based on these observations, we design a real-time streaming system for incremental graph computing called RisGraph. Our novel design on scheduling, trade-offs on data structures and the computing engine make RisGraph satisfy the three requirements at the same time. The evaluation shows RisGraph can ingest millions of updates per second and its 99.9%ile latency is within 20 milliseconds for graphs with hundreds of millions of vertices and billions of edges on a single commodity machine.
Solving the multi-level Maxwell-Bloch equations using GPGPU computing for the simulation of nonlinear optics in atomic gases

We present a numerical implementation of a solver for the Maxwell-Bloch equations to calculate the propagation of a light pulse in a nonlinear medium composed of an atomic gas in one, two and three dimensional systems. This implementation solves the wave equation of light using a finite difference method in the time domain scheme, while the Bloch equations for the atomic population in each point of the simulation domain are integrated using splitting methods. We present numerical simulations of atomic-gas systems and performance benchmarks.
Supersymmetric AdS4 black holes from tri-sasakian truncation

A class of BPS $AdS_4$ black hole solutions within four-dimensional $N=4$ gauged supergravity coupled to three-vector multiplets with dyonic gauging is identified. The gauged supergravity has non-semisimple $SO(3)\ltimes (\mathbf{T}^3,\hat{\mathbf{T}}^3)$ gauge group and can be obtained from a consistent truncation of eleven-dimensional supergravity on a tri-sasakian manifold. The maximally symmetric vacua contain $AdS_4$ geometries with $N=1,3$ supersymmetry corresponding to $N=1$ and $N=3$ superconformal field theories (SCFTs) in three dimensions. We find supersymmetric solutions of the form $AdS_2\times \Sigma_2$, with $\Sigma_2$ being a two-sphere or a hyperbolic space, preserving two supercharges. These solutions describe twisted compactifications of the dual $N=1$ and $N=3$ SCFTs and arise as near horizon geometries of dyonic black holes in asymptotically $AdS_4$ space-time. Most solutions have hyperbolic horizons although some of them exhibit spherical horizons. These provide a new class of dyonic BPS black holes with known M-theory origin.
Circadian regulation of metabolism across photosynthetic organisms

SUMMARY Circadian regulation produces a biological measure of time within cells. The daily cycle in the availability of light for photosynthesis causes dramatic changes in biochemical processes in photosynthetic organisms, with the circadian clock having crucial roles in adaptation to these fluctuating conditions. Correct alignment between the circadian clock and environmental day–night cycles maximizes plant productivity through its regulation of metabolism. Therefore, the processes that integrate circadian regulation with metabolism are key to understanding how the circadian clock contributes to plant productivity. This forms an important part of exploiting knowledge of circadian regulation to enhance sustainable crop production. Here, we examine the roles of circadian regulation in metabolic processes in source and sink organ structures of Arabidopsis. We also evaluate possible roles for circadian regulation in root exudation processes that deposit carbon into the soil, and the nature of the rhythmic interactions between plants and their associated microbial communities. Finally, we examine shared and differing aspects of the circadian regulation of metabolism between Arabidopsis and other model photosynthetic organisms, and between circadian control of metabolism in photosynthetic and non‐photosynthetic organisms. This synthesis identifies a variety of future research topics, including a focus on metabolic processes that underlie biotic interactions within ecosystems.
Analysis of single-cell sequencing results of an elderly patient with myeloid leukemia reveals high expression of multiple oncogenes in monocytes and hematopoietic stem cells

ABSTRACT Objective Older patients with acute myeloid leukemia (AML) face a higher risk of death. This study analyzed the gene sequencing results of an elderly AML patient to provide new ideas for treatment. Methods This study performed single-cell RNA sequencing (scRNA-seq) bioinformatics analysis of blood cells in the peripheral blood and bone marrow of a 64-year-old AML-M5 patient before chemotherapy. The differentially expressed genes (DEGs) were identified, and functional enrichment analyses were performed. Results A total of 7990 and 123 DEGs were identified in monocytes and hematopoietic stem cells (HSCs), respectively. Among the top 40 DEGs analyzed, MYB showed high expression in peripheral blood monocytes, while 13 other tumor-related genes exhibited high expression in monocytes in the bone marrow. Peripheral blood and bone marrow HSCs had 6 and 12 highly expressed tumor-related genes respectively, including MCL1, JUN, and JUNB. These genes may form a interconnected network contributing to the progression and heterogeneity of AML, which can have an impact on patient treatment and prognosis. Conclusions In conclusion, when treating elderly AML patients, it is important to consider their individual characteristics in order to optimize treatment strategies.
„APP family member dimeric complexes are formed predominantly in synaptic compartments“

The amyloid precursor protein (APP), a key player in Alzheime’s disease (AD), is part of a larger gene family, including the APP like proteins APLP1 and APLP2. They share similar structures, form homo- and heterotypic dimers and exhibit overlapping functions. We investigated complex formation of the APP family members via two inducible dimerization systems, the FKBP-rapamycin based dimerization as well as cysteine induced dimerization, combined with coimmunoprecipitations and Blue Native (BN) gel analyses. Within the APP family, APLP1 shows the highest degree of dimerization and high molecular weight (HMW) complex formation. Interestingly, about 20% of APP is dimerized in cultured cells while about 50% of APP is dimerized in mouse brains, independent of age and splice forms. Furthermore, we could show that dimerized APP originates mostly from neurons and is enriched in synaptosomes. Finally, BN gel analysis of human cortex samples shows a significant decrease of APP dimers in AD patients compared to controls, suggesting that loss of dimers of full-length APP might correlate with loss of synapses in the process of AD.