nils-herrmann/clean_scirepeval huggingface dataset:
Attentive Memory Networks: Efficient Machine Reading for Conversational Search

Recent advances in conversational systems have changed the search paradigm. Traditionally, a user poses a query to a search engine that returns an answer based on its index, possibly leveraging external knowledge bases and conditioning the response on earlier interactions in the search session. In a natural conversation, there is an additional source of information to take into account: utterances produced earlier in a conversation can also be referred to and a conversational IR system has to keep track of information conveyed by the user during the conversation, even if it is implicit. We argue that the process of building a representation of the conversation can be framed as a machine reading task, where an automated system is presented with a number of statements about which it should answer questions. The questions should be answered solely by referring to the statements provided, without consulting external knowledge. The time is right for the information retrieval community to embrace this task, both as a stand-alone task and integrated in a broader conversational search setting. In this paper, we focus on machine reading as a stand-alone task and present the Attentive Memory Network (AMN), an end-to-end trainable machine reading algorithm. Its key contribution is in efficiency, achieved by having an hierarchical input encoder, iterating over the input only once. Speed is an important requirement in the setting of conversational search, as gaps between conversational turns have a detrimental effect on naturalness. On 20 datasets commonly used for evaluating machine reading algorithms we show that the AMN achieves performance comparable to the state-of-theart models, while using considerably fewer computations.
Job satisfaction among nurses: a literature review.

The current nursing shortage and high turnover is of great concern in many countries because of its impact upon the efficiency and effectiveness of any health-care delivery system. Recruitment and retention of nurses are persistent problems associated with job satisfaction. This paper analyses the growing literature relating to job satisfaction among nurses and concludes that more research is required to understand the relative importance of the many identified factors to job satisfaction. It is argued that the absence of a robust causal model incorporating organizational, professional and personal variables is undermining the development of interventions to improve nurse retention.
A meta-analysis of state-of-the-art electoral prediction from Twitter data

Electoral prediction from Twitter data is an appealing research topic. It seems relatively straightforward and the prevailing view is overly optimistic. This is problematic because while simple approaches are assumed to be good enough, core problems are not addressed. Thus, this paper aims to (1) provide a balanced and critical review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) depict a roadmap to push forward the field. Hence, a scheme to characterize Twitter prediction methods is proposed. It covers every aspect from data collection to performance evaluation, through data processing and vote inference. Using that scheme, prior research is analyzed and organized to explain the main approaches taken up to date but also their weaknesses. This is the first meta-analysis of the whole body of research regarding electoral prediction from Twitter data. It reveals that its presumed predictive power regarding electoral prediction has been somewhat exaggerated: although social media may provide a glimpse on electoral outcomes current research does not provide strong evidence to support it can currently replace traditional polls. Finally, future lines of work are suggested.
A Semantic Loss Function for Deep Learning Under Weak Supervision ∗

This paper develops a novel methodology for using symbolic knowledge in deep learning. We define a semantic loss function that bridges between neural output vectors and logical constraints. This loss function captures how close the neural network is to satisfying the constraints on its output. An experimental evaluation shows that our semantic loss function effectively guides the learner to achieve (near-)state-of-the-art results on semi-supervised multi-class classification. Moreover, it significantly increases the ability of the neural network to predict structured objects under weak supervision, such as rankings and shortest paths.
wmin . ac . uk Music and memory in advertising : music as a device of implicit learning and recall

Music may play several roles and have many effects in advertising; it may attract attention, carry the product message, act as a mnemonic device, and create excitement or a state of relaxation. There have been numerous studies that have focused on the general perceptual, cognitive and affective processing that occurs in response to exposure to music; there also have been studies on the effects of music on shortand long-term memory. However, few of these have examined the specific importance of music as a mnemonic device within filmed events (Boltz et al. 1991) or TV commercials (Yalch 1991, Stewart et al. 1998). In this paper, the role of music will be evaluated within advertising and during low-attention conditions. A series of experiments was carried out whereby musicians and nonmusicians were exposed to an advert that was embedded into a group of other adverts, presented in the middle of an engaging TV program, thus replicating very naturalistic conditions. There were 4 audio conditions examined in an example advertisement: jingle, instrumental music, instrumental music with voice-over and environmental sounds with voice-over. Results indicate that music is effective in facilitating implicit learning and recall of the advertised product, showing that, under non-attentive conditions, there is a certain mechanism of unconscious elaboration of the musical signal. The role of previous musical training seems to have a little significance under low-attentive conditions, thus we observe an unconscious physiological reaction to the information carried with the music of a commercial which is common to musicians and non-musicians. Conclusions concerning the function of music on listeners and on memory stimulation could prove effective in an analysis of the communicative role of music in advertising, and might also have wider ramifications for current research into more generalised analysis of music and meaning.
Clinically Significant Drug Interactions with Antacids

One may consider that drug-drug interactions (DDIs) associated with antacids is an obsolete topic because they are prescribed less frequently by medical professionals due to the advent of drugs that more effectively suppress gastric acidity (i.e. histamine H2-receptor antagonists [H2RAs] and proton pump inhibitors [PPIs]). Nevertheless, the use of antacids by ambulant patients may be ever increasing, because they are freely available as over-the-counter (OTC) drugs. Antacids consisting of weak basic substances coupled with polyvalent cations may alter the rate and/or the extent of absorption of concomitantly administered drugs via different mechanisms. Polyvalent cations in antacid formulations may form insoluble chelate complexes with drugs and substantially reduce their bioavailability. Clinical studies demonstrated that two classes of antibacterial s (tetracyclines and fluoroquinolones) are susceptible to clinically relevant DDIs with antacids through this mechanism. Countermeasures against this type of DDI include spacing out the dosing interval —taking antacid either 4 hours before or 2 hours after administration of these antibacterials. Bisphosphonates may be susceptible to DDIs with antacids by the same mechanism, as described in the prescription information of most bisphosphonates, but no quantitative data about the DDIs are available. For drugs with solubility critically dependent on pH, neutralization of gastric fluid by antacids may alter the dissolution of these drugs and the rate and/or extent of their absorption. However, the magnitude of DDIs elicited by antacids through this mechanism is less than that produced by H2RAs or PPIs; therefore, the clinical relevance of such DDIs is often obscure. Magnesium ions contained in some antacid formulas may increase gastric emptying, thereby accelerating the rate of absorption of some drugs. However, the clinical relevance of this is unclear in most cases because the difference in plasma drug concentration observed after dosing shortly disappears. Recent reports have indicated that some of the molecular-targeting agents such as the tyrosine kinase inhibitors dasatinib and imatinib, and the thrombopoietin receptor agonist eltrombopag may be susceptible to DDIs with antacids. Finally, the recent trend of developing OTC drugs as combination formulations of an antacid and an H2RA is a concern because these drugs will increase the risk of DDIs by dual mechanisms, i.e. a gastric pH-dependent mechanism by H2RAs and a cation-mediated chelation mechanism by antacids.
Im2Avatar: Colorful 3D Reconstruction from a Single Image

Existing works on single-image 3D reconstruction mainly focus on shape recovery. In this work, we study a new problem, that is, simultaneously recovering 3D shape and surface color from a single image, namely “colorful 3D reconstruction”. This problem is both challenging and intriguing because the ability to infer textured 3D model from a single image is at the core of visual understanding. Here, we propose an end-to-end trainable framework, Colorful Voxel Network (CVN), to tackle this problem. Conditioned on a single 2D input, CVN learns to decompose shape and surface color information of a 3D object into a 3D shape branch and a surface color branch, respectively. Specifically, for the shape recovery, we generate a shape volume with the state of its voxels indicating occupancy. For the surface color recovery, we combine the strength of appearance hallucination and geometric projection by concurrently learning a regressed color volume and a 2Dto-3D flow volume, which are then fused into a blended color volume. The final textured 3D model is obtained by sampling color from the blended color volume at the positions of occupied voxels in the shape volume. To handle the severe sparse volume representations, a novel loss function, Mean Squared False Cross-Entropy Loss (MSFCEL), is designed. Extensive experiments demonstrate that our approach achieves significant improvement over baselines, and shows great generalization across diverse object categories and arbitrary viewpoints.
Intelligent automation entering the business world

Robotic process automation or intelligent automation (the combination of artificial intelligence and automation) is starting to change the way business is done in nearly every sector of the economy. Intelligent automation systems detect and produce vast amounts of information and can automate entire processes or workflows, learning and adapting as they go. Applications range from the routine to the revolutionary: from collecting, analysing, and making decisions about textual information to guiding autonomous vehicles and advanced robots. It is already helping companies transcend conventional performance trade-offs to achieve unprecedented levels of efficiency and quality. Until recently, robotics has found most of its applications in the primary sector, automating and removing the human element from the production chain. Replacing menial tasks was its first foray, and many organisations introduced robotics into their assembly line, warehouse, and cargo bay operations.
Coupled inductor three-phase bidirectional buck/boost converter for energy storage application

This research designs and realizes a zero-voltage switching (ZVS) three-phase DC-DC buck/boost converter that reduces the current ripple, switching losses and increases converter efficiency. The size and cost can be reduced when the proposed converter is designed with the coupled inductor scheme. This paper describes a three-phase DC-DC buck/boost converter with the coupled inductor and ZVS soft switching operation under different inductor current conduction modes. Simulation and experimental results are employed compare the performance of the proposed three-phase bidirectional converter in the PV system under battery charge and discharge operating modes for energy storage systems.
Neural Extractive Summarization with Side Information

Most extractive summarization methods focus on the main body of the document from which sentences need to be extracted. However, the gist of the document may lie in side information, such as the title and image captions which are often available for newswire articles. We propose to explore side information in the context of single-document extractive summarization. We develop a framework for singledocument summarization composed of a hierarchical document encoder and an attention-based extractor with attention over side information. We evaluate our model on a large scale news dataset. We show that extractive summarization with side information consistently outperforms its counterpart that does not use any side information, in terms of both informativeness and fluency.
Building better boards.

Companies facing new requirements for governance are scrambling to buttress financial-reporting systems, overhaul board structures--whatever it takes to comply. But there are limits to how much good governance can be imposed from the outside. Boards know what they ought to be: seats of challenge and inquiry that add value without meddling and make CEOs more effective but not all-powerful. A board can reach that goal only if it functions as a high-performance team, one that is competent, coordinated, collegial, and focused on an unambiguous goal. Such entities don't just evolve; they must be constructed to an exacting blueprint--what the author calls board building. In this article, Nadler offers an agenda and a set of tools that boards can use to define and achieve their objectives. It's important for a board to conduct regular self-assessments and to pay attention to the results of those analyses. As a first step, the directors and the CEO should agree on which of the following common board models best fits the company: passive, certifying, engaged, intervening, or operating. The directors and the CEO should then analyze which business tasks are most important and allot sufficient time and resources to them. Next, the board should take inventory of each director's strengths to ensure that the group as a whole possesses the skills necessary to do its work. Directors must exert more influence over meeting agendas and make sure they have the right information at the right time and in the right format to perform their duties. Finally, the board needs to foster an engaged culture characterized by candor and a willingness to challenge. An ambitious board-building process, devised and endorsed both by directors and by management, can potentially turn a good board into a great one.
Robust Accurate Statistical Annotation of General Text

We describe a robust accurate domain-independent approach t statistical parsing incorporated into the new release of the ANLT toolkit, and publicly available as a research tool. The system has bee n used to parse many well known corpora in order to produce dat a for lexical acquisition efforts; it has also been used as a component in a open-domain question answering project. The performance of the system is competitive with that of statistical parsers using highl y lexicalised parse selection models. However, we plan to ex end the system to improve parse coverage, depth and accuracy.
Study on the Analysis and Optimization of Brake Disc : A Review

In view of significant increase in the research work on the brake disc in past few years, this article attempts to identify and highlight the various researches that are most relevant to analysis and optimization of brake disc. In the present article a keen review on the studies done on brake disc by previous researchers between (19982015) is presented. This literature review covers the important aspects of brake disc with the emphasis on material selection methods, thermal analysis, structural analysis, FEA and optimization of disc brake. This literature progressively discusses about the research methodology adopted and the outcome of the research work done by past researchers. This review is intended to give the readers a brief about the variety of the research work done on brake disc. Brake disc, FEA,
Local partial-likelihood estimation for lifetime data

This paper considers a proportional hazards model, which allows one to examine the extent to which covariates interact nonlinearly with an exposure variable, for analysis of lifetime data. A local partial-likelihood technique is proposed to estimate nonlinear interactions. Asymptotic normality of the proposed estimator is established. The baseline hazard function, the bias and the variance of the local likelihood estimator are consistently estimated. In addition, a one-step local partial-likelihood estimator is presented to facilitate the computation of the proposed procedure and is demonstrated to be as efficient as the fully iterated local partial-likelihood estimator. Furthermore, a penalized local likelihood estimator is proposed to select important risk variables in the model. Numerical examples are used to illustrate the effectiveness of the proposed procedures.
Neural Networks and Intellect: Using Model-Based Concepts

With the first few words of Neural Networks and Intellect: Using Model-Based Concepts, Leonid Perlovsky embarks on the daring task of creating a mathematical concept of “the mind.” The content of the book actually exceeds even the most daring of expectations. A wide variety of concepts are linked together intertwining the development of artificial intelligence, evolutionary computation, and even the philosophical observations ranging from Aristotle and Plato to Kant and Gvdel. Perlovsky discusses fundamental questions with a number of engineering applications to filter them through philosophical categories (both ontological and epistemological). In such a fashion, the inner workings of the human mind, consciousness, language-mind relationships, learning, and emotions are explored mathematically in amazing details. Perlovsky even manages to discuss the concept of beauty perception in mathematical terms. Beginners will appreciate that Perlovsky starts with the basics. The first chapter contains an introduction to probability, statistics, and pattern recognition, along with the intuitive explanation of the complicated mathematical concepts. The second chapter reviews numerous mathematical approaches, algorithms, neural networks, and the fundamental mathematical ideas underlying each method. It analyzes fundamental limitations of the nearest neighbor methods and the simple neural network. Vapnik’s statistical learning theory, support vector machines, and Grossberg’s neural field theories are clearly explained. Roles of hierarchical organization and evolutionary computation are analyzed. Even experts in the field might find interesting the relationships among various algorithms and approaches. Fundamental mathematical issues include origins of combinatorial complexity (CC) of many algorithms and neural networks (operations or training) and its relationship to di-
What it will take to achieve the as-yet-unfulfilled promises of health information technology.

A team of RAND Corporation researchers projected in 2005 that rapid adoption of health information technology (IT) could save the United States more than $81 billion annually. Seven years later the empirical data on the technology's impact on health care efficiency and safety are mixed, and annual health care expenditures in the United States have grown by $800 billion. In our view, the disappointing performance of health IT to date can be largely attributed to several factors: sluggish adoption of health IT systems, coupled with the choice of systems that are neither interoperable nor easy to use; and the failure of health care providers and institutions to reengineer care processes to reap the full benefits of health IT. We believe that the original promise of health IT can be met if the systems are redesigned to address these flaws by creating more-standardized systems that are easier to use, are truly interoperable, and afford patients more access to and control over their health data. Providers must do their part by reengineering care processes to take full advantage of efficiencies offered by health IT, in the context of redesigned payment models that favor value over volume.
SD codes: erasure codes designed for how storage systems really fail

Traditionally, when storage systems employ erasure codes, they are designed to tolerate the failures of entire disks. However, the most common types of failures are latent sector failures, which only affect individual disk sectors, and block failures which arise through wear on SSD’s. This paper introduces SD codes, which are designed to tolerate combinations of disk and sector failures. As such, they consume far less storage resources than traditional erasure codes. We specify the codes with enough detail for the storage practitioner to employ them, discuss their practical properties, and detail an open-source implementation.
Improving Human Action Recognition Using Fusion of Depth Camera and Inertial Sensors

This paper presents a fusion approach for improving human action recognition based on two differing modality sensors consisting of a depth camera and an inertial body sensor. Computationally efficient action features are extracted from depth images provided by the depth camera and from accelerometer signals provided by the inertial body sensor. These features consist of depth motion maps and statistical signal attributes. For action recognition, both feature-level fusion and decision-level fusion are examined by using a collaborative representation classifier. In the feature-level fusion, features generated from the two differing modality sensors are merged before classification, while in the decision-level fusion, the Dempster-Shafer theory is used to combine the classification outcomes from two classifiers, each corresponding to one sensor. The introduced fusion framework is evaluated using the Berkeley multimodal human action database. The results indicate that because of the complementary aspect of the data from these sensors, the introduced fusion approaches lead to 2% to 23% recognition rate improvements depending on the action over the situations when each sensor is used individually.