nils-herrmann/clean_scirepeval huggingface dataset:
signSGD with Majority Vote is Communication Efficient And Byzantine Fault Tolerant

Training neural networks on large datasets can be accelerated by distributing the workload over a network of machines. As datasets grow ever larger, networks of hundreds or thousands of machines become economically viable. The time cost of communicating gradients limits the effectiveness of using such large machine counts, as may the increased chance of network faults. We explore a particularly simple algorithm for robust, communication-efficient learning—SIGNSGD. Workers transmit only the sign of their gradient vector to a server, and the overall update is decided by a majority vote. This algorithm uses 32× less communication per iteration than full-precision, distributed SGD. Under natural conditions verified by experiment, we prove that SIGNSGD converges in the large and minibatch settings, establishing convergence for a parameter regime of ADAM as a byproduct. We model adversaries as those workers who may compute a stochastic gradient estimate and manipulate it, but may not coordinate with other adversaries. Aggregating sign gradients by majority vote means that no individual worker has too much power. We prove that unlike SGD, majority vote is robust when up to 50% of workers behave adversarially. On the practical side, we built our distributed training system in Pytorch. Benchmarking against the state of the art collective communications library (NCCL), our framework—with the parameter server housed entirely on one machine—led to a 25% reduction in time for training resnet50 on Imagenet when using 15 AWS p3.2xlarge machines.
A New Learning Approach to Malware Classification Using Discriminative Feature Extraction

With the development of the Internet, malware has become one of the most significant threats. Recognizing specific types of malware is an important step toward effective removal. Malware visualization is an important branch of malware static analysis techniques, where a piece of malware is turned into an image for visualization and classification. Despite great success, it is still difficult to extract effective texture feature representations for challenging datasets. The existing methods use global image features which are sensitive to relative code locations. In this paper, we present a new learning framework to obtain more discriminative and robust feature descriptors. The proposed method works with the existing local descriptors such as local binary patterns and dense scale-invariant feature transform, by grouping them into blocks and by using a new bag-of-visual-words model to obtain robust features, which are more flexible than global features and more robust than local features. We evaluate the proposed method on three malware databases. The experimental results demonstrate that the obtained descriptors lead to the state-of-the-art classification performance.
Resilient 3D hierarchical architected metamaterials.

Hierarchically designed structures with architectural features that span across multiple length scales are found in numerous hard biomaterials, like bone, wood, and glass sponge skeletons, as well as manmade structures, like the Eiffel Tower. It has been hypothesized that their mechanical robustness and damage tolerance stem from sophisticated ordering within the constituents, but the specific role of hierarchy remains to be fully described and understood. We apply the principles of hierarchical design to create structural metamaterials from three material systems: (i) polymer, (ii) hollow ceramic, and (iii) ceramic-polymer composites that are patterned into self-similar unit cells in a fractal-like geometry. In situ nanomechanical experiments revealed (i) a nearly theoretical scaling of structural strength and stiffness with relative density, which outperforms existing nonhierarchical nanolattices; (ii) recoverability, with hollow alumina samples recovering up to 98% of their original height after compression to ≥ 50% strain; (iii) suppression of brittle failure and structural instabilities in hollow ceramic hierarchical nanolattices; and (iv) a range of deformation mechanisms that can be tuned by changing the slenderness ratios of the beams. Additional levels of hierarchy beyond a second order did not increase the strength or stiffness, which suggests the existence of an optimal degree of hierarchy to amplify resilience. We developed a computational model that captures local stress distributions within the nanolattices under compression and explains some of the underlying deformation mechanisms as well as validates the measured effective stiffness to be interpreted as a metamaterial property.
Exercise and well-being: a review of mental and physical health benefits associated with physical activity.

This review highlights recent work evaluating the relationship between exercise, physical activity and physical and mental health. Both cross-sectional and longitudinal studies, as well as randomized clinical trials, are included. Special attention is given to physical conditions, including obesity, cancer, cardiovascular disease and sexual dysfunction. Furthermore, studies relating physical activity to depression and other mood states are reviewed. The studies include diverse ethnic populations, including men and women, as well as several age groups (e.g. adolescents, middle-aged and older adults).



Results of the studies continue to support a growing literature suggesting that exercise, physical activity and physical-activity interventions have beneficial effects across several physical and mental-health outcomes. Generally, participants engaging in regular physical activity display more desirable health outcomes across a variety of physical conditions. Similarly, participants in randomized clinical trials of physical-activity interventions show better health outcomes, including better general and health-related quality of life, better functional capacity and better mood states.



The studies have several implications for clinical practice and research. Most work suggests that exercise and physical activity are associated with better quality of life and health outcomes. Therefore, assessment and promotion of exercise and physical activity may be beneficial in achieving desired benefits across several populations. Several limitations were noted, particularly in research involving randomized clinical trials. These trials tend to involve limited sample sizes with short follow-up periods, thus limiting the clinical implications of the benefits associated with physical activity.
The long war and parental combat deployment: effects on military children and at-home spouses.

Given the growing number of military service members with families and the multiple combat deployments characterizing current war time duties, the impact of deployments on military children requires clarification. Behavioral and emotional adjustment problems were examined in children (aged 6 through 12) of an active duty Army or Marine Corps parent currently deployed (CD) or recently returned (RR) from Afghanistan or Iraq.



Children (N = 272) and their at-home civilian (AHC) (N = 163) and/or recently returned active duty (AD) parent (N = 65) were interviewed. Child adjustment outcomes were examined in relation to parental psychological distress and months of combat deployment (of the AD) using mixed effects linear models.



Parental distress (AHC and AD) and cumulative length of parental combat-related deployments during the child's lifetime independently predicted increased child depression and externalizing symptoms. Although behavioral adjustment and depression levels were comparable to community norms, anxiety was significantly elevated in children in both deployment groups. In contrast, AHC parental distress was greater in those with a CD (vs. RR) spouse.



Findings indicate that parental combat deployment has a cumulative effect on children that remains even after the deployed parent returns home, and that is predicted by psychological distress of both the AD and AHC parent. Such data may be informative for screening, prevention, and intervention strategies.
High bandwidth underwater optical communication.

We report error-free underwater optical transmission measurements at 1 Gbit/s (10(9) bits/s) over a 2 m path in a laboratory water pipe with up to 36 dB of extinction. The source at 532 nm was derived from a 1064 nm continuous-wave laser diode that was intensity modulated, amplified, and frequency doubled in periodically poled lithium niobate. Measurements were made over a range of extinction by the addition of a Mg(OH)(2) and Al(OH)(3) suspension to the water path, and we were not able to observe any evidence of temporal pulse broadening. Results of Monte Carlo simulations over ocean water paths of several tens of meters indicate that optical communication data rates >1 Gbit/s can be supported and are compatible with high-capacity data transfer applications that require no physical contact.
A Two-Step Method for Clustering Mixed Categroical and Numeric Data

Various clustering algorithms have been developed to group data into clusters in diverse domains. However, these clustering algorithms work effectively either on pure numeric data or on pure categorical data, most of them perform poorly on mixed categorical and numeric data types. In this paper, a new two-step clustering method is presented to find clusters on this kind of data. In this approach the items in categorical attributes are processed to construct the similarity or relationships among them based on the ideas of co-occurrence; then all categorical attributes can be converted into numeric attributes based on these constructed relationships. Finally, since all categorical data are converted into numeric, the existing clustering algorithms can be applied to the dataset without pain. Nevertheless, the existing clustering algorithms suffer from some disadvantages or weakness, the proposed two-step method integrates hierarchical and partitioning clustering algorithm with adding attributes to cluster objects. This method defines the relationships among items, and improves the weaknesses of applying single clustering algorithm. Experimental evidences show that robust results can be achieved by applying this method to cluster mixed numeric and categorical data.
Economic Benefit of Powerful Credit Scoring ∗

We study the economic benefits from using credit scoring models. We contribute to the literature by relating the discriminatory power of a credit scoring model to the optimal credit decision. Given the Receiver Operating Characteristic (ROC) curve, we derive a) the profit-maximizing cutoff and b) the pricing curve. Using these two concepts and a mixture thereof, we study a stylized loan market model with banks differing in the quality of their credit scoring model. Even for small quality differences, the variation in profitability among lenders is large and economically significant. We end our analysis by quantifying the impact on profits when information leaks from a competitor’s scoring model into the market.
Mining Frequent Patterns by Pattern-Growth: Methodology and Implications

Mining frequent patterns has been a focused topic in data mining research in recent years, with the developmeht of numerous interesting algorithms for mining association, correlation, causality, sequential patterns, partial periodicity, constraint-based frequent pattern mining, associative classification, emerging patterns, etc. Most of the previous studies adopt an Apriori-like, candidate generation-and-test approach. However, based on our analysis, candidate generation and test may still be expensive, especially when encountering long and numerous patterns. A new methodology, called f r e q u e n t p a t t e r n g rowth , which mines frequent patterns without candidate generation, has been developed. The method adopts a divide-andconquer philosophy to project and partition databases based on the currently discovered frequent patterns and grow such patterns to longer ones in the projected databases. Moreover, efficient data structures have been developed for effective database compression and fast in-memory traversal. Such a methodology may eliminate or substantially reduce the number of candidate sets to be generated and also reduce the size of the database to be iteratively examined, and, therefore, lead to high performance. In this paper, we provide an overview of this approach and examine its methodology and implications for mining several kinds of frequent patterns, including association, frequent closed itemsets, max-patterns, sequential patterns, and constraint-based mining of frequent patterns. We show that frequent pattern growth is efficient at mining large databases and its further development may lead to scalable mining of many other kinds of patterns as well.
A Hybrid Word-Character Model for Abstractive Summarization

Automatic abstractive text summarization is an important and challenging research topic of natural language processing. Among many widely used languages, the Chinese language has a special property that a Chinese character contains rich information comparable to a word. Existing Chinese text summarization methods, either adopt totally character-based or word-based representations, fail to fully exploit the information carried by both representations. To accurately capture the essence of articles, we propose a hybrid word-character approach (HWC) which preserves the advantages of both wordbased and character-based representations. We evaluate the advantage of the proposed HWC approach by applying it to two existing methods, and discover that it generates state-of-the-art performance with a margin of 24 ROUGE points on a widely used dataset LCSTS. In addition, we find an issue contained in the LCSTS dataset and offer a script to remove overlapping pairs (a summary and a short text) to create a clean dataset for the community. The proposed HWC approach also generates the best performance on the new, clean LCSTS dataset.
Data mining techniques for customer relationship management

Advancements in technology have made relationship marketing a reality in recent years. Technologies such as data warehousing, data mining, and campaign management software have made customer relationship management a new area where firms can gain a competitive advantage. Particularly through data mining—the extraction of hidden predictive information from large databases—organizations can identify valuable customers, predict future behaviors, and enable firms to make proactive, knowledge-driven decisions. The automated, future-oriented analyses made possible by data mining move beyond the analyses of past events typically provided by history-oriented tools such as decision support systems. Data mining tools answer business questions that in the past were too time-consuming to pursue. Yet, it is the answers to these questions make customer relationship management possible. Various techniques exist among data mining software, each with their own advantages and challenges for different types of applications. A particular dichotomy exists between neural networks and chi-square automated interaction detection (CHAID). While differing approaches abound in the realm of data mining, the use of some type of data mining is necessary to accomplish the goals of today’s customer relationship management philosophy.
An Approach for Selecting Software-as-a-Service (SaaS) Product

Software-as-a-Service (SaaS) helps organizations avoid capital expenditure and pay for the functionality as an operational expenditure. Though enterprises are unlikely to use SaaS model for all their information systems needs, certain business functionalities such as Sales Force Automation (SFA), are more seen to be implemented using SaaS model. Such demand has prompted quite a few vendors to offer SFA functionality as SaaS. Enterprises need to adopt an objective approach to ensure they select the most appropriate SaaS product for their needs. This paper presents an approach that makes use of Analytic Hierarchy Process (AHP) technique for prioritizing the product features and also for expert-led scoring of the products.
Miniaturized dual-band dipole antenna loaded with metamaterial based structure

In this paper, a new miniaturized double-sided printed dipole antenna loaded with balanced Capacitively Loaded Loops (CLLs) as Metamaterial structure is presented. CLLs placed close to the edge of the printed antenna cause the antenna to radiate at two different frequencies, one of which is lower than self-resonant frequency of dipole antenna. In the other words, the loaded dipole antenna can perform at low frequency as compared with natural resonance frequency of unload half wavelength dipole. Finally, the CLL element is integrated with chip capacitor to provide a larger capacitance which in turn allows the resulting CLL element to resonate at a lower frequency. It is demonstrated that the proposed loaded dipole antenna is a dual band radiator with sufficient gain suitable for applications such as mobile communication and Industrial, Scientific and Medical (ISM) system. Prototype of miniaturized double resonant dipole antenna is fabricated and tested. The measured results are in good agreement with those obtained from simulation.
Domainless Adaptation by Constrained Decoding on a Schema Lattice

In many applications such as personal digital assistants, there is a constant need for new domains to increase the system’s coverage of user queries. A conventional approach is to learn a separate model every time a new domain is introduced. This approach is slow, inefficient, and a bottleneck for scaling to a large number of domains. In this paper, we introduce a framework that allows us to have a single model that can handle all domains: including unknown domains that may be created in the future as long as they are covered in the master schema. The key idea is to remove the need for distinguishing domains by explicitly predicting the schema of queries. Given permitted schema of a query, we perform constrained decoding on a lattice of slot sequences allowed under the schema. The proposed model achieves competitive and often superior performance over the conventional model trained separately per domain.
Learning concepts and categories: is spacing the "enemy of induction"?

Inductive learning -- that is, learning a new concept or category by observing exemplars -- happens constantly, for example, when a baby learns a new word or a doctor classifies x-rays. What influence does the spacing of exemplars have on induction? Compared with massing, spacing enhances long-term recall, but we expected spacing to hamper induction by making the commonalities that define a concept or category less apparent. We asked participants to study multiple paintings by different artists, with a given artist's paintings presented consecutively (massed) or interleaved with other artists' paintings (spaced). We then tested induction by asking participants to indicate which studied artist (Experiments 1a and 1b) or whether any studied artist (Experiment 2) painted each of a series of new paintings. Surprisingly, induction profited from spacing, even though massing apparently created a sense of fluent learning: Participants rated massing as more effective than spacing, even after their own test performance had demonstrated the opposite.
Effects of a Whatsapp-delivered physical activity intervention to enhance health-related physical fitness components and cardiovascular disease risk factors in older adults.

This pilot study evaluated the feasibility and preliminary effectiveness of a 10-week WhatsApp-based intervention aimed at enhancing health-related physical fitness components and cardiovascular disease (CVD) risk factors compared with a face-to-face condition.



Participants (N.=32) were assigned to one of three groups: training group (N.=16), mobile group (N.=7) and control group (N.=9). Training group and mobile group performed the same training program, based on strength training with elastics bands and aerobic exercise, during 10 weeks; only the delivery mode differed.



The mobile group increased handgrip strength, aerobic capacity and decreased systolic blood pressure and heart rate after exercise though there were no significant differences respect to control group. The training group decreased significantly systolic blood pressure (P=0.038), diastolic blood pressure (P=0.005), mean arterial pressure (P=0.006) and heart rate after exercise (P=0.002), respect to control group.



Comparison between training and mobile group showed that WhatsApp-based physical activity intervention was less effective than face-to-face condition. The results indicate that the use of an online social network produced slight changes in some health-related physical fitness components and CVD risk factors.
Types and tokens in visual processing: a double dissociation between the attentional blink and repetition blindness.

In rapid serial visual presentation tasks, correct identification of a target triggers a deficit for reporting a 2nd target appearing within 500 ms: an attentional blink (AB). A different phenomenon, termed repetition blindness (RB), refers to a deficit for the 2nd of 2 stimuli that are identical. What is the relationship between these 2 deficits? The present study obtained a double dissociation between AB and RB. AB and RB followed different time courses (Experiments 1 and 4A), increased target-distractor discriminability alleviated AB but not RB (Experiments 2 and 4A), and enhanced episodic distinctiveness of the two targets eliminated RB but not AB (Experiments 3 and 4B). The implications of the double dissociation between AB and RB for theories of visual processing are discussed.
Making the Best of Mifare Classic Update

What would you do if you would be instructed to make a secure application built on the Mifare Classic? Arguably, due to the vulnerabilities shown in  and hinted at on [6], this is rather difficult and it may be easier to use to another chip. This document explores what the best is you can get, if the only option is Mifare Classic. We propose countermeasures against state restoration and against cloning. The effectiveness of these countermeasures depends on the absense of other vulnerabilities of