nils-herrmann/clean_scirepeval huggingface dataset:
Meta Multi-Task Learning for Sequence Modeling

Semantic composition functions have been playing a pivotal role in neural representation learning of text sequences. In spite of their success, most existing models suffer from the underfitting problem: they use the same shared compositional function on all the positions in the sequence, thereby lacking expressive power due to incapacity to capture the richness of compositionality. Besides, the composition functions of different tasks are independent and learned from scratch. In this paper, we propose a new sharing scheme of composition function across multiple tasks. Specifically, we use a shared meta-network to capture the meta-knowledge of semantic composition and generate the parameters of the taskspecific semantic composition models. We conduct extensive experiments on two types of tasks, text classification and sequence tagging, which demonstrate the benefits of our approach. Besides, we show that the shared meta-knowledge learned by our proposed model can be regarded as off-theshelf knowledge and easily transferred to new tasks.
Current Concepts in the Evaluation and Treatment of the Shoulder in Overhead Throwing Athletes, Part 2

The overhead throwing athlete is an extremely challenging patient in sports medicine. The repetitive microtraumatic stresses imposed on the athlete's shoulder joint complex during the throwing motion constantly place the athlete at risk for injury. Treatment of the overhead athlete requires the understanding of several principles based on the unique physical characteristics of the overhead athlete and the demands endured during the act of throwing. These principles are described and incorporated in a multiphase progressive rehabilitation program designed to prevent injuries and rehabilitate the injured athlete, both nonoperatively and postoperatively.
Feature extraction with regularized siamese networks for outlier detection: application to lesion screening in medical imaging

Computer aided diagnosis (CAD) systems are designed to assist clinicians in various tasks, including highlighting abnormal regions in a medical image. A common approach consists in training a voxel-level binary classifier on a set of feature vectors extracted from normal and pathological areas in patients’ scans. However, many pathologies (such as epilepsy) are characterized by lesions that may be located anywhere in the brain, have various shapes, sizes and texture. An adequate representation of such a heterogeneity requires a significant amount of annotated data which is a major issue in the medical domain. Therefore, we built on a previously proposed approach that considers epilepsy lesion detection task as a voxel-level outlier detection problem. It consists in building a oc-SVM classifier for each voxel in the brain volume using a small number of clinically-guided features [EAHJ16]. Our goal in this study is to make a step forward by replacing the handcrafted features with automatically learnt representations using neural networks. We propose a novel version of siamese networks trained on patches extracted from healthy patients’ scans only. This network, composed of stacked autoencoders as subnetworks, is regularized by the reconstruction error of the patches. It is designed to learn representations that bring patches centered at the same voxel localization ’closer’ with respect to the chosen metric (i.e. cosine). Finally, the middle layer representations of the subnetworks are fed to oc-SVM classifiers at voxel-level. The method is validated on 3 patients’ MRI scans with confirmed epilepsy lesions and shows a promising performance.
Optimum Design Rules for CMOS Hall Sensors

This manuscript analyzes the effects of design parameters, such as aspect ratio, doping concentration and bias, on the performance of a general CMOS Hall sensor, with insight on current-related sensitivity, power consumption, and bandwidth. The article focuses on rectangular-shaped Hall probes since this is the most general geometry leading to shape-independent results. The devices are analyzed by means of 3D-TCAD simulations embedding galvanomagnetic transport model, which takes into account the Lorentz force acting on carriers due to a magnetic field. Simulation results define a set of trade-offs and design rules that can be used by electronic designers to conceive their own Hall probes.
Temporal window of integration in auditory-visual speech perception

Forty-three normal hearing participants were tested in two experiments, which focused on temporal coincidence in auditory visual (AV) speech perception. In these experiments, audio recordings of/pa/and/ba/were dubbed onto video recordings of /ba/or/ga/, respectively (ApVk, AbVg), to produce the illusory "fusion" percepts /ta/, or /da/ [McGurk, H., & McDonald, J. (1976). Hearing lips and seeing voices. . In Experiment 1, an identification task using McGurk pairs with asynchronies ranging from -467 ms (auditory lead) to +467 ms was conducted. Fusion responses were prevalent over temporal asynchronies from -30 ms to +170 ms and more robust for audio lags. In Experiment 2, simultaneity judgments for incongruent and congruent audiovisual tokens (AdVd, AtVt) were collected. McGurk pairs were more readily judged as asynchronous than congruent pairs. Characteristics of the temporal window over which simultaneity and fusion responses were maximal were quite similar, suggesting the existence of a 200 ms duration asymmetric bimodal temporal integration window.
Aqueous stability of leuprolide acetate: effect of temperature, dissolved oxygen, pH and complexation with β-cyclodextrin.

In the present research, the aqueous stability of leuprolide acetate (LA) in phosphate buffered saline (PBS) medium was studied (pH = 2.0-7.4). For this purpose, the effect of temperature, dissolved oxygen and pH on the stability of LA during 35 days was investigated. Results showed that the aqueous stability of LA was higher at low temperatures. Degassing of the PBS medium partially increased the stability of LA at 4 °C, while did not change at 37 °C. The degradation of LA was accelerated at lower pH values. In addition, complexes of LA with different portions of β-cyclodextrin (β-CD) were prepared through freeze-drying procedure and characterized by Fourier transform infrared (FTIR) and differential scanning calorimetry (DSC) analyses. Studying their aqueous stability at various pH values (2.0-7.4) showed LA/β-CD complexes exhibited higher stability when compared with LA at all pH values. The stability of complexes was also improved by increasing the portion of LA/β-CD up to 1/10.
Emotional and Linguistic Cues of Depression from Social Media

Health outcomes in modern society are often shaped by peer interactions. Increasingly, a significant fraction of such interactions happen online and can have an impact on various mental health and behavioral health outcomes. Guided by appropriate social and psychological research, we conduct an observational study to understand the interactions between clinically depressed users and their ego-network when contrasted with a differential control group of normal users and their ego-network. Specifically, we examine if one can identify relevant linguistic and emotional signals from social media exchanges to detect symptomatic cues of depression. We observe significant deviations in the behavior of depressed users from the control group. Reduced and nocturnal online activity patterns, reduced active and passive network participation, increase in negative sentiment or emotion, distinct linguistic styles (e.g. self-focused pronoun usage), highly clustered and tightly-knit neighborhood structure, and little to no exchange of influence between depressed users and their ego-network over time are some of the observed characteristics. Based on our observations, we then describe an approach to extract relevant features and show that building a classifier to predict depression based on such features can achieve an F-score of 90%.
Evaluating Word Embeddings in Multi-label Classification Using Fine-grained Name Typing

Embedding models typically associate each word with a single real-valued vector, representing its different properties. Evaluation methods, therefore, need to analyze the accuracy and completeness of these properties in embeddings. This requires fine-grained analysis of embedding subspaces. Multi-label classification is an appropriate way to do so. We propose a new evaluation method for word embeddings based on multi-label classification given a word embedding. The task we use is finegrained name typing: given a large corpus, find all types that a name can refer to based on the name embedding. Given the scale of entities in knowledge bases, we can build datasets for this task that are complementary to the current embedding evaluation datasets in: they are very large, contain fine-grained classes, and allow the direct evaluation of embeddings without confounding factors like sentence context.
Dysproteinemias and Glomerular Disease.

Dysproteinemia is characterized by the overproduction of an Ig by clonal expansion of cells from the B cell lineage. The resultant monoclonal protein can be composed of the entire Ig or its components. Monoclonal proteins are increasingly recognized as a contributor to kidney disease. They can cause injury in all areas of the kidney, including the glomerular, tubular, and vascular compartments. In the glomerulus, the major mechanism of injury is deposition. Examples of this include Ig amyloidosis, monoclonal Ig deposition disease, immunotactoid glomerulopathy, and cryoglobulinemic GN specifically from types 1 and 2 cryoglobulins. Mechanisms that do not involve Ig deposition include the activation of the complement system, which causes complement deposition in C3 glomerulopathy, and cytokines/growth factors as seen in thrombotic microangiopathy and precipitation, which is involved with cryoglobulinemia. It is important to recognize that nephrotoxic monoclonal proteins can be produced by clones from any of the B cell lineages and that a malignant state is not required for the development of kidney disease. The nephrotoxic clones that do not meet requirement for a malignant condition are now called monoclonal gammopathy of renal significance. Whether it is a malignancy or monoclonal gammopathy of renal significance, preservation of renal function requires substantial reduction of the monoclonal protein. With better understanding of the pathogenesis, clone-directed strategies, such as rituximab against CD20 expressing B cell and bortezomib against plasma cell clones, have been used in the treatment of these diseases. These clone-directed therapies been found to be more effective than immunosuppressive regimens used in nonmonoclonal protein-related kidney diseases.
Dynamical Modeling and Distributed Control of Connected and Automated Vehicles: Challenges and Opportunities

The platooning of connected and automated vehicles (CAVs) is expected to have a transformative impact on road transportation, e.g., enhancing highway safety, improving traffic utility, and reducing fuel consumption. Requiring only local information, distributed control schemes are scalable approaches to the coordination of multiple CAVs without using centralized communication and computation. From the perspective of multi-agent consensus control, this paper introduces a decomposition framework to model, analyze, and design the platoon system. In this framework, a platoon is naturally decomposed into four interrelated components, i.e., 1) node dynamics, 2) information flow network, 3) distributed controller, and 4) geometry formation. The classic model of each component is summarized according to the results of the literature survey; four main performance metrics, i.e., internal stability, stability margin, string stability, and coherence behavior, are discussed in the same fashion. Also, the basis of typical distributed control techniques is presented, including linear consensus control, distributed robust control, distributed sliding mode control, and distributed model predictive control.
A theory of multiclass boosting

Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclass setting, the “correct” requirements on the weak classifier, or the notion of the most efficient boosting algorithms are missing. In this paper, we create a broad and general framework, within which we make precise and identify the optimal requirements on the weak-classifier, as well as design the most effective, in a certain sense, boosting algorithms that assume such requirements.
A New Approach to Compute CNNs for Extremely Large Images

CNN (Convolution Neural Network) is widely used in visual analysis and achieves exceptionally high performances in image classification, face detection, object recognition, image recoloring, and other learning jobs. Using deep learning frameworks, such as Torch and Tensorflow, CNN can be efficiently computed by leveraging the power of GPU. However, one drawback of GPU is its limited memory which prohibits us from handling large images. Passing a 4K resolution image to the VGG network will result in an exception of out-of-memory for Titan-X GPU. In this paper, we propose a new approach that adopts the BSP (bulk synchronization parallel) model to compute CNNs for images of any size. Before fed to a specific CNN layer, the image is split into smaller pieces which go through the neural network separately. Then, a specific padding and normalization technique is adopted to merge sub-images back into one image. Our approach can be easily extended to support distributed multi-GPUs. In this paper, we use neural style network as our example to illustrate the effectiveness of our approach. We show that using one Titan-X GPU, we can transfer the style of an image with 10,000×10,000 pixels within 1 minute.
Management of Upper Lateral Cartilages (ULCs) in Rhinoplasty

Handling of upper lateral cartilages (ULCs) is of prime importance in rhinoplasty. This study presents the experiences among 2500 cases of rhinoplasty in the past 10 years for managing of ULCs to minimize unwilling results of the shape and functional problems of the nose.



All cases of rhinoplasties were done by the same surgeon from 2002 to 2013. Management of ULCs changed from resection to preserving the ULCs and to enhance their structural and functional roles. The techniques were spreader grafts, suturing of ULC together at the level or above the septum, using ULCs as auto-spreader flaps and very rarely trimming of ULCs unilaterally or bilaterally for making symmetric dorsal aesthetic lines. Fifty cases were operated based on this classification. Most cases were in type II and III. There were 7 cases in type I and 8 cases in type IV.



Among most cases, the results were satisfactory although there were 8 cases for revision and among them, 2 cases had some fullness on dorsum and supra-tip because of inappropriate judgment on keeping the relationship between dorsum and tip. The problems in the shape and airways role of the nose reduced dramatically and a useful algorithm was presented.



ULCs have great important roles in shape and function of nose. Preserving methods to keep these structures are of importance in surgical treatments of primary rhinoplasties. The presented algorithm helps to manage the ULCs in different anatomic types of the noses especially for surgeons who are in learning curve period.
Notions of Chaotic Cryptography: Sketch of a Chaos based Cryptosystem

Then, we can start by answering the questions: what exactly is chaos?, how is it used in cryptography?. First of all, let us say that there is not an universally mathematical accepted definition of the term “chaos”. In general sense, it refers to some dynamical phenomena considered to be complex (lack of time and spatial order) and unpredictable (erratic). Although it was precluded by Poincaré at the end of the XIX century (Poincaré, 1890), chaos theory begins to take form in the second half of the XX century (Lorenz, 1963;  1977) after observations of the evolution of different physical systems. These systems revealed that despite of the knowledge of their evolution rules and initial conditions, their future seemed to be arbitrary and unpredictable. That opened quite a revolution in modern physics, terminating with Laplace’s ideas of casual determinism (Laplace, 1825).
Neural Representations of Self versus Other: Visual-Spatial Perspective Taking and Agency in a Virtual Ball-tossing Game

Human self-consciousness relies on the ability to distinguish between oneself and others. We sought to explore the neural correlates involved in self-other representations by investigating two critical processes: perspective taking and agency. Although recent research has shed light on the neural processes underlying these phenomena, little is known about how they overlap or interact at the neural level. In a two-factorial functional magnetic resonance imaging (fMRI) experiment, participants played a ball-tossing game with two virtual characters (avatars). During an active/agency (ACT) task, subjects threw a ball to one of the avatars by pressing a button. During a passive/nonagency (PAS) task, they indicated which of the other avatars threw the ball. Both tasks were performed from a first-person perspective (1PP), in which subjects interacted from their own perspective, and a third-person perspective (3PP), in which subjects interacted from the perspective of an avatar with another location in space. fMRI analyses revealed overlapping activity in medial prefrontal regions associated with representations of one's own perspective and actions (1PP and ACT), and overlapping activity in temporal-occipital, premotor, and inferior frontal, as well as posterior parietal regions associated with representation of others' perspectives and actions (3PP and PAS). These findings provide evidence for distinct neural substrates underlying representations of the self and others and provide support for the idea that the medial prefrontal cortex crucially contributes to a neural basis of the self. The lack of a statistically significant interaction suggests that perspective taking and agency represent independent constituents of self-consciousness.
Deep Neural Language Models for Machine Translation

Neural language models (NLMs) have been able to improve machine translation (MT) thanks to their ability to generalize well to long contexts. Despite recent successes of deep neural networks in speech and vision, the general practice in MT is to incorporate NLMs with only one or two hidden layers and there have not been clear results on whether having more layers helps. In this paper, we demonstrate that deep NLMs with three or four layers outperform those with fewer layers in terms of both the perplexity and the translation quality. We combine various techniques to successfully train deep NLMs that jointly condition on both the source and target contexts. When reranking nbest lists of a strong web-forum baseline, our deep models yield an average boost of 0.5 TER / 0.5 BLEU points compared to using a shallow NLM. Additionally, we adapt our models to a new sms-chat domain and obtain a similar gain of 1.0 TER / 0.5 BLEU points.
Functional magnetic resonance imaging of the effects of low-frequency transcutaneous electrical nerve stimulation on central pain modulation: a double-blind, placebo-controlled trial.

Transcutaneous electrical nerve stimulation (TENS) is an analgesic current that is used in many acute and chronic painful states. The aim of this study was to investigate central pain modulation by low-frequency TENS.



Twenty patients diagnosed with subacromial impingement syndrome of the shoulder were enrolled in the study. Patients were randomized into 2 groups: low-frequency TENS and sham TENS. Painful stimuli were delivered during which functional magnetic resonance imaging scans were performed, both before and after treatment. Ten central regions of interest that were reported to have a role in pain perception were chosen and analyzed bilaterally on functional magnetic resonance images. Perceived pain intensity during painful stimuli was evaluated using visual analog scale (VAS).



In the low-frequency TENS group, there was a statistically significant decrease in the perceived pain intensity and pain-specific activation of the contralateral primary sensory cortex, bilateral caudal anterior cingulate cortex, and of the ipsilateral supplementary motor area. There was a statistically significant correlation between the change of VAS value and the change of activity in the contralateral thalamus, prefrontal cortex, and the ipsilateral posterior parietal cortex. In the sham TENS group, there was no significant change in VAS value and activity of regions of interest.



We suggest that a 1-session low-frequency TENS may induce analgesic effect through modulation of discriminative, affective, and motor aspects of central pain perception.
Doppio: A Reconfigurable Dual-Face Smartwatch for Tangible Interaction

Doppio is a reconfigurable smartwatch with two touch sensitive display faces. The orientation of the top relative to the base and how the top is attached to the base, creates a very large interaction space. We define and enumerate possible configurations, transitions, and manipulations in this space. Using a passive prototype, we conduct an exploratory study to probe how people might use this style of smartwatch interaction. With an instrumented prototype, we conduct a controlled experiment to evaluate the transition times between configurations and subjective preferences. We use the combined results of these two studies to generate a set of characteristics and design considerations for applying this interaction space to smartwatch applications. These considerations are illustrated with a proof-of-concept hardware prototype demonstrating how Doppio interactions can be used for notifications, private viewing, task switching, temporary information access, application launching, application modes, input, and sharing the top.
Simulation-based optimization: practical introduction to simulation optimization

The merging of optimization and simulation technologies has seen a rapid growth in recent years. A Google search on "Simulation Optimization" returns more than six thousand pages where this phrase appears. The content of these pages ranges from articles, conference presentations and books to software, sponsored work and consultancy. This is an area that has sparked as much interest in the academic world as in practical settings. In this paper, we first summarize some of the most relevant approaches that have been developed for the purpose of optimizing simulated systems. We then concentrate on the metaheuristic black-box approach that leads the field of practical applications and provide some relevant details of how this approach has been implemented and used in commercial software. Finally, we present an example of simulation optimization in the context of a simulation model developed to predict performance and measure risk in a real world project selection problem.