Miracle-dz/UniFAR-FTUs huggingface dataset:
Floquet analysis of a quantum system with modulated periodic driving

We consider a quantum system periodically driven with a strength which varies slowly on the scale of the driving period. The analysis is based on a general formulation of the Floquet theory relying on the extended Hilbert space. It is shown that the dynamics of the system can be described in terms of a slowly varying effective Floquet Hamiltonian that captures the long-term evolution, as well as rapidly oscillating micromotion operators. We obtain a systematic high-frequency expansion of all these operators. Generalizing the previous studies, the expanded effective Hamiltonian is now time dependent and contains extra terms appearing due to changes in the periodic driving. The same applies to the micromotion operators which exhibit a slow temporal dependence in addition to the rapid oscillations. As an illustration, we consider a quantum-mechanical spin in an oscillating magnetic field with a slowly changing direction. The effective evolution of the spin is then associated with non-Abelian geometric phases reflecting the geometry of the extended Floquet space. The developed formalism is general and also applies to other periodically driven systems, such as shaken optical lattices with a time-dependent shaking strength, a situation relevant to the cold-atom experiments.
Measurement Allocation by Shapley Value in Wireless Sensor Networks

In this paper, we consider measurement allocation problem in a spatially correlated sensor field. Our goal is to determine the probability of each sensor’s being measured based on its contribution to the estimation reliability; it is desirable that a sensor improving the estimation reliability is measured more frequently. We consider a spatial correlation model of a sensor field reflecting transmission power limit, noise in measurement and transmission channel, and channel attenuation. Then the estimation reliability is defined distortion error between event source and its estimation at sink. Motivated by the correlation nature, we model the measurement allocation problem into a cooperative game, and then quantify each sensor’s contribution using Shapley value. Against the intractability in the computation of exact Shapley value, we deploy a randomized method that enables to compute the approximate Shapley value within a reasonable time. Besides, we envisage a measurement scheduling achieving the balance between network lifetime and estimation reliability.
Tighter Connections Between Formula-SAT and Shaving Logs

A noticeable fraction of Algorithms papers in the last few decades improve the running time of well-known algorithms for fundamental problems by logarithmic factors. For example, the $O(n^2)$ dynamic programming solution to the Longest Common Subsequence problem (LCS) was improved to $O(n^2/\log^2 n)$ in several ways and using a variety of ingenious tricks. This line of research, also known as "the art of shaving log factors", lacks a tool for proving negative results. Specifically, how can we show that it is unlikely that LCS can be solved in time $O(n^2/\log^3 n)$? Perhaps the only approach for such results was suggested in a recent paper of Abboud, Hansen, Vassilevska W. and Williams (STOC'16). The authors blame the hardness of shaving logs on the hardness of solving satisfiability on Boolean formulas (Formula-SAT) faster than exhaustive search. They show that an $O(n^2/\log^{1000} n)$ algorithm for LCS would imply a major advance in circuit lower bounds. Whether this approach can lead to tighter barriers was unclear. In this paper, we push this approach to its limit and, in particular, prove that a well-known barrier from complexity theory stands in the way for shaving five additional log factors for fundamental combinatorial problems. For LCS, regular expression pattern matching, as well as the Fr\'echet distance problem from Computational Geometry, we show that an $O(n^2/\log^{7+\varepsilon} n)$ runtime would imply new Formula-SAT algorithms. Our main result is a reduction from SAT on formulas of size $s$ over $n$ variables to LCS on sequences of length $N=2^{n/2} \cdot s^{1+o(1)}$. Our reduction is essentially as efficient as possible, and it greatly improves the previously known reduction for LCS with $N=2^{n/2} \cdot s^c$, for some $c \geq 100$.
SAMBA: hardware accelerator for biological sequence comparison

MOTIVATION SAMBA (Systolic Accelerator for Molecular Biological Applications) is a 128 processor hardware accelerator for speeding up the sequence comparison process. The short-term objective is to provide a low-cost board to boost PC or workstation performance on this class of applications. This paper places SAMBA amongst other existing systems and highlights the original features. RESULTS Real performance obtained from the prototype is demonstrated. For example, a sequence of 300 amino acids is scanned against SWISS-PROT-34 (21 210 389 residues) in 30 s using the Smith and Waterman algorithm. More time-consuming applications, like the bank-to-bank comparison, are computed in a few hours instead of days on standard workstations. Technology allows the prototype to fit onto a single PCI board for plugging into any PC or workstation. AVAILABILITY SAMBA can be tested on the WEB server at URL http://www.irisa. fr/SAMBA/.
A neural network approach for non-Cartesian k-space parallel imaging reconstruction

Introduction: Parallel MR Imaging utilizes implicit or explicit knowledge of coil sensitivity functions to reconstruct unaliased images from undersampled multicoil data. Image based methods such as SENSE [1], k-space based methods such as GRAPPA [2] and hybrids [3] have been proposed. Most of these techniques have focused on Cartesian trajectories. With increasing popularity of non-Cartesian trajectories like radial, spiral due to their inherent over-sampling of low k-space, there is a felt need for adapting parallel imaging to these sampling methods. However, due to the complex nature of the aliasing PSF for non-Cartesian trajectories, parallel imaging reconstruction has proved non trivial. We propose a novel algorithm using neural networks (NN) to reconstruct non-Cartesian undersampled data.
Methylene blue inhibits lumefantrine-resistant Plasmodium berghei.

INTRODUCTION Chemotherapy still is the most effective way to control malaria, a major public health problem in sub-Saharan Africa. The large-scale use of the combination therapy artemether-lumefantrine for malaria treatment in Africa predisposes lumefantrine to emergence of resistance. There is need to identify drugs that can be used as substitutes to lumefantrine for use in combination therapy. Methylene blue, a synthetic anti-methemoglobinemia drug, has been shown to contain antimalarial properties, making it a candidate for drug repurposing. The present study sought to determine antiplasmodial effects of methylene blue against lumefantrine- and pyrimethamine-resistant strains of P. berghei. METHODOLOGY Activity of methylene blue was assessed using the classical four-day test on mice infected with lumefantrine-resistant and pyrimethamine-resistant P. berghei. A dose of 45 mg/kg/day was effective for testing ED90. Parasitemia and mice survival was determined. RESULTS At 45 mg/kg/day, methylene blue sustained significant parasite inhibition, over 99%, for at least 6 days post-treatment against lumefantrine-resistant and pyrimethamine-resistant P. berghei (p = 0.0086 and p = 0.0191, respectively). No serious adverse effects were observed. CONCLUSIONS Our results indicate that methylene blue at a concentration of 45 mg/kg/day confers over 99% inhibition against lumefantrine- and pyrimethamine-resistant P. berghei for six days. This shows the potential use methylene blue in the development of antimalarials against lumefantrine- and pyrimethamine-resistant parasites.
Ray-of-Arrival Passing for Indirect Beam Training in Cooperative Millimeter Wave MIMO Networks

This paper is concerned with the channel estimation problem in multi-cell Millimeter (mmWave) wireless systems. We develop a novel Ray-of-Arrival Passing for Indirect (RAPID) Beam Training framework, in which a network consisting of multiple BS are able to work cooperatively to estimate jointly the UE channels. To achieve this aim, we consider the spatial geometry of the mmWave environment and transform conventional angular domain beamforming concepts into the more euclidean, Ray-based domain. Leveraging this model, we then consider the conditional probabilities that pilot signals are received in each direction, given that the deployment of each BS is known to the network. Simulation results show that RAPID is able to improve the average estimation of the network and significantly increase the rate of poorer quality links. Furthermore, we also show that, when a coverage rate threshold is considered, RAPID is able to improve greatly the probability that multiple link options will be available to a user at any given time.
A Survey on Real-Time Communications in Wireless Sensor Networks

Generally, various traffic requirements in wireless sensor network are mostly dependent on specific application types, that is, event-driven, continuous, and query-driven types. In these applications, real-time delivery is one of the important research challenges. However, due to harsh networking environment around a node, many researchers usually take different approach from conventional networks. In order to discuss and analyze the advantage or disadvantage of these approaches, some comprehensive survey literatures were published; however they are either out of date or compiled for communication protocols on single layer. Based on this deficiency, in this paper, we present the up-to-date research approaches and discuss the important features related to real-time communications in wireless sensor networks. As for grouping, we categorize the approaches into hard, soft, and firm real-time model. Furthermore, in all these categories, research has been focused on MAC and scheduling and routing according to research area or objective in second level. Finally, the article also suggests potential directions for future research in the field.
Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification

Person re-identification (re-ID) requires rapid, flexible yet discriminant representations to quickly generalize to unseen observations on-the-fly and recognize the same identity across disjoint camera views. Recent effective methods are developed in a pair-wise similarity learning system to detect a fixed set of features from distinct regions which are mapped to their vector embeddings for the distance measuring. However, the most relevant and crucial parts of each image are detected independently without referring to the dependency conditioned on one and another. Also, these region based methods rely on spatial manipulation to position the local features in comparable similarity measuring. To combat these limitations, in this paper we introduce the Deep Co-attention based Comparators (DCCs) that fuse the co-dependent representations of the paired images so as to focus on the relevant parts of both images and produce their \textit{relative representations}. Given a pair of pedestrian images to be compared, the proposed model mimics the foveation of human eyes to detect distinct regions concurrent on both images, namely co-dependent features, and alternatively attend to relevant regions to fuse them into the similarity learning. Our comparator is capable of producing dynamic representations relative to a particular sample every time, and thus well-suited to the case of re-identifying pedestrians on-the-fly. We perform extensive experiments to provide the insights and demonstrate the effectiveness of the proposed DCCs in person re-ID. Moreover, our approach has achieved the state-of-the-art performance on three benchmark data sets: DukeMTMC-reID \cite{DukeMTMC}, CUHK03 \cite{FPNN}, and Market-1501 \cite{Market1501}.
Perspective of Nurses toward the Patient Safety Culture in Neonatal Intensive Care Units

Background: Infants hospitalized in neonatal intensive care units (NICUs) are often severely ill, highly prone to various diseases, and exposed to complex and prolonged intensive care interventions. Consequently, they are susceptible to the lapses in teamwork and patient safety. Patient safety culture a fundamental step to improve patient safety. The present study aimed to evaluate the perspective of NICU nurses toward the patient safety culture. Methods: This cross-sectional study was conducted in 2016. Participants were selected via census sampling, including 156 nurses working in the NICU of the teaching hospitals affiliated to Isfahan University of Medical Sciences in Isfahan, Iran. Data were collected using a demographic questionnaire and Hospital Survey on Patient Safety Culture (HSOPSC). Data analysis was performed in SPSS version 16 (SPSS Inc, Chicago, IL, USA) using descriptive statistics (mean and standard deviation). Results: Among the 12 dimensions of the patient safety culture, the highest scores were observed in the ג��teamwork within unitsג�� (98.5),ג��organizational learning-continuous developmentג�� (87.8), ג��feedback and communication about errorsג�� (80.3), and ג��frequency of events reportedג�� (78.8). The lowest scores belonged to the dimensions of ג��handoffs and transitionsג�� (15.3), ג��non-punitive response to errorג�� (21.5), and ג��staffingג�� (37.1). Conclusion: According to the results, adherence to the dimensions of the patient safety culture was poor in the studied hospitals. Therefore, the patient safety culture requires special attention by providing proper facilities, adequate staff, developing checklists for handoffs and transitions, and surveillance and continuous monitoring by healthcare centers. Furthermore, a system-based approach should be implemented to deal with errors, while a persuasive reporting approach is needed to promote the patient safety culture in the NICUs of these hospitals. \xC2
ChainSmith: Automatically Learning the Semantics of Malicious Campaigns by Mining Threat Intelligence Reports

Modern cyber attacks consist of a series of steps and are generally part of larger campaigns. Large-scale field data provides a quantitative measurement of these campaigns. On the other hand, security practitioners extract and report qualitative campaign characteristics manually. Linking the two sources provides new insights about attacker strategies from measurements. However, this is a time-consuming task because qualitative measurements are generally reported in natural language and are not machine-readable. We propose an approach to bridge measurement data with manual analysis. We borrow the idea from threat intelligence: we define campaigns using a 4-stage model, and describe each stage using IOCs (indicators of compromise), e.g. URLs and IP addresses. We train a multi-class classifier to extract IOCs and further categorize them into different stages. We implement these ideas in a system called ChainSmith. Our system can achieve 91.9% precision and 97.8% recall in extracting IOCs, and can determine the campaign roles for 86.2% of IOCs with 78.2% precision and 80.7% recall. We run ChainSmith on 14,155 online security articles, from which we collect 24,653 IOCs. The semantic roles allow us to link manual attack analysis with large scale field measurements. In particular, we study the effectiveness of different persuasion techniques used on enticing user to download the payloads. We find that the campaign usually starts from social engineering and "missing codec" ruse is a common persuasion technique that generates the most suspicious downloads each day.
StpA and Hha stimulate pausing by RNA polymerase by promoting DNA–DNA bridging of H-NS filaments

Abstract In enterobacteria, AT-rich horizontally acquired genes, including virulence genes, are silenced through the actions of at least three nucleoid-associated proteins (NAPs): H-NS, StpA and Hha. These proteins form gene-silencing nucleoprotein filaments through direct DNA binding by H-NS and StpA homodimers or heterodimers. Both linear and bridged filaments, in which NAPs bind one or two DNA segments, respectively, have been observed. Hha can interact with H-NS or StpA filaments, but itself lacks a DNA-binding domain. Filaments composed of H-NS alone can inhibit transcription initiation and, in the bridged conformation, slow elongating RNA polymerase (RNAP) by promoting backtracking at pause sites. How the other NAPs modulate these effects of H-NS is unknown, despite evidence that they help regulate subsets of silenced genes in vivo (e.g. in pathogenicity islands). Here we report that Hha and StpA greatly enhance H-NS-stimulated pausing by RNAP at 20°C. StpA:H-NS or StpA-only filaments also stimulate pausing at 37°C, a temperature at which Hha:H-NS or H-NS-only filaments have much less effect. In addition, we report that both Hha and StpA greatly stimulate DNA–DNA bridging by H-NS filaments. Together, these observations indicate that Hha and StpA can affect H-NS-mediated gene regulation by stimulating bridging of H-NS/DNA filaments.
Responses of kukupa (Hemiphaga novaeseelandiae) and other birds to mammal pest control at Motatau, Northland

The kukupa or New Zealand pigeon (Hemiphaga novaeseelandiae) is gradually declining on the New Zealand mainland, due mostly to predation by introduced pest mammals including ship rats (Rattus rattus) and brushtail possums (Trichosurus vulpecula). We report on a co-operative project between Maori landowners, the Department of Conservation, and Manaaki Whenua-Landcare Research researchers to restore a Northland kukupa population and to examine kukupa nesting success in relation to pest abundance. Ship rats and possums were targeted by trapping and poisoning throughout Motatau Forest (350 ha) from 1997 to 1999; only possums were targeted in 2000. All 13 kukupa nests located before pest control started in late 1997 failed at the egg stage, but all seven nests located in 1998-99 successfully fledged young when trapping and tracking indices of possums and ship rats were less than 4%. After pest control, counts of kukupa and some other bird species increased at Motatau compared with counts in a nearby non-treatment block, suggesting numbers of adult kukupa can be increased in small forest areas by intensive pest control. This increase is due at least partly to increased nest success. Evidence from time-lapse video cameras, sign remaining at nests, and nest success rates under different pest control regimes suggest both ship rats and possums are important predators at kukupa nests.
Biculturalism: The relationship between education policy and art education practice in secondary schools in Aotearoa New Zealand

Introduction In this research I take the position that biculturalism in Aotearoa New Zealand is not so much a theoretical construct as a proclamation of a political stance. I aim to uncover the interpretations of biculturalism that have guided state education and to investigate practices which respond to what I call the ‘bicultural imperative’. Although the research focused on art education in secondary schools, the enactment of statutes establishing bicultural policy in education has implications for other curriculum areas.
Aspect Term Extraction with History Attention and Selective Transformation

Aspect Term Extraction (ATE), a key sub-task in Aspect-Based Sentiment Analysis, aims to extract explicit aspect expressions from online user reviews. We present a new framework for tackling ATE. It can exploit two useful clues, namely opinion summary and aspect detection history. Opinion summary is distilled from the whole input sentence, conditioned on each current token for aspect prediction, and thus the tailor-made summary can help aspect prediction on this token. On the other hand, the aspect detection history information is distilled from the previous aspect predictions, and it can leverage the coordinate structure and tagging schema constraints to upgrade the aspect prediction. Experimental results over four benchmark datasets clearly demonstrate that our framework can outperform all state-of-the-art methods.
Algorithms for Canonical Polyadic Decomposition With Block-Circulant Factors

Higher order tensors and their decompositions are well-known tools in signal processing. More specifically, tensors admitting decompositions with structured factor matrices arise in various applications, such as telecommunications or convolutive independent component analysis. These applications motivate the development of efficient algorithms for structured tensor decompositions. In this letter, we develop a method for canonical polyadic decompositions (CPDs) with block-circulant factor matrices by extending a method for circulant factors. Block-circulant factor matrices arise in the identification of homogeneous Wiener– Hammerstein models. By transforming the data tensor to the frequency domain, the available structure is exploited using simple element-wise divisions. This approach reformulates the structured CPD as an unstructured CPD of lower rank. The resulting CPD of a possibly incomplete tensor can then be solved algebraically or using optimization-based methods.
Extracting Lungs from CT Images Using Fully Convolutional Networks

Analysis of cancer and other pathological diseases, like the interstitial lung diseases (ILDs), is usually possible through Computed Tomography (CT) scans. To aid this, a preprocessing step of segmentation is performed to reduce the area to be analyzed, segmenting the lungs and removing unimportant regions. Generally, complex methods are developed to extract the lung region, also using hand-made feature extractors to enhance segmentation. With the popularity of deep learning techniques and its automated feature learning, we propose a lung segmentation approach using fully convolutional networks (FCNs) combined with fully connected conditional random fields (CRF), employed in many state-of-the-art segmentation works. Aiming to develop a generalized approach, the publicly available datasets from University Hospitals of Geneva (HUG) and VESSEL12 challenge were studied, including many healthy and pathological CT scans for evaluation. Experiments using the dataset individually, its trained model on the other dataset and a combination of both datasets were employed. Dice scores of 98.67%±0.94% for the HUG-ILD dataset and 99.19%±0.37% for the VESSEL12 dataset were achieved, outperforming works in the former and obtaining similar state-of-the-art results in the latter dataset, showing the capability in using deep learning approaches.
Mulberry Fruit Extract Affords Protection against Ethyl Carbamate-Induced Cytotoxicity and Oxidative Stress

Ethyl carbamate (EC) is a food and environmental toxicant and is a cause of concern for human exposure. Several studies indicated that EC-induced toxicity was associated with oxidative stress. Mulberry fruits are reported to have a wide range of bioactive compounds and pharmacological activities. The present study was therefore aimed to investigate the protective property of mulberry fruit extract (MFE) on EC-induced cytotoxicity and oxidative stress. Chemical composition analysis showed that total phenolic content and total flavonoid content in MFE were 502.43 ± 5.10 and 219.12 ± 4.45 mg QE/100 g FW. Cyanidin-3-O-glucoside and cyanidin-3-O-rutinoside were the major anthocyanins in MFE. In vitro antioxidant studies (DPPH, ABTS, and FRAP assays) jointly exhibited the potent antioxidant capacity of MFE. Further study indicated that MFE protected human liver HepG2 cells from EC-induced cytotoxicity by scavenging overproduced cellular ROS. EC treatment promoted intracellular glutathione (GSH) depletion and caused mitochondrial membrane potential (MMP) collapse, as well as mitochondrial membrane lipid peroxidation, whereas MFE pretreatment significantly inhibited GSH depletion and restored the mitochondrial membrane function. Overall, our study suggested that polyphenolic-rich MFE could afford a potent protection against EC-induced cytotoxicity and oxidative stress.