Miracle-dz/UniFAR-FTUs huggingface dataset:
A High-performance Hardware Implementation of Saber Based on Karatsuba Algorithm

Abstract. Although large numbers of hardware and software implementations have been proposed to accelerate lattice-based cryptography, Saber, a module-LWR-based algorithm, which has advanced to second round of the NIST standardization process, has not been adequately supported by the current solutions. Based on these motivations, a high-performance crypto-processor is proposed based on an algorithm-hardware co-design in this paper. First, a hierarchical Karatsuba calculating framework, a hardware-efﬁcient Karatsuba scheduling strategy and an optimized circuit structure are utilized to enable high-throughput polynomial multiplication. Furthermore, a task-level pipeline and truncated multipliers are proposed to enable algorithm-speciﬁc ﬁne-grained processing. Enabled by all of the above optimizations, our processor takes 943, 1156, and 408 clock cycles for key generation, encryption, and de-cryption, respectively. Enabled by these optimizations, our processor takes 943, 1156 and 408 clock cycles for key generation, encryption, and decryption of Saber768, achieving 5.4 × , 5.2 × and 4.2 × reductions compared with the state-of-the-art FPGA solutions, respectively. The post-layout simulation of our design is implemented with TSMC 40 nm CMOS process within 0.35 mm 2 . The throughput for Saber768 is up to 346k encryption operations per second and the energy efﬁciency is 0.12 uJ/encryption while operating at 400 MHz, achieving nearly 52 × improvement and 30 × improvement, respectively compared with current PQC hardware solutions.
Rapidly reversible mutations generate subclonal genetic diversity and reversible drug resistance

Most genetic changes have negligible reversion rates. As most mutations that confer resistance to an adversary condition (e.g., drug treatment) also confer a growth defect in its absence, it is challenging for cells to genetically adapt to transient environmental changes. Here we identify a set of rapidly reversible drug resistance mutations in S. pombe that are caused by Microhomology mediated Tandem Duplication (MTD), and reversion back to the wild-type sequence. Using 10,000x coverage whole-genome sequencing we identify over 6000 subclonal MTDs in a single clonal population, and determine using machine learning how MTD frequency is encoded in DNA. We find that sequences with the highest predicted MTDs rates tend to generate insertions that maintain the correct reading frame; MTD formation has shaped the evolution of coding sequences. Our study reveals a mechanism of reversible genetic variation that is beneficial for adaptation to environmental fluctuations and facilitates evolutionary divergence.
P-DNN: An Effective Intrusion Detection Method based on Pruning Deep Neural Network

Today, the scale of global Internet users continues to grow; the Internet has become the main driver of global economic growth; IoT technology is also constantly pushing the process of the Internet of Everything. However, the ever-changing cybersecurity situation is not optimistic and the people’s demand for secure network is also increasing. In this paper, for the biggest challenge of building anomaly-based Network Intrusion Detection System: building a high-performance intrusion detection classifier model, we first propose an effective intrusion detection method based on pruning deep neural network: P-DNN. Firstly, we train a deep neural network with complex structure and good intrusion detection performance. Secondly, through the pruning operation, only the connections with more important information in the weight are reserved, reducing the complexity of the model. Finally, retrain the deep neural network to find the best model. We use the KDD Cup 99 dataset to evaluate the effectiveness of the method and achieve exciting results. The model constructed by P-DNN achieves a detection rate of 0.9904 for known attacks and a detection rate of 0.1050 for unknown attacks. By comparing with related work, the model achieves the best intrusion detection performance: COST is reduced to 0.1875 and ACC is increased to 0.9317.
The Effect of Government Mortgage Guarantees on Home Buying and Home Ownership

The U.S. federal government guarantees a vast majority of mortgages through the government sponsored enterprises and the federal housing administration. Although the government’s involvement in mortgage financing is controversial, it is often justified to make mortgage credits more available and thereby promote home ownership. However, very little is known about the effects of government mortgage guarantees on home buying and home ownership. To estimate the effects, we implement a difference-in-difference design, with detailed property-level data, that exploits regional changes in upper limits of the mortgage size that can be guaranteed by the government. We find that government guarantees do not have their intended effects on home ownership: although the guarantees have positive effects on house transactions coupled with an increase in mortgage origination, home ownership rates do not increase. ∗Meher Islam provided outstanding research assistance. Federal Reserve Board of Governors, serafin.j.grundl@frb.gov, you.kim@frb.gov. We thank Scott Frame for helpful comments. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the staff, by the Board of Governors, or by the Federal Reserve System.
Honey and its beneficial therapeutic effects: A review

Honey is highly nutritious, natural product obtained from honey bees which is wholesome food for old, children and adults. Children, young and old can consume honey, without worrying any side effects. Therapeutic benefits of honey were reported on the basis of various in vivo and in vitro studies carried out on human and animals. Studies revealed that due to the presence of various phenolic acid and flavonoid compounds in honey, it exhibit antibacterial, antifungal and antiviral activity. In addition to exhibiting antimicrobial activity these phenolic and flavonoid compounds present in honey also act as rich source of antioxidants. Long term consumption of honey can be beneficial against various disorders like diabetes mellitus, cough, wound healing, ophthalmology, cancer, gastro-intestinal and cardiovascular disorders etc. Further research is needed to evaluate the health benefits of honey and its formulations against more diseases.
Affine invariant iterated function systems and the minimal simplex problem

The AIFS (Affine invariant Iterated Function System) is a slightly modified IFS defined by Barnsley [Fractal Everywhere, Academic Press, 1988]. Instead of the usual Descartes coordinates, the barycentric system has been used. This allows more handy manipulation with the attractors generated by the IFS. The open question is still determination of the smallest simplex that contains the attractor. Some ideas concerning this problem are presented and elaborated in this note. M.S.C. 2000: 28A80,65D17.
GFD: A Weighted Heterogeneous Graph Embedding Based Approach for Fraud Detection in Mobile Advertising

Online mobile advertising plays a vital role in the mobile app ecosystem. The mobile advertising frauds caused by fraudulent clicks or other actions on advertisements are considered one of the most critical issues in mobile advertising systems. To combat the evolving mobile advertising frauds, machine learning methods have been successfully applied to identify advertising frauds in tabular data, distinguishing suspicious advertising fraud operation from normal one. However, such approaches may suffer from labor-intensive feature engineering and robustness of the detection algorithms, since the online advertising big data and complex fraudulent advertising actions generated by malicious codes, botnets, and click-firms are constantly changing. In this paper, we propose a novel weighted heterogeneous graph embedding and deep learning-based fraud detection approach, namely, GFD, to identify fraudulent apps for mobile advertising. In the proposed GFD approach, (i) we construct a weighted heterogeneous graph to represent behavior patterns between users, mobile apps, and mobile ads and design a weighted metapath to vector algorithm to learn node representations (graph-based features) from the graph; (ii) we use a time window based statistical analysis method to extract intrinsic features (attribute-based features) from the tabular sample data; (iii) we propose a hybrid neural network to fuse graph-based features and attribute-based features for classifying the fraudulent apps from normal apps. The GFD approach was applied on a large real-world mobile advertising dataset, and experiment results demonstrate that the approach significantly outperforms well-known learning methods.
APPROXIMATION LIMITATIONS OF PURE DYNAMIC PROGRAMMING\ast

We prove the first, even superpolynomial, lower bounds on the size of tropical (min,+) and (max,+) circuits approximating given optimization problems. Many classical dynamic programming (DP) algorithms for optimization problems are pure in that they only use the basic min, max, + operations in their recursion equations. Tropical circuits constitute a rigorous mathematical model for this class of algorithms. An algorithmic consequence of our lower bounds for tropical circuits is that the approximation powers of pure DP algorithms and greedy algorithms are incomparable. That pure DP algorithms can hardly beat greedy in approximation is long known. New in this consequence is that the converse also holds.
Fe b 20 06 AN OPTIMIZATION PROBLEM WITH VOLUME CONSTRAIN FOR A DEGENERATE QUASILINEAR OPERATOR

We consider the optimization problem of minimizing ∫ Ω |∇u| dx with a constrain on the volume of {u > 0}. We consider a penalization problem, and we prove that for small values of the penalization parameter, the constrained volume is attained. In this way we prove that every solution u is locally Lipschitz continuous and that the free boundary, ∂{u > 0} ∩ Ω, is smooth.
Moving the Goalpost Closer: Do Flexible Targets Improve the Behavioral Impact of Incentives?

Incentives are increasingly used to motivate behavior change. An unexplored yet critical question in incentive design is how to set the eligibility condition for receiving the reward; should everyone be subject to a uniform, “fixed” level of the targeted behavior, or does “flexible” targets that may vary across individuals, lead to greater improvements in outcomes? Participants at an HIV clinic in Uganda were randomly assigned to either receive incentives with “Fixed” or “Flexible” targets for improving medication adherence over nine months. Setting a high “Fixed” target did not affect adherence while allowing participants to adjust their target relative to their baseline performance improved adherence, with particularly large improvements among those with low levels of adherence at baseline. Findings suggest commitment to a goal and subsequent effort to achieve it may depend critically on perceiving the goal to be attainable.
A Comparison of the Rudin-Osher-Fatemi Total Variation model and the Nonlocal Means Algorithm

In this study, we compare two image denoising methods which are the Rudin-Osher-Fatemi total variation (TV) model and the nonlocal means (NLM) algorithm on medical images. To evaluate those methods, we used two well known measuring metrics. The methods are tested with a CT image, one X-Ray image, and three MRI images. Experimental result shows that the NML algorithm can give better results than the ROF TV model, but computational complexity is high.
Ghost species and optimal diversity: shared patterns between two tropical mountains within Auchenorrhyncha (Insecta: Hemiptera)

Altitudinal studies are usually carried out during a specific period of the year, which is supposed to be representative of the targeted insect diversity. Intra-annual variability is seldom investigated within tropical gradients, when it might influence diversity patterns. Two altitudinal gradients were compared here in tropical forests on two mountains, Mount Wilhelm (Papua New Guinea, 200–3,700 m.a.s.l) and Doi Inthanon (Thailand, 400–2,556 m.a.s.l), during the corresponding early wet-season sampling periods, to test a general spatial pattern of species diversity within tropical Auchenorrhyncha assemblages. We investigate the impact of selecting a seasonally optimal sampling period on determination of overall species diversity patterns. Shannon and Simpson diversity indices decreased as elevation increased on both gradients, as did species richness and abundance on Mount Wilhelm. Cluster analysis revealed numerous differences in altitudinal composition of dominant families between gradients. Our results revealed (1) the optimal sampling period with distinct seasonal variability in the diversity of assemblages, (2) that sampling only at an optimal period, had consequences on the number and the kind of species and families collected, but not on the diversity pattern, and (3) that the decrease of diversity indices with increasing elevation for the Auchenorrhyncha is not restricted to a local tropical gradient.
Improving Feature’s Capability of Carrying Category-specific Information for Adversarial Domain Adaptation

Recent research has shown that generative adversarial networks (GANs) have been successfully applied in aligning features for domain adaptation. However, the extracted features might lose category-specific information, because they are distinguished as either a source or a target during the adversarial training. To address this issue, a two-stage training framework consisting of two sets of GANs and a dedicated classifier is proposed in this study. In the pretraining stage, we use an encoder–decoder–classifier structure to obtain discriminative and representative features of the source domain and use it as a reference in the subsequent training. In the adversarial training stage, two sets of GANs are used to align target-domain features with those of the source domain and transfer samples of the target domain to the source domain simultaneously. A dedicated classifier is trained along with the adversarial loss to force the generated features of the target domain to carry category-specific information, which significantly improves classification performance. The features of the source domain stay intact in the adversarial training stage. Thus, our approach can alleviate the training burden of GANs. The proposed method has been validated on digital datasets and office31 datasets. Experimental results demonstrate that an average accuracy of 96.5% and 95.1% is achieved, which leads to superior or comparable performance to state-of-the-art results.
Review of advances in microwave and millimetre-wave NDT&E: principles and applications

Microwave and millimetre-wave non-destructive testing and evaluation (NDT&E) has a long history dating back to the late 1950s (Bahr 1982 Microwave non-destructive testing methods ; Zoughi 2000 Microwave Non-destructive testing and evaluation principles ; Feinstein 1967 Surface crack detection by microwave methods ; Ash 1973 In 3rd European Microwave Conference ; Auld 1981 Phys. Technol. 12, 149–154; Case 2017 Mater. Eval. 75). However, sustained activities in this field date back to the early 1980s (Zoughi 1995 Res. Nondestr. Eval. 7, 71–74; Zoughi 2018 Mater. Eval. 76, 1051–1057; Kharkovsky 2007 IEEE Instrumentation & Measurement Magazine 10, 26–38). Owing to various limitations associated with using microwaves and millimetre waves for NDT&E, these techniques did not see much utility in the early days. However, with the advent and prevalence of composite materials and structures, in a wide range of applications, and technological advances in high-frequency component design and availability, these techniques are no longer considered as ‘emerging techniques’ (Zoughi 2018 Mater. Eval. 76, 1051–1057; Schull 2002 Nondestructive evaluation: theory, techniques, and applications ). Currently, microwave and millimetre-wave NDT&E is a rapidly growing field and has been more widely acknowledged and accepted by practitioners over the last 25+ years (Case 2017 Mater. Eval. 75; Bakhtiari 1994 IEEE Trans. Microwave Theory Tech . 42, 389–395; Bakhtiari 1993 Mater. Eval. 51, 740–743; Bakhtiari 1993 IEEE Trans. Instrum. Meas. 42, 19–24; Ganchev 1995 IEEE Trans. Instrum. Meas. 44, 326–328; Bois 1999 IEEE Trans. Instrum. Meas. 48, 1131–1140; Ghasr 2009 IEEE Trans. Instrum. Meas. 58, 1505–1513). Microwave non-destructive testing was recently recognized and designated by the American Society for Nondestructive Testing (ASNT) as a ‘Method’ on its own (Case 2017 Mater. Eval. 75). These techniques are well suited for materials characterization; layered composite inspection for thickness, disbond, delamination and corrosion under coatings; surface-breaking crack detection and evaluation; and cure-state monitoring in concrete and resin-rich composites, to name a few. This work reviews recent advances in four major areas of microwave and millimetre-wave NDT&E, namely materials characterization, surface crack detection, imaging and sensors. The techniques, principles and some of the applications in each of these areas are discussed. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.
Machine learning at the interface of structural health monitoring and non-destructive evaluation

While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objective of damage detection and identification in structures, they are distinct in many respects. This paper will discuss the differences and commonalities and consider ultrasonic/guided-wave inspection as a technology at the interface of the two methodologies. It will discuss how data-based/machine learning analysis provides a powerful approach to ultrasonic NDE/SHM in terms of the available algorithms, and more generally, how different techniques can accommodate the very substantial quantities of data that are provided by modern monitoring campaigns. Several machine learning methods will be illustrated using case studies of composite structure monitoring and will consider the challenges of high-dimensional feature data available from sensing technologies like autonomous robotic ultrasonic inspection. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.
Analysis of the interventions adopted due to the COVID-19 on ARI morbility for Colombia

Acute Respiratory Infections are among the leading causes of death globally, particularly in developing countries, and are highly correlated with the quality of health and surveillance systems and effective early interventions in high-risk age groups. According to the World Health Organization, about four million people die each year from mostly preventable respiratory tract infections, making it a public health concern. The official declaration of a pandemic in March 2020 due to the Sars-CoV-2 virus coincided with the influenza season in Colombia and with environmental alerts about low air quality that increase its incidence. The objective of this document is the application of a flexible model for the identification of the pattern and monitoring of ARI morbility for Colombia by age group that shows atypical patterns in the reported series for 5 departments and that coincide with the decisions implemented to contain the COVID-19
Screening electrocardiogram in young athletes and military members: a systematic review and meta-analysis

Objective: To determine the effect of electrocardiogram (ECG) screening on prevention of sudden cardiac arrest and death (SCA/D) in young athletes and military members. Design: Systematic review and meta-analysis Data Sources: MEDLINE, Embase, Cochrane CENTRAL, Web of Science, BIOSIS, Scopus, SPORT discus, PEDro, were searched from inception to date searched between 2/21/19 and 3/1/19, and Clinicaltrials.gov on 7/29/19. All databases were searched without limits on language. Eligibility criteria for selected studies: Randomized and non-randomized controlled trials, where pre-participation examination including ECG was the primary intervention used to screen athletes or military 40 years of age or younger. Accepted controls were no screening, usual care, or pre-participation examination without ECG Results: 4 studies (11,689,172 participants) were included, all at high risk of bias. Pooled data from two studies (n= 3,869,274; very low quality) showed a 42% relative decrease in sudden cardiac death, equating to an absolute risk reduction of . 0016%. Uncertainty was high, with a potential 67% relative decrease to a 45% relative increase in those screened with ECG based on 95% confidence intervals (RR 0.58; 95%CI 0.23, 1.45). Heterogeneity was found to be high as measured with I2 statistic (71%). Conclusion: There is very low quality evidence ECG screening decreases risk of sudden cardiac death in young athletes and military members. Decisions need to consider evidence that ECG screening could also increase risk of sudden cardiac death based on the findings of meta- analysis.
Investigating the effect of tactile input and output locations for drivers’ hands on in-car tasks performance

This paper reports a study investigating the effects of tactile input and output from the steering wheel and the centre console on non-driving task performance. While driving, participants were asked to perform list selection tasks using tactile switches and to experience tactile feedback on either the non-dominant, dominant or both hands as they were browsing the list. Our results show the average duration for selecting an item is 30% shorter when interacting with the steering wheel. They also show a 20% increase in performance when tactile feedback is provided. Our findings reveal that input prevails over output location when designing interaction for drivers. However, tactile feedback on the steering wheel is beneficial when provided at the same location as the input or to both hands. The results will help designers understand the trade-offs of using different interaction locations in the car.
Impact of Hand Signals on Safety: Two Controlled Studies With Novice E-Scooter Riders

The introduction of micro-mobility, such as e-scooters, brings new challenges. Nevertheless, these trend devices are spreading rapidly without a comprehensive study of their interactions with other road users. For example, many countries currently require drivers of e-scooters to signal turns by hand. In this work, we investigate whether e-scooter riders can do this without losing control and whether they perceive hand signals as safe enough to use in traffic. We have conducted two studies with 10 and 24 participants, respectively. Each participant was able to perform hand signals without apparent problems. We also observed an intensive training effect regarding the handling of e-scooters. Nevertheless, our results indicate that a considerable number of inexperienced riders will, outside the laboratory, turn without signs with the currently prevailing e-scooter designs and regulations due to uncertainties.