nils-herrmann/clean_scirepeval huggingface dataset:
Leveraging Contact Forces for Learning to Grasp

Grasping objects under uncertainty remains an open problem in robotics research. This uncertainty is often due to noisy or partial observations of the object pose or shape. To enable a robot to react appropriately to unforeseen effects, it is crucial that it continuously takes sensor feedback into account. While visual feedback is important for inferring a grasp pose and reaching for an object, contact feedback offers valuable information during manipulation and grasp acquisition. In this paper, we use model-free deep reinforcement learning to synthesize control policies that exploit contact sensing to generate robust grasping under uncertainty. We demonstrate our approach on a multi-fingered hand that exhibits more complex finger coordination than the commonly used twofingered grippers. We conduct extensive experiments in order to assess the performance of the learned policies, with and without contact sensing. While it is possible to learn grasping policies without contact sensing, our results suggest that contact feedback allows for a significant improvement of grasping robustness under object pose uncertainty and for objects with a complex shape.
A Comprehensive Survey of Deep Learning for Image Captioning

Generating a description of an image is called image captioning. Image captioning requires recognizing the important objects, their attributes, and their relationships in an image. It also needs to generate syntactically and semantically correct sentences. Deep-learning-based techniques are capable of handling the complexities and challenges of image captioning. In this survey article, we aim to present a comprehensive review of existing deep-learning-based image captioning techniques. We discuss the foundation of the techniques to analyze their performances, strengths, and limitations. We also discuss the datasets and the evaluation metrics popularly used in deep-learning-based automatic image captioning.
Comparison of Elastic Resistance Band Exercises and Yoga in Physiotherapy Students with Chronic Non-Specific Low Back Pain: A Randomized Clinical Trial

Low back pain (LBP) is a problem worldwide with a lifetime prevalence reported to be as high as 84%. The lifetime prevalence of low back pain is reported to be as high as 84%, and the prevalence of chronic low back pain is about 23%, with 11–12% of the population being disabled by low back pain . LBP is defined as pain experienced between the twelfth rib and the inferior gluteal fold, with or without associated leg pain . Based on the etiology LBP is classified as Specific Low Back Pain and Non-specific Low Back Pain. Of all the LBP patients 10% are attributed to Specific and 90% are attributed to NonSpecific Low Back Pain (NSLBP) . Specific LBP are those back pains which have specific etiology causes like Sponylolisthesis, Spondylosis, Ankylosing Spondylitis, Prolapsed disc etc.
UniBench: A Benchmark for Multi-model Database Management Systems

Unlike traditional database management systems which are organized around a single data model, a multi-model database (MMDB) utilizes a single, integrated back-end to support multiple data models, such as document, graph, relational, and key-value. As more and more platforms are proposed to deal with multi-model data, it becomes crucial to establish a benchmark for evaluating the performance and usability of MMDBs. Previous benchmarks, however, are inadequate for such scenario because they lack a comprehensive consideration for multiple models of data. In this paper, we present a benchmark, called UniBench, with the goal of facilitating a holistic and rigorous evaluation of MMDBs. UniBench consists of a mixed data model, a synthetic multi-model data generator, and a set of core workloads. Specifically, the data model simulates an emerging application: Social Commerce, a Web-based application combining E-commerce and social media. The data generator provides diverse data format including JSON, XML, key-value, tabular, and graph. The workloads are comprised of a set of multi-model queries and transactions, aiming to cover essential aspects of multi-model data management. We implemented all workloads on ArangoDB and OrientDB to illustrate the feasibility of our proposed benchmarking system and show the learned lessons through the evaluation of these two multi-model databases. The source code and data of this benchmark can be downloaded at
Secure Smart Environment Using IOT based on RFID

IOT( Internet of Things) relying on exchange of information through radio frequency identification(RFID), is emerging as one of important technologies that find its use in various applications ranging from healthcare, construction, hospitality to transportation sector and many more. This paper describes about IOT, concentrating its use in improving and securing future shopping. This paper shows how RFID technology makes life easier and secure and thus helpful in the future. KeywordsIOT,RFID, Intelligent shopping, RFID tags, RFID reader, Radio frequency
Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics

Despite outstanding success in vision amongst other domains, many of the recent deep learning approaches have evident drawbacks for robots. This manuscript surveys recent work in the literature that pertain to applying deep learning systems to the robotics domain, either as means of estimation or as a tool to resolve motor commands directly from raw percepts. These recent advances are only a piece to the puzzle. We suggest that deep learning as a tool alone is insufficient in building a unified framework to acquire general intelligence. For this reason, we complement our survey with insights from cognitive development and refer to ideas from classical control theory, producing an integrated direction for a lifelong learning architecture.
The career of metaphor.

A central question in metaphor research is how metaphors establish mappings between concepts from different domains. The authors propose an evolutionary path based on structure-mapping theory. This hypothesis--the career of metaphor--postulates a shift in mode of mapping from comparison to categorization as metaphors are conventionalized. Moreover, as demonstrated by 3 experiments, this processing shift is reflected in the very language that people use to make figurative assertions. The career of metaphor hypothesis offers a unified theoretical framework that can resolve the debate between comparison and categorization models of metaphor. This account further suggests that whether metaphors are processed directly or indirectly, and whether they operate at the level of individual concepts or entire conceptual domains, will depend both on their degree of conventionality and on their linguistic form.
A Retrieve-and-Edit Framework for Predicting Structured Outputs

For the task of generating complex outputs such as source code, editing existing outputs can be easier than generating complex outputs from scratch. With this motivation, we propose an approach that first retrieves a training example based on the input (e.g., natural language description) and then edits it to the desired output (e.g., code). Our contribution is a computationally efficient method for learning a retrieval model that embeds the input in a task-dependent way without relying on a hand-crafted metric or incurring the expense of jointly training the retriever with the editor. Our retrieve-and-edit framework can be applied on top of any base model. We show that on a new autocomplete task for GitHub Python code and the Hearthstone cards benchmark, retrieve-and-edit significantly boosts the performance of a vanilla sequence-to-sequence model on both tasks.
10 Communicative Language Teaching in the twenty-first century : the ' Principled Communicative Approach '

Earl Stevick has always been interested in improving language teaching methodology, and he has never been afraid of innovation. His seminal work,  A Way and Ways (Stevick 1980), introduced many of us to Counselling-Learning and Suggestopedia for the first time, and in Memory, Meaning and  A View of Language Teaching. (Stevick 1996) he discussed a wide range of theoretical and practical considerations to help us better understand the intricate cognitive and interpersonal processes whereby a language is acquired and then used for meaningful communication. The proposal in this chapter to revitalize Communicative Language Teaching (CLT) in the light of contemporary scholarly advances is fully within the spirit of Earl's approach.' By the turn of the new millennium, CLT had become a real buzzword in language teaching methodology, but the extent to which the term covers a well-defined and uniform teaching method is highly questionable. In fact, since the genesis of CLT in the early 1970s, its proponents have developed a very wide range of variants that were only loosely related to each other (for overviews, see Savignon 2005; Spada 2007). In this chapter I first look at the core characteristics of CLT to explore the roots of the diverse interpretations and then argue that in order for CLT to fulfil all the expectations attached to it in the twenty-first century, the method needs to be revised according to the latest findings of psycholinguistic research. I will conclude the chapter by outlining the main principles of a proposed revised approach that I have termed the `Principled Communicative Approach' (PCA).
Radiation Pattern Synthesis for Monopulse Radar Applications With a Reconfigurable Transmitarray Antenna

We investigate here the capabilities of a 400-element reconfigurable transmitarray antenna to synthesize monopulse radiation patterns for radar applications in X-band. The generation of the sum (Σ) and difference (A) patterns are demonstrated both theoretically and experimentally for broadside as well as tilted beams in different azimuthal planes. Two different feed configurations have been considered, namely, a single focal source and a four-element focal source configuration. The latter enables the simultaneous generation of a Σ- and two A-patterns in orthogonal planes, which is an important advantage for tracking applications with stringent requirements in speed and accuracy.
Being a DevOps Developer

THE IDYLL has been going on for decades. DevOps, the synergy between software development and IT operations, was an open secret before it became a mass movement. Passionate programmers were often also closet system administrators—sometimes literally so, by nurturing recycled hardware in their home’s closet. These same programmers were also drawn to the machine room, chatting with the administrators about disk-partitioning schemes, backup strategies, and new OS releases. Not to be outdone, zealous administrators would nd endless excuses to develop all sorts of nifty software: deployment automation, monitoring, provisioning, and reporting tools. Many factors are propelling the increased adoption of DevOps. First, software is increasingly being offered over the Internet as a service instead of being developed as an organization’s bespoke system or a shrinkwrapped product. This makes operations an integral part of the offering, driving demands for service quality. Then there’s the agile movement. Its emphasis on cooperation between all stakeholders has helped formalize the relationship between development and operations. Its acceptance of change has driven demand for processes and tools that will let systems respond to change swiftly and ef ciently. Another enabler has been the availability of powerful and plentiful hardware. It has allowed the abstraction of system infrastructure and its expression as code amenable to established software development practices. Resource virtualization and cloud computing have provided the required building blocks. In many IT sectors, DevOps is here to stay, helping deliver higherquality services more ef ciently. How can you, as a software practitioner, embrace DevOps to increase your organization’s effectiveness?
Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review

To conduct a systematic review of deep learning models for electronic health record (EHR) data, and illustrate various deep learning architectures for analyzing different data sources and their target applications. We also highlight ongoing research and identify open challenges in building deep learning models of EHRs.



We searched PubMed and Google Scholar for papers on deep learning studies using EHR data published between January 1, 2010, and January 31, 2018. We summarize them according to these axes: types of analytics tasks, types of deep learning model architectures, special challenges arising from health data and tasks and their potential solutions, as well as evaluation strategies.



We surveyed and analyzed multiple aspects of the 98 articles we found and identified the following analytics tasks: disease detection/classification, sequential prediction of clinical events, concept embedding, data augmentation, and EHR data privacy. We then studied how deep architectures were applied to these tasks. We also discussed some special challenges arising from modeling EHR data and reviewed a few popular approaches. Finally, we summarized how performance evaluations were conducted for each task.



Despite the early success in using deep learning for health analytics applications, there still exist a number of issues to be addressed. We discuss them in detail including data and label availability, the interpretability and transparency of the model, and ease of deployment.
Occupational safety and health criteria for responsible development of nanotechnology

Organizations around the world have called for the responsible development of nanotechnology. The goals of this approach are to emphasize the importance of considering and controlling the potential adverse impacts of nanotechnology in order to develop its capabilities and benefits. A primary area of concern is the potential adverse impact on workers, since they are the first people in society who are exposed to the potential hazards of nanotechnology. Occupational safety and health criteria for defining what constitutes responsible development of nanotechnology are needed. This article presents five criterion actions that should be practiced by decision-makers at the business and societal levels-if nanotechnology is to be developed responsibly. These include (1) anticipate, identify, and track potentially hazardous nanomaterials in the workplace; (2) assess workers' exposures to nanomaterials; (3) assess and communicate hazards and risks to workers; (4) manage occupational safety and health risks; and (5) foster the safe development of nanotechnology and realization of its societal and commercial benefits. All these criteria are necessary for responsible development to occur. Since it is early in the commercialization of nanotechnology, there are still many unknowns and concerns about nanomaterials. Therefore, it is prudent to treat them as potentially hazardous until sufficient toxicology, and exposure data are gathered for nanomaterial-specific hazard and risk assessments. In this emergent period, it is necessary to be clear about the extent of uncertainty and the need for prudent actions.
Towards Energy-Efficient Wireless Networking in the Big Data Era: A Survey

With the proliferation of wireless devices, wireless networks in various forms have become global information infrastructure and an important part of our daily life, which, at the same time, incur fast escalations of both data volumes and energy demand. In other words, energy-efficient wireless networking is a critical and challenging issue in the big data era. In this paper, we provide a comprehensive survey of recent developments on energy-efficient wireless networking technologies that are effective or promisingly effective in addressing the challenges raised by big data. We categorize existing research into two main parts depending on the roles of big data. The first part focuses on energy-efficient wireless networking techniques in dealing with big data and covers studies in big data acquisition, communication, storage, and computation; while the second part investigates recent approaches based on big data analytics that are promising to enhance energy efficiency of wireless networks. In addition, we identify a number of open issues and discuss future research directions for enhancing energy efficiency of wireless networks in the big data era.
Orchestrated Objective Reduction of Quantum Coherence in Brain Microtubules : The " Orch OR " Model for Consciousness

Features of consciousness difficult to understand in terms of conventional neuroscience have evoked application of quantum theory, which describes the fundamental behavior of matter and energy. In this paper we propose that aspects of quantum theory (e.g. quantum coherence) and of a newly proposed physical phenomenon of quantum wave function "self-collapse"(objective reduction: OR Penrose, 1994) are essential for consciousness, and occur in cytoskeletal microtubules and other structures within each of the brain's neurons. The particular characteristics of microtubules suitable for quantum effects include their crystal-like lattice structure, hollow inner core, organization of cell function and capacity for information processing. We envisage that conformational states of microtubule subunits (tubulins) are coupled to internal quantum events, and cooperatively interact (compute) with other tubulins. We further assume that macroscopic coherent superposition of quantum-coupled tubulin conformational states occurs throughout significant brain volumes and provides the global binding essential to consciousness. We equate the emergence of the microtubule quantum coherence with pre-conscious processing which grows (for up to 500 milliseconds) until the mass-energy difference among the separated states of tubulins reaches a threshold related to quantum gravity. According to the arguments for OR put forth in Penrose (1994), superpositioned states each have their own space-time geometries. When the degree of coherent mass-energy difference leads to sufficient separation of space-time geometry, the system must choose and decay (reduce, collapse) to a single universe state. In this way, a transient superposition of slightly differing space-time geometries persists until an abrupt quantum classical reduction occurs. Unlike the random, "subjective reduction"(SR, or R) of standard quantum theory caused by observation or environmental entanglement, the OR we propose in microtubules is a self-collapse and it results in particular patterns of microtubule-tubulin conformational states that regulate neuronal activities including synaptic functions. Possibilities and probabilities for postreduction tubulin states are influenced by factors including attachments of microtubule-associated proteins (MAPs) acting as "nodes"which tune and "orchestrate"the quantum oscillations. We thus term the self-tuning OR process in microtubules "orchestrated objective reduction"("B>Orch OR", and calculate an estimate for the number of tubulins (and neurons) whose coherence for relevant time periods (e.g. 500 milliseconds) will elicit Orch OR. In providing a connection among 1) pre-conscious to conscious transition, 2) fundamental space-time notions, 3) noncomputability, and 4) binding of various (time scale and spatial) reductions into an instantaneous event ("conscious now", we believe Orch OR in brain microtubules is the most specific and plausible model for consciousness yet proposed.
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data

We present a convolutional network that is equivariant to rigid body motions. The model uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and equivariant convolutions to map between such representations. These SE(3)-equivariant convolutions utilize kernels which are parameterized as a linear combination of a complete steerable kernel basis, which is derived analytically in this paper. We prove that equivariant convolutions are the most general equivariant linear maps between fields over R. Our experimental results confirm the effectiveness of 3D Steerable CNNs for the problem of amino acid propensity prediction and protein structure classification, both of which have inherent SE(3) symmetry.
Renoprotective effects of febuxostat compared with allopurinol in patients with hyperuricemia: A systematic review and meta-analysis

Hyperuricemia is reported to be related to rapid progression of renal function in patients with chronic kidney disease (CKD). Allopurinol, a uric acid lowering agent, protects renal progression. However, it is not widely used in patients with CKD because of its serious adverse event. Febuxostat can be alternatively used for patients who are intolerable to allopurinol. We aimed to determine renoprotective effect and urate-lowering effect between the two drugs.



We performed a systematic review and meta-analysis of randomized controlled trials to assess the effects of febuxostat compared to allopurinol in patients with hyperuricemia. MEDLINE, Embase, and Cochrane Library databases were searched to identify research publications.



Four relevant publications were selected from among 3,815 studies. No significant differences were found in the changes in serum creatinine from baseline between the febuxostat and allopurinol groups. Changes in estimated glomerular filtration rate (eGFR) were observed between the two groups at 1 month (mean difference 1.65 mL/min/1.73 m2, 95% confidence interval [CI] 0.38, 2.91 mL/min/1.73 m2; heterogeneity χ2 = 1.25, I2 = 0%, P = 0.01); however, the changes in eGFR were not significantly different at 3 months. A significant difference did exist in the changes in albuminuria levels from baseline between the febuxostat and allopurinol groups (mean difference -80.47 mg/gCr, 95% CI -149.29, -11.64 mg/gCr; heterogeneity χ2 = 0.81, I2 = 0%, P = 0.02). A significant difference was also observed in the changes in serum uric acid from baseline between the febuxostat and allopurinol groups (mean difference -0.92 mg/dL, 95% CI -1.29, -0.56 mg/dL; heterogeneity χ2 = 6.24, I2 = 52%, P < 0.001).



Febuxostat might be more renoprotective than allopurinol.
A 75-GHz Phase-Locked Loop in 90-nm CMOS Technology

The design and experimental verification of a 75-GHz phase-locked loop (PLL) fabricated in 90-nm CMOS technology is presented. The circuit incorporates a three-quarter wavelength oscillator to achieve high-frequency operation and a novel phase-frequency detector (PFD) based on SSB mixers to suppress the reference feedthrough. The PLL demonstrates an operation range of 320 MHz and reference sidebands of less than -72 dBc while consuming 88 mW from a 1.45-V supply.
Recent Modifications of Chitosan for Adsorption Applications: A Critical and Systematic Review

Chitosan is considered to be one of the most promising and applicable materials in adsorption applications. The existence of amino and hydroxyl groups in its molecules contributes to many possible adsorption interactions between chitosan and pollutants (dyes, metals, ions, phenols, pharmaceuticals/drugs, pesticides, herbicides, etc.). These functional groups can help in establishing positions for modification. Based on the learning from previously published works in literature, researchers have achieved a modification of chitosan with a number of different functional groups. This work summarizes the published works of the last three years (2012-2014) regarding the modification reactions of chitosans (grafting, cross-linking, etc.) and their application to adsorption of different environmental pollutants (in liquid-phase).