Miracle-dz/UniFAR-FTUs huggingface dataset:
Obtaining Regularization for Free via Iterate Averaging

Regularization for optimization is a crucial technique to avoid overﬁtting in machine learning. In order to obtain the best performance, we usually train a model by tuning the regularization parameters. It becomes costly, however, when a single round of training takes signiﬁcant amount of time. Very recently, Neu and Rosasco [13] shows that if we run stochastic gradient descent (SGD) on linear regression problems, then by averaging the SGD iterates properly, we obtain a regularized solution. It left open whether the same phenomenon can be achieved for other optimization problems and algorithms. In this paper, we establish a complete theory by showing an averaging scheme that converts the iterates of GD on an arbitrary strongly convex and smooth objective function to its regularized counterpart with an adjustable regularization parameter. Our method can be used for accelerated SGD as well. We derive our results by leveraging the power of approximating the algorithmic path by a continuous differential equation and its discretization. In sum, we obtain regularization for free for a large class of optimization problems and resolve an open question in [13].
łThe Government Spies Using Our Webcams:ž The Language of Conspiracy Theories in Online Discussions

Conspiracy theories are omnipresent in online discussionsÐwhether to explain a late-breaking event that still lacks official report or to give voice to political dissent. Conspiracy theories evolve, multiply, and interconnect, further complicating efforts to limit their propagation. It is therefore crucial to develop scalable methods to examine the nature of conspiratorial discussions in online communities. What do users talk about when they discuss conspiracy theories online? What are the recurring elements in their discussions? What do these elements tell us about the way users think? This work answers these questions by analyzing over ten years of discussions in r/conspiracyÐan online community on Reddit dedicated to conspiratorial discussions. We focus on the key elements of a conspiracy theory: the conspiratorial agents , the actions they perform, and their targets . By computationally detecting agentśactionśtarget triplets in conspiratorial statements, and grouping them into semantically coherent clusters, we develop a notion of narrative-motif to detect recurring patterns of triplets. For example, a narrative-motif such as łgovernmental agencyścontrolsścommunicationsž appears in diverse conspiratorial statements alleging that governmental agencies control information to nefarious ends. Thus, narrative-motifs expose commonalities between multiple conspiracy theories even when they refer to different events or circumstances. In the process, these representations help us understand how users talk about conspiracy theories and offer us a means to interpret what they talk about. Our approach enables a population-scale study of conspiracy theories in alternative news and social media with implications for understanding their adoption and combating their spread.
A Survey of Compiler Optimization Techniques

: This survey paper aims to illustrate the major advancements in techniques for optimization of compilers. Optimizing compilers is a crucial task and often requires extensive work for different types of applications. Compiler suites that employ the latest optimization techniques can offer a plethora of benefits to developers as well as routinely produce code 20-30% faster than standard compilers. Superior optimization would also allow developers to cut cost and improve products without having to fall back on higher speed and higher cost processors. In this paper we discuss the various techniques of obtaining better compiler optimization by using different cache optimization techniques, dynamic optimization techniques as well as optimizing compilers for dynamic languages such as JavaScript. Machine learning techniques that can be employed for better optimization in compilers are also discussed.
A Novel Feature Extraction Method for Multi-Class Kernel CSSD

— In order to relax the presumption of strictly linear patterns between source signals and recorded EEG in Common Spatial Subspace Decomposition (CSSD), in this paper we study Kernel CSSD and propose to extend it to multi-class. To obtain the solution of the spatial filter, we adopt cross validation on the training data to get the best number m of the eigenvector, and the filter is optimized through KL divergence to determine the optimal spatial filter. We selects III_3a data set in 2005 BCI competition in the experiment, where a linear kernel function and a nearest neighbor classifier are used. The experiment shows that our method in this paper can extract the corresponding effective feature from multi-class EEG data of single trial, and get better classification results.
Towards Modular and Adaptive Assistance Systems for Manual Assembly: A Semantic Description and Interoperability Framework

—With the advent of Industry 4.0 and human-in-the-loop paradigms, Cyber-Physical Systems (CPS) are becoming increasingly common in production facilities, and, consequently, there has been a surge of interest in the ﬁeld. In production systems, CPS which assist humans in completing tasks are called assistance systems. Most recent designs proposed for assistance systems in the production domain are monolithic and allow only limited modiﬁcations. In contrast, this work considers an assistance system to have a hybrid architecture consisting of a central entity containing the process description (or instructions) and one or more plug-and-play Cyber-Physical Systems to retrieve relevant information from the physical environment. Such a design allows the overall system capabilities to be adapted to the needs of workers and tasks. In this paper, a framework is presented for designing the CPS modules using Semantic Web technologies which will allow (i) interpretation of all data, and (ii) interoperability among the modules, from the very outset. Furthermore, a knowledge description model and ontology development of a CPS module is described. An approach is illustrated with the help of a use case for implementing the framework to design a module, data exchange among modules, and to build a sustainable ecosystem of ontologies which enables rapid development of third-party CPS modules. An implementation using Prot´eg´e is provided and future direction of research is discussed.
Coherent Theme Discovery in Images Based On Visual Pattern

: Discovering Thematic objects that appear frequently in a number of images is a challenging problem, due to the appearance variations of the same common object and the enormous computational cost involved in exploring the huge solution space, including the location, scale, and the number of common objects. We characterize each image as a collection of visual primitives and propose a novel bottom up approach to gradually prune local primitives to recover the whole common object. A multi-layer candidate pruning procedure is designed to accelerate the image data mining process. Our solution provides accurate localization of the common object, thus is able to crop the common objects despite their variations due to scale, view-point, lighting condition changes. Moreover, it can extract common objects even with few numbers of images. Experiments on challenging image datasets validate the effectiveness and efficiency of our method.
Effect of cutting frequency on species richness and dry matter yield of permanent grassland after grazing cessation

ABSTRACT The effect of grazing cessation management has only rarely been investigated. In this study, the influence of cutting regime in the absence of fertilization was examined during 2005–2011 on a semi-natural grassland in Rapotin (Czech Republic) which had been grazed for more than 30 years before the start of the trial. This was conducted as a randomized plot design with five levels of treatment: 4 C – intensive (four cuts per year); 3 C – medium intensive (three cuts per year); 2 C – low intensive (two cuts per year); 1 C – extensive (one cut per year); 0 C – abandoned (control; without any management). Species richness was significantly lower in treatments 1 C and 0 C than in other treatments. Management and year had similar and significant influence on species composition and explained 23% of its variability. Dry matter (DM) yields were found to be lower in three-cut than in two-cut treatments (5.56 t ha−1 and 6.22 t ha−1, respectively). In the case of grazing cessation, a suitable compromise of three cuts per year can be recommended to guarantee maintenance of species richness, botanical composition, and appropriate grass forage yield under similar site conditions.
Speaker Identification for Japanese Prefectural Assembly Minutes

Recently, we have been creating a corpus of Japanese prefectural assembly minutes. The corpus contains assembly minutes of all 47 prefectures between April 2011 and March 2015. This four-year period represents one term of office for the assembly members in most prefectures. In prefectural assembly minutes, the name of the speakers can be recorded in several different ways such as Japanese Hiragana and Katakana characters, and Chinese characters. Our purpose of this study is to uniquely identify the speakers by hand, reconciling the different representations of their names. This paper describes how we annotated a Japanese political corpus with speaker identity information.
Large Very Dense Subgraphs in a Stream of Edges

We study the detection and the reconstruction of a large very dense subgraph in a social graph with n nodes and m edges given as a stream of edges, when the graph follows a power law degree distribution, in the regime when $m=O(n. łog n)$. A subgraph is very dense if its edge density is comparable to a clique. We uniformly sample the edges with a Reservoir of size $k=O(\sqrtn.łog n)$. The detection algorithm of a large very dense subgraph checks whether the Reservoir has a giant component. We show that if the graph contains a very dense subgraph of size $Ømega(\sqrtn )$, then the detection algorithm is almost surely correct. On the other hand, a random graph that follows a power law degree distribution almost surely has no large very dense subgraph, and the detection algorithm is almost surely correct. We define a new model of random graphs which follow a power law degree distribution and have large very dense subgraphs. We then show that on this class of random graphs we can reconstruct a good approximation of the very dense subgraph with high probability. We generalize these results to dynamic graphs defined by sliding windows in a stream of edges.
Parrotfishes (Scaridae) of the Eastern Pacific, with a Generic Rearrangement of the Scarinae' 2

Pacific parrotfishes are not cosely related to those of the Atlantic. S. cornpressus and perrico are endemic to the eastern Pacific, and the other species range across the Pacific into the Indian Ocean. This contrasts sharply with the distribution pattern of most other shore fish families represented in the new world, and indicates that the parrotfishes have had a different history. Field and laboratory studies indicate that the several eastern Pacific species of Scarus exhibit differing patterns of sexual dichromatism and dimorphism. S. perrico exhibits little dichromatism; both males and females are light blue-green. Apparent dimorphism in the development of the nuchal hump is related to allometric growth and the attainment of larger size in the male. S. rubroviolaceus and S. ghobban exhibit marked dichromatism, but small mature males are similar to the female, as are juveniles. S. compressus is unique in that both sexes develop distinctive color patterns as large adults. From known characters, it is not feasible to recognize more than two genera within the Scarinae: Scarus and Bolbometopon. The following changes in the taxonomy of parrotfish species occurring in the eastern Pacific are proposed: Scarus compressus (Osburn and Nichols) is a valid species. S. dussumieri Curvier and Valenciennes, S. californiensis (Pellegrin),
An Experimental Investigation of Price Dispersion and Cycles

We report a continuous-time experiment studying the Burdett-Judd model, whose unique Nash equilibrium (NE) features dispersed prices. Adaptive dynamics predict that the NE is stable for one of our parameter sets and unstable for another. The empirical price distribution is close to the NE distribution for the stable parameter set, but for the unstable parameter set it skews toward higher prices in its NE support interval. We offer an empirical definition of price cycles in terms of changes over time in robust measures of central tendency and dispersion, by which the data exhibit persistent cycles in both treatments but larger cycles for the unstable parameters. Results are roughly similar for professional and student sellers and for limited-information treatments.
Dark Side of the Moon: Wernher von Braun, the Third Reich, and the Space Race

Wayne Biddle’s Dark Side of the Moon joins a growing list of Wernher von Braun biographies published in the last two decades in Germany and the United States. (1) This renewed interest in the charismatic rocket engineer and manager of both the V-2 program for the Nazi regime and the Saturn V rocket development program for NASA seems reflective of a major re-evaluation and shift in public representations of the man who was voted #2 in Aviation Week’s 2003 ‘Top 100 Stars of Aerospace’ poll and whose name to this day adorns not only high schools and streets in Germany but also the Civic Center and a Research Hall at the University of Alabama in Huntsville.
Known Species of Cordyceps from China and Their Distribution

Known species and their names of genus Cordyceps(Ascomycota)from China are sorted out, amended and briefly discussed in this paper.Up to now,139 names have been reported.They are dis- tributed in 29 provinces and regions of China.Among them,125 species,3 varieties and 2 forma are rec- ognized,3 are invalidation,2 are misspelled,4 are suspicious,38 have anamorph,and 46 are endemic to china.
Management of puddled soil through organic amendments for post-rice mungbean

Soil puddling is considered favorable for rice but unfavorable for post-rice upland crops. The deleterious effects of puddling in post-rice mungbean and management of puddled soil have not been adequately addressed earlier in Bangladesh. This research work was conducted to amend differential puddled soil through organic amendments for improved post-rice mungbean production. The treatments were puddling by four passes, puddling by eight passes, and non-puddling with three organic amendments i.e. control, crop residue, and cow dung. Puddling generally increased bulk density of soil under moisture deficit condition. Puddling had no significant effect on soil pH, total-N, available-P, exchangeable - K, and organic-C. Application of cow dung in the puddle soil increased the amount of N (75.0%), P (35.5%), K (13.3%), and organic-C (38.8%) compared to control. Puddling (eight passes) impaired root growth resulting reduced total dry matter production by 15.6% over non-puddling condition. Cow dung management in differential puddling intensity gave higher root (0.13 g plant -1 ) and total dry matter (1.16 g plant -1 ) production of mungbean than without amendment at 60 days after seedling. Puddling intensity adversely affected the number of pods plant -1 , thousand-seed weight and seed yield. Cow dung application gave significantly higher number of pods, seeds pod -1 and thousand-seed weight. The mungbean seed yield achieved through cow dung incorporation (8.74 g plant -1 ) was better than crop residue (7.38 g plant -1 ) and control (7.34 g plant -1 ). The adverse effects of puddling intensity on soil strength and bulk density and succeeding plant growth and seed yield of mungbean were highly remarkable. Cow dung management indicated a positive response on the improvement of soil properties and mungbean seed yield.
A Object Retrieval Based on Fuzzy Flood Fill Method

— An Efficient and effective image segmentation is an important task in image processing and object recognition. Since fully automatic image segmentation is usually very hard for natural images, interactive schemes with a human interaction provide us good solutions. If we want to retrieve the particular selected area of any object for any special purpose like quality of the image or any security purpose, we need to select the pixels of the image and retrieve the particular object. This paper presents a new region merging based on fuzzy flood fill method. The users only need to roughly indicate the location and region of the object and background by using strokes, after that use fuzzy flood fill to extraction of object. A region merging mechanism is proposed to guide the merging process with the help of initial segmented image. A region is merged with its adjacent region if they have good similarity among them, here good is fuzzy variable. The proposed method is merging the initially segmented regions and then effectively extracts the object contour. The region merging process is the image content and it does not need to set the similarity threshold in advance, but it based on some fuzzy variables e.g. low, medium, high similarity etc.
SiblingClassTestDetector: Finding Untested Sibling Functions

Methods which are not covered by a project’s test suite are especially prone to exhibiting unexpected behaviours and being more challenging to maintain over time. Some methods are untested but have related implementations in sibling classes (sharing a common superclass) that are tested. Our goal is to help developers improve test suites by identifying such untested methods. We have implemented a prototype tool, SiblingClassTestDetector, which accepts programs along with their test suites, and outputs a set of Completable Candidates (CCs). We have applied our prototype tool to 17 open-source benchmarks and identified 107 CCs within these benchmarks. We have also manually produced two tests for Completable Candidates and submitted Pull Requests for these tests, one of which has been merged by developers.
BATTERY MANAGEMENT OF NEW ENERGY VEHICLE BASED ON CFD

In order to meet the needs of actual working conditions and people's expectations, electric vehicles using lithium ion batteries as power sources are not completely comparable to traditional fuel vehicles in terms of endurance mileage and driving performance. The main technologies of battery thermal management system are summarized, and the research status of air cooling, liquid cooling and phase change material cooling and heat generation characteristics of batteries are summarized. Based on CFD (Computational Fluid Dynamics) analysis method, the model of liquid cooling and heat dissipation system of batteries is established, and the flow of cold fluid in cooling channel and the temperature change of batteries are analyzed. Based on the simulation results of the original model, the existing problems are analyzed and the cooling passage structure of the cooling system is optimized. The temperature field and flow field of the improved scheme are simulated, analyzed and compared. The results show that, compared with other kinds of batteries, lithium ion batteries have many advantages, such as high voltage, light weight and no pollution. However, the working performance of lithium ion is closely related to the working temperature. When the working temperature is too high, the working safety of lithium ion is not guaranteed; when the working temperature is too low, the chemical reaction rate decreases, and the capacity of lithium ion batteries decreases, which has adverse effects on the endurance mileage and driving performance. Therefore, it is necessary to study the temperature field control of batteries to ensure the normal operation of electric vehicles.
A Handbook for Designing, Implementing, and Evaluating Successful Pilots

Since at least the late 1970s, electric utilities and their regulators have recognized the value of experimentation to motivate innovation. The industry has a long history of using pilots to help inform future decision making about electric utility rates, customer technology adoption and integration, and even changes to the utility’s regulatory or business model. Although utility pilots have become almost ubiquitous proving grounds for new rates, technologies, and alterations to the traditional utility regulatory and business model, some regulators are beginning to raise questions about what constitutes a “good” pilot. Much has been written about utility pilots over the years; however, what is missing from the literature is the identification of a comprehensive process for not only designing and evaluating a pilot, but also implementing, successful utility pilots that provide actionable outcomes upon which more informed decisions can be made. This report provides a step-by-step process that regulators, policymakers, and utilities can follow to help promote a more successful pilot, even if whatever is being tested fails to produce the intended or expected result(s). It is worth noting that this report is not intended to serve as a technical resource for those designing, implementing, and evaluating pilots. However, there are myriad references provided for those wishing to delve deeper into the technical details.
A Survey on Deployment Strategies and Energy Efficiency of Wireless Sensor Networks

: Wireless sensor network (WSN) consisting of a large number of sensor nodes can be deployed in vehicles, agriculture fields, forest, environment etc. for a variety of application areas like monitoring an earthquake observation, military, medical, surveillance etc. A sensor node has a small computing power, less memory, and minor range for communications. The deployment of the sensor nodes can be done in various manners including single hop contact and multi hop contact strategies etc. in various WSN applications. Energy consumption in this network is a very important issue to increase the lifetime of the sensor network. Now a days researcher considers energy utilization as an important task. Energy consumption depends on the communication range for the wireless sensor network node, by increase or decrease in the range of the node the energy consumption is affected. The increase in communication distance increases the energy consumption for a particular node in the wireless sensor network. In this paper, we focus on the research challenges of optimization model of the WSN, increase the WSN coverage, and minimization of energy consumption of the node.
Location-Centric Social Media Analytics: Challenges and Opportunities for Smart Cities

With the proliferation of increasingly powerful smartphones, location-centric social media platforms, such as Foursquare, have attracted millions of users sharing their physical activity online, resulting in an invaluable source of fine-grained, semantically rich, and spatiotemporal user activity data. Such data provides us with an unprecedented opportunity for analyzing urban dynamics and developing smart city applications. In this article, we first systematically discuss the unique characteristics of location-centric social media data, which consist of four data dimensions, i.e., spatial, temporal, semantic, and social dimensions. We then highlight three key challenges relating to data analytics, i.e., data heterogeneity, data quality, and privacy. Finally, we discuss the opportunities of leveraging location-centric social media data for urban analytics and smart cities, including both data analytics within and across the four data dimensions, and data fusion with further urban data.