nils-herrmann/clean_scirepeval huggingface dataset:
Fusion of Stereo and Still Monocular Depth Estimates in a Self-Supervised Learning Context

We study how autonomous robots can learn by themselves to improve their depth estimation capability. In particular, we investigate a self-supervised learning setup in which stereo vision depth estimates serve as targets for a convolutional neural network (CNN) that transforms a single still image to a dense depth map. After training, the stereo and mono estimates are fused with a novel fusion method that preserves high confidence stereo estimates, while leveraging the CNN estimates in the low-confidence regions. The main contribution of the article is that it is shown that the fused estimates lead to a higher performance than the stereo vision estimates alone. Experiments are performed on the KITTI dataset, and on board of a Parrot SLAMDunk, showing that even rather limited CNNs can help provide stereo vision equipped robots with more reliable depth maps for autonomous navigation.
MIMO and beamforming solutions for 5G technology

Multi-antenna technologies such as beamforming and Multiple-Input, Multiple-Output (MIMO) are anticipated to play a key role in “5G” systems, which are expected to be deployed in the year 2020 and beyond. With a class of 5G systems expected to be deployed in both cm-wave (3-30 GHz) and mm-wave (30-300 GHz) bands, the unique characteristics and challenges of those bands have prompted a revisiting of the design and performance tradeoffs associated with existing multi-antenna techniques in order to determine the preferred framework for deploying MIMO technology in 5G systems. In this paper, we discuss key implementation issues surrounding the deployment of transmit MIMO processing for 5G systems. We describe MIMO architectures where the transmit MIMO processing is implemented at baseband, RF, and a combination of RF and baseband (a hybrid approach). We focus on the performance and implementation issues surrounding several candidate techniques for multi-user-MIMO (MU-MIMO) transmission in the mm-wave bands.
The TRMM Multisatellite Precipitation Analysis ( TMPA ) : Quasi-Global , Multiyear , Combined-Sensor Precipitation Estimates at Fine Scales

The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining precipitation estimates from multiple satellites, as well as gauge analyses where feasible, at fine scales (0.25° 0.25° and 3 hourly). TMPA is available both after and in real time, based on calibration by the TRMM Combined Instrument and TRMM Microwave Imager precipitation products, respectively. Only the after-real-time product incorporates gauge data at the present. The dataset covers the latitude band 50°N–S for the period from 1998 to the delayed present. Early validation results are as follows: the TMPA provides reasonable performance at monthly scales, although it is shown to have precipitation rate–dependent low bias due to lack of sensitivity to low precipitation rates over ocean in one of the input products [based on Advanced Microwave Sounding Unit-B (AMSU-B)]. At finer scales the TMPA is successful at approximately reproducing the surface observation–based histogram of precipitation, as well as reasonably detecting large daily events. The TMPA, however, has lower skill in correctly specifying moderate and light event amounts on short time intervals, in common with other finescale estimators. Examples are provided of a flood event and diurnal cycle determination.
Universal Deep Neural Network Compression

In this paper, we investigate lossy compression of deep neural networks (DNNs) 1 by weight quantization and lossless source coding for memory-efficient deploy2 ment. Whereas the previous work addressed non-universal scalar quantization and 3 entropy coding of DNN weights, we for the first time introduce universal DNN 4 compression by universal vector quantization and universal source coding. In 5 particular, we examine universal randomized lattice quantization of DNNs, which 6 randomizes DNN weights by uniform random dithering before lattice quantization 7 and can perform near-optimally on any source without relying on knowledge of 8 its probability distribution. Moreover, we present a method of fine-tuning vector 9 quantized DNNs to recover the performance loss after quantization. Our experi10 mental results show that the proposed universal DNN compression scheme com11 presses the 32-layer ResNet (trained on CIFAR-10) and the AlexNet (trained on 12 ImageNet) with compression ratios of 47.1 and 42.5, respectively.
Automatic Construction and Evaluation of a Large Semantically Enriched Wikipedia

The hyperlink structure of Wikipedia constitutes a key resource for many Natural Language Processing tasks and applications, as it provides several million semantic annotations of entities in context. Yet only a small fraction of mentions across the entire Wikipedia corpus is linked. In this paper we present the automatic construction and evaluation of a Semantically Enriched Wikipedia (SEW) in which the overall number of linked mentions has been more than tripled solely by exploiting the structure of Wikipedia itself and the wide-coverage sense inventory of BabelNet. As a result we obtain a sense-annotated corpus with more than 200 million annotations of over 4 million different concepts and named entities. We then show that our corpus leads to competitive results on multiple tasks, such as Entity Linking and Word Similarity.
News framing: Theory and typology

The increasing popularity of the framing concept in media analysis goes hand in hand with significant inconsistency in its application. This paper outlines an integrated process model of framing that includes production, content, and media use perspectives. A typology of generic and issue-specific frames is proposed based on previous studies of media frames. An example is given of how generic news frames may be identified and used to understand cross-national differences in news coverage. The paper concludes with an identification of contentious issues in current framing research.
Job sculpting: the art of retaining your best people.

Hiring good people is tough, but keeping them can be even tougher. The professionals streaming out of today's MBA programs are so well educated and achievement oriented that they could do well in virtually any job. But will they stay? According to noted career experts Timothy Butler and James Waldroop, only if their jobs fit their deeply embedded life interests--that is, their long-held, emotionally driven passions. Butler and Waldroop identify the eight different life interests of people drawn to business careers and introduce the concept of job sculpting, the art of matching people to jobs that resonate with the activities that make them truly happy. Managers don't need special training to job sculpt, but they do need to listen more carefully when employees describe what they like and dislike about their jobs. Once managers and employees have discussed deeply embedded life interests--ideally, during employee performance reviews--they can work together to customize future work assignments. In some cases, that may mean simply adding another assignment to existing responsibilities. In other cases, it may require moving that employee to a new position altogether. Skills can be stretched in many directions, but if they are not going in the right direction--one that is congruent with deeply embedded life interests--employees are at risk of becoming dissatisfied and uncommitted. And in an economy where a company's most important asset is the knowledge, energy, and loyalty of its people, that's a large risk to take.
University of Mannheim @ CLSciSumm-17: Citation-Based Summarization of Scientific Articles Using Semantic Textual Similarity

The number of publications is rapidly growing and it is essential to enable fast access and analysis of relevant articles. In this paper, we describe a set of methods based on measuring semantic textual similarity, which we use to semantically analyze and summarize publications through other publications that cite them. We report the performance of our approach in the context of the third CL-SciSumm shared task and show that our system performs favorably to competing systems in terms of produced summaries.
Object-Oriented Software Engineering : Measuring and Controlling the Development Process

Although the benefits of Object-Orientation are manifold and it is, for certain, one of the mainstays for software production in the future, it will only achieve widespread practical acceptance when the management aspects of the software development process using this technology are carefully addressed. Here, software metrics play an important role allowing, among other things, better planning, the assessment of improvements, the reduction of unpredictability, early identification of potential problems and productivity evaluation. This paper proposes a set of metrics suitable for evaluating the use of the main abstractions of the Object-Oriented paradigm such as inheritance, encapsulation, information hiding or polymorphism and the consequent emphasis on reuse that, together, are believed to be responsible for the increase in software quality and development productivity. Those metrics are aimed at helping to establish comparisons throughout the practitioners’ community and setting design recommendations that may eventually become organization standards. Some desirable properties for such a metrics set are also presented. Future lines of research are envisaged.
Structure Fusion and Propagation for Zero-Shot Learning

The key of zero-shot learning (ZSL) is how to find the information transfer model for bridging the gap between images and semantic information (texts or attributes). Existing ZSL methods usually construct the compatibility function between images and class labels with the consideration of the relevance on the semantic classes (the manifold structure of semantic classes). However, the relationship of image classes (the manifold structure of image classes) is also very important for the compatibility model construction. It is difficult to capture the relationship among image classes due to unseen classes, so that the manifold structure of image classes often is ignored in ZSL. To complement each other between the manifold structure of image classes and that of semantic classes information, we propose structure propagation (SP) for improving the performance of ZSL for classification. SP can jointly consider the manifold structure of image classes and that of semantic classes for approximating to the intrinsic structure of object classes. Moreover, the SP can describe the constrain condition between the compatibility function and these manifold structures for balancing the influence of the structure propagation iteration. The SP solution provides not only unseen class labels but also the relationship of two manifold structures that encode the positive transfer in structure propagation. Experimental results demonstrate that SP can attain the promising results on the AwA, CUB, Dogs and SUN databases.
Novel Treponema pallidum serologic tests: a paradigm shift in syphilis screening for the 21st century.

The mainstay of diagnosis for Treponema pallidum infections is based on nontreponemal and treponemal serologic tests. Many new diagnostic methods for syphilis have been developed, using specific treponemal antigens and novel formats, including rapid point-of-care tests, enzyme immunoassays, and chemiluminescence assays. Although most of these newer tests are not yet cleared for use in the United States by the Food and Drug Administration, their performance and ease of automation have promoted their application for syphilis screening. Both sensitive and specific, new screening tests detect antitreponemal IgM and IgG antibodies by use of wild-type or recombinant T. pallidum antigens. However, these tests cannot distinguish between recent and remote or treated versus untreated infections. In addition, the screening tests require confirmation with nontreponemal tests. This use of treponemal tests for screening and nontreponemal serologic tests as confirmatory tests is a reversal of long-held practice. Clinicians need to understand the science behind these tests to use them properly in syphilis management.
Pharmacological effects of garlic (Allium sativum L.)

Singh VK, Singh DK. Pharmacological Effects of Garlic (Allium sativum L.). ARBS Annu Rev Biomed Sci 2008;10:6-26. Garlic (Allium sativum L.) is a bulbous herb used as a food item, spice and medicine in different parts of the world. Its medicinal use is based on traditional experience passed from generation to generation. Researchers from various disciplines are now directing their efforts towards discovering the effects of garlic on human health. Interest in garlic among researchers, particularly those in medical profession, has stemmed from the search for a drug that has a broad-spectrum therapeutic effect with minimal toxicity. Recent studies indicate that garlic extract has antimicrobial activity against many genera of bacteria, fungi and viruses. The role of garlic in preventing cardiovascular disease has been acclaimed by several authors. Chemical constituents of garlic have been investigated for treatment of hyperlipidemia, hypertension, platelet aggregation and blood fibrinolytic activity. Experimental data indicate that garlic may have anticarcinogenic effect. Recent researches in the area of pest control show that garlic has strong insecticidal, nematicidal, rodenticidal and molluscicidal activity. Despite field trials and laboratory experiments on the pesticidal activity of garlic have been conducted, more studies on the way of delivery in environment and mode of action are still recommended for effective control of pest. Adverse effects of oral ingestion and topical exposure of garlic include body odor, allergic reactions, acceleration in the effects of anticoagulants and reduction in the efficacy of anti-AIDS drug Saquinavir.
First Workshop on Knowledge Base Construction, Mining and Reasoning

1.  Meanwhile, the ability of mining and reasoning over such constructed databases is at the core of powering various downstream applications on web and mobile devices. Recently, we have witnessed a signicant amount of interests in building large-scale knowledge bases (KBs) from massive, unstructured data sources (e.g., Wikipedia-based methods such as DBpedia [9], YAGO [19], Wikidata [22], automated systems like Snowball , KnowItAll , NELL [4] and DeepDive , and opendomain approaches like Open IE  and Universal Schema ); as well as mining and reasoning over such knowledge bases to empower a wide variety of intelligent services, including question answering [6], recommender systems  and semantic search . Automated construction, mining and reasoning of the knowledge bases have become possible as research advances in many related areas such as information extraction, natural language processing, data mining, search, machine learning, databases and data integration. However, there are still substantial scientic and engineering challenges in advancing and integrating such relevant methodologies. e goal of this proposed workshop is to gather together leading experts from industry and academia to share their visions about the eld, discuss latest research results, and exchange exciting ideas. With a focus on invited talks and position papers, the workshop aims to provide a vivid forum of discussion about knowledge base-related research. 2. Relevance to WSDM. Knowledge base construction, mining and reasoning is closely related to a wide variety of applications in WSDM, including web search, question answering, and recommender systems. Building a high-quality knowledge base from
L O ] 2 0 Ju l 2 01 8 Learning Heuristics for Automated Reasoning through Deep Reinforcement Learning

We demonstrate how to learn efficient heuristics for automated reasoning algorithms through deep reinforcement learning. We consider search algorithms for quantified Boolean logics, that already can solve formulas of impressive size up to 100s of thousands of variables. The main challenge is to find a representation which lends to making predictions in a scalable way. The heuristics learned through our approach significantly improve over the handwritten heuristics for several sets of formulas.
Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery

We introduce a dynamical spatio-temporal model formalized as a recurrent neural network for forecasting time series of spatial processes, i.e. series of observations sharing temporal and spatial dependencies. The model learns these dependencies through a structured latent dynamical component, while a decoder predicts the observations from the latent representations. We consider several variants of this model, corresponding to different prior hypothesis about the spatial relations between the series. The model is evaluated and compared to state-of-the-art baselines, on a variety of forecasting problems representative of different application areas: epidemiology, geo-spatial statistics and car-traffic prediction. Besides these evaluations, we also describe experiments showing the ability of this approach to extract relevant spatial relations.
Human-Like Playtesting with Deep Learning

We present an approach to learn and deploy human-like playtesting in computer games based on deep learning from player data. We are able to learn and predict the most "human" action in a given position through supervised learning on a convolutional neural network. Furthermore, we show how we can use the learned network to predict key metrics of new content — most notably the difficulty of levels. Our player data and empirical data come from Candy Crush Saga (CCS) and Candy Crush Soda Saga (CCSS). However, the method is general and well suited for many games, in particular where content creation is sequential. CCS and CCSS are non-deterministic match-3 puzzle games with multiple game modes spread over a few thousand levels, providing a diverse testbed for this technique. Compared to Monte Carlo Tree Search (MCTS) we show that this approach increases correlation with average level difficulty, giving more accurate predictions as well as requiring only a fraction of the computation time.
Hierarchical Deep Reinforcement Learning Agent with Counter Self-play on Competitive Games

Deep Reinforcement Learning algorithms lead to agents that can solve difficult decision making problems in complex environments. However, many difficult multi-agent competitive games, especially real-time strategy games are still considered beyond the capability of current deep reinforcement learning algorithms, although there has been a recent effort to change this (OpenAI, 2017; Vinyals et al., 2017). Moreover, when the opponents in a competitive game are suboptimal, the current Nash Equilibrium seeking, selfplay algorithms are often unable to generalize their strategies to opponents that play strategies vastly different from their own. This suggests that a learning algorithm that is beyond conventional self-play is necessary. We develop Hierarchical Agent with Self-Play , a learning approach for obtaining hierarchically structured policies that can achieve higher performance than conventional self-play on competitive games through the use of a diverse pool of sub-policies we get from Counter Self-Play (CSP). We demonstrate that the ensemble policy generated by Hierarchical Agent with Self-Play can achieve better performance while facing unseen opponents that use sub-optimal policies. On a motivating iterated Rock-Paper-Scissor game and a partially observable real-time strategic game (http://generals.io/), we are led to the conclusion that Hierarchical Agent with Self-Play can perform better than conventional self-play as well as achieve 77% win rate against FloBot, an open-source agent which has ranked at position number 2 on the online leaderboards.
Petri Nets approach to simulate and control of Flexible Manufacturing Systems

This paper presents deadlock prevention are use to solve the deadlock problem of flexible manufacturing systems (FMS). Petri nets have been successfully as one of the most powerful tools for modeling of FMS. Their modeling power and a mathematical arsenal supporting the analysis of the modeled systems stimulate the increasing interest in Petri nets. With the structural object of Petri nets, siphons are important in the analysis and control of deadlocks in Petri nets (PNs) excellent properties. The deadlock prevention method are caused by the unmarked siphons, during the Petri nets are an effective way to model, analyze, simulation and control deadlocks in FMS is presented in this work. The characterization of special structural elements in Petri net so-called siphons has been a major approach for the investigation of deadlock-freeness in the center of FMS. The siphons are structures which allow for some implications on the net's can be well controlled by adding a control place (called monitor) for each uncontrolled siphon in the net in order to become deadlock-free situation in the system. Finally, We proposed method of modeling, simulation, control of FMS by using Petri nets, where deadlock analysis have Production line in parallel processing is demonstrate by a practical example used Petri Net-tool in MATLAB, is effective, and explicitly although its off-line computation.
Microcrystalline cellulose, a direct compression binder in a quality by design environment--a review.

The ICH quality vision introduced the concept of quality by design (QbD), which requires a greater understanding of the raw material attributes, of process parameters, of their variability and their interactions. Microcrystalline cellulose (MCC) is one of the most important tableting excipients thanks to its outstanding dry binding properties, enabling the manufacture of tablets by direct compression (DC). DC remains the most economical technique to produce large batches of tablets, however its efficacy is directly impacted by the raw material attributes. Therefore excipients' variability and their impact on drug product performance need to be thoroughly understood. To help with this process, this review article gathers prior knowledge on MCC, focuses on its use in DC and lists some of its potential critical material attributes (CMAs).
A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts

Most existing zero-shot learning methods consider the problem as a visual semantic embedding one. Given the demonstrated capability of Generative Adversarial Networks(GANs) to generate images, we instead leverage GANs to imagine unseen categories from text descriptions and hence recognize novel classes with no examples being seen. Specifically, we propose a simple yet effective generative model that takes as input noisy text descriptions about an unseen class (e.g. Wikipedia articles) and generates synthesized visual features for this class. With added pseudo data, zero-shot learning is naturally converted to a traditional classification problem. Additionally, to preserve the inter-class discrimination of the generated features, a visual pivot regularization is proposed as an explicit supervision. Unlike previous methods using complex engineered regularizers, our approach can suppress the noise well without additional regularization. Empirically, we show that our method consistently outperforms the state of the art on the largest available benchmarks on Text-based Zero-shot Learning.
The Experiential Aspects of Consumption : Consumer Fantasies , Feeiings , and Fun

This paper argues for the recognition of important experiential aspects of consumption. Specifically, a general framework is constructed to represent typical consumer behavior variables. Based on this paradigm, the prevailing information processing model is contrasted with an experiential view that focuses on the symbolic, hedonic, and esthetic nature of consumption. This view regards the consumption experience as a phenomenon directed toward the pursuit of fantasies, feelings, and fun.