nils-herrmann/clean_scirepeval huggingface dataset:
Recycling and recovery routes of plastic solid waste (PSW): a review.

Plastic solid waste (PSW) presents challenges and opportunities to societies regardless of their sustainability awareness and technological advances. In this paper, recent progress in the recycling and recovery of PSW is reviewed. A special emphasis is paid on waste generated from polyolefinic sources, which makes up a great percentage of our daily single-life cycle plastic products. The four routes of PSW treatment are detailed and discussed covering primary (re-extrusion), secondary (mechanical), tertiary (chemical) and quaternary (energy recovery) schemes and technologies. Primary recycling, which involves the re-introduction of clean scrap of single polymer to the extrusion cycle in order to produce products of the similar material, is commonly applied in the processing line itself but rarely applied among recyclers, as recycling materials rarely possess the required quality. The various waste products, consisting of either end-of-life or production (scrap) waste, are the feedstock of secondary techniques, thereby generally reduced in size to a more desirable shape and form, such as pellets, flakes or powders, depending on the source, shape and usability. Tertiary treatment schemes have contributed greatly to the recycling status of PSW in recent years. Advanced thermo-chemical treatment methods cover a wide range of technologies and produce either fuels or petrochemical feedstock. Nowadays, non-catalytic thermal cracking (thermolysis) is receiving renewed attention, due to the fact of added value on a crude oil barrel and its very valuable yielded products. But a fact remains that advanced thermo-chemical recycling of PSW (namely polyolefins) still lacks the proper design and kinetic background to target certain desired products and/or chemicals. Energy recovery was found to be an attainable solution to PSW in general and municipal solid waste (MSW) in particular. The amount of energy produced in kilns and reactors applied in this route is sufficiently investigated up to the point of operation, but not in terms of integration with either petrochemical or converting plants. Although primary and secondary recycling schemes are well established and widely applied, it is concluded that many of the PSW tertiary and quaternary treatment schemes appear to be robust and worthy of additional investigation.
Ternary Weight Networks

We introduce Ternary Weight Networks (TWNs) neural networks with weights constrained to +1, 0 and -1. The L2 distance between the full (float or double) precision weights and the ternary weights along with a scaling factor is minimized. With the optimization, the TWNs own high capacity of model expression that is good enough to approximate the Full Precision Weight Networks (FPWNs) counterpart. Besides, the TWNs achieve up to 16x or 32x model compression rate and own much fewer multiplications compared with the FPWNs. Compared with recently proposed Binary Precision Weight Networks (BPWNs), the TWNs own nearly 38x more power of expression in a 3×3 size filter, which is commonly used in most of the state-of-the-art CNN models like residual networks or VGG. Besides, the TWNs eliminate the singularity at zero and converge faster and more stablely at training time. Benchmarks on MNIST, CIFAR-10, and the large scale ImageNet dataset show that TWNs achieve state-of-the-art performance which is only slightly worse than the FPWNs counterpart but outperforms the analogous BPWNs.
Deep Successor Reinforcement Learning

Learning robust value functions given raw observations and rewards is now possible with model-free and model-based deep reinforcement learning algorithms. There is a third alternative, called Successor Representations (SR), which decomposes the value function into two components – a reward predictor and a successor map. The successor map represents the expected future state occupancy from any given state and the reward predictor maps states to scalar rewards. The value function of a state can be computed as the inner product between the successor map and the reward weights. In this paper, we present DSR, which generalizes SR within an end-to-end deep reinforcement learning framework. DSR has several appealing properties including: increased sensitivity to distal reward changes due to factorization of reward and world dynamics, and the ability to extract bottleneck states (subgoals) given successor maps trained under a random policy. We show the efficacy of our approach on two diverse environments given raw pixel observations – simple grid-world domains (MazeBase) and the Doom game engine. 2
Detecting Personal Medication Intake in Twitter: An Annotated Corpus and Baseline Classification System

Social media sites (e.g., Twitter) have been used for surveillance of drug safety at the population level, but studies that focus on the effects of medications on specific sets of individuals have had to rely on other sources of data. Mining social media data for this information would require the ability to distinguish indications of personal medication intake in this media. Towards that end, this paper presents an annotated corpus that can be used to train machine learning systems to determine whether a tweet that mentions a medication indicates that the individual posting has taken that medication (at a specific time). To demonstrate the utility of the corpus as a training set, we present baseline results of supervised classification.
Neural information retrieval: at the end of the early years

A recent “third wave” of neural network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing. Because these modern NNs often comprise multiple interconnected layers, work in this area is often referred to as deep learning. Recent years have witnessed an explosive growth of research into NN-based approaches to information retrieval (IR). A significant body of work has now been created. In this paper, we survey the current landscape of Neural IR research, paying special attention to the use of learned distributed representations of textual units. We highlight the successes of neural IR thus far, catalog obstacles to its wider adoption, and suggest potentially promising directions for future research.
Griddler: Intelligent computer aided design of complex solar cell metallization patterns

Solar cell designs with complex metallization geometries such as metal wrap through (MWT), interdigitated-back-contact (IBC) cells, and metal grids with non-ideal features like finger breaks, finger striations and non-uniform contact resistance, are not amenable to simple series resistance (Rs) analysis based on small unit cells. In order to accurately simulate these cells, we developed a program that captures the cell metallization geometry from rastered images/CAD files, and efficiently meshes the cell plane for finite element analysis, yielding standard data such as the I-V curve, voltage and Rs distribution. The program also features a powerful post processor that predicts the rate of change in efficiency with respect to incremental changes in the metallization pattern, opening up the possibility of intelligent computer aided design procedures.
Consensus Attention-based Neural Networks for Chinese Reading Comprehension

Reading comprehension has embraced a booming in recent NLP research. Several institutes have released the Cloze-style reading comprehension data, and these have greatly accelerated the research of machine comprehension. In this work, we firstly present Chinese reading comprehension datasets, which consist of People Daily news dataset and Children’s Fairy Tale (CFT) dataset. Also, we propose a consensus attention-based neural network architecture to tackle the Cloze-style reading comprehension problem, which aims to induce a consensus attention over every words in the query. Experimental results show that the proposed neural network significantly outperforms the state-of-the-art baselines in several public datasets. Furthermore, we setup a baseline for Chinese reading comprehension task, and hopefully this would speed up the process for future research.
Residual encoder-decoder network introduced for multisource SAR image despeckling

Synthetic Aperture Radar (SAR) image is disturbed by multiplicative noise known as speckle. In this paper, based on the power of deep fully convolutional network, an encoding-decoding framework is introduced for multisource SAR image despeckling. The network contains a series of convolution and deconvolution layers, forming an end-to-end non-linear mapping between noise and clean SAR images. With addition of skip connection, the network can keep image details and accomplish the strategy for residual learning which solves the notorious problem of vanishing gradients and accelerates convergence. The experimental results on simulated and real SAR images show that the introduced approach achieves improvements in both despeckling performance and time efficiency over the state-of-the-art despeckling methods.
Shape-controlled synthesis of gold and silver nanoparticles.

Monodisperse samples of silver nanocubes were synthesized in large quantities by reducing silver nitrate with ethylene glycol in the presence of poly(vinyl pyrrolidone) (PVP). These cubes were single crystals and were characterized by a slightly truncated shape bounded by [100], [110], and [111] facets. The presence of PVP and its molar ratio (in terms of repeating unit) relative to silver nitrate both played important roles in determining the geometric shape and size of the product. The silver cubes could serve as sacrificial templates to generate single-crystalline nanoboxes of gold: hollow polyhedra bounded by six [100] and eight [111] facets. Controlling the size, shape, and structure of metal nanoparticles is technologically important because of the strong correlation between these parameters and optical, electrical, and catalytic properties.
A Dataset for Multimodal Question Answering in the Cultural Heritage Domain

Multimodal question answering in the cultural heritage domain allows visitors to museums, landmarks or other sites to ask questions in a more natural way. This in turn provides better user experiences. In this paper, we propose the construction of a golden standard dataset dedicated to aiding research into multimodal question answering in the cultural heritage domain. The dataset, soon to be released to the public, contains multimodal content about the fascinating old-Egyptian Amarna period, including images of typical artworks, documents about these artworks (containing images) and over 800 multimodal queries integrating visual and textual questions. The multimodal questions and related documents are all in English. The multimodal questions are linked to relevant paragraphs in the related documents that contain the answer to the multimodal query.
Building brands without mass media.

Costs, market fragmentation, and new media channels that let customers bypass advertisements seem to be in league against the old ways of marketing. Relying on mass media campaigns to build strong brands may be a thing of the past. Several companies in Europe, making a virtue of necessity, have come up with alternative brand-building approaches and are blazing a trail in the post-mass-media age. In England, Nestlé's Buitoni brand grew through programs that taught the English how to cook Italian food. The Body Shop garnered loyalty with its support of environmental and social causes. Cadbury funded a theme park tied to its history in the chocolate business. Häagen-Dazs opened posh ice-cream parlors and got itself featured by name on the menus of fine restaurants. Hugo Boss and Swatch backed athletic or cultural events that became associated with their brands. The various campaigns shared characteristics that could serve as guidelines for any company hoping to build a successful brand: senior managers were closely involved with brand-building efforts; the companies recognized the importance of clarifying their core brand identity; and they made sure that all their efforts to gain visibility were tied to that core identity. Studying the methods of companies outside one's own industry and country can be instructive for managers. Pilot testing and the use of a single and continuous measure of brand equity also help managers get the most out of novel approaches in their ever more competitive world.
The central surface density of “ dark halos ” predicted by MOND

Prompted by the recent claim, by Donato & al., of a quasi-universal central surface density of galaxy dark matter halos, I look at what MOND has to say on the subject. MOND, indeed, predicts a quasi-universal value of this quantity for objects of all masses and of any internal structure, provided they are mostly in the Newtonian regime; i.e., that their mean acceleration is at or above a 0. The predicted value is γΣ M , with Σ M ≡ a 0 /2πG = 138(a 0 /1.2 × 10 −8 cm s −2)M ⊙ pc −2 , and γ a constant of order 1 that depends only on the form of the MOND interpolating function. For the nominal value of a 0 , log(Σ M /M ⊙ pc −2) = 2.14, which is consistent with that found by Doanato & al. of 2.15 ± 0.2. MOND predicts, on the other hand, that this quasi-universal value is not shared by objects with much lower mean accelerations. It permits halo central surface densities that are arbitrarily small, if the mean acceleration inside the object is small enough. However, for such low-surface-density objects, MOND predicts a halo surface density that scales as the square root of the baryonic one, and so the range of the former is much compressed relative to the latter. This explains, in part, the finding of Donato & al. that the universal value applies to low acceleration systems as well. Looking at literature results for a number of the lowest surface-density disk galaxies with rotation-curve analysis, I find that, indeed, their halo surface densities are systematically lower then the above " universal " value. The prediction of Σ M as an upper limit, and accumulation value, of halo central surface densities, pertains, unlike most other MOND predictions, to a pure " halo " property, not to a relation between baryonic and " dark matter " properties.
A Question-Answering framework for plots using Deep learning

Deep Learning has managed to push boundaries in a wide variety of tasks. One area of interest is to tackle problems in reasoning and understanding, in an aim to emulate human intelligence. In this work, we describe a deep learning model that addresses the reasoning task of question-answering on bar graphs and pie charts. We introduce a novel architecture that learns to identify various plot elements, quantify the represented values and determine a relative ordering of these statistical values. We test our model on the recently released FigureQA dataset, which provides images and accompanying questions, for bar graphs and pie charts, augmented with rich annotations. Our approach outperforms the state-of-the-art Relation Networks baseline and traditional CNN-LSTM models when evaluated on this dataset. Our model also has a considerably faster training time of approximately 2 days on 1 GPU compared to the Relation Networks baseline which requires around two weeks to train on 4 GPUs.
The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries

This paper presents a method for combining query-relevance with information-novelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce redundancy while maintaining query relevance in re-ranking retrieved documents and in selecting apprw priate passages for text summarization. Preliminary results indicate some benefits for MMR diversity ranking in document retrieval and in single document summarization. The latter are borne out by the recent results of the SUMMAC conference in the evaluation of summarization systems. However, the clearest advantage is demonstrated in constructing non-redundant multi-document summaries, where MMR results are clearly superior to non-MMR passage selection.
A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches

This paper presents and compares WordNetbased and distributional similarity approaches. The strengths and weaknesses of each approach regarding similarity and relatedness tasks are discussed, and a combination is presented. Each of our methods independently provide the best results in their class on the RG and WordSim353 datasets, and a supervised combination of them yields the best published results on all datasets. Finally, we pioneer cross-lingual similarity, showing that our methods are easily adapted for a cross-lingual task with minor losses.
Latent Alignment and Variational Attention

Neural attention has become central to many state-of-the-art models in natural language processing and related domains. Attention networks are an easy-to-train and effective method for softly simulating alignment; however, the approach does not marginalize over latent alignments in a probabilistic sense. This property makes it difficult to compare attention to other alignment approaches, to compose it with probabilistic models, and to perform posterior inference conditioned on observed data. A related latent approach, hard attention, fixes these issues, but is generally harder to train and less accurate. This work considers variational attention networks, alternatives to soft and hard attention for learning latent variable alignment models, with tighter approximation bounds based on amortized variational inference. We further propose methods for reducing the variance of gradients to make these approaches computationally feasible. Experiments show that for machine translation and visual question answering, inefficient exact latent variable models outperform standard neural attention, but these gains go away when using hard attention based training. On the other hand, variational attention retains most of the performance gain but with training speed comparable to neural attention.
Red Media , Blue Media : Evidence of Ideological Selectivity in Media Use

We show that the demand for news varies with the perceived affinity of the news organization to the consumer’s political preferences. In an experimental setting, conservatives and Republicans preferred to read news reports attributed to Fox News and to avoid news from CNN and NPR. Democrats and liberals exhibited exactly the opposite syndrome—dividing their attention equally between CNN and NPR, but avoiding Fox News. This pattern of selective exposure based on partisan affinity held not only for news coverage of controversial issues but also for relatively ‘‘soft’’ subjects such as crime and travel. The tendency to select news based on anticipated agreement was also strengthened among more politically engaged partisans. Overall, these results suggest that the further proliferation of new media and enhanced media choices may contribute to the further polarization of the news audience.
Crowd Scene Understanding from Video: A Survey

Crowd video analysis has applications in crowd management, public space design, and visual surveillance. Example tasks potentially aided by automated analysis include anomaly detection (such as a person walking against the grain of traffic or rapid assembly/dispersion of groups of people), population and density measurements, and interactions between groups of people. This survey explores crowd analysis as it relates to two primary research areas: crowd statistics and behavior understanding. First, we survey methods for counting individuals and approximating the density of the crowd. Second, we showcase research efforts on behavior understanding as related to crowds. These works focus on identifying groups, interactions within small groups, and abnormal activity detection such as riots and bottlenecks in large crowds. Works presented in this section also focus on tracking groups of individuals, either as a single entity or a subset of individuals within the frame of reference. Finally, a summary of datasets available for crowd activity video research is provided.
Minimum Bayes-Risk Decoding for Statistical Machine Translation

We present Minimum Bayes-Risk (MBR) decoding for statistical machine translation. This statistical approach aims to minimize expected loss of translation errors under loss functions that measure translation performance. We describe a hierarchy of loss functions that incorporate different levels of linguistic information from word strings, word-to-word alignments from an MT system, and syntactic structure from parse-trees of source and target language sentences. We report the performance of the MBR decoders on a Chinese-to-English translation task. Our results show that MBR decoding can be used to tune statistical MT performance for specific loss functions.
Developing a Reliable Core Stability Assessment Battery for Patients with Nonspecific Low Back Pain.

Test-retest design.



The objective was to examine the intrarater (test-retest) reliability of the core stability related tests and to develop a reliable core stability assessment battery.



Studies suggest that core stability exercises may improve function and decrease pain in patients with nonspecific low back pain (LBP). Reliable clinical tests are required to implement adequate rehabilitation and to evaluate results of these interventions.



The study had a test-retest design. Thirty-three different tests that might relate to core stability were identified with their mostly used protocols. Five different components of core stability including endurance, flexibility, strength, functional performance, and motor control were assessed in 38 patients with nonspecific LBP. The same testing procedure was performed again after 48 to 72 hours. Intraclass correlation coefficients (ICCs), standard error of measurement, and minimal detectable change were calculated to assess the intrarater reliability.



The intrarater reliability of the tests ranged from little to very high (ICC = 0.08-0.98). Partial curl-up (ICC = 0.90), lateral bridge (ICC = 0.95-0.96), trunk flexor endurance (ICC = 0.97), sit and reach (ICC = 0.98), single-legged hop (ICC = 0.98-0.97), lateral step-down (ICC = 0.93-0.92), eyes open right and left leg unilateral stance (ICC = 0.97 and 0.91) tests had the highest intrarater reliability for each core stability component.



The results indicated that the partial curl-up test (strength), side bridge and trunk flexor tests (endurance), sit-and-reach test (flexibility), single-legged hop, and lateral step-down (functional), unilateral stance test with eyes open (motor control) had very high intrarater reliability. A core stability assessment battery involving these tests can be used in patients with nonspecific LBP to assess all components of core stability.
Correcting Chinese Word Usage Errors for Learning Chinese as a Second Language

With more and more people around the world learning Chinese as a second language, the need of Chinese error correction tools is increasing. In the HSK dynamic composition corpus, word usage error (WUE) is the most common error type. In this paper, we build a neural network model that considers both target erroneous token and context to generate a correction vector and compare it against a candidate vocabulary to propose suitable corrections. To deal with potential alternative corrections, the top five proposed candidates are judged by native Chinese speakers. For more than 91% of the cases, our system can propose at least one acceptable correction within a list of five candidates. To the best of our knowledge, this is the first research addressing general-type Chinese WUE correction. Our system can help non-native Chinese learners revise their sentences by themselves. Title and Abstract in Chinese