Miracle-dz/UniFAR-FTUs huggingface dataset:
Unifying the Global and Local Approaches: An Efficient Power Iteration with Forward Push

Personalized PageRank (PPR) is a critical measure of the importance of a node t to a source node s in a graph. The Single-Source PPR (SSPPR) query computes the PPR's of all the nodes with respect to s on a directed graph G with n nodes and m edges; and it is an essential operation widely used in graph applications. In this paper, we propose novel algorithms for answering two variants of SSPPR queries: (i) high-precision queries and (ii) approximate queries. For high-precision queries, Power Iteration (PowItr) and Forward Push (FwdPush) are two fundamental approaches. Given an absolute error threshold λ (which is typically set to as small as 10-8), the only known bound of FwdPush is O(m/λ), much worse than the O(m log 1/λ)-bound of PowItr. Whether FwdPush can achieve the same running time bound as PowItr does still remains an open question in the research community. We give a positive answer to this question. We show that the running time of a common implementation of FwdPush is actually bounded by O(m · log 1/λ). Based on this finding, we propose a new algorithm, called Power Iteration with Forward Push (PowerPush), which incorporates the strengths of both PowItr and FwdPush. For approximate queries (with a relative error ε), we propose a new algorithm, called SpeedPPR, with overall expected time bounded by $O(n · log n · log 1/ε) on scale-free graphs. This improves the state-of-the-art O((n · log n)/ε) bound. We conduct extensive experiments on six real datasets. The experimental results show that PowerPush outperforms the state-of-the-art high-precision algorithm BePi by up to an order of magnitude in both efficiency and accuracy. Furthermore, our SpeedPPR also outperforms the state-of-the-art approximate algorithm FORA by up to an order of magnitude in all aspects including query time, accuracy, pre-processing time as well as index size.
The Proper Basis for a Zero-dimensional Polynomial Ideal

The proper basis formulated herein constitutes an improvement on the Gr\"obner basis for a zero-dimensional polynomial ideal. Let $K[\mathbf{x}]$ be a polynomial ring over a field $K$ with $\mathbf{x}:=(x_1,\dotsc,x_n)$. With $x_1$ being the least variable, a zero-dimensional polynomial ideal $I\subset K[\mathbf{x}]$ always has an eliminant $\chi\in K[x_1]\setminus K$ such that $I\cap K[x_1]=(\chi)$ after eliminating the other variables $\tilde{\mathbf{x}}:=(x_2,\dotsc,x_n)$. Hence it is excessive computation for the elimination process involving the variable $x_1$ in Buchberger's algorithm for the Gr\"obner basis. It is natural to treat $K[\mathbf{x}]$ as the algebra $K[x_1][\tilde{\mathbf{x}}]$ and define a new type of basis over $K[x_1]$ for $I$ called the proper basis. The proper basis is based on a new type of polynomial division called the proper division, which improves the division mechanism in M\"oller's algorithm over $K[x_1]$ for the Gr\"obner basis. We develop a modular algorithm over a principal ideal ring with zero divisors. The convincing efficiency of the proper basis over both Buchberger's Gr\"obner basis over $K$ and M\"oller's one over $K[x_1]$ is corroborated by a series of benchmark testings with respect to the typical \textnormal{\textsc{lex}} ordering.
A Fault Tolerant Mechanism for Partitioning and Offloading Framework in Pervasive Environments

Application partitioning and code offloading are being researched extensively during the past few years. Several frameworks for code offloading have been proposed. However, fewer works attempted to address issues occurred with its implementation in pervasive environments such as frequent network disconnection due to high mobility of users. Thus, in this paper, we proposed a fault tolerant algorithm that helps in consolidating the efficiency and robustness of application partitioning and offloading frameworks. To permit the usage of different fault tolerant policies such as replication and checkpointing, the devices are grouped into high and low reliability clusters. Experimental results shown that the fault tolerant algorithm can easily adapt to different execution conditions while incurring minimum overhead.
An Ultra Fast Low Power Convolutional Neural Network Image Sensor with Pixel-level Computing

—The separation of the data capture and analysis in modern vision systems has led to a massive amount of data transfer between the end devices and cloud computers, resulting in long latency, slow response, and high power consumption. Efﬁcient hardware architectures are under focused development to enable Artiﬁcial Intelligence (AI) at the resource-limited end sensing devices. This paper proposes a Processing-In-Pixel (PIP) CMOS sensor architecture, which allows convolution operation before the column readout circuit to signiﬁcantly improve the image reading speed with much lower power consumption. The simulation results show that the proposed architecture enables convolution operation (kernel size=3 × 3, stride=2, input channel=3, output channel=64) in a 1080P image sensor array with only 22.62 mW power consumption. In other words, the computational efﬁciency is 4.75 TOPS/w, which is about 3.6 times as higher as the state-of-the-art.
The Linearised Response of the Mass Burning Rate of a Premixed Flame to Rapid Pressure Changes

ABSTRACT An investigation is made into the response of the mass burning rate of a premixed flame to small but sharp changes in pressure. In this paper a fast time scale is considered such that (K1 = thermal diffusivity, u01 1 = initial burning velocity, l1 a = a typical acoustic length, a01 1 = frozen sound speed, θ = nondimensional activation energy). Previous studies by the author (Mclntosh 1990)and by other researchers (e.g. Ledder and Kapila 1991) have considered the case of r = 1 which is the usual value corresponding to audible resonance of flames in tubes, burner ports etc. However the case r = Θ2 (Mclntosh 1991)is a most interesting and distinct case since then the fast time scale alters the inner reaction zone which consequently obeys a different and essentially unsteady diffusion-reaction equation. The mass burning rate is then on an appreciably larger scale. The time response of the mass burning rate to pressure fluctuations at this fast time scale is governed in general by a non-linear partial.
Generalize Ultrasound Image Segmentation Via Instant And Plug & Play Style Transfer

Deep segmentation models that generalize to images with unknown appearance are important for real-world medical image analysis. Retraining models leads to high latency and complex pipelines, which are impractical in clinical settings. The situation becomes more severe for ultrasound image analysis because of their large appearance shifts. In this paper, we propose a novel method for robust segmentation under unknown appearance shifts. Our contribution is three-fold. First, we advance a one-stage plug-and-play solution by embedding hierarchical style transfer units into a segmentation architecture. Our solution can remove appearance shifts and perform segmentation simultaneously. Second, we adopt Dynamic Instance Normalization to conduct precise and dynamic style transfer in a learnable manner, rather than previously fixed style normalization. Third, our solution is fast and lightweight for routine clinical adoption. Given $400\times 400$ image input, our solution only needs an additional 0.2 ms and 1.92M FLOPs to handle appearance shifts compared to the baseline pipeline. Extensive experiments are conducted on a large dataset from three vendors demonstrate our proposed method enhances the robustness of deep segmentation models.
High performance organic field-effect transistor with oxide/metal bilayer electrodes

Pentacene organic field-effect transistors (OFETs) were fabricated by inserting a thin metallic oxide material MoO3 between pentacene and gold (Au) electrodes as an interlayer. Comparing with the corresponding single layer OFETs without any interlayer, theses OFETs with a thin MoO3 interlayer showed an obvious enhancement of hole mobility and slightly decrease of threshold voltage. The improvement of performance was investigated by interfacial energy level of the organic/metal interface, which showed that the MoO3 interlayer could significantly reduce the injection barrier between Au and pentacene. Moreover, the reduction of the injection barrier leads to a decrease of contact resistance at organic/metal interface, which improve the performance of the devices.
Generation of Traffic Flows in Multi-Agent Traffic Simulation with Agent Behavior Model based on Deep Reinforcement Learning

In multi-agent based traffic simulation, agents are always supposed to move following existing instructions, and mechanically and unnaturally imitate human behavior. The human drivers perform acceleration or deceleration irregularly all the time, which seems unnecessary in some conditions. For letting agents in traffic simulation behave more like humans and recognize other agents' behavior in complex conditions, we propose a unified mechanism for agents learn to decide various accelerations by using deep reinforcement learning based on a combination of regenerated visual images revealing some notable features, and numerical vectors containing some important data such as instantaneous speed. By handling batches of sequential data, agents are enabled to recognize surrounding agents' behavior and decide their own acceleration. In addition, we can generate a traffic flow behaving diversely to simulate the real traffic flow by using an architecture of fully decentralized training and fully centralized execution without violating Markov assumptions.
Comparison of the effects of investor attention using search volume data before and after mobile device popularization

In this study, we will study investor attention measurement using the Search Volume Index in the recent market. Since 2009, the popularity of mobile devices and the spread of the Internet have made the speed of information delivery faster and the investment information retrieval data for obtaining investment information has increased dramatically. In these circumstances, investor attention measurement using search volume data can be measured more accurately and faster than before mobile device popularization. To confirm this, we will compare the effect of measuring investor attention using search volume data before and after mobile device popularization. In addition, it is confirmed that the measured investor attention is that of retail traders, not institutional traders or professional traders, and the relationship between investor attention and short-term price pressure theory. Using SVI data provided by Google Trends, we will experiment with Russell 3000 stocks and IPO stocks and compare the results. In addition, the results of investigating the investor's interest using the search volume data from various angles through experiments such as the comparison of the results based on the inclusion of the noise ticker group, the comparison of the limitations of the existing investor attention measurement method, and the comparison of explanatory variables with existing IPO related studies. We would like to verify its practicality and significance.
Remote Pulse Estimation in the Presence of Face Masks

Remote photoplethysmography (rPPG), a family of techniques for monitoring blood volume changes, may be especially useful for contactless health monitoring via face videos from consumer-grade cameras. The COVID-19 pandemic caused widespread use of protective face masks, which results in a domain shift from the typical region of interest. In this paper we show that augmenting unmasked face videos by adding patterned synthetic face masks forces the deep learning-based rPPG model to attend to the periocular and forehead regions, improving performance and closing the gap between masked and unmasked pulse estimation. This paper offers several novel contributions: (a) deep learning-based method designed for remote photoplethysmography in a presence of face masks, (b) new dataset acquired from 54 masked subjects with recordings of their face and ground-truth pulse waveforms, (c) data augmentation method to add a synthetic mask to a face video, and (d) evaluations of handcrafted algorithms and two 3D convolutional neural network-based architectures trained on videos of unmasked faces and with masks synthetically added.
Learning Better Sentence Representation with Syntax Information

Sentence semantic understanding is a key topic in the field of natural language processing. Recently, contextualized word representations derived from pre-trained language models such as ELMO and BERT have shown significant improvements for a wide range of semantic tasks, e.g. question answering, text classification and sentiment analysis. However, how to add external knowledge to further improve the semantic modeling capability of model is worth probing. In this paper, we propose a novel approach to combining syntax information with a pre-trained language model. In order to evaluate the effect of the pre-training model, first, we introduce RNN-based and Transformer-based pre-trained language models; secondly, to better integrate external knowledge, such as syntactic information integrate with the pre-training model, we propose a dependency syntax expansion (DSE) model. For evaluation, we have selected two subtasks: sentence completion task and biological relation extraction task. The experimental results show that our model achieves 91.2\% accuracy, outperforming the baseline model by 37.8\% on sentence completion task. And it also gets competitive performance by 75.1\% $F_{1}$ score on relation extraction task.
TIB's Visual Analytics Group at MediaEval '20: Detecting Fake News on Corona Virus and 5G Conspiracy

Fake news on social media has become a hot topic of research as it negatively impacts the discourse of real news in the public. Specifically, the ongoing COVID-19 pandemic has seen a rise of inaccurate and misleading information due to the surrounding controversies and unknown details at the beginning of the pandemic. The FakeNews task at MediaEval 2020 tackles this problem by creating a challenge to automatically detect tweets containing misinformation based on text and structure from Twitter follower network. In this paper, we present a simple approach that uses BERT embeddings and a shallow neural network for classifying tweets using only text, and discuss our findings and limitations of the approach in text-based misinformation detection.
PARAFAC Decomposition of Three‐Way Kinetic‐Spectrophotometric Spectral Matrices Based on Phosphomolymbdenum Blue Complex Chemistry for Nitrite Determination in Water and Meat Samples

Abstract A three‐way analytical methodology experimentally based on kinetic‐spectrophotometric and parallel factor analysis (PARAFAC) chemometrics analysis was assessed for the quantification of nitrite in water and meat samples. This method is based on the reduction of phosphomolybdic acid to phosphomolymbdenum blue complex by sodium sulfide. The obtained phosphomolymbdenum blue complex is oxidized by the addition of nitrite and this causes a reduction in intensity of the blue color. Three‐way data matrices were generated by acquisition of ultraviolet‐visible (UV‐Vis) spectra (600–900 nm) as a function of the time and of different relative concentration of the nitrite (0.10–2.10 µg mL−1). The PARAFAC trilinear model, without restrictions, was used in the data analysis. A full decomposition of the data matrices was obtained (spectra, concentration, and time profile). It was shown that kinetic methods coupled to three‐way chemometrics analytical methods can be used for the development of robust sensors for the analysis of nitrite in water and meat samples. The accuracy of the method, evaluated through the root mean square error of prediction (RMSEP), was 0.0515 and 0.1181 for nitrite by PARAFAC and partise least squares (PLS) models respectively. The results with the PARAFAC model are better than those of the PLS model, according to results, it being possible to recover the spectra and kinetic profiles, as well as the initial concentration of nitrite with good accuracy.
Palm oil markets and future supply

This article considers the recent trends in the palm oil market balance and the future prospects for palm oil fundamentals. The article finds that there exists potential for the demand for palm oil and its products to increase quite steeply, partly due to emerging demand as a relatively cheap biofuel (whether as palm biodiesel or for direct burning), and partly due to its price advantages in edible applications. In terms of supply, as long as there is willingness to plant more area with oil palm in environmentally sensitive areas, there will be continued growth in the oil palm industry. This development is likely to be led primarily by Indonesia with increasing areas planted with oil palm, notably in Kalimantan, and by continued yield improvements in Malaysia.
Acetaminophen-induced apoptosis: Facts versus fiction

An overdose of the widely used analgesic acetaminophen (APAP) is the most common cause of acute liver failure in the western world and hence is a clinically significant problem. Thus, mechanisms of APAP-induced hepatotoxicity have been the focus of extensive investigation for decades and it was established that APAP induces hepatocyte cell death by necrosis. Although APAP-induced necrosis shares some features of apoptosis induced by the intrinsic pathway, apoptotic cell death in this context was ruled out due to the absence of caspase activation and lack of protection by caspase inhibitors and missing morphological characteristics of apoptotic cells. Deeper mechanistic understanding of the cell death process after APAP in recent years has now revealed that cells die by programmed necrosis and apoptosis is not a relevant mode of cell death in this context. Hence, it is alarming to note that an increasing number of studies are being published purporting to indicate that APAP induces apoptotic cell death. These papers broadly measure “apoptotic markers” with questionable specificity such as Bax, Bcl-2 and caspase-3 protein expression, or use the terminal deoxynucleotidyl transferase dUTP nick end labeling assay as basis for the conclusion that there is apoptosis after APAP overdose. The misguided use of these apoptosis parameters in correlative studies without context or scientific rationale confuses the field and threatens to undo decades of careful mechanistic investigation into this topic. This review examines this emerging problem in detail and recommends approaches to correct it. Relevance for Patients Hepatotoxicity and acute liver failure caused by an acetaminophen overdose is a serious clinical problem in western countries. Understanding the mode of cell death and the signaling pathways involved is critical for developing new therapeutic approaches. Recent trends to claim that apoptosis is a relevant mode of cell death in acetaminophen hepatotoxicity are not justified by sound scientific data and will not lead to effective new drug development.
Radioprotective effect of a combination of melatonin and metformin on mice spermatogenesis: A histological study

Abstract Background The spermatogenesis system includes highly radiosensitive cells. Hence, this system is a potential target for toxic effects of ionizing radiation during radiotherapy of abdomen and pelvis cancers, as well as after accidental radiation events. Accordingly, metformin and melatonin are two important radioprotectors that have shown an ability to prevent cell death through neutralization of free radicals and stimulating DNA damage responses. Objective To evaluate the radioprotective effects of melatonin and metformin on mice spermatogenesis when administered alone or as a combination. Materials and Methods In this histological Study, 40 (6-8 wk, 30 gr) NMRI mice were divided into 8 groups (n = 5/each) as control, metformin, melatonin, melatonin + metformin, radiation, radiation + melatonin, radiation + metformin, and radiation + melatonin + metformin. 37 days after the irradiation, the testicular tissues were collected for histological evaluation. Results Single administration of melatonin could ameliorate effectively radiation toxicity in mice testis. Metformin showed radioprotective effects on some parameters such as the numbers of spermatogonia and mature sperms. Interestingly, the melatonin and metformin combination reversed the reduced number of sperms rather than single drug administration. Conclusion The combination of melatonin with metformin can protect mice spermatogenesis against ionizing radiation more effectively compared to the single forms of these drugs.
Anisotropic Cardiac Conduction

Anisotropy is the property of directional dependence. In cardiac tissue, conduction velocity is anisotropic and its orientation is determined by myocyte direction. Cell shape and size, excitability, myocardial fibrosis, gap junction distribution and function are all considered to contribute to anisotropic conduction. In disease states, anisotropic conduction may be enhanced, and is implicated, in the genesis of pathological arrhythmias. The principal mechanism responsible for enhanced anisotropy in disease remains uncertain. Possible contributors include changes in cellular excitability, changes in gap junction distribution or function and cellular uncoupling through interstitial fibrosis. It has recently been demonstrated that myocyte orientation may be identified using diffusion tensor magnetic resonance imaging in explanted hearts, and multisite pacing protocols have been proposed to estimate myocyte orientation and anisotropic conduction in vivo. These tools have the potential to contribute to the understanding of the role of myocyte disarray and anisotropic conduction in arrhythmic states.
Utility of a simplified ultrasonography scoring system among patients with rheumatoid arthritis

Supplemental Digital Content is available in the text Abstract We aimed to evaluate the utility of a simplified ultrasonography (US) scoring system, which is desired in daily clinical practice, among patients with rheumatoid arthritis (RA) receiving biological/targeted synthetic disease-modifying antirheumatic drugs (DMARDs). A total of 289 Japanese patients with RA who were started on tumor necrosis factor inhibitors, abatacept, tocilizumab, or Janus kinase inhibitors between June 2013 and April 2019 at one of the 15 participating rheumatology centers were reviewed. We performed US assessment of articular synovia over 22 joints among bilateral wrist and finger joints, and the 22-joint (22j)-GS and 22-joint (22j)-PD scores were evaluated as an indicator of US activity using the sum of the GS and PD scores, respectively. The top 6 most affected joints included the bilateral wrist and second/third metacarpophalangeal joints. Therefore, 6-joint (6j)-GS and -PD scores were defined as the sum of the GS and PD scores from the 6 synovial sites over the aforementioned 6 joints, respectively. Although the 22j- or 6j-US scores were significantly correlated with DAS28-ESR or -CRP scores, the correlations were weak. Conversely, 6j-US scores were significantly and strongly correlated with 22j-US scores not only at baseline but also after therapy initiation. Using a multicenter cohort data, our results indicated that a simplified US scoring system could be adequately tolerated during any disease course among patients with RA receiving biological/targeted synthetic DMARDs.