nils-herrmann/clean_scirepeval huggingface dataset:
American College of Rheumatology 2012 recommendations for the use of nonpharmacologic and pharmacologic therapies in osteoarthritis of the hand, hip, and knee.

To update the American College of Rheumatology (ACR) 2000 recommendations for hip and knee osteoarthritis (OA) and develop new recommendations for hand OA.



A list of pharmacologic and nonpharmacologic modalities commonly used to manage knee, hip, and hand OA as well as clinical scenarios representing patients with symptomatic hand, hip, and knee OA were generated. Systematic evidence-based literature reviews were conducted by a working group at the Institute of Population Health, University of Ottawa, and updated by ACR staff to include additions to bibliographic databases through December 31, 2010. The Grading of Recommendations Assessment, Development and Evaluation approach, a formal process to rate scientific evidence and to develop recommendations that are as evidence based as possible, was used by a Technical Expert Panel comprised of various stakeholders to formulate the recommendations for the use of nonpharmacologic and pharmacologic modalities for OA of the hand, hip, and knee.



Both “strong” and “conditional” recommendations were made for OA management. Modalities conditionally recommended for the management of hand OA include instruction in joint protection techniques, provision of assistive devices, use of thermal modalities and trapeziometacarpal joint splints, and use of oral and topical nonsteroidal antiinflammatory drugs (NSAIDs), tramadol, and topical capsaicin. Nonpharmacologic modalities strongly recommended for the management of knee OA were aerobic, aquatic, and/or resistance exercises as well as weight loss for overweight patients. Nonpharmacologic modalities conditionally recommended for knee OA included medial wedge insoles for valgus knee OA, subtalar strapped lateral insoles for varus knee OA, medially directed patellar taping, manual therapy, walking aids, thermal agents, tai chi, self management programs, and psychosocial interventions. Pharmacologic modalities conditionally recommended for the initial management of patients with knee OA included acetaminophen, oral and topical NSAIDs, tramadol, and intraarticular corticosteroid injections; intraarticular hyaluronate injections, duloxetine, and opioids were conditionally recommended in patients who had an inadequate response to initial therapy. Opioid analgesics were strongly recommended in patients who were either not willing to undergo or had contraindications for total joint arthroplasty after having failed medical therapy. Recommendations for hip OA were similar to those for the management of knee OA.



These recommendations are based on the consensus judgment of clinical experts from a wide range of disciplines, informed by available evidence, balancing the benefits and harms of both nonpharmacologic and pharmacologic modalities, and incorporating their preferences and values. It is hoped that these recommendations will be utilized by health care providers involved in the management of patients with OA.
Cracking the Code of Biodiversity Responses to Past Climate Change.

How individual species and entire ecosystems will respond to future climate change are among the most pressing questions facing ecologists. Past biodiversity dynamics recorded in the paleoecological archives show a broad array of responses, yet significant knowledge gaps remain. In particular, the relative roles of evolutionary adaptation, phenotypic plasticity, and dispersal in promoting survival during times of climate change have yet to be clarified. Investigating the paleo-archives offers great opportunities to understand biodiversity responses to future climate change. In this review we discuss the mechanisms by which biodiversity responds to environmental change, and identify gaps of knowledge on the role of range shifts and tolerance. We also outline approaches at the intersection of paleoecology, genomics, experiments, and predictive models that will elucidate the processes by which species have survived past climatic changes and enhance predictions of future changes in biological diversity.
Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing

We initiate the study of privacy in pharmacogenetics, wherein machine learning models are used to guide medical treatments based on a patient's genotype and background. Performing an in-depth case study on privacy in personalized warfarin dosing, we show that suggested models carry privacy risks, in particular because attackers can perform what we call model inversion: an attacker, given the model and some demographic information about a patient, can predict the patient's genetic markers. As differential privacy (DP) is an oft-proposed solution for medical settings such as this, we evaluate its effectiveness for building private versions of pharmacogenetic models. We show that DP mechanisms prevent our model inversion attacks when the privacy budget is carefully selected. We go on to analyze the impact on utility by performing simulated clinical trials with DP dosing models. We find that for privacy budgets effective at preventing attacks, patients would be exposed to increased risk of stroke, bleeding events, and mortality. We conclude that current DP mechanisms do not simultaneously improve genomic privacy while retaining desirable clinical efficacy, highlighting the need for new mechanisms that should be evaluated in situ using the general methodology introduced by our work.
Analyzing Behavior Specialized Acceleration

Hardware specialization has become a promising paradigm for overcoming the inefficiencies of general purpose microprocessors. Of significant interest are Behavioral Specialized Accelerators (BSAs), which are designed to efficiently execute code with only certain properties, but remain largely configurable or programmable. The most important strength of BSAs -- their ability to target a wide variety of codes -- also makes their interactions and analysis complex, raising the following questions: can multiple BSAs be composed synergistically, what are their interactions with the general purpose core, and what combinations favor which workloads? From a methodological standpoint, BSAs are also challenging, as they each require ISA development, compiler and assembler extensions, and either simulator or RTL models.
 To study the potential of BSAs, we propose a novel modeling technique called the Transformable Dependence Graph (TDG) - a higher level alternative to the time-consuming traditional compiler+simulator approach, while still enabling detailed microarchitectural models for both general cores and accelerators. We then propose a multi-BSA organization, called ExoCore, which we model and study using the TDG. A design space exploration reveals that an ExoCore organization can push designs beyond the established energy-performance frontiers for general purpose cores. For example, a 2-wide OOO processor with three BSAs matches the performance of a conventional 6-wide OOO core, has 40% lower area, and is 2.6x more energy efficient.
ANAPSID: An Adaptive Query Processing Engine for SPARQL Endpoints

Following the design rules of Linked Data, the number of available SPARQL endpoints that support remote query processing is quickly growing; however, because of the lack of adaptivity, query executions may frequently be unsuccessful. First, fixed plans identified following the traditional optimize-thenexecute paradigm, may timeout as a consequence of endpoint availability. Second, because blocking operators are usually implemented, endpoint query engines are not able to incrementally produce results, and may become blocked if data sources stop sending data. We present ANAPSID, an adaptive query engine for SPARQL endpoints that adapts query execution schedulers to data availability and run-time conditions. ANAPSID provides physical SPARQL operators that detect when a source becomes blocked or data traffic is bursty, and opportunistically, the operators produce results as quickly as data arrives from the sources. Additionally, ANAPSID operators implement main memory replacement policies to move previously computed matches to secondary memory avoiding duplicates. We compared ANAPSID performance with respect to RDF stores and endpoints, and observed that ANAPSID speeds up execution time, in some cases, in more than one order of magnitude.
Self-Powered ZigBee Wireless Sensor Nodes for Railway Condition Monitoring

A track-borne energy transducer is a smart device for harvesting energy of trains or rail transportation systems. In this paper, the authors extend this application through introducing a self-powered ZigBee wireless sensor node. The proposed hardware prototype consists of a ZigBee coordinator at road-side and a series of sensors (Accelerometer, temperature sensor, humidity sensor, and infrared detector) connected to a ZigBee end device at rail-side. The ZigBee end device is powered by the magnetic levitation energy harvester and communicated wirelessly with the ZigBee coordinator. The magnetic levitation oscillator is selected due to its broad-band response characteristics. The results indicate a peak–peak output voltage of 2.3 V under the condition that the vehicle travels over the rail-borne device at the speed of 105 km/h.
Spiking Deep Networks with LIF Neurons

We train spiking deep networks using leaky integrate-and-fire (LIF) neurons, and achieve state-of-the-art results for spiking networks on the CIFAR-10 and MNIST datasets. This demonstrates that biologically-plausible spiking LIF neurons can be integrated into deep networks can perform as well as other spiking models (e.g. integrate-and-fire). We achieved this result by softening the LIF response function, such that its derivative remains bounded, and by training the network with noise to provide robustness against the variability introduced by spikes. Our method is general and could be applied to other neuron types, including those used on modern neuromorphic hardware. Our work brings more biological realism into modern image classification models, with the hope that these models can inform how the brain performs this difficult task. It also provides new methods for training deep networks to run on neuromorphic hardware, with the aim of fast, power-efficient image classification for robotics applications.
Enhancement of Sentence-Generation Based Summarization Method By Modelling Inter-Sentential Consequent-Relationships

In the text summarization field, based on how to create the summary, there are two main theoretic approaches (E. Lloret [22], K. Jezek and J. Steinberger [13]): the trend based on "extraction" in which the most important sentences in the source text will be chosen; the trend based on "abstraction" in which a new reduced grammatical and meaningful text will be created based on understanding the original text. In this paper, we focus on "abstraction" approach to summarize Vietnamese texts having only two sentences. We also restrict this research to consider verbs representing "actions" and "states", which appear in both two sentences of source text. Our method is based on modeling and processing Consequent-Relations between the verbs in the first sentence and the second sentence. The aim of this research is to generate new Vietnamese meaning-summarizing sentences which satisfy two requirements: (i) summarize the true meaning of the source pair of sentences; (ii) have natural expression for summarization. We also experiment and assess our approach by proposing a new quantitative evaluation method that suitable for the research approach.
A case for stateful forwarding plane

In Named Data Networking (NDN), packets carry data names instead of source and destination addresses. This paradigm shift leads to a new network forwarding plane: data consumers send Interest packets to request desired data, routers forward Interest packets and maintain the state of all pending Interests, which is then used to guide Data packets back to the consumers. Maintaining the pending Interest state, together with the two-way Interest and Data exchange, enables NDN routers’ forwarding process to measure performance of different paths, quickly detect failures and retry alternative paths. In this paper we describe an initial design of NDN’s forwarding plane and evaluate its data delivery performance under adverse conditions. Our results show that this stateful forwarding plane can successfully circumvent prefix hijackers, avoid failed links, and utilize multiple paths to mitigate congestion. We also compare NDN’s performance with that of IP-based solutions to highlight the advantages of a stateful forwarding plane.
System in wafer-level package technology with RDL-first process

We have developed a new system-in-package (SiP) called a “System in Wafer-Level Package” (SiWLP). It is fabricated using “RDL-first” technology for fan-out wafer-level-packages (FO-WLPs) and provides high chip-I/O density, design flexibility, and package miniaturization. We developed this SiWLP by using multilayer RDLs and evaluated its unique packaging processes. We achieved high-throughput fabrication by using die-to-wafer (D2W) bonding with fine-pitch reflow soldering and simultaneous molding/underfilling at the wafer level.
Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks

Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification. However, many short texts occur in sequences (e.g., sentences in a document or utterances in a dialog), and most existing ANN-based systems do not leverage the preceding short texts when classifying a subsequent one. In this work, we present a model based on recurrent neural networks and convolutional neural networks that incorporates the preceding short texts. Our model achieves state-of-the-art results on three different datasets for dialog act prediction.
Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers

In the deep neural network (DNN), the hidden layers can be considered as increasingly complex feature transformations and the final softmax layer as a log-linear classifier making use of the most abstract features computed in the hidden layers. While the loglinear classifier should be different for different languages, the feature transformations can be shared across languages. In this paper we propose a shared-hidden-layer multilingual DNN (SHL-MDNN), in which the hidden layers are made common across many languages while the softmax layers are made language dependent. We demonstrate that the SHL-MDNN can reduce errors by 3-5%, relatively, for all the languages decodable with the SHL-MDNN, over the monolingual DNNs trained using only the language specific data. Further, we show that the learned hidden layers sharing across languages can be transferred to improve recognition accuracy of new languages, with relative error reductions ranging from 6% to 28% against DNNs trained without exploiting the transferred hidden layers. It is particularly interesting that the error reduction can be achieved for the target language that is in different families of the languages used to learn the hidden layers.
How to construct random functions

A constructive theory of randomness for functions, based on computational complexity, is developed, and a pseudorandom function generator is presented. This generator is a deterministic polynomial-time algorithm that transforms pairs (<italic>g</italic>, <italic>r</italic>), where <italic>g</italic> is <italic>any</italic> one-way function and <italic>r</italic> is a random <italic>k</italic>-bit string, to polynomial-time computable functions ƒ<italic><subscrpt>r</subscrpt></italic>: {1, … , 2<italic><supscrpt>k</supscrpt></italic>} → {1, … , 2<italic><supscrpt>k</supscrpt></italic>}. These ƒ<italic><subscrpt>r</subscrpt></italic>'s cannot be distinguished from <italic>random</italic> functions by any probabilistic polynomial-time algorithm that asks and receives the value of a function at arguments of its choice. The result has applications in cryptography, random constructions, and complexity theory.
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning

The mutual information is a core statistical quantity that has applications in all areas of machine learning, whether this is in training of density models over multiple data modalities, in maximising the efficiency of noisy transmission channels, or when learning behaviour policies for exploration by artificial agents. Most learning algorithms that involve optimisation of the mutual information rely on the Blahut-Arimoto algorithm — an enumerative algorithm with exponential complexity that is not suitable for modern machine learning applications. This paper provides a new approach for scalable optimisation of the mutual information by merging techniques from variational inference and deep learning. We develop our approach by focusing on the problem of intrinsically-motivated learning, where the mutual information forms the definition of a well-known internal drive known as empowerment. Using a variational lower bound on the mutual information, combined with convolutional networks for handling visual input streams, we develop a stochastic optimisation algorithm that allows for scalable information maximisation and empowerment-based reasoning directly from pixels to actions.
Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales

We address therating-inference problem, wherein rather than simply decide whether a review is “thumbs up” or “thumbs down”, as in previous sentiment analysis work, one must determine an author’s evaluation with respect to a multi-point scale (e.g., one to five “stars”). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, “three stars” is intuitively closer to “four stars” than to “one star”. We first evaluate human performance at the task. Then, we apply a metaalgorithm, based on a metric labelingformulation of the problem, that alters a given n-ary classifier’s output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem.
Qualitative Simulation

Qualitative simulation is a key inference process in qualitative causal reasoning. However, the precise meaning of the different proposals and their relation with differential equations is often unclear. In this paper, we present a precise definition of qualitative structure and behavior descriptions as abstractions of differential equations and continuously differentiable functions. We present a new algorithm for qualitative simulation that generalizes the best features of existing algorithms, and allows direct comparisons among alternate approaches. Starting with a set of constraints abstracted from a differential equation, we prove that the OSIM algorithm is guaranteed to produce a qualitative behavior corresponding to any solution to the original equation. We also show that any qualitative simulation algorithm will sometimes produce spurious qualitative behaviors: ones which do not correspond to any mechanism satisfying the given constraints. These observations suggest specific types of care that must be taken in designing applications of qualitative causal reasoning systems, and in constructing and validating a knowledge base of mechanism descriptions.
A Flash-Memory Based File System

A flash memory device driver that supports a conventional UNIX file system transparently was designed. To avoid the limitations due to flash memory's restricted number of write cycles and its inability to be overwritten, this driver writes data to the flash memory system sequentially as a Log-structured File System (LFS) does and uses a cleaner to collect valid data blocks and reclaim invalid ones by erasing the corresponding flash memory regions. Measurements showed that the overhead of the cleaner has little effect on the performance of the prototype when utilization is low but that the effect becomes critical as the utilization gets higher, reducing the random write throughput from 222 Kbytes/s at 30% utilization to 40 Kbytes/s at 90% utilization. The performance of the prototype in the Andrew Benchmark test is roughly equivalent to that of the 4.4BSD Pageable Memory based File System (MFS).
Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association

Person re-identification is an important task that requires learning discriminative visual features for distinguishing different person identities. Diverse auxiliary information has been utilized to improve the visual feature learning. In this paper, we propose to exploit natural language description as additional training supervisions for effective visual features. Compared with other auxiliary information, language can describe a specific person from more compact and semantic visual aspects, thus is complementary to the pixel-level image data. Our method not only learns better global visual feature with the supervision of the overall description but also enforces semantic consistencies between local visual and linguistic features, which is achieved by building global and local image-language associations. The global image-language association is established according to the identity labels, while the local association is based upon the implicit correspondences between image regions and noun phrases. Extensive experiments demonstrate the effectiveness of employing language as training supervisions with the two association schemes. Our method achieves state-of-the-art performance without utilizing any auxiliary information during testing and shows better performance than other joint embedding methods for the image-language association.
Understanding Instructions on Large Scale for Human-Robot Interaction

Correctly interpreting human instructions is the first step to human-robot interaction. Previous approaches to semantically parsing the instructions relied on large numbers of training examples with annotation to widely cover all words in a domain. Annotating large enough instructions with semantic forms needs exhaustive engineering efforts. Hence, we propose propagating the semantic lexicon to learn a semantic parser from limited annotations, whereas the parser still has the ability of interpreting instructions on a large scale. We assume that the semantically-close words have the same semantic form based on the fact that human usually uses different words to refer to a same object or task. Our approach softly maps the unobserved words/phrases to the semantic forms learned from the annotated copurs through a metric for knowledge-based lexical similarity. Experiments on the collected instructions showed that the semantic parser learned with lexicon propagation outperformed the baseline. Our approach provides an opportunity for the robots to understand the human instructions on a large scale.