nils-herrmann/clean_scirepeval huggingface dataset:
Realization of three-qubit quantum error correction with superconducting circuits

Quantum computers could be used to solve certain problems exponentially faster than classical computers, but are challenging to build because of their increased susceptibility to errors. However, it is possible to detect and correct errors without destroying coherence, by using quantum error correcting codes. The simplest of these are three-quantum-bit (three-qubit) codes, which map a one-qubit state to an entangled three-qubit state; they can correct any single phase-flip or bit-flip error on one of the three qubits, depending on the code used. Here we demonstrate such phase- and bit-flip error correcting codes in a superconducting circuit. We encode a quantum state, induce errors on the qubits and decode the error syndrome—a quantum state indicating which error has occurred—by reversing the encoding process. This syndrome is then used as the input to a three-qubit gate that corrects the primary qubit if it was flipped. As the code can recover from a single error on any qubit, the fidelity of this process should decrease only quadratically with error probability. We implement the correcting three-qubit gate (known as a conditional-conditional NOT, or Toffoli, gate) in 63 nanoseconds, using an interaction with the third excited state of a single qubit. We find 85 ± 1 per cent fidelity to the expected classical action of this gate, and 78 ± 1 per cent fidelity to the ideal quantum process matrix. Using this gate, we perform a single pass of both quantum bit- and phase-flip error correction and demonstrate the predicted first-order insensitivity to errors. Concatenation of these two codes in a nine-qubit device would correct arbitrary single-qubit errors. In combination with recent advances in superconducting qubit coherence times, this could lead to scalable quantum technology.
Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud

Consumer reviews are now part of everyday decision-making. Yet, the credibility of these reviews is fundamentally undermined when businesses commit review fraud, creating fake reviews for themselves or their competitors. We investigate the economic incentives to commit review fraud on the popular review platform Yelp, using two complementary approaches and datasets. We begin by analyzing restaurant reviews that are identified by Yelp’s filtering algorithm as suspicious, or fake – and treat these as a proxy for review fraud (an assumption we provide evidence for). We present four main findings. First, roughly 16% of restaurant reviews on Yelp are filtered. These reviews tend to be more extreme (favorable or unfavorable) than other reviews, and the prevalence of suspicious reviews has grown significantly over time. Second, a restaurant is more likely to commit review fraud when its reputation is weak, i.e., when it has few reviews, or it has recently received bad reviews. Third, chain restaurants – which benefit less from Yelp – are also less likely to commit review fraud. Fourth, when restaurants face increased competition, they become more likely to receive unfavorable fake reviews. Using a separate dataset, we analyze businesses that were caught soliciting fake reviews through a sting conducted by Yelp. These data support our main results, and shed further light on the economic incentives behind a business’s decision to leave fake reviews.
Stochastic Direct Reinforcement : Application to Simple Games with Recurrence

We investigate repeated matrix games with stochastic players as a microcosm for studying dynamic, multi-agent interactions using the Stochastic Direct Reinforcement (SDR) policy gradient algorithm. SDR is a generalization of Recurrent Reinforcement Learning (RRL) that supports stochastic policies. Unlike other RL algorithms, SDR and RRL use recurrent policy gradients to properly address temporal credit assignment resulting from recurrent structure. Our main goals in this paper are to (1) distinguish recurrent memory from standard, non-recurrent memory for policy gradient RL, (2) compare SDR with Q-type learning methods for simple games, (3) distinguish reactive from endogenous dynamical agent behavior and (4) explore the use of recurrent learning for interacting, dynamic agents. We find that SDR players learn much faster and hence outperform recently-proposed Q-type learners for the simple game Rock, Paper, Scissors (RPS). With more complex, dynamic SDR players and opponents, we demonstrate that recurrent representations and SDR’s recurrent policy gradients yield better performance than non-recurrent players. For the Iterated Prisoners Dilemma, we show that non-recurrent SDR agents learn only to defect (Nash equilibrium), while SDR agents with recurrent gradients can learn a variety of interesting behaviors, including cooperation.
Advanced Photoconductive Terahertz Optoelectronics Based on Nano-Antennas and Nano-Plasmonic Light Concentrators

High power sources and high sensitivity detectors are highly in demand for terahertz imaging and sensing systems. Use of nano-antennas and nano-plasmonic light concentrators in photoconductive terahertz sources and detectors has proven to offer significantly higher terahertz radiation powers and detection sensitivities by enhancing photoconductor quantum efficiency while maintaining its ultrafast operation. This is because of the unique capability of nano-antennas and nano-plasmonic structures in manipulating the concentration of photo-generated carriers within the device active area, allowing a larger number of photocarriers to efficiently contribute to terahertz radiation and detection. An overview of some of the recent advancements in terahertz optoelectronic devices through use of various types of nano-antennas and nano-plasmonic light concentrators is presented in this article.
Customer-centered brand management.

Most executives today agree that their efforts should be focused on growing the lifetime value of their customers. Yet few companies have come to terms with the implications of that idea for their marketing management. Oldsmobile, for example, enjoyed outstanding brand equity with many customers through the 1980s. But as the century wore further on, the people who loved the Olds got downright old. So why did General Motors spend so many years and so much money trying to reposition and refurbish the tired,tarnished brand? Why didn't GM managers instead move younger buyers along a path of less resistance, toward another of the brands in GM's stable--or even launch a wholly new brand geared to their tastes? Catering to new customers, even at the expense of the brand, would surely have been the path to profits. The reason, argue the authors, is that in large consumer-goods companies like General Motors, brands are the raison d'etre. They are the focus of decision making and the basis of accountability. But this overwhelming focus on growing brand equity is inconsistent with the goal of growing customer equity. Drawing on a wide range of current examples, the authors offer seven tactics that will put brands in the service of growing customer equity. These include replacing traditional brand managers with a new position--the customer segment manager; targeting brands to as narrow an audience as possible; developing the capability and the mind-set to hand off customers from one brand to another within the company; and changing the way brand equity is measured by basing calculations on individual, rather than average, customer data.
Rumors detection in Chinese via crowd responses

In recent years, microblogging platforms have become good places to spread various spams, making the problem of gauging information credibility on social networks receive considerable attention especially under an emergency situation. Unlike previous studies on detecting rumors using tweets' inherent attributes generally, in this work, we shift the premise and focus on identifying event rumors on Weibo by extracting features from crowd responses that are texts of retweets (reposting tweets) and comments under a certain social event. Firstly the paper proposes a method of collecting theme data, including a sample set of tweets which have been confirmed to be false rumors based on information from the official rumor-busting service provided by Weibo. Secondly clustering analysis of tweets are made to examine the text features extracted from retweets and comments, and a classifier is trained based on observed feature distribution to automatically judge rumors from a mixed set of valid news and false information. The experiments show that the new features we propose are indeed effective in the classification, and especially some stop words and punctuations which are treated as noises in previous works can play an important role in rumor detection. To the best of our knowledge, this work is the first to detect rumors in Chinese via crowd responses under an emergency situation.
Introducing arguments

In her book Introducing Arguments, Linda Pylkkänen distinguishes between the core and noncore arguments of verbs by means of a detailed discussion of applicative and causative constructions. The term applicative refers to structures that in more general linguistic terms are defined as ditransitive, i.e. when both a direct and an indirect object are associated with the verb, as exemplified in (1) (Pylkkänen, 2008: 13):
Event Recommendation in Event-based Social Networks

With the large number of events published all the time in event-based social networks (EBSN), it has become increasingly difficult for users to find the events that best match their preferences. Recommender systems appear as a natural solution to this problem. However, the event recommendation scenario is quite different from typical recommendation domains (e.g. movies), since there is an intrinsic new item problem involved (i.e. events can not be ”consumed” before their occurrence) and scarce collaborative information. Although some few works have appeared in this area, there is still lacking in the literature an extensive analysis of the different characteristics of EBSN data that can affect the design of event recommenders. In this paper we provide a contribution in this direction, where we investigate and discuss important features of EBSN such as sparsity, events life time, co-participation of users in events and geographic features. We also shed some light on the performance and limitations of several well known recommendation algorithms and combinations of them on real data collected from meetup.com.
Assessing Internet Enabled Business Value : An Exploratory Investigation ?

While the focus of electronic commerce has often been on “dot coms” or pure Internet based companies, a major transformation is under way in many traditional “bricks-and-mortar” organizations. The latter are investing heavily in Internet based technologies and applications in order to attain new heights of efficiency, productivity and business value. While anecdotes in the business press suggest that some firms have achieved unprecedented performance gains by leveraging the Internet, there is no systematic evidence in the Information Technology (IT) productivity or business value literature regarding the payoffs from Internet enabled business initiatives. We propose an exploratory model of electronic business value involving IT applications, processes, business partner readiness, and operational and financial performance measures. This model is rooted in IT business value and productivity research, and is empirically tested with data from over 1000 firms in manufacturing, retail, distribution and wholesale sectors. We find that electronic business initiatives involving customer-facing technologies lead to operational excellence in customer interactions and improved financial performance. Further, supplier related operational excellence is a key determinant of customer excellence, suggesting the related nature of customer and supplier related performance. Customer and supplier readiness to engage in online business have strong positive impacts on customer and supplier related operational excellence respectively, indicating the need for all entities in a value chain to simultaneously adopt Internet applications and business practices. To the best of our knowledge, this is the first study to address the business value of Internet initiatives.
Estimating Rule Quality for Knowledge Base Completion with the Relationship between Coverage Assumption

Currently, there are many large, automatically constructed knowledge bases (KBs). One interesting task is learning from a knowledge base to generate new knowledge either in the form of inferred facts or rules that define regularities. One challenge for learning is that KBs are necessarily open world: we cannot assume anything about the truth values of tuples not included in the KB. When a KB only contains facts (i.e., true statements), which is typically the case, we lack negative examples, which are often needed by learning algorithms. To address this problem, we propose a novel score function for evaluating the quality of a first-order rule learned from a KB. Our metric attempts to include information about the tuples not in the KB when evaluating the quality of a potential rule. Empirically, we find that our metric results in more precise predictions than previous approaches.
Algoritmic methods for segmentation of time series: An overview

Adaptive and innovative application of classical data mining principles and techniques in time series analysis has resulted in development of a concept known as time series data mining. Since the time series are present in all areas of business and scientific research, attractiveness of mining of time series datasets should not be seen only in the context of the research challenges in the scientific community, but also in terms of usefulness of the research results, as a support to the process of business decision-making. A fundamental component in the mining process of time series data is time series segmentation. As a data mining research problem, segmentation is focused on the discovery of rules in movements of observed phenomena in a form of interpretable, novel, and useful temporal patterns. In this Paper, a comprehensive review of the conceptual determinations, including the elements of comparative analysis, of the most commonly used algorithms for segmentation of time series, is being considered.
Text Recognition from Silent Lip Movement Video

Lip reading, the ability to recognize text information from the movement of a speaker’s mouth, is a difficult and challenging task. Recently, the end-to-end model that maps a variable-length sequence of video frames to text performs poorly in real life situation where people unintentionally move the lips instead of speaking. The goal of this work is to improve the performance of lip reading task in real life. The model proposed in this article consists of two networks that are visual to audio feature network and audio feature to text network. Our experiments showed that the model proposed in this article can achieve 92.76% accuracy in lip reading task on the dataset that the unintentional lips movement was added.
Information Management Capabilities : Antecedents And Consequences Completed Research Paper

With organizations immersed in data (Davenport et al., 2012), developing the organizational capabilities to take advantage of this huge flow of heterogeneous data (Bharadwaj et al., 2013) has become essential for creating strategic value. This paper specifically analyzes the antecedents and consequences of IMC Information Management Capabilities (Carmichael et al.,2010; Mihtas et al.,2011; Phadtare, 2011). To this end, we sought to identify the works that proposed to develop the construct, mapping the theoretical assumptions on which it’s founded. We found 98 citations of such works through different types of searches. We selected those publications that contributed or analyzed IMC in some way as a fundamental part of the work. By synthesizing these searches, it was possible to analyze their contributions to IMC and to point out suggestions for future advances on this subject.
Insertion , Evasion , and Denial of Service : Eluding Network Intrusion Detection

All currently available network intrusion detection (ID) systems rely upon a mechanism of data collection---passive protocol analysis---which is fundamentally flawed. In passive protocol analysis, the intrusion detection system (IDS) unobtrusively watches all traffic on the network, and scrutinizes it for patterns of suspicious activity. We outline in this paper two basic problems with the reliability of passive protocol analysis: (1) there isn't enough information on the wire on which to base conclusions about what is actually happening on networked machines, and (2) the fact that the system is passive makes it inherently "fail-open," meaning that a compromise in the availability of the IDS doesn't compromise the availability of the network. We define three classes of attacks which exploit these fundamental problems---insertion, evasion, and denial of service attacks --and describe how to apply these three types of attacks to IP and TCP protocol analysis. We present the results of tests of the efficacy of our attacks against four of the most popular network intrusion detection systems on the market. All of the ID systems tested were found to be vulnerable to each of our attacks. This indicates that network ID systems cannot be fully trusted until they are fundamentally redesigned. Insertion, Evasion, and Denial of Service: Eluding Network Intrusion Detection http://www.robertgraham.com/mirror/Ptacek-Newsham-Evasion-98.html (1 of 55) [17/01/2002 08:32:46 p.m.]
Molecular pathophysiology of Parkinson's disease.

Parkinson's disease (PD) is a progressive neurodegenerative movement disorder that results primarily from the death of dopaminergic neurons in the substantia nigra. Although the etiology of PD is incompletely understood, the recent discovery of genes associated with rare monogenic forms of the disease, together with earlier studies and new experimental animal models, has provided important and novel insight into the molecular pathways involved in disease pathogenesis. Increasing evidence indicates that deficits in mitochondrial function, oxidative and nitrosative stress, the accumulation of aberrant or misfolded proteins, and ubiquitin-proteasome system dysfunction may represent the principal molecular pathways or events that commonly underlie the pathogenesis of sporadic and familial forms of PD .
Garlic: nature's protection against physiological threats.

Currently reliance on natural products is gaining popularity to combat various physiological threats including oxidative stress, cardiovascular complexities, cancer insurgence, and immune dysfunction. The use of traditional remedies may encounter more frequently due to an array of scientific evidence in their favor. Garlic (Allium sativum) holds a unique position in history and was recognized for its therapeutic potential. Recent advancements in the field of immunonutrition, physiology, and pharmacology further explored its importance as a functional food against various pathologies. Extensive research work has been carried out on the health promoting properties of garlic, often referred to its sulfur containing metabolites i.e. allicin and its derivatives. Garlic in its preparations are effective against health risks and even used as dietary supplements such as age garlic extract (AGE) and garlic oil etc. Its components/formulations can scavenge free radicals and protect membranes from damage and maintains cell integrity. It also provides cardiovascular protection mediated by lowering of cholesterol, blood pressure, anti-platelet activities, and thromboxane formation thus providing protection against atherosclerosis and associated disorders. Besides this, it possesses antimutagenic and antiproliferative properties that are interesting in chemopreventive interventions. Several mechanisms have been reviewed in this context like activation of detoxification phase-I and II enzymes, reactive oxygen species (ROS) generation, and reducing DNA damage etc. Garlic could be useful in preventing the suppression of immune response associated with increased risk of malignancy as it stimulates the proliferation of lymphocytes, macrophage phagocytosis, stimulates the release of interleukin-2, tumor necrosis factor-alpha and interferon-gamma, and enhances natural killer cells. In this paper much emphasis has been placed on garlic's ability to ameliorate oxidative stress, core role in cardiovascular cure, chemopreventive strategies, and indeed its prospective as immune booster.
Digital image processing

As most regular readers of this TRANSACTIONS know, the development of digital signal processing techniques for applications involving image or picture data has been an increasingly active research area for the past decade. Collectively, t h s work is normally characterized under the generic heading “digital image processing.” Interestingly, the two books under review here share this heading as their title. Both are quite ambitious undertakings in that they attempt to integrate contributions from many disciplines (classical systems theory, digital signal processing, computer science, statistical communications, etc.) into unified, comprehensive presentations. In this regard it can be said that both are to some extent successful, although in quite different ways. Why the unusual step of a joint review? A brief overview of the two books reveals that they share not only a common title, but also similar objectives/purposes, intended audiences, structural organizations, and lists of topics considered. A more careful study reveals that substantial differences do exist, however, in the style and depth of subject treatment (as reflected in the difference in their lengths). Given their almost simultaneous publication, it seems appropriate to discuss these similarities/differences in a common setting. After much forethought (and two drafts), the reviewer decided to structure this review by describing the general topical material in their (joint) major sections, with supplementary comments directed toward the individual texts. It is hoped that this will provide the reader with a brief survey of the books’ contents and some flavor of their contrasting approaches. To avoid the identity problems of the joint title, each book will be subsequently referred to using the respective authors’ names: Gonzalez/Wintz and  Pratt. Subjects will be correlated  with chapter  number(s) and approximate  l ngth of coverage.
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards lowor high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both lowand highorder feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide & Deep model from Google, DeepFM has a shared input to its “wide” and “deep” parts, with no need of feature engineering besides raw features. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of DeepFM over the existing models for CTR prediction, on both benchmark data and commercial data.