nils-herrmann/clean_scirepeval huggingface dataset:
Conjugated polymer-based organic solar cells.

Photovoltaics (PVs) consists of the harvesting of energy from sunlight and its conversion into electrical power. This technology is being increasingly recognized as one of the key components in our future global energy scenario. Limited fossil fuel resources and steadily increasing costs of supplying fossil fuels to the market induce a natural limit where renewable energies will begin to kick in as major energy suppliers. In addition, the detrimental long-term effects of CO2 and other emissions from burning fossil fuels into our atmosphere underscore the multiple benefits of developing renewable energy resources and commercializing them. The worldwide photovoltaics (PV) industry reached a cell/module production of 1200 MWp in 2004, exhibiting an average annual growth of 40% over the last 5 years . The same year, this market recorded a spectacular increase of 58.5% (US$ 5.8 billion), making the industry one of the fastest growing worldwide at present. As shown in Fig. 11.1, this development is not only in Japan, but also in Europe and the USA. Besides, most Japanese, USA and European forecasts seem to agree on one point: This trend is expected to last at least for the next 30 years to come . A significant capacity of about 200 GWp is forecast to be operational around the globe in 2020. This number is the equivalent of 200 state-of-the-art nuclear power plants. PV technology is only one of many alternative renewable energies such as wind, biomass, and water. However, though all of these technologies are expected to contribute significantly to the world’s energy supply in the next century, PVs possesses three key properties which make it unique: 455
Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem

In this paper we introduce Ant-Q, a family of algorithms which present many similarities with Q-learning (Watkins, 1989), and which we apply to the solution of symmetric and asymmetric instances of the traveling salesman problem (TSP). Ant-Q algorithms were inspired by work on the ant system (AS), a distributed algorithm for combinatorial optimization based on the metaphor of ant colonies which was recently proposed in (Dorigo, 1992; Dorigo, Maniezzo and Colorni, 1996). We show that AS is a particular instance of the Ant-Q family, and that there are instances of this family which perform better than AS. We experimentally investigate the functioning of Ant-Q and we show that the results obtained by Ant-Q on symmetric TSP's are competitive with those obtained by other heuristic approaches based on neural networks or local search. Finally, we apply Ant-Q to some difficult asymmetric TSP's obtaining very good results: Ant-Q was able to find solutions of a quality which usually can be found only by very specialized algorithms.
Cross-modal plasticity: where and how?

Animal studies have shown that sensory deprivation in one modality can have striking effects on the development of the remaining modalities. Although recent studies of deaf and blind humans have also provided convincing behavioural, electrophysiological and neuroimaging evidence of increased capabilities and altered organization of spared modalities, there is still much debate about the identity of the brain systems that are changed and the mechanisms that mediate these changes. Plastic changes across brain systems and related behaviours vary as a function of the timing and the nature of changes in experience. This specificity must be understood in the context of differences in the maturation rates and timing of the associated critical periods, differences in patterns of transiently existing connections, and differences in molecular factors across brain systems.
End-to-End Joint Semantic Segmentation of Actors and Actions in Video

Traditional video understanding tasks include human action recognition and actor/object semantic segmentation. However, the combined task of providing semantic segmentation for different actor classes simultaneously with their action class remains a challenging but necessary task for many applications. In this work, we propose a new end-to-end architecture for tackling this task in videos. Our model effectively leverages multiple input modalities, contextual information, and multitask learning in the video to directly output semantic segmentations in a single unified framework. We train and benchmark our model on the Actor-Action Dataset (A2D) for joint actor-action semantic segmentation, and demonstrate state-of-the-art performance for both segmentation and detection. We also perform experiments verifying our approach improves performance for zero-shot recognition, indicating generalizability of our jointly learned feature space.
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

We introduce a large scale MAchine Reading COmprehension dataset, which we name MS MARCO. The dataset comprises of 1,010,916 anonymized questions— sampled from Bing’s search query logs—each with a human generated answer and 182,669 completely human rewritten generated answers. In addition, the dataset contains 8,841,823 passages—extracted from 3,563,535 web documents retrieved by Bing—that provide the information necessary for curating the natural language answers. A question in the MS MARCO dataset may have multiple answers or no answers at all. Using this dataset, we propose three different tasks with varying levels of difficulty: (i) predict if a question is answerable given a set of context passages, and extract and synthesize the answer as a human would (ii) generate a well-formed answer (if possible) based on the context passages that can be understood with the question and passage context, and finally (iii) rank a set of retrieved passages given a question. The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering. We believe that the scale and the real-world nature of this dataset makes it attractive for benchmarking machine reading comprehension and question-answering models.
Business Intelligence

Business intelligence systems combine operational data with analytical tools to present complex and competitive information to planners and decision makers. The objective is to improve the timeliness and quality of inputs to the decision process. Business Intelligence is used to understand the capabilities available in the firm; the state of the art, trends, and future directions in the markets, the technologies, and the regulatory environment in which the firm competes; and the actions of competitors and the implications of these actions. The emergence of the data warehouse as a repository, advances in data cleansing, increased capabilities of hardware and software, and the emergence of the web architecture all combine to create a richer business intelligence environment than was available previously. Although business intelligence systems are widely used in industry, research about them is limited. This paper, in addition to being a tutorial, proposes a BI framework and potential research topics. The framework highlights the importance of unstructured data and discusses the need to develop BI tools for its acquisition, integration, cleanup, search, analysis, and delivery. In addition, this paper explores a matrix for BI data types (structured vs. unstructured) and data sources (internal and external) to guide research.
Neural Networks for Open Domain Targeted Sentiment

Open domain targeted sentiment is the joint information extraction task that finds target mentions together with the sentiment towards each mention from a text corpus. The task is typically modeled as a sequence labeling problem, and solved using state-of-the-art labelers such as CRF. We empirically study the effect of word embeddings and automatic feature combinations on the task by extending a CRF baseline using neural networks, which have demonstrated large potentials for sentiment analysis. Results show that the neural model can give better results by significantly increasing the recall. In addition, we propose a novel integration of neural and discrete features, which combines their relative advantages, leading to significantly higher results compared to both baselines.
Time-Varying Graphs and Social Network Analysis: Temporal Indicators and Metrics

Most instruments formalisms, concepts, and metrics for social networks analysis fail to capture their dynamics. Typical systems exhibit different scales of dynamics, ranging from the fine-grain dynamics of interactions (which recently led researchers to consider temporal versions of distance, connectivity, and related indicators), to the evolution of network properties over longer periods of time. This paper proposes a general approach to study that evolution for both atemporal and temporal indicators, based respectively on sequences of static graphs and sequences of time-varying graphs that cover successive time-windows. All the concepts and indicators, some of which are new, are expressed using a time-varying graph formalism recently proposed in .
Breaking Mifare DESFire MF3ICD40: Power Analysis and Templates in the Real World

With the advent of side-channel analysis, implementations of mathematically secure ciphers face a new threat: by exploiting the physical characteristics of a device, adversaries are able to break algorithms such as AES or Triple-DES (3DES), for which no efficient analytical or brute-force attacks exist. In this paper, we demonstrate practical, noninvasive side-channel attacks on the Mifare DESFire MF3ICD40 contactless smartcard, a 3DES-based alternative to the cryptanalytically weak Mifare Classic [9, 25]. We detail on how to recover the complete 112-bit secret key of the employed 3DES algorithm, using non-invasive power analysis and template attacks. Our methods can be put into practice at a low cost with standard equipment, thus posing a severe threat to many real-world applications that employ the DESFire MF3ICD40 smartcard.
A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks

Modeling and predicting people's opinions plays an important role in today's life. For viral marketing and political strategy design, it is particularly important to be able to analyze competing opinions, such as pro-Democrat vs. pro-Republican. While observing the evolution of polar opinions in a social network over time, can we tell when the network "behaved"' abnormally? Furthermore, can we predict how the opinions of individual users will change in the future? To answer such questions, it is insufficient to study individual user behavior, since opinions spread beyond users' ego-networks. Instead, we need to consider the opinion dynamics of all users simultaneously. In this work, we introduce the Social Network Distance (SND)—a distance measure that quantifies the likelihood of evolution of one snapshot of a social network into another snapshot under a chosen opinion dynamics model. SND has a rich semantics of a transportation problem, yet, is computable in pseudo-linear time, thereby, being applicable to large-scale social networks analysis. We demonstrate the effectiveness of SND in experiments with Twitter data.
Extracting aspects and mining opinions in product reviews using supervised learning algorithm

Social media is emerging rapidly on the internet. This media knowledge helps people, company and organizations to analyze information for important decision making. Opinion mining is also called as sentiment analysis which involves in building a system to gather and examine opinions about the product made in reviews or tweets, comments, blog posts on the web. Sentiment is classified automatically for important applications such as opinion mining and summarization. To make valuable decisions in marketing analysis where implement sentiment classification efficiently. Reviews contain sentiment which is expressed in a different way in different domains and it is costly to annotate data for each new domain. The analysis of online customer reviews in which firms cannot discover what exactly people liked and did not like in document-level and sentence-level opinion mining. So, now opinion mining ongoing research is in phrase-level opinion mining. It performs finer-grained analysis and directly looks at the opinion in online reviews. The proposed system is based on phrase-level to examine customer reviews. Phrase-level opinion mining is also well-known as aspect based opinion mining. It is used to extract most important aspects of an item and to predict the orientation of each aspect from the item reviews. The projected system implements aspect extraction using frequent itemset mining in customer product reviews and mining opinions whether it is positive or negative opinion. It identifies sentiment orientation of each aspect by supervised learning algorithms in customer reviews.
A Fiber Tracking Method for Building Patient Specific Dynamic Musculoskeletal Models from Diffusion Tensor Data

A new musculoskeltal simulation primitive, the strand, can provide an efficient way of simulating complicated muscle fiber architectures . In this paper we present a new fiber tracking algorithm based on an energy minimizing active curve that is well suited for building these strand type models from diffusion tensor imaging data. We present fiber tracking results for the Brachioradialis muscle in the left forearm of a male subject. The algorithm produces a space filling arrangement of fibers that are uniformly distributed throughout the muscle and aligned with the underlying muscle fiber direction.
Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting

We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. -step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form , the prediction of at time is based on the point estimates of the previous outputs. In this paper, we show how, using an analytical Gaussian approximation, we can formally incorporate the uncertainty about intermediate regressor values, thus updating the uncertainty on the current prediction.
The Cricket location-support system

This paper presents the design, implementation, and evaluation of Cricket, a location-support system for in-building, mobile, location-dependent applications. It allows applications running on mobile and static nodes to learn their physical location by using listeners that hear and analyze information from beacons spread throughout the building. Cricket is the result of several design goals, including user privacy, decentralized administration, network heterogeneity, and low cost. Rather than explicitly tracking user location, Cricket helps devices learn where they are and lets them decide whom to advertise this information to; it does not rely on any centralized management or control and there is no explicit coordination between beacons; it provides information to devices regardless of their type of network connectivity; and each Cricket device is made from off-the-shelf components and costs less than U.S. $10. We describe the randomized algorithm used by beacons to transmit information, the use of concurrent radio and ultrasonic signals to infer distance, the listener inference algorithms to overcome multipath and interference, and practical beacon configuration and positioning techniques that improve accuracy. Our experience with Cricket shows that several location-dependent applications such as in-building active maps and device control can be developed with little effort or manual configuration.
A layered naming architecture for the internet

Currently the Internet has only one level of name resolution, DNS, which converts user-level domain names into IP addresses. In this paper we borrow liberally from the literature to argue that there should be three levels of name resolution: from user-level descriptors to service identifiers; from service identifiers to endpoint identifiers; and from endpoint identifiers to IP addresses. These additional levels of naming and resolution (1) allow services and data to be first class Internet objects (in that they can be directly and persistently named), (2) seamlessly accommodate mobility and multi-homing and (3) integrate middleboxes (such as NATs and firewalls) into the Internet architecture. We further argue that flat names are a natural choice for the service and endpoint identifiers. Hence, this architecture requires scalable resolution of flat names, a capability that distributed hash tables (DHTs) can provide.
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ∼90% scaling efficiency when moving from 8 to 256 GPUs. This system enables us to train visual recognition models on internetscale data with high efficiency.
On the compression of recurrent neural networks with an application to LVCSR acoustic modeling for embedded speech recognition

We study the problem of compressing recurrent neural networks (RNNs). In particular, we focus on the compression of RNN acoustic models, which are motivated by the goal of building compact and accurate speech recognition systems which can be run efficiently on mobile devices. In this work, we present a technique for general recurrent model compression that jointly compresses both recurrent and non-recurrent inter-layer weight matrices. We find that the proposed technique allows us to reduce the size of our Long Short-Term Memory (LSTM) acoustic model to a third of its original size with negligible loss in accuracy.
Fine-grained classification via mixture of deep convolutional neural networks

We present a novel deep convolutional neural network (DCNN) system for fine-grained image classification, called a mixture of DCNNs (MixDCNN). The fine-grained image classification problem is characterised by large intra-class variations and small inter-class variations. To overcome these problems our proposed MixDCNN system partitions images into K subsets of similar images and learns an expert DCNN for each subset. The output from each of the K DCNNs is combined to form a single classification decision. In contrast to previous techniques, we provide a formulation to perform joint end-to-end training of the K DCNNs simultaneously. Extensive experiments, on three datasets using two network structures (AlexNet and GoogLeNet), show that the proposed MixDCNN system consistently outperforms other methods. It provides a relative improvement of 12.7% and achieves state-of-the-art results on two datasets.
Emotion Recognition from Text Based on Automatically Generated Rules

With the growth of the Internet community, textual data has proven to be the main tool of communication in human-machine and human-human interaction. This communication is constantly evolving towards the goal of making it as human and real as possible. One way of humanizing such interaction is to provide a framework that can recognize the emotions present in the communication or the emotions of the involved users in order to enrich user experience. For example, by providing insights to users for personal preferences and automated recommendations based on their emotional state. In this work, we propose a framework for emotion classification in English sentences where emotions are treated as generalized concepts extracted from the sentences. We start by generating an intermediate emotional data representation of a given input sentence based on its syntactic and semantic structure. We then generalize this representation using various ontologies such as Word Net and Concept Net, which results in an emotion seed that we call an emotion recognition rule (ERR). Finally, we use a suite of classifiers to compare the generated ERR with a set of reference ERRs extracted from a training set in a similar fashion. The used classifiers are k-nearest neighbors (KNN) with handcrafted similarity measure, Point Mutual Information (PMI), and PMI with Information Retrieval (PMI-IR). When applied on different datasets, the proposed approach significantly outperformed the existing state-of-the art machine learning and rule-based classifiers with an average F-Score of 84%.
Collaborative Filtering with Graph Information: Consistency and Scalable Methods

Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space. Often, additional information about the variables is known, and it is reasonable to assume that incorporating this information will lead to better predictions. We tackle the problem of matrix completion when pairwise relationships among variables are known, via a graph. We formulate and derive a highly efficient, conjugate gradient based alternating minimization scheme that solves optimizations with over 55 million observations up to 2 orders of magnitude faster than state-of-the-art (stochastic) gradient-descent based methods. On the theoretical front, we show that such methods generalize weighted nuclear norm formulations, and derive statistical consistency guarantees. We validate our results on both real and synthetic datasets.