nils-herrmann/clean_scirepeval huggingface dataset:
Application of Deep Learning to Algorithmic Trading

Trading Framework Deep Learning has become a robust machine learning tool in recent years, and models based on deep learning has been applied to various fields. However, applications of deep learning in the field of computational finance are still limited[1]. In our project, Long Short Term Memory (LSTM) Networks, a time series version of Deep Neural Networks model, is trained on the stock data in order to forecast the next day‘s stock price of Intel Corporation (NASDAQ: INTC): our model predicts next day’s adjusted closing price based on information/features available until the present day. Based on the predicted price, we trade the Intel stock according to the strategy that we developed, which is described below. Locally Weighted Regression has also been performed in lieu of the unsupervised learning model for comparison.
The training intensity distribution among well-trained and elite endurance athletes

Researchers have retrospectively analyzed the training intensity distribution (TID) of nationally and internationally competitive athletes in different endurance disciplines to determine the optimal volume and intensity for maximal adaptation. The majority of studies present a "pyramidal" TID with a high proportion of high volume, low intensity training (HVLIT). Some world-class athletes appear to adopt a so-called "polarized" TID (i.e., significant % of HVLIT and high-intensity training) during certain phases of the season. However, emerging prospective randomized controlled studies have demonstrated superior responses of variables related to endurance when applying a polarized TID in well-trained and recreational individuals when compared with a TID that emphasizes HVLIT or threshold training. The aims of the present review are to: (1) summarize the main responses of retrospective and prospective studies exploring TID; (2) provide a systematic overview on TIDs during preparation, pre-competition, and competition phases in different endurance disciplines and performance levels; (3) address whether one TID has demonstrated greater efficacy than another; and (4) highlight research gaps in an effort to direct future scientific studies.
Thinking across Cultures : Implications for Dual Processes

In A History of God, Karen Armstrong describes a division, made by fourth century Christians, between kerygma and dogma: 'religious truth … capable of being expressed and defined clearly and logically,' versus 'religious insights [that] had an inner resonance that could only be apprehended by each individual in his own time during … contemplation' (Armstrong, 1993, p.114). This early dual-process theory had its roots in Plato and Aristotle, who suggested a division between 'philosophy,' which could be 'expressed in terms of reason and thus capable of proof,' and knowledge contained in myths, 'which eluded scientific demonstration' (Armstrong, 1993, 113–14). This division—between what can be known and reasoned logically versus what can only be experienced and apprehended—continued to influence Western culture through the centuries, and arguably underlies our current dual-process theories of reasoning. In psychology, the division between these two forms of understanding have been described in many different ways. The underlying theme of 'overtly reasoned' versus 'perceived, intuited' often ties these dual process theories together. In Western culture, the latter form of thinking has often been maligned (Dijksterhuis and Nordgren, 2006; Gladwell, 2005; Lieberman, 2000). Recently, cultural psychologists have suggested that although the distinction itself—between reasoned and intuited knowl-edge—may have precedents in the intellectual traditions of other cultures, the privileging of the former rather than the latter may be peculiar to Western cultures The Chinese philosophical tradition illustrates this difference of emphasis. Instead of an epistemology that was guided by abstract rules, 'the Chinese in esteeming what was immediately percepti-ble—especially visually perceptible—sought intuitive instantaneous understanding through direct perception' (Nakamura, 1960/1988, p.171). Taoism—the great Chinese philosophical school besides Confucianism—developed an epistemology that was particularly oriented towards concrete perception and direct experience (Fung, 1922; Nakamura, 1960/1988). Moreover, whereas the Greeks were concerned with definitions and devising rules for the purposes of classification, for many influential Taoist philosophers, such as Chuang Tzu, '… the problem of … how terms and attributes are to be delimited, leads one in precisely the wrong direction. Classifying or limiting knowledge fractures the greater knowledge' (Mote, 1971, p.102).
On Graduated Optimization for Stochastic Non-Convex Problems

The graduated optimization approach, also known as the continuation method, is a popular heuristic to solving non-convex problems that has received renewed interest over the last decade. Despite being popular, very little is known in terms of its theoretical convergence analysis. In this paper we describe a new first-order algorithm based on graduated optimization and analyze its performance. We characterize a family of non-convex functions for which this algorithm provably converges to a global optimum. In particular, we prove that the algorithm converges to an ε-approximate solution within O(1/ε) gradient-based steps. We extend our algorithm and analysis to the setting of stochastic non-convex optimization with noisy gradient feedback, attaining the same convergence rate. Additionally, we discuss the setting of “zeroorder optimization”, and devise a variant of our algorithm which converges at rate of O(d/ε).
Chaining Mutual Information and Tightening Generalization Bounds

Bounding the generalization error of learning algorithms has a long history, which yet falls short in explaining various generalization successes including those of deep learning. Two important difficulties are (i) exploiting the dependencies between the hypotheses, (ii) exploiting the dependence between the algorithm’s input and output. Progress on the first point was made with the chaining method, originating from the work of Kolmogorov, and used in the VC-dimension bound. More recently, progress on the second point was made with the mutual information method by Russo and Zou ’15. Yet, these two methods are currently disjoint. In this paper, we introduce a technique to combine chaining and mutual information methods, to obtain a generalization bound that is both algorithm-dependent and that exploits the dependencies between the hypotheses. We provide an example in which our bound significantly outperforms both the chaining and the mutual information bounds. As a corollary, we tighten Dudley’s inequality when the learning algorithm chooses its output from a small subset of hypotheses with high probability.
Investigating how the message framing strategy impacts brand marketing

The current study, integrating relevant concepts derived from self-regulatory focus, prospect/involvement and knowledge structure theories, proposes a conceptual framework that depicts how the message framing strategy of advertising may have an impact on the persuasiveness of brand marketing. As empirical examination of the framework shows, the consumer characteristics of self-construal, consumer involvement, and product knowledge are three key antecedents of the persuasiveness that message framing generates at the dimensions of advertising attitude, brand attitude, and purchase intention. Besides, significant interaction exists among these three variables. Implications of the research findings, both for academics and practitioners, are discussed.
Watch Your Step: Learning Graph Embeddings Through Attention

Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e.g. by sampling random walks). There are many hyper-parameters to these methods (such as random walk length) which have to be manually tuned for every graph. In this paper, we replace random walk hyperparameters with trainable parameters that we automatically learn via backpropagation. In particular, we learn a novel attention model on the power series of the transition matrix, which guides the random walk to optimize an upstream objective. Unlike previous approaches to attention models, the method that we propose utilizes attention parameters exclusively on the data (e.g. on the random walk), and not used by the model for inference. We experiment on link prediction tasks, as we aim to produce embeddings that best-preserve the graph structure, generalizing to unseen information. We improve state-of-the-art on a comprehensive suite of real world datasets including social, collaboration, and biological networks. Adding attention to random walks can reduce the error by 20% to 45% on datasets we attempted. Further, our learned attention parameters are different for every graph, and our automatically-found values agree with the optimal choice of hyper-parameter if we manually tune existing methods.
A Study on Overfitting in Deep Reinforcement Learning

Recent years have witnessed significant progresses in deep Reinforcement Learning (RL). Empowered with large scale neural networks, carefully designed architectures, novel training algorithms and massively parallel computing devices, researchers are able to attack many challenging RL problems. However, in machine learning, more training power comes with a potential risk of more overfitting. As deep RL techniques are being applied to critical problems such as healthcare and finance, it is important to understand the generalization behaviors of the trained agents. In this paper, we conduct a systematic study of standard RL agents and find that they could overfit in various ways. Moreover, overfitting could happen “robustly”: commonly used techniques in RL that add stochasticity do not necessarily prevent or detect overfitting. In particular, the same agents and learning algorithms could have drastically different test performance, even when all of them achieve optimal rewards during training. The observations call for more principled and careful evaluation protocols in RL. We conclude with a general discussion on overfitting in RL and a study of the generalization behaviors from the perspective of inductive bias.
Real-Time Optimizing Control of an Experimental Crosswind Power Kite

The contribution of this paper is to propose and experimentally validate an optimizing control strategy for power kites flying crosswind. The algorithm ensures the kite follows a reference path (control) and also periodically optimizes the reference path (efficiency optimization). The path-following part of the controller is capable of consistently following a reference path, despite significant time delays and wind variations, using position measurements only. The path-optimization part adjusts the reference path in order to maximize line tension. It uses a real-time optimization algorithm that combines off-line modeling knowledge and on-line measurements. The algorithm has been tested comprehensively on a small-scale prototype, and this paper focuses on experimental results.
SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability

In Semantic Textual Similarity (STS), systems rate the degree of semantic equivalence between two text snippets. This year, the participants were challenged with new datasets in English and Spanish. For the English subtask, we exposed the systems to a diversity of testing scenarios, by preparing additional pairs from headlines and image descriptions, as well as introducing new genres, including answer pairs from a tutorial dialogue system, answer pairs from Q&A websites, and pairs from a committed belief dataset. For the Spanish subtask, additional pairs from news and Wikipedia articles were selected. The annotations for both subtasks leveraged crowdsourcing. The English subtask attracted 29 teams with 74 system runs, and the Spanish subtask engaged 7 teams participating with 16 system runs. In addition, this year we ran a pilot task on Interpretable STS, where the systems needed to add an explanatory layer, that is, they had to align the chunks in the sentence pair, explicitly annotating the kind of relation and the score for the chunk pair. The train and test data were manually annotated by an expert, and included headline and image sentence pairs from previous years. 7 teams participated with 29 runs.
Interval efficiency assessment using data envelopment analysis

This paper studies how to conduct efficiency assessment using data envelopment analysis (DEA) in interval and/or fuzzy input–output environments. A new pair of interval DEA models is constructed on the basis of interval arithmetic, which differs from the existing DEA models handling interval data in that the former is a linear CCR model without the need of extra variable alternations and uses a fixed and unified production frontier (i.e. the same constraint set) to measure the efficiencies of decision-making units (DMUs) with interval input and output data, while the latter is usually a nonlinear optimization problem with the need of extra variable alternations or scale transformations and utilizes variable production frontiers (i.e. different constraint sets) to measure interval efficiencies. Ordinal preference information and fuzzy data are converted into interval data through the estimation of permissible intervals and -levelsets, respectively, and are incorporated into the interval DEA models. The proposed interval DEA models are developed for measuring the lower and upper bounds of the best relative efficiency of each DMU with interval input and output data, which are different from the interval formed by the worst and the best relative efficiencies of each DMU. A minimax regret-based approach (MRA) is introduced to compare and rank This research was supported by the project on Human Social Science of MOE, P.R.China under the Grant No. 01JA790082, the National Natural Science Foundation of China (NSFC) under the Grant No: 70271056, and also in part by the European Commission under the Grant No: IPS-2000-00030, the UK Engineering and Physical Science Research Council (EPSRC) under the Grant No: GR/R32413/01, and Fok Ying Tung Education Foundation under the Grant No: 71080. ∗ Corresponding author. Manchester Business School East, The University of Manchester, P.O. Box 88, Manchester M60 1QD, UK. Tel.: +44 161 2750788; fax: +44 161 2003505. E-mail addresses: msymwang@hotmail.com , Yingming.Wang@Manchester.ac.uk (Y.-M. Wang). 0165-0114/$ see front matter  Sets and Systems 153 (2005) 347–370 the efficiency intervals of DMUs. Two numerical examples are provided to show the applications of the proposed interval DEA models and the preference ranking approach.
Reliability of 3D laser-based anthropometry and comparison with classical anthropometry

Anthropometric quantities are widely used in epidemiologic research as possible confounders, risk factors, or outcomes. 3D laser-based body scans (BS) allow evaluation of dozens of quantities in short time with minimal physical contact between observers and probands. The aim of this study was to compare BS with classical manual anthropometric (CA) assessments with respect to feasibility, reliability, and validity. We performed a study on 108 individuals with multiple measurements of BS and CA to estimate intra- and inter-rater reliabilities for both. We suggested BS equivalents of CA measurements and determined validity of BS considering CA the gold standard. Throughout the study, the overall concordance correlation coefficient (OCCC) was chosen as indicator of agreement. BS was slightly more time consuming but better accepted than CA. For CA, OCCCs for intra- and inter-rater reliability were greater than 0.8 for all nine quantities studied. For BS, 9 of 154 quantities showed reliabilities below 0.7. BS proxies for CA measurements showed good agreement (minimum OCCC > 0.77) after offset correction. Thigh length showed higher reliability in BS while upper arm length showed higher reliability in CA. Except for these issues, reliabilities of CA measurements and their BS equivalents were comparable.
Securing Bitcoin wallets via threshold signatures

The Bitcoin ecosystem has suffered frequent thefts and losses affecting both businesses and individuals. The insider threat faced by a business is particularly serious. Due to the irreversibility, automation, and pseudonymity of transactions, Bitcoin currently lacks support for the sophisticated internal control systems deployed by modern businesses to deter fraud. We seek to bridge this gap. We show that a thresholdsignature scheme compatible with Bitcoin’s ECDSA signatures can be used to enforce complex yet useful security policies including: (1) shared control of a wallet, (2) secure bookkeeping, a Bitcoin-specific form of accountability, (3) secure delegation of authority, and (4) two-factor security for personal wallets.
Compressing Convolutional Neural Networks in the Frequency Domain

Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities of labeled data through millions of parameters. However, as model sizes increase, so do the storage and memory requirements of the classifiers, hindering many applications such as image and speech recognition on mobile phones and other devices. In this paper, we present a novel net- work architecture, Frequency-Sensitive Hashed Nets (FreshNets), which exploits inherent redundancy in both convolutional layers and fully-connected layers of a deep learning model, leading to dramatic savings in memory and storage consumption. Based on the key observation that the weights of learned convolutional filters are typically smooth and low-frequency, we first convert filter weights to the frequency domain with a discrete cosine transform (DCT) and use a low-cost hash function to randomly group frequency parameters into hash buckets. All parameters assigned the same hash bucket share a single value learned with standard back-propagation. To further reduce model size, we allocate fewer hash buckets to high-frequency components, which are generally less important. We evaluate FreshNets on eight data sets, and show that it leads to better compressed performance than several relevant baselines.
Monetizing Network Hospitality: Hospitality and Sociability in the Context of Airbnb

We present a qualitative study of hospitality exchange processes that take place via the online peer-to-peer platform Airbnb. We explore 1) what motivates individuals to monetize network hospitality and 2) how the presence of money ties in with the social interaction related to network hospitality. We approach the topic from the perspective of hosts -- that is, Airbnb users who participate by offering accommodation for other members in exchange for monetary compensation. We found that participants were motivated to monetize network hospitality for both financial and social reasons. Our analysis indicates that the presence of money can provide a helpful frame for network hospitality, supporting hosts in their efforts to accomplish desired sociability, select guests consistent with their preferences, and control the volume and type of demand. We conclude the paper with a critical discussion of the implications of our findings for network hospitality and, more broadly, for the so-called sharing economy.
Kernel Regression for Image Processing and Reconstruction

In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some popular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples
A Quantitative Ergodic Theory Proof of Szemerédi's Theorem

A famous theorem of Szemerédi asserts that given any density 0 < δ ≤ 1 and any integer k ≥ 3, any set of integers with density δ will contain infinitely many proper arithmetic progressions of length k. For general k there are essentially four known proofs of this fact; Szemerédi’s original combinatorial proof using the Szemerédi regularity lemma and van der Waerden’s theorem, Furstenberg’s proof using ergodic theory, Gowers’ proof using Fourier analysis and the inverse theory of additive combinatorics, and the more recent proofs of Gowers and Rödl-Skokan using a hypergraph regularity lemma. Of these four, the ergodic theory proof is arguably the shortest, but also the least elementary, requiring passage (via the Furstenberg correspondence principle) to an infinitary measure preserving system, and then decomposing a general ergodic system relative to a tower of compact extensions. Here we present a quantitative, self-contained version of this ergodic theory proof, and which is “elementary” in the sense that it does not require the axiom of choice, the use of infinite sets or measures, or the use of the Fourier transform or inverse theorems from additive combinatorics. It also gives explicit (but extremely poor) quantitative bounds.
CHOICE AND PROCRASTINATION *

Recent models of procrastination due to self-control problems assume that a procrastinator considers just one option and is unaware of her self-control problems. We develop a model where a person chooses from a menu of options and is partially aware of her self-control problems. This menu model replicates earlier results and generates new ones. A person might forego completing an attractive option because she plans to complete a more attractive but never-to-be-completed option. Hence, providing a non-procrastinator additional options can induce procrastination, and a person may procrastinate worse pursuing important goals than unimportant ones.
An Efficient Data Fingerprint Query Algorithm Based on Two-Leveled Bloom Filter

The function of the comparing fingerprints algorithm was to judge whether a new partitioned data chunk was in a storage system a decade ago. At present, in the most de-duplication backup system the fingerprints of the big data chunks are huge and cannot be stored in the memory completely. The performance of the system is unavoidably retarded by data chunks accessing the storage system at the querying stage. Accordingly, a new query mechanism namely Two-stage Bloom Filter (TBF) mechanism is proposed. Firstly, as a representation of the entirety for the first grade bloom filter, each bit of the second grade bloom filter in the TBF represents the chunks having the identical fingerprints reducing the rate of false positives. Secondly, a two-dimensional list is built corresponding to the two grade bloom filter for the absolute addresses of the data chunks with the identical fingerprints. Finally, a new hash function class with the strong global random characteristic is set up according to the data fingerprints’ random characteristics. To reduce the comparing data greatly, TBF decreases the number of accessing disks, improves the speed of detecting the redundant data chunks, and reduces the rate of false positives which helps the improvement of the overall performance of system.