nils-herrmann/clean_scirepeval huggingface dataset:
Information Technology , Workplace Organization , and the Demand for Skilled Labor : Firm-Level Evidence

We investigate the hypothesis that the combination of three related innovations—1) information technology (IT), 2) complementary workplace reorganization, and 3) new products and services—constitute a significant skill-biased technical change affecting labor demand in the United States. Using detailed firm-level data, we find evidence of complementarities among all three of these innovations in factor demand and productivity regressions. In addition, firms that adopt these innovations tend to use more skilled labor. The effects of IT on labor demand are greater when IT is combined with the particular organizational investments we identify, highlighting the importance of IT-enabled organizational change. Disciplines Business Administration, Management, and Operations | Economics | Labor Economics | Other Business | Technology and Innovation This journal article is available at ScholarlyCommons: http://repository.upenn.edu/oid_papers/108 For more information, ebusiness@mit.edu or 617-253-7054 please visit our website at http://ebusiness.mit.edu or contact the Center directly at A research and education initiative at the MIT Sloan School of Management Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-level Evidence
Dialog-based Language Learning

A long-term goal of machine learning research is to build an intelligent dialog agent. Most research in natural language understanding has focused on learning from fixed training sets of labeled data, with supervision either at the word level (tagging, parsing tasks) or sentence level (question answering, machine translation). This kind of supervision is not realistic of how humans learn, where language is both learned by, and used for, communication. In this work, we study dialog-based language learning, where supervision is given naturally and implicitly in the response of the dialog partner during the conversation. We study this setup in two domains: the bAbI dataset of [30] and large-scale question answering from [4]. We evaluate a set of baseline learning strategies on these tasks, and show that a novel model incorporating predictive lookahead is a promising approach for learning from a teacher’s response. In particular, a surprising result is that it can learn to answer questions correctly without any reward-based supervision at all.
A steganography embedding method based on edge identification and XOR coding

In this paper, we present a novel image steganography algorithm that combines the strengths of edge detection and XOR coding, to conceal a secret message either in the spatial domain or an Integer Wavelet Transform (IWT) based transform domain of the cover image. Edge detection enables the identification of sharp edges in the cover image that when embedding in would cause less degradation to the image quality compared to embedding in a pre-specified set of pixels that do not differentiate between sharp and smooth areas. This is motivated by the fact that the human visual system (HVS) is less sensitive to changes in sharp contrast areas compared to uniform areas of the image. The edge detection method presented here is capable of estimating the exact edge intensities for both the cover and stego images (before and after embedding the message), which is essential when extracting the message. The XOR coding, on the other hand, is a simple, yet effective, process that helps in reducing differences between the cover and stego images. In order to embed three secret message bits, the algorithm requires four bits of the cover image, but due to the coding mechanism, no more than two of the four bits will be changed when producing the stego image. The proposed method utilizes the sharpest regions of the image first and then gradually moves to the less sharp regions. Experimental results demonstrate that the proposed method has achieved better imperceptibility results than other popular steganography methods. Furthermore, when applying a textural feature steganalytic algorithm to differentiate between cover and stego images produced using various embedding rates, the proposed method maintained a good level of security compared to other steganography methods.
Impact of FOUP environment on product yield in advanced technologies

Historically, much attention has been given to the unit processes and the integration of those unit processes to improve product yield. Less attention has been given to the wafer environment, either during or post processing. This paper contains a detailed discussion on how particles and Airborne Molecular Contaminants (AMCs) from the wafer environment interact and produce undesired effects on the wafer. Sources of wafer environmental contamination are the process itself, ambient environment, outgassing from wafers, and FOUP contamination. Establishing a strategy that reduces contamination inside the FOUP will increase yield and decrease defect variability. Three primary variables that greatly impact this strategy are FOUP contamination mitigation, FOUP material, and FOUP metrology and cleaning method.
Table detection in heterogeneous documents

Detecting tables in document images is important since not only do tables contain important information, but also most of the layout analysis methods fail in the presence of tables in the document image. Existing approaches for table detection mainly focus on detecting tables in single columns of text and do not work reliably on documents with varying layouts. This paper presents a practical algorithm for table detection that works with a high accuracy on documents with varying layouts (company reports, newspaper articles, magazine pages, ...). An open source implementation of the algorithm is provided as part of the Tesseract OCR engine. Evaluation of the algorithm on document images from publicly available UNLV dataset shows competitive performance in comparison to the table detection module of a commercial OCR system.
A survey of the state-of-the-art in patch-based synthesis

This paper surveys the state-of-the-art of research in patch-based synthesis. Patch-based methods synthesize output images by copying small regions from exemplar imagery. This line of research originated from an area called “texture synthesis”, which focused on creating regular or semi-regular textures from small exemplars. However, more recently, much research has focused on synthesis of larger and more diverse imagery, such as photos, photo collections, videos, and light fields. Additionally, recent research has focused on customizing the synthesis process for particular problem domains, such as synthesizing artistic or decorative brushes, synthesis of rich materials, and synthesis for 3D fabrication. This report investigates recent papers that follow these themes, with a particular emphasis on papers published since 2009, when the last survey in this area was published. This survey can serve as a tutorial for readers who are not yet familiar with these topics, as well as provide comparisons between these papers, and highlight some open problems in this area.
Symmetric Key Cryptography: Current Trends

The present day & age is the time when Information has become the most powerful commodity & communication online or through wireless networks has become an integral part of our lives. The data being transmitted is prone to various passive and active attacks. Thus, information security is in all likelihood the most formidable part of correspondence today. The solution to this problem is the use of cryptography techniques. Cryptography is the art and science of secret writing that changes the message from its typical structure into a garbled structure. The point of this paper is to portray an expansive review of Encryption & Decryption & provide an involved review of the various Symmetric Key Cryptography algorithms.  Cryptography, Encryption, Decryption, Survey, Symmetric Key
The Lombard effect: a reflex to better communicate with others in noise

To study the Lombard reflex, more realistic databases representing real-world conditions need to be recorded and analyzed. In this paper we 1) summarize a procedure to record Lombard data which provides a good approximation of realistic conditions, 2) present an analysis per class of sounds for duration and energy of words recorded while subjects are listening to noise through open-ear headphones a) when speakers are in communication with a recognition device and b) when reading a list, and 3) report on the influence of speaking style on speakerdependent and speaker-independent experiments. This paper extends a previous study aimed at analyzing the influence of the communication factor on the Lombard reflex. We also show evidence that it is difficult to separate the speaker from the environment stressor (in this case the noise) when studying the Lombard reflex. The main conclusion of our pilot study is that the communication factor should not be neglected because it strongly influences the Lombard reflex.
Exploiting Semantic Information and Deep Matching for Optical Flow

Research • TorontoCity: Seeing the World with a Million Eyes. S. Wang, M. Bai, G. Mattyus, H. Chu, W. Luo, B. Yang, J. Liang, J. Cheverie, S. Fidler, R. Urtasun.  Transformation for Instance Segmentation. M. Bai, R. Urtasun. CVPR 2017.  Exploiting Semantic Information and Deep Matching for Optical Flow. M. Bai∗, W. Luo∗, K. Kundu, R. Urtasun. ECCV 2016. ∗ denotes equal contributions.
Introductory tutorial: Agent-based modeling and simulation

Agent-based modeling and simulation (ABMS) is an approach to modeling systems comprised of individual, autonomous, interacting "agents." There is much interest in many application problem domains in developing agent-based models. Agent-based modeling offers ways to model individual behaviors and how behaviors affect others in ways that have not been available before. Applications range from modeling agent behavior in supply chains and the stock market, to predicting the success of marketing campaigns and the spread of epidemics, to projecting the future needs of the healthcare system. Progress in the area suggests that ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use agent-based models as electronic laboratories to aid in discovery. This brief tutorial introduces agent-based modeling by describing the basic ideas of ABMS, discussing some applications, and addressing methods for developing agent-based models.
Cross-domain Recommendation via Deep Domain Adaptation

e behavior of users in certain services could be a clue that can be used to infer their preferences and may be used to make recommendations for other services they have never used. However, the cross-domain relationships between items and user consumption paerns are not simple, especially when there are few or no common users and items across domains. To address this problem, we propose a content-based cross-domain recommendation method for cold-start users that does not require userand itemoverlap. We formulate recommendation as extreme multi-class classication where labels (items) corresponding to the users are predicted. With this formulation, the problem is reduced to a domain adaptation seing, in which a classier trained in the source domain is adapted to the target domain. For this, we construct a neural network that combines an architecture for domain adaptation, Domain Separation Network, with a denoising autoencoder for item representation. We assess the performance of our approach in experiments on a pair of data sets collected from movie and news services of Yahoo! JAPAN and show that our approach outperforms several baseline methods including a cross-domain collaborative ltering method.
Crude oil price forecasting using XGBoost

One of the most important role of economic variables in today's world countries are the price and the change of the price of crude oil. Changes in the price of crude oil have a very critical role in terms of treasury and budget, both in company and state planning. For example, one may choose one of the energy or natural gas indexed energy production plans based on the trend of the crude oil price, for planning to meet the need for electricity next year. Accurate forecasting of the crude oil price and realization of the forecasts based on this forecast will provide savings or gains in government and corporate economies, which can reach billions of dollars. There is a great need for this estimation in countries where crude oil production is low and heavily dependent on crude oil import. In this paper, the parameters which are the factors affecting the crude oil prices will be interpreted using XGBoost, a gradient boosting model, from machine learning libraries and estimation will be made.
Developing an embodied gait on a compliant quadrupedal robot

Incorporating the body dynamics of compliant robots into their controller architectures can drastically reduce the complexity of locomotion control. An extreme version of this embodied control principle was demonstrated in highly compliant tensegrity robots, for which stable gait generation was achieved by using only optimized linear feedback from the robot's sensors to its actuators. The morphology of quadrupedal robots has previously been used for sensing and for control of a compliant spine, but never for gait generation. In this paper, we successfully apply embodied control to the compliant, quadrupedal Oncilla robot. As initial experiments indicated that mere linear feedback does not suffice, we explore the minimal requirements for robust gait generation in terms of memory and nonlinear complexity. Our results show that a memoryless feedback controller can generate a stable trot by learning the desired nonlinear relation between the input and the output signals. We believe this method can provide a very useful tool for transferring knowledge from open loop to closed loop control on compliant robots.
Compliant locomotion using whole-body control and Divergent Component of Motion tracking

This paper presents a compliant locomotion framework for torque-controlled humanoids using model-based whole-body control. In order to stabilize the centroidal dynamics during locomotion, we compute linear momentum rate of change objectives using a novel time-varying controller for the Divergent Component of Motion (DCM). Task-space objectives, including the desired momentum rate of change, are tracked using an efficient quadratic program formulation that computes optimal joint torque setpoints given frictional contact constraints and joint position / torque limits. In order to validate the effectiveness of the proposed approach, we demonstrate push recovery and compliant walking using THOR, a 34 DOF humanoid with series elastic actuation. We discuss details leading to the successful implementation of optimization-based whole-body control on our hardware platform, including the design of a “simple” joint impedance controller that introduces inner-loop velocity feedback into the actuator force controller.
Device Concepts for Graphene-Based Terahertz Photonics

Graphene is establishing itself as a new photonic material with huge potential in a variety of applications ranging from transparent electrodes in displays and photovoltaic modules to saturable absorber in mode-locked lasers. Its peculiar bandstructure and electron transport characteristics naturally suggest graphene could also form the basis for a new generation of high-performance devices operating in the terahertz (THz) range of the electromagnetic spectrum. The region between 300 GHz and 10 THz is in fact still characterized by a lack of efficient, compact, solid state photonic components capable of operating well at 300 K. Recent works have already shown very promising results in the development of high-speed modulators as well as of bolometer and plasma-wave detectors. Furthermore, several concepts have been proposed aiming at the realization of lasers and oscillators. This paper will review the latest achievements in graphene-based THz photonics and discuss future perspectives of this rapidly developing research field.
Automating physical database design in a parallel database

Physical database design is important for query performance in a shared-nothing parallel database system, in which data is horizontally partitioned among multiple independent nodes. We seek to automate the process of data partitioning. Given a workload of SQL statements, we seek to determine automatically how to partition the base data across multiple nodes to achieve overall optimal (or close to optimal) performance for that workload. Previous attempts use heuristic rules to make those decisions. These approaches fail to consider all of the interdependent aspects of query performance typically modeled by today's sophisticated query optimizers.We present a comprehensive solution to the problem that has been tightly integrated with the optimizer of a commercial shared-nothing parallel database system. Our approach uses the query optimizer itself both to recommend candidate partitions for each table that will benefit each query in the workload, and to evaluate various combinations of these candidates. We compare a rank-based enumeration method with a random-based one. Our experimental results show that the former is more effective.
Expanding the scope of health information systems. Challenges and developments.

To identify current challenges and developments in health information systems.



Reports on HIS, eHealth and process support were analyzed, core problems and challenges were identified.



Health information systems are extending their scope towards regional networks and health IT infrastructures. Integration, interoperability and interaction design are still today's core problems. Additional problems arise through the integration of genetic information into the health care process. There are noticeable trends towards solutions for these problems.
Biological Water Dynamics and Entropy: A Biophysical Origin of Cancer and Other Diseases

This paper postulates that water structure is altered by biomolecules as well as by disease-enabling entities such as certain solvated ions, and in turn water dynamics and structure affect the function of biomolecular interactions. Although the structural and dynamical alterations are subtle, they perturb a well-balanced system sufficiently to facilitate disease. We propose that the disruption of water dynamics between and within cells underlies many disease conditions. We survey recent advances in magnetobiology, nanobiology, and colloid and interface science that point compellingly to the crucial role played by the unique physical properties of quantum coherent nanomolecular clusters of magnetized water in enabling life at the cellular level by solving the “problems” of thermal diffusion, intracellular crowding, and molecular self-assembly. Interphase water and cellular surface tension, normally maintained by biological sulfates at membrane surfaces, are compromised by exogenous interfacial water stressors such as cationic aluminum, with consequences that include greater local water hydrophobicity, increased water tension, and interphase stretching. The ultimate result is greater “stiffness” in the extracellular matrix and either the “soft” cancerous state or the “soft” neurodegenerative state within cells. Our hypothesis provides a basis for understanding why so many idiopathic diseases of today are highly stereotyped and pluricausal. OPEN ACCESS Entropy 2013, 15 3823
Deep Semantic Role Labeling With Self-Attention

Semantic Role Labeling (SRL) is believed to be a crucial step towards natural language understanding and has been widely studied. Recent years, end-to-end SRL with recurrent neural networks (RNN) has gained increasing attention. However, it remains a major challenge for RNNs to handle structural information and long range dependencies. In this paper, we present a simple and effective architecture for SRL which aims to address these problems. Our model is based on self-attention which can directly capture the relationships between two tokens regardless of their distance. Our single model achieves F1 = 83.4 on the CoNLL-2005 shared task dataset and F1 = 82.7 on the CoNLL-2012 shared task dataset, which outperforms the previous state-of-the-art results by 1.8 and 1.0 F1 score respectively. Besides, our model is computationally efficient, and the parsing speed is 50K tokens per second on a single Titan X GPU.
Evidence for wavelike energy transfer through quantum coherence in photosynthetic systems

Photosynthetic complexes are exquisitely tuned to capture solar light efficiently, and then transmit the excitation energy to reaction centres, where long term energy storage is initiated. The energy transfer mechanism is often described by semiclassical models that invoke ‘hopping’ of excited-state populations along discrete energy levels. Two-dimensional Fourier transform electronic spectroscopy has mapped these energy levels and their coupling in the Fenna–Matthews–Olson (FMO) bacteriochlorophyll complex, which is found in green sulphur bacteria and acts as an energy ‘wire’ connecting a large peripheral light-harvesting antenna, the chlorosome, to the reaction centre. The spectroscopic data clearly document the dependence of the dominant energy transport pathways on the spatial properties of the excited-state wavefunctions of the whole bacteriochlorophyll complex. But the intricate dynamics of quantum coherence, which has no classical analogue, was largely neglected in the analyses—even though electronic energy transfer involving oscillatory populations of donors and acceptors was first discussed more than 70 years ago, and electronic quantum beats arising from quantum coherence in photosynthetic complexes have been predicted and indirectly observed. Here we extend previous two-dimensional electronic spectroscopy investigations of the FMO bacteriochlorophyll complex, and obtain direct evidence for remarkably long-lived electronic quantum coherence playing an important part in energy transfer processes within this system. The quantum coherence manifests itself in characteristic, directly observable quantum beating signals among the excitons within the Chlorobium tepidum FMO complex at 77 K. This wavelike characteristic of the energy transfer within the photosynthetic complex can explain its extreme efficiency, in that it allows the complexes to sample vast areas of phase space to find the most efficient path.
News Topic Evolution Tracking by Incorporating Temporal Information

Time stamped texts or text sequences are ubiquitous in real life, such as news reports. Tracking the topic evolution of these texts has been an issue of considerable interest. Recent work has developed methods of tracking topic shifting over long time scales. However, most of these researches focus on a large corpus. Also, they only focus on the text itself and no attempt have been made to explore the temporal distribution of the corpus, which could provide meaningful and comprehensive clues for topic tracking. In this paper, we formally address this problem and put forward a novel method based on the topic model. We investigate the temporal distribution of news reports of a specific event and try to integrate this information with a topic model to enhance the performance of topic model. By focusing on a specific news event, we try to reveal more details about the event, such as, how many stages are there in the event, what aspect does each stage focus on, etc.