nils-herrmann/clean_scirepeval huggingface dataset:
High-Frequency Equity Index Futures Trading Using Recurrent Reinforcement Learning with Candlesticks

In 1997, Moody and Wu presented recurrent reinforcement learning (RRL) as a viable machine learning method within algorithmic trading. Subsequent research has shown a degree of controversy with regards to the benefits of incorporating technical indicators in the recurrent reinforcement learning framework. In 1991, Nison introduced Japanese candlesticks to the global research community as an alternative to employing traditional indicators within the technical analysis of financial time series. The literature accumulated over the past two and a half decades of research contains conflicting results with regards to the utility of using Japanese candlestick patterns to exploit inefficiencies in financial time series. In this paper, we combine features based on Japanese candlesticks with recurrent reinforcement learning to produce a high-frequency algorithmic trading system for the E-mini S&P 500 index futures market. Our empirical study shows a statistically significant increase in both return and Sharpe ratio compared to relevant benchmarks, suggesting the existence of exploitable spatio-temporal structure in Japanese candlestick patterns and the ability of recurrent reinforcement learning to detect and take advantage of this structure in a high-frequency equity index futures trading environment.
Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking

Every year, novel NVIDIA GPU designs are introduced. This rapid architectural and technological progression, coupled with a reluctance by manufacturers to disclose low-level details, makes it difficult for even the most proficient GPU software designers to remain up-to-date with the technological advances at a microarchitectural level. To address this dearth of public, microarchitectural-level information on the novel NVIDIA GPUs, independent researchers have resorted to microbenchmarks-based dissection and discovery. This has led to a prolific line of publications that shed light on instruction encoding, and memory hierarchy's geometry and features at each level. Namely, research that describes the performance and behavior of the Kepler, Maxwell and Pascal architectures. In this technical report, we continue this line of research by presenting the microarchitectural details of the NVIDIA Volta architecture, discovered through microbenchmarks and instruction set disassembly. Additionally, we compare quantitatively our Volta findings against its predecessors, Kepler, Maxwell and Pascal.
Gestational diabetes and the incidence of type 2 diabetes: a systematic review.

To examine factors associated with variation in the risk for type 2 diabetes in women with prior gestational diabetes mellitus (GDM).



We conducted a systematic literature review of articles published between January 1965 and August 2001, in which subjects underwent testing for GDM and then testing for type 2 diabetes after delivery. We abstracted diagnostic criteria for GDM and type 2 diabetes, cumulative incidence of type 2 diabetes, and factors that predicted incidence of type 2 diabetes.



A total of 28 studies were examined. After the index pregnancy, the cumulative incidence of diabetes ranged from 2.6% to over 70% in studies that examined women 6 weeks postpartum to 28 years postpartum. Differences in rates of progression between ethnic groups was reduced by adjustment for various lengths of follow-up and testing rates, so that women appeared to progress to type 2 diabetes at similar rates after a diagnosis of GDM. Cumulative incidence of type 2 diabetes increased markedly in the first 5 years after delivery and appeared to plateau after 10 years. An elevated fasting glucose level during pregnancy was the risk factor most commonly associated with future risk of type 2 diabetes.



Conversion of GDM to type 2 diabetes varies with the length of follow-up and cohort retention. Adjustment for these differences reveals rapid increases in the cumulative incidence occurring in the first 5 years after delivery for different racial groups. Targeting women with elevated fasting glucose levels during pregnancy may prove to have the greatest effect for the effort required.
A survey on reactive programming

Reactive programming has recently gained popularity as a paradigm that is well-suited for developing event-driven and interactive applications. It facilitates the development of such applications by providing abstractions to express time-varying values and automatically managing dependencies between such values. A number of approaches have been recently proposed embedded in various languages such as Haskell, Scheme, JavaScript, Java, .NET, etc. This survey describes and provides a taxonomy of existing reactive programming approaches along six axes: representation of time-varying values, evaluation model, lifting operations, multidirectionality, glitch avoidance, and support for distribution. From this taxonomy, we observe that there are still open challenges in the field of reactive programming. For instance, multidirectionality is supported only by a small number of languages, which do not automatically track dependencies between time-varying values. Similarly, glitch avoidance, which is subtle in reactive programs, cannot be ensured in distributed reactive programs using the current techniques.
Probiotics: an overview of beneficial effects

Food products fermented by lactic acid bacteria have long been used for their proposed health promoting properties. In recent years, selected probiotic strains have been thoroughly investigated for specific health effects. Properties like relief of lactose intolerance symptoms and shortening of rotavirus diarrhoea are now widely accepted for selected probiotics. Some areas, such as the treatment and prevention of atopy hold great promise. However, many proposed health effects still need additional investigation. In particular the potential benefits for the healthy consumer, the main market for probiotic products, requires more attention. Also, the potential use of probiotics outside the gastrointestinal tract deserves to be explored further. Results from well conducted clinical studies will expand and increase the acceptance of probiotics for the treatment and prevention of selected diseases.
A Survey of Stealth Malware Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions

As our professional, social, and financial existences become increasingly digitized and as our government, healthcare, and military infrastructures rely more on computer technologies, they present larger and more lucrative targets for malware. Stealth malware in particular poses an increased threat because it is specifically designed to evade detection mechanisms, spreading dormant, in the wild for extended periods of time, gathering sensitive information or positioning itself for a high-impact zero-day attack. Policing the growing attack surface requires the development of efficient anti-malware solutions with improved generalization to detect novel types of malware and resolve these occurrences with as little burden on human experts as possible. In this paper, we survey malicious stealth technologies as well as existing solutions for detecting and categorizing these countermeasures autonomously. While machine learning offers promising potential for increasingly autonomous solutions with improved generalization to new malware types, both at the network level and at the host level, our findings suggest that several flawed assumptions inherent to most recognition algorithms prevent a direct mapping between the stealth malware recognition problem and a machine learning solution. The most notable of these flawed assumptions is the closed world assumption: that no sample belonging to a class outside of a static training set will appear at query time. We present a formalized adaptive open world framework for stealth malware recognition and relate it mathematically to research from other machine learning domains.
LIFTING LINE METHOD FOR MODELLING COVERING AND BLANKETING EFFECTS FOR YACHT FLEET RACE SIMULATION

An approach is presented that can be used to enhance the realism of yacht fleet race simulations. The wake of an upwind sailing yacht is represented as a single heeled horseshoe vortex (and image) system. At each time step changes in vortex strength are convected into the wake as a pair of vortex line elements. These subsequently move in accordance with the local wind, self-induced velocity and velocity induced by the presence of the wakes of other yachts. An empirical based decay factor is used to eventually remove the far wake. A synthesis of sail yacht wake representations based on detailed 3D Reynolds Averaged Navier-Stokes (RANS) Computational Fluid Dynamics (CFD) calculations with wind tunnel test results are used to capture the initial strength of the combined main-jib vortex system and its vertical height. These were based on a typical upwind sail arrangement for a range of heel angles and in-line calculations for a pair of yachts separated by three boat lengths. This paper details the basis of the validated CFD results for a yacht at heel and the analysis of the CFD results to provide an approximate single line vortex method for the yacht. The developed algorithm will eventually run within the Robo-Race which is a real-time yacht race strategy analysis tool based on MATLAB-Simulink developed at the University of Southampton. 1 Research Student,   Engineer, Wolfson Unit for Marine Technology and Industrial Aerodynamics, University of Southampton, UK NOMENCLATURE ACC America's Cup Class AoA Angle of Attack CFD Computational Fluid Dynamics RANS Reynolds Averaged Navier-Stokes SST Shear Stress Transport CL, CD Lift and drag coefficients L Sail rig and yacht length r = {x, y, z} Position U Freestream velocity q = {u, v, w} Induced velocities in x-, y-, -z directions Γi Vortex strength of element i ω Vorticity
Baseball throwing mechanics as they relate to pathology and performance - a review.

It is a commonly held perception amongst biomechanists, sports medicine practitioners, baseball coaches and players, that an individual baseball player's style of throwing or pitching influences their performance and susceptibility to injury. With the results of a series of focus groups with baseball managers and pitching coaches in mind, the available scientific literature was reviewed regarding the contribution of individual aspects of pitching and throwing mechanics to potential for injury and performance. After a discussion of the limitations of kinematic and kinetic analyses, the individual aspects of pitching mechanics are discussed under arbitrary headings: Foot position at stride foot contact; Elbow flexion; Arm rotation; Arm horizontal abduction; Arm abduction; Lead knee position; Pelvic orientation; Deceleration-phase related issues; Curveballs; and Teaching throwing mechanics. In general, popular opinion of baseball coaching staff was found to be largely in concordance with the scientific investigations of biomechanists with several notable exceptions. Some difficulties are identified with the practical implementation of analyzing throwing mechanics in the field by pitching coaches, and with some unquantified aspects of scientific analyses. Key pointsBiomechanical analyses including kinematic and kinetic analyses allow for estimation of pitching performance and potential for injury.Some difficulties both theoretic and practical exist for the implementation and interpretation of such analyses.Commonly held opinions of baseball pitching authorities are largely held to concur with biomechanical analyses.Recommendations can be made regarding appropriate pitching and throwing technique in light of these investigations.
A Proposed Solution and Future Direction for Blockchain-Based Heterogeneous Medicare Data in Cloud Environment

The healthcare data is an important asset and rich source of healthcare intellect. Medical databases, if created properly, will be large, complex, heterogeneous and time varying. The main challenge nowadays is to store and process this data efficiently so that it can benefit humans. Heterogeneity in the healthcare sector in the form of medical data is also considered to be one of the biggest challenges for researchers. Sometimes, this data is referred to as large-scale data or big data. Blockchain technology and the Cloud environment have proved their usability separately. Though these two technologies can be combined to enhance the exciting applications in healthcare industry. Blockchain is a highly secure and decentralized networking platform of multiple computers called nodes. It is changing the way medical information is being stored and shared. It makes the work easier, keeps an eye on the security and accuracy of the data and also reduces the cost of maintenance. A Blockchain-based platform is proposed that can be used for storing and managing electronic medical records in a Cloud environment.
Contextual Semantic Search Navigation

Search has for a long time been an important tool for users seeking information. Many popular search systems have been based on keyword search. However, such systems are not that well adapted to explore the search space. Effective exploration requires systems which can guide the user in his quest. The main focus of this thesis is to generate a framework for semantic navigational search which is compatible with the vector space model and unites formal and informal semantics. In this thesis, formal semantics is represented by ontologies, while informal semantics is represented by folksonomies. The research conducted has been divided into four main phases with different foci that together fulfill our research focus: (i) Formal semantics, (ii) Informal semantics, (iii) Structure verification, and (iv) Contextual semantic search navigation. In the first phase, the focus was ontologies and extending concepts with concept signatures. A concept signature is a shallow semantic representation of a concept based on how the concept is referred to in textual documents bridging the semantic and syntactic representation of concepts. The second phase focused on structuring folksonomy tags and extending tags with concept signatures. This phase also focused on consolidating the tag vocabulary. These two phases together unify formal and informal semantics with a common format. The third phase developed an approach for assessing the hierarchical relations of ontologies and structured folksonomies based on concept signatures. Finally, the fourth research phase developed an approach to contextual semantic search navigation based on a hierarchical model and concept signatures. The approach is based on semantic navigators which use the concept signatures actively to generate and rank navigation suggestions to the user. Several experiments have been conducted to evaluate the proposed approaches, and they show promising results. The main contributions of this thesis can be summarized as follows: • A semantic representation which unifies formal and informal semantics. • An unsupervised approach to identifying semantic similarity in tags. • An approach to contextualizing semantic navigators for search using tag signatures and hierarchical relations. • An approach to evaluating the taxonomic quality of ontologies and folksonomies. • An approach to generating concept signatures from formal semantics (ontology). • An approach to generating concept signatures from informal semantics (folksonomy).
Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport

Network-based cloud computing is rapidly expanding as an alternative to conventional office-based computing. As cloud computing becomes more widespread, the energy consumption of the network and computing resources that underpin the cloud will grow. This is happening at a time when there is increasing attention being paid to the need to manage energy consumption across the entire information and communications technology (ICT) sector. While data center energy use has received much attention recently, there has been less attention paid to the energy consumption of the transmission and switching networks that are key to connecting users to the cloud. In this paper, we present an analysis of energy consumption in cloud computing. The analysis considers both public and private clouds, and includes energy consumption in switching and transmission as well as data processing and data storage. We show that energy consumption in transport and switching can be a significant percentage of total energy consumption in cloud computing. Cloud computing can enable more energy-efficient use of computing power, especially when the computing tasks are of low intensity or infrequent. However, under some circumstances cloud computing can consume more energy than conventional computing where each user performs all computing on their own personal computer (PC).
Optimizing daily agent scheduling in a multiskill call center

We examine and compare simulation-based algorithms for solving the agent scheduling problem in a multiskill call center. This problem consists in minimizing the total costs of agents under constraints on the expected service level per call type, per period, and aggregated. We propose a solution approach that combines simulation with integer or linear programming, with cut generation. In our numerical experiments with realistic problem instances, this approach performs better than all other methods proposed previously for this problem. We also show that the two-step approach, which is the standard method for solving this problem, sometimes yield solutions that are highly suboptimal and inferior to those obtained by our proposed method.
Balls of Light : The Questionable Science of Crop Circles

Three papers published by W. C. Levengood (1994), W. C. Levengood and N. P. Talbott (1999) and by E. H. Haselhoff (2001) suggested the involvement of some kind of electromagnetic radiation during the creation of crop circles. Here we discuss the methods and conclusions of the three articles, pointing out the misrepresentation of the experimental protocols, the misleading application of statistical procedures, the arbitrary discarding of unwanted results and the weakness of the proposed physical model to the suggested hypothesis. In particular, we show that Haselhoff’s conclusions are unsubstantiated and do not prove the involvement of an electromagnetic radiation source in the creation event.
Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer

Attention plays a critical role in human visual experience. Furthermore, it has recently been demonstrated that attention can also play an important role in the context of applying artificial neural networks to a variety of tasks from fields such as computer vision and NLP. In this work we show that, by properly defining attention for convolutional neural networks, we can actually use this type of information in order to significantly improve the performance of a student CNN network by forcing it to mimic the attention maps of a powerful teacher network. To that end, we propose several novel methods of transferring attention, showing consistent improvement across a variety of datasets and convolutional neural network architectures. Code and models for our experiments are available at https://github.com/szagoruyko/attention-transfer.
Illiquidity and stock returns : cross-section and time-series effects

This paper shows that over time, expected market illiquidity positively affects ex ante stock excess return, suggesting that expected stock excess return partly represents an illiquidity premium. This complements the cross-sectional positive return–illiquidity relationship. Also, stock returns are negatively related over time to contemporaneous unexpected illiquidity. The illiquidity measure here is the average across stocks of the daily ratio of absolute stock return to dollar volume, which is easily obtained from daily stock data for long time series in most stock markets. Illiquidity affects more strongly small firm stocks, thus explaining time series variations in their premiums over time. r 2002 Elsevier Science B.V. All rights reserved.
Robust LSTM-Autoencoders for Face De-Occlusion in the Wild

Face recognition techniques have been developed significantly in recent years. However, recognizing faces with partial occlusion is still challenging for existing face recognizers, which is heavily desired in real-world applications concerning surveillance and security. Although much research effort has been devoted to developing face de-occlusion methods, most of them can only work well under constrained conditions, such as all of faces are from a pre-defined closed set of subjects. In this paper, we propose a robust LSTM-Autoencoders (RLA) model to effectively restore partially occluded faces even in the wild. The RLA model consists of two LSTM components, which aims at occlusion-robust face encoding and recurrent occlusion removal respectively. The first one, named multi-scale spatial LSTM encoder, reads facial patches of various scales sequentially to output a latent representation, and occlusion-robustness is achieved owing to the fact that the influence of occlusion is only upon some of the patches. Receiving the representation learned by the encoder, the LSTM decoder with a dual channel architecture reconstructs the overall face and detects occlusion simultaneously, and by feat of LSTM, the decoder breaks down the task of face de-occlusion into restoring the occluded part step by step. Moreover, to minimize identify information loss and guarantee face recognition accuracy over recovered faces, we introduce an identity-preserving adversarial training scheme to further improve RLA. Extensive experiments on both synthetic and real data sets of faces with occlusion clearly demonstrate the effectiveness of our proposed RLA in removing different types of facial occlusion at various locations. The proposed method also provides significantly larger performance gain than other de-occlusion methods in promoting recognition performance over partially-occluded faces.
Collaborative competitive filtering: learning recommender using context of user choice

While a user's preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learning recommender models. In particular, existing collaborative filtering (CF) approaches take into account only the binary events of user actions but totally disregard the contexts in which users' decisions are made. In this paper, we propose Collaborative Competitive Filtering (CCF), a framework for learning user preferences by modeling the choice process in recommender systems. CCF employs a multiplicative latent factor model to characterize the dyadic utility function. But unlike CF, CCF models the user behavior of choices by encoding a local competition effect. In this way, CCF allows us to leverage dyadic data that was previously lumped together with missing data in existing CF models. We present two formulations and an efficient large scale optimization algorithm. Experiments on three real-world recommendation data sets demonstrate that CCF significantly outperforms standard CF approaches in both offline and online evaluations.