nils-herrmann/clean_scirepeval huggingface dataset:
On the Instability of Bitcoin Without the Block Reward

Bitcoin provides two incentives for miners: block rewards and transaction fees. The former accounts for the vast majority of miner revenues at the beginning of the system, but it is expected to transition to the latter as the block rewards dwindle. There has been an implicit belief that whether miners are paid by block rewards or transaction fees does not affect the security of the block chain.
 We show that this is not the case. Our key insight is that with only transaction fees, the variance of the block reward is very high due to the exponentially distributed block arrival time, and it becomes attractive to fork a "wealthy" block to "steal" the rewards therein. We show that this results in an equilibrium with undesirable properties for Bitcoin's security and performance, and even non-equilibria in some circumstances. We also revisit selfish mining and show that it can be made profitable for a miner with an arbitrarily low hash power share, and who is arbitrarily poorly connected within the network. Our results are derived from theoretical analysis and confirmed by a new Bitcoin mining simulator that may be of independent interest.
 We discuss the troubling implications of our results for Bitcoin's future security and draw lessons for the design of new cryptocurrencies.
Visceral and serum lysozyme activities in some freshwater fish (three catfish and two carps)

The lysozyme activities were investigated in three catfish: hybrid catfish [Clarias macrocephalus (Günther 1864) x Clarias gariepinus (Burchell 1822)], Hemibagrus filamentus (Fang and Chaux 1949), and Kryptopterus apogon (Bleeker 1851) and two carps: Oreochromis niloticus (Linnaeus 1758) and Channa striata (Bloch 1793). Fish samples used in this study were from two sites of Chiang Mai province; C. macrocephalus x C. gariepinus and O. niloticus were from Ton Payom Market, Amphoe Muang. H. filamentus, K. apogon, and C. striata were from Doi Tao Lake, Amphoe Doi Tao. The serum, brain, liver, and kidney of the catfish and carps from both sites were spectrophotometrically measured at 450 nm by ELISA reader. C. macrocephalus x C. gariepinus and H. filamentus showed the highest lysozyme activity in the serum, followed by the kidney, brain, and liver, respectively, while the highest lysozyme activity was observed in the brain of K. apogon, and the least one was observed in the liver. Among the catfish, lysozyme activities of K. apogon were significantly higher than those of the other fish (P < 0.01). However, no significant difference in lysozyme activity from the three investigated visceral organs was found. In carps, O. niloticus had the highest lysozyme activity in the serum, followed by the kidney, brain, and liver, respectively. Meanwhile, the highest lysozyme activity in C. striata was recorded in the serum, followed by the liver, brain, and kidney. However, there was no significant difference in lysozyme activity from the serum and the internal organs in these two fish. In conclusion, the innate immunity arms in these five fish species encompassed the similar function pattern, although they were differently present in their organs. The data will be used as reference ranks for these species to evaluate health status when they are exposed to some pathogens or chemical contamination in streams and reservoirs.
Cloud Computing: Today and Tomorrow

During the past few years, cloud computing has become a key IT buzzword. Although the definition of cloud computing is still “cloudy”, the trade press and bloggers label many vendors as cloud computing vendors, and report on their services and issues. Cloud computing is in its infancy in terms of market adoption. However, it is a key IT megatrend that will take root. This article reviews its definition and status, adoption issues, and provides a glimpse of its future and discusses technical issues that are expected to be addressed.
Modelling Competitive Sports: Bradley-Terry-Élő Models for Supervised and On-Line Learning of Paired Competition Outcomes

Prediction and modelling of competitive sports outcomes has received much recent attention, especially from the Bayesian statistics and machine learning communities. In the real world setting of outcome prediction, the seminal Élő update still remains, after more than 50 years, a valuable baseline which is difficult to improve upon, though in its original form it is a heuristic and not a proper statistical “model”. Mathematically, the Élő rating system is very closely related to the Bradley-Terry models, which are usually used in an explanatory fashion rather than in a predictive supervised or on-line learning setting. Exploiting this close link between these two model classes and some newly observed similarities, we propose a new supervised learning framework with close similarities to logistic regression, low-rank matrix completion and neural networks. Building on it, we formulate a class of structured log-odds models, unifying the desirable properties found in the above: supervised probabilistic prediction of scores and wins/draws/losses, batch/epoch and on-line learning, as well as the possibility to incorporate features in the prediction, without having to sacrifice simplicity, parsimony of the Bradley-Terry models, or computational efficiency of Élő’s original approach. We validate the structured log-odds modelling approach in synthetic experiments and English Premier League outcomes, where the added expressivity yields the best predictions reported in the state-of-art, close to the quality of contemporary betting odds. ∗f.kiraly@ucl.ac.uk †zhaozhi.qian.15@ucl.ac.uk 1 ar X iv :1 70 1. 08 05 5v 1 [ st at .M L ] 2 7 Ja n 20 17
Lexical and Discourse Analysis of Online Chat Dialog

One of the ultimate goals of natural language processing (NLP) systems is understanding the meaning of what is being transmitted, irrespective of the medium (e.g., written versus spoken) or the form (e.g., static documents versus dynamic dialogues). Although much work has been done in traditional language domains such as speech and static written text, little has yet been done in the newer communication domains enabled by the Internet, e.g., online chat and instant messaging. This is in part due to the fact that there are no annotated chat corpora available to the broader research community. The purpose of this research is to build a chat corpus, tagged with lexical (token part-of-speech labels), syntactic (post parse tree), and discourse (post classification) information. Such a corpus can then be used to develop more complex, statistical-based NLP applications that perform tasks such as author profiling, entity identification, and social network analysis.
Post‐Activation Potentiation Increases Recruitment of Fast Twitch Fibers: A Potential Practical Application in Runners

To determine the relationship between fatigue and post-activation potentiation, we examined the effects of sub-maximal continuous running on neuromuscular function tests, as well as on the squat jump and counter movement jump in endurance athletes. The height of the squat jump and counter movement jump and the estimate of the fast twitch fiber recruiting capabilities were assessed in seven male middle distance runners before and after 40 min of continuous running at an intensity corresponding to the individual lactate threshold. The same test was then repeated after three weeks of specific aerobic training. Since the three variables were strongly correlated, only the estimate of the fast twitch fiber was considered for the results. The subjects showed a significant improvement in the fast twitch fiber recruitment percentage after the 40 min run. Our data show that submaximal physical exercise determined a change in fast twitch muscle fiber recruitment patterns observed when subjects performed vertical jumps; however, this recruitment capacity was proportional to the subjects' individual fast twitch muscle fiber profiles measured before the 40 min run. The results of the jump tests did not change significantly after the three-week training period. These results suggest that pre-fatigue methods, through sub-maximal exercises, could be used to take advantage of explosive capacity in middle-distance runners.
Geometric modeling using octree encoding

A geometric modeling technique called Octree Encoding is presented. Arbitrary 3-D objects can be represented to any specified resolution in a hierarchical I-ary tree structure or “octree.” Objects may be concave or convex, have holes (including interior holes), consist of disjoint parts, and possess sculptured (i.e., “free-form”) surfaces. The memory required for representation and manipulation is on the order of the surface area of the object. A complexity metric is proposed based on the number of nodes in an object’s tree representation. Efficient (linear time) algorithms have been developed for the Boolean operations (union, intersection and difference), geometric operations (translation, scaling and rotation), N-dimensional interference detection, and display from any point in space with hidden surfaces removed. The algorithms require neither floating-point operations, integer multiplications, nor integer divisions. In addition, many independent sets of very simple calculations are typically generated, allowing implementation over many inexpensive high-bandwidth processors operating in parallel. Real time analysis and manipulation of highly complex situations thus becomes possible.
Convolutional Neural Networks

The earliest Convolution Neural Network (CNN) model is leNet-5 model proposed by LeCun in 1998. However, in the next few years, the development of CNN had been almost stopped until the article ‘Reducing the dimensionality of data with neural networks’ presented by Hinton in 2006. CNN started entering a period of rapid development. AlexNet won the championship in the image classification contest of ImageNet with the huge superiority of 11% beyond the second place in 2012, and the proposal of DeepFace and DeepID, as two relatively successful models for high-performance face recognition and authentication in 2014, marking the important position of CNN. Convolution Neural Network (CNN) is an efficient recognition algorithm widely used in image recognition and other fields in recent years. That the core features of CNN include local field, shared weights and pooling greatly reducing the parameters, as well as simple structure, make CNN become an academic focus. In this paper, the Convolution Neural Network’s history and structure are summarized. And then several areas of Convolutional Neural Network applications are enumerated. At last, some new insights for the future research of CNN are presented.
The sleep-deprived human brain

How does a lack of sleep affect our brains? In contrast to the benefits of sleep, frameworks exploring the impact of sleep loss are relatively lacking. Importantly, the effects of sleep deprivation (SD) do not simply reflect the absence of sleep and the benefits attributed to it; rather, they reflect the consequences of several additional factors, including extended wakefulness. With a focus on neuroimaging studies, we review the consequences of SD on attention and working memory, positive and negative emotion, and hippocampal learning. We explore how this evidence informs our mechanistic understanding of the known changes in cognition and emotion associated with SD, and the insights it provides regarding clinical conditions associated with sleep disruption.
Skip-Thought Memory Networks

Answering (QA) is fundamental to natural language processing in that most nlp problems can be phrased as QA (Kumar et al., 2015). Current weakly supervised memory network models that have been proposed so far struggle at answering questions that involve relations among multiple entities (such as face-book's bAbi qa5-three-arg-relations in (Weston et al., 2015)). To address this problem of learning multi-argument multi-hop semantic relations for the purpose of QA, we propose a method that combines the jointly learned long-term read-write memory and attentive inference components of end-to-end memory networks (MemN2N) (Sukhbaatar et al., 2015) with distributed sentence vector representations encoded by a Skip-Thought model (Kiros et al., 2015). This choice to append Skip-Thought Vectors to the existing MemN2N framework is motivated by the fact that Skip-Thought Vectors have been shown to accurately model multi-argument semantic relations (Kiros et al., 2015).
Modeling Large-Scale Structured Relationships with Shared Memory for Knowledge Base Completion

Recent studies on knowledge base completion, the task of recovering missing relationships based on recorded relations, demonstrate the importance of learning embeddings from multi-step relations. However, due to the size of knowledge bases, learning multi-step relations directly on top of observed triplets could be costly. Hence, a manually designed procedure is often used when training the models. In this paper, we propose Implicit ReasoNets (IRNs), which is designed to perform multi-step inference implicitly through a controller and shared memory. Without a human-designed inference procedure, IRNs use training data to learn to perform multi-step inference in an embedding neural space through the shared memory and controller. While the inference procedure does not explicitly operate on top of observed triplets, our proposed model outperforms all previous approaches on the popular FB15k benchmark by more than 5.7%.
Knowledge Graph Entity Representation and Retrieval

Recent studies indicate that nearly 75% of queries issued to Web search engines aim at finding information about entities, which are material objects or concepts that exist in the real world or fiction (e.g. people, organizations, products, etc.). Most common information needs underlying this type of queries include finding a certain entity (e.g. “Einstein relativity theory”), a particular attribute or property of an entity (e.g. “Who founded Intel?”) or a list of entities satisfying a certain criteria (e.g. “Formula 1 drivers that won the Monaco Grand Prix”). These information needs can be efficiently addressed by presenting structured information about a target entity or a list of entities retrieved from a knowledge graph either directly as search results or in addition to the ranked list of documents. This tutorial provides a summary of the recent research in knowledge graph entity representation methods and retrieval models. The first part of this tutorial introduces state-of-the-art methods for entity representation, from multi-fielded documents with flat and hierarchical structure to latent dimensional representations based on tensor factorization, while the second part presents recent developments in entity retrieval models, including Fielded Sequential Dependence Model (FSDM) and its parametric extension (PFSDM), as well as entity set expansion and ranking methods.
Data mining and medical world: breast cancers' diagnosis, treatment, prognosis and challenges.

The amount of data in electronic and real world is constantly on the rise. Therefore, extracting useful knowledge from the total available data is very important and time consuming task. Data mining has various techniques for extracting valuable information or knowledge from data. These techniques are applicable for all data that are collected inall fields of science. Several research investigations are published about applications of data mining in various fields of sciences such as defense, banking, insurances, education, telecommunications, medicine and etc. This investigation attempts to provide a comprehensive survey about applications of data mining techniques in breast cancer diagnosis, treatment & prognosis till now. Further, the main challenges in these area is presented in this investigation. Since several research studies currently are going on in this issues, therefore, it is necessary to have a complete survey about all researches which are completed up to now, along with the results of those studies and important challenges which are currently exist in this area for helping young researchers and presenting to them the main problems that are still exist in this area.
Automated Theorem Proving in Intuitionistic Propositional Logic by Deep Reinforcement Learning

The problem-solving in automated theorem proving (ATP) can be interpreted as a search problem where the prover constructs a proof tree step by step. In this paper, we propose a deep reinforcement learning algorithm for proof search in intuitionistic propositional logic. The most significant challenge in the application of deep learning to the ATP is the absence of large, public theorem database. We, however, overcame this issue by applying a novel data augmentation procedure at each iteration of the reinforcement learning. We also improve the efficiency of the algorithm by representing the syntactic structure of formulas by a novel compact graph representation. Using the large volume of augmented data, we train highly accurate graph neural networks that approximate the value function for the set of the syntactic structures of formulas. Our method is also cost-efficient in terms of computational time. We will show that our prover outperforms Coq’s tauto tactic, a prover based on human-engineered heuristics. Within the specified time limit, our prover solved 84% of the theorems in a benchmark library, while tauto was able to solve only 52%.
Memory and the brain.

This review summarizes some of the recent advances in the neurobiology of memory. Current research helps us to understand how memories are created and, conversely, how our memories can be influenced by stress, drugs, and aging. An understanding of how memories are encoded by the brain may also lead to new ideas about how to maximize the long-term retention of important information. There are multiple memory systems with different functions and, in this review, we focus on the conscious recollection of one's experience of events and facts and on memories tied to emotional responses. Memories are also classified according to time: from short-term memory, lasting only seconds or minutes, to long-term memory, lasting months or years. The advent of new functional neuroimaging methods provides an opportunity to gain insight into how the human brain supports memory formation. Each memory system has a distinct anatomical organization, where different parts of the brain are recruited during phases of memory storage. Within the brain, memory is a dynamic property of populations of neurons and their interconnections. Memories are laid down in our brains via chemical changes at the neuron level. An understanding of the neurobiology of memory may stimulate health educators to consider how various teaching methods conform to the process of memory formation.
Information Filtering and Information Retrieval: Two Sides of the Same Coin?

Information filtering is a name used to describe a variety of processes involving the delivery of information to people who need it. Although this term is appearing quite often in popular and technical articles describing applications such as electronic mail, multimedia distributed systems, and electronic office documents, the distinction between filtering and related processes such as retrieval, routing, categorization, and extraction is often not dear. It is only by making that distinction, however, that the specific research issues associated with filtering can be identified and addressed. A reasonable first step in defining information filtering is to list the typical characteristics or features of this process. The following features are the most commonly mentioned: • An information filtering system is an information system designed for unstructured or semistructured data. This contrasts with a typical database application that involves very structured data, such as employee records. The notion of structure being used here is not only that the data conforms to a format such as a record type description, but also that the fields of the records consist of simple data types with well-defined meanings. It is possible, for example, to define a database type for a complex document, such as a journal article, but the meaning of the text, figure and table components of that type
GNSS Spoofing Detection using Two-Antenna Differential Carrier Phase

A method is developed to detect GNSS spoofing by processing beat carrier-phase measurements from a pair of antennas in a CDGPS-type calculation. This system  Todd E. Humphreys, & Andrew Schofield. All rights reserved.  2014 detects spoofing attacks that are resistant to standard RAIM technique, and it can sense an attack in a fraction of a second without external aiding. The signal-in-space properties used to detect spoofing are the relationships of the signal arrival directions to the vector that points from one antenna to the other. In the un-spoofed case, there are a multiplicity of relationships between the interantenna vector and the arrival directions of the multiple signals, which results in a quantifiable multiplicity of carrier-phase single-differences between the antennas. In the spoofed case, there is a single direction of arrival, assuming a single spoofer transmission antenna, and the carrier phase single-differences are identical for all channels, up to an integer cycle ambiguity. A real-time implementation of this detection method has been developed, and it has been tested against live-signal spoofing attacks aboard a superyacht that was cruising around Italy en route from Monaco to Venice. The prototype system demonstrated an ability to detect spoofing attacks in a fraction of a second, though lags in the system’s signal processing lengthened the detection delay to as much as 6 seconds. The system experienced challenges during the initial phase of a spoofing attack if the spoofer power was not much greater than that of the true signal. The true and spoofed signals interfere in a beating pattern in this case, making the composite signal harder to track and harder to classify as being either spoofed or non-spoofed. After the spoofer drags the victim receiver off to an erroneous position or timing fix, the beating subsides, and the new spoofing detection system performs well.
Charging Schemes for Plug-In Hybrid Electric Vehicles in Smart Grid: A Survey

Plug-in hybrid electric vehicles (PHEVs) have emerged as an important tool in reducing greenhouse gas emissions, due to their lower dependency on fossil fuel. Since, for cost efficiency, PHEVs have a limited battery capacity, they must be recharged often and especially after trips. Thus, efficient battery charging plays an important role on the success of PHEVs commercial adoption. This paper surveys the state-of-the-art of existing PHEV battery charging schemes. We classify these schemes into four classes, namely, uncontrolled, indirectly controlled, smart, and bidirectional charging, and review various existing techniques within each class. For uncontrolled charging, existing studies focus on evaluating the impact of adding variable charging load on the smart grid. Various indirectly controlled charging schemes have been proposed to control energy prices, in order to indirectly influence the charging operations. Smart charging schemes can directly control a rich set of charging parameters to achieve various performance objectives, such as minimizing power loss, maximizing operator's profit, ensuring fairness, and so on. Finally, bidirectional charging allows a PHEV to discharge energy into smart grid, such that the vehicle can act as a mobile energy source to further stabilize the grid, which is partially supplied by intermittent renewable energy sources. This survey provides a comprehensive one-stop introductory reference to quickly learn about the key features and technical challenges, addressed by existing PHEV battery charging schemes in smart grid.