nils-herrmann/clean_scirepeval huggingface dataset:
Deep learning for detecting inappropriate content in text

Today, there are a large number of online discussion fora on the internet which are meant for users to express, discuss and exchange their views and opinions on various topics. For example, news portals, blogs, social media channels such as youtube. typically allow users to express their views through comments. In such fora, it has been often observed that user conversations sometimes quickly derail and become inappropriate such as hurling abuses, passing rude and discourteous comments on individuals or certain groups/communities. Similarly, some virtual agents or bots have also been found to respond back to users with inappropriate messages. As a result, inappropriate messages or comments are turning into an online menace slowly degrading the effectiveness of user experiences. Hence, automatic detection and filtering of such inappropriate language has become an important problem for improving the quality of conversations with users as well as virtual agents. In this paper, we propose a novel deep learning-based technique for automatically identifying such inappropriate language. We especially focus on solving this problem in two application scenarios—(a) Query completion suggestions in search engines and (b) Users conversations in messengers. Detecting inappropriate language is challenging due to various natural language phenomenon such as spelling mistakes and variations, polysemy, contextual ambiguity and semantic variations. For identifying inappropriate query suggestions, we propose a novel deep learning architecture called “Convolutional Bi-Directional LSTM (C-BiLSTM)" which combines the strengths of both Convolution Neural Networks (CNN) and Bi-directional LSTMs (BLSTM). For filtering inappropriate conversations, we use LSTM and Bi-directional LSTM (BLSTM) sequential models. The proposed models do not rely on hand-crafted features, are trained end-end as a single model, and effectively capture both local features as well as their global semantics. Evaluating C-BiLSTM, LSTM and BLSTM models on real-world search queries and conversations reveals that they significantly outperform both pattern-based and other hand-crafted feature-based baselines.
Human Shape from Silhouettes Using Generative HKS Descriptors and Cross-Modal Neural Networks

In this work, we present a novel method for capturing human body shape from a single scaled silhouette. We combine deep correlated features capturing different 2D views, and embedding spaces based on 3D cues in a novel convolutional neural network (CNN) based architecture. We first train a CNN to find a richer body shape representation space from pose invariant 3D human shape descriptors. Then, we learn a mapping from silhouettes to this representation space, with the help of a novel architecture that exploits correlation of multi-view data during training time, to improve prediction at test time. We extensively validate our results on synthetic and real data, demonstrating significant improvements in accuracy as compared to the state-of-the-art, and providing a practical system for detailed human body measurements from a single image.
A micropower envelope detector for audio applications

A low-power envelope-detecting circuit for analog audio applications is implemented using a wide-linear-range transconductor (WLR OTA), a classB mirror, and a simple current-mode peak detector. A DC-offset correction loop ensures insensitivity to DC input conditions. In a 1.5" technology, and over most of the 100Hz-10kHz audio frequency range, an experimental implementation yielded almost 60-dB of linear operation while consuming less than 5.3pW ofpower from a 2.8V supply.
High Performance Perovskite Solar Cells

Perovskite solar cells fabricated from organometal halide light harvesters have captured significant attention due to their tremendously low device costs as well as unprecedented rapid progress on power conversion efficiency (PCE). A certified PCE of 20.1% was achieved in late 2014 following the first study of long-term stable all-solid-state perovskite solar cell with a PCE of 9.7% in 2012, showing their promising potential towards future cost-effective and high performance solar cells. Here, notable achievements of primary device configuration involving perovskite layer, hole-transporting materials (HTMs) and electron-transporting materials (ETMs) are reviewed. Numerous strategies for enhancing photovoltaic parameters of perovskite solar cells, including morphology and crystallization control of perovskite layer, HTMs design and ETMs modifications are discussed in detail. In addition, perovskite solar cells outside of HTMs and ETMs are mentioned as well, providing guidelines for further simplification of device processing and hence cost reduction.
A Multiport Microsecond Optical Circuit Switch for Data Center Networking

We experimentally evaluate the network-level switching time of a functional 23-host prototype hybrid optical circuit-switched/electrical packet-switched network for datacenters called Mordia (Microsecond Optical Research Datacenter Interconnect Architecture). This hybrid network uses a standard electrical packet switch and an optical circuit-switched architecture based on a wavelength-selective switch that has a measured mean port-to-port network reconfiguration time of 11.5 $\mu{\rm s}$  including the signal acquisition by the network interface card. Using multiple parallel rings, we show that this architecture can scale to support the large bisection bandwidth required for future datacenters.
Physician Presence in an Ambulance Car Is Associated with Increased Survival in Out-of-Hospital Cardiac Arrest: A Prospective Cohort Analysis

The presence of a physician seems to be beneficial for pre-hospital cardiopulmonary resuscitation (CPR) of patients with out-of-hospital cardiac arrest. However, the effectiveness of a physician's presence during CPR before hospital arrival has not been established. We conducted a prospective, non-randomized, observational study using national data from out-of-hospital cardiac arrests between 2005 and 2010 in Japan. We performed a propensity analysis and examined the association between a physician's presence during an ambulance car ride and short- and long-term survival from out-of-hospital cardiac arrest. Specifically, a full non-parsimonious logistic regression model was fitted with the physician presence in the ambulance as the dependent variable; the independent variables included all study variables except for endpoint variables plus dummy variables for the 47 prefectures in Japan (i.e., 46 variables). In total, 619,928 out-of-hospital cardiac arrest cases that met the inclusion criteria were analyzed. Among propensity-matched patients, a positive association was observed between a physician's presence during an ambulance car ride and return of spontaneous circulation (ROSC) before hospital arrival, 1-month survival, and 1-month survival with minimal neurological or physical impairment (ROSC: OR = 1.84, 95% CI 1.63-2.07, p = 0.00 in adjusted for propensity and all covariates); 1-month survival: OR = 1.29, 95% CI 1.04-1.61, p = 0.02 in adjusted for propensity and all covariates); cerebral performance category (1 or 2): OR = 1.54, 95% CI 1.03-2.29, p = 0.04 in adjusted for propensity and all covariates); and overall performance category (1 or 2): OR = 1.50, 95% CI 1.01-2.24, p = 0.05 in adjusted for propensity and all covariates). A prospective observational study using national data from out-of-hospital cardiac arrests shows that a physician's presence during an ambulance car ride was independently associated with increased short- and long-term survival.
An Efficient Approach to Informative Feature Extraction from Multimodal Data

One primary focus in multimodal feature extraction is to find the representations of individual modalities that are maximally correlated. As a well-known measure of dependence, the Hirschfeld-Gebelein-Rényi (HGR) maximal correlation becomes an appealing objective because of its operational meaning and desirable properties. However, the strict whitening constraints formalized in the HGR maximal correlation limit its application. To address this problem, this paper proposes Soft-HGR, a novel framework to extract informative features from multiple data modalities. Specifically, our framework prevents the “hard” whitening constraints, while simultaneously preserving the same feature geometry as in the HGR maximal correlation. The objective of Soft-HGR is straightforward, only involving two inner products, which guarantees the efficiency and stability in optimization. We further generalize the framework to handle more than two modalities and missing modalities. When labels are partially available, we enhance the discriminative power of the feature representations by making a semi-supervised adaptation. Empirical evaluation implies that our approach learns more informative feature mappings and is more efficient to optimize.
Wideband 3×4 Butler matrix using Wilkinson divider for MIMO applications

This paper presents the design and simulation of a broadband 3×4 Butler matrix for the IEEE 802.11 b/g/n and ISM bands. The aim of this study is to develop an antenna array feeding networks for Multiple-Input Multiple-Output (MIMO) applications, based on an asymmetric Butler matrix. The asymmetric structure allows to create a further beam on the array's normal axis, in addition to the same beams which are created by the symmetrical version. The proposed circuit presents a high isolation and wideband features. The circuit can be used for both transmission and reception systems to ensure the Multi-User MIMO (MU-MIMO) service.
Hematopoietic cell transplantation for Wiskott-Aldrich syndrome: advances in biology and future directions for treatment.

The Wiskott-Aldrich syndrome (WAS) is an X-linked disorder characterized by a triad of diagnostic clinical elements: immunodeficiency, eczema, and hemorrhage caused by thrombocytopenia with small-sized platelets. The formal proof that hematopoietic cell transplantation (HCT) could be used to cure WAS revealed a requirement for both immunosuppression and myelosuppression that still underlies the standard approach to curative therapy today. The current short- and long-term toxicities of HCT are the main stumbling block for the ability to cure every patient with WAS and X-linked thrombocytopenia, and much remains to be done.
Automatic malware categorization using cluster ensemble

In this paper, resting on the analysis of instruction frequency and function-based instruction sequences, we develop an Automatic Malware Categorization System (AMCS) for automatically grouping malware samples into families that share some common characteristics using a cluster ensemble by aggregating the clustering solutions generated by different base clustering algorithms. We propose a principled cluster ensemble framework for combining individual clustering solutions based on the consensus partition. The domain knowledge in the form of sample-level constraints can be naturally incorporated in the ensemble framework. In addition, to account for the characteristics of feature representations, we propose a hybrid hierarchical clustering algorithm which combines the merits of hierarchical clustering and k-medoids algorithms and a weighted subspace K-medoids algorithm to generate base clusterings. The categorization results of our AMCS system can be used to generate signatures for malware families that are useful for malware detection. The case studies on large and real daily malware collection from Kingsoft Anti-Virus Lab demonstrate the effectiveness and efficiency of our AMCS system.
G-Free: defeating return-oriented programming through gadget-less binaries

Despite the numerous prevention and protection mechanisms that have been introduced into modern operating systems, the exploitation of memory corruption vulnerabilities still represents a serious threat to the security of software systems and networks. A recent exploitation technique, called Return-Oriented Programming (ROP), has lately attracted a considerable attention from academia. Past research on the topic has mostly focused on refining the original attack technique, or on proposing partial solutions that target only particular variants of the attack.
 In this paper, we present G-Free, a compiler-based approach that represents the first practical solution against any possible form of ROP. Our solution is able to eliminate all unaligned free-branch instructions inside a binary executable, and to protect the aligned free-branch instructions to prevent them from being misused by an attacker. We developed a prototype based on our approach, and evaluated it by compiling GNU libc and a number of real-world applications. The results of the experiments show that our solution is able to prevent any form of return-oriented programming.
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.
Distributed virtual machine consolidation: A systematic mapping study

Virtual Machine (VM) consolidation is an effective technique to improve resource utilization and reduce energy footprint in cloud data centers. It can be implemented in a centralized or a distributed fashion. Distributed VM consolidation approaches are currently gaining popularity because they are often more scalable than their centralized counterparts and they avoid a single point of failure.  To present a comprehensive, unbiased overview of the state-of-the-art on distributed VM consolidation approaches.  A Systematic Mapping Study (SMS) of the existing distributed VM consolidation approaches.  19 papers on distributed VM consolidation categorized in a variety of ways. The results show that the existing distributed VM consolidation approaches use four types of algorithms, optimize a number of different objectives, and are often evaluated with experiments involving simulations.  There is currently an increasing amount of interest on developing and evaluating novel distributed VM consolidation approaches. A number of research gaps exist where the focus of future research may be directed.
Outrageous but meaningful coincidences: dependent type-safe syntax and evaluation

Tagless interpreters for well-typed terms in some object language are a standard example of the power and benefit of precise indexing in types, whether with dependent types, or generalized algebraic datatypes. The key is to reflect object language types as indices (however they may be constituted) for the term datatype in the host language, so that host type coincidence ensures object type coincidence. Whilst this technique is widespread for simply typed object languages, dependent types have proved a tougher nut with nontrivial computation in type equality. In their type-safe representations, Danielsson [2006] and Chapman [2009] succeed in capturing the equality rules, but at the cost of representing equality derivations explicitly within terms. This article constructs a type-safe representation for a dependently typed object language, dubbed KIPLING, whose computational type equality just appropriates that of its host, Agda. The KIPLING interpreter example is not merely de rigeur - it is key to the construction. At the heart of the technique is that key component of generic programming, the universe.
Deep learning massively accelerates super-resolution localization microscopy

The speed of super-resolution microscopy methods based on single-molecule localization, for example, PALM and STORM, is limited by the need to record many thousands of frames with a small number of observed molecules in each. Here, we present ANNA-PALM, a computational strategy that uses artificial neural networks to reconstruct super-resolution views from sparse, rapidly acquired localization images and/or widefield images. Simulations and experimental imaging of microtubules, nuclear pores, and mitochondria show that high-quality, super-resolution images can be reconstructed from up to two orders of magnitude fewer frames than usually needed, without compromising spatial resolution. Super-resolution reconstructions are even possible from widefield images alone, though adding localization data improves image quality. We demonstrate super-resolution imaging of >1,000 fields of view containing >1,000 cells in ∼3 h, yielding an image spanning spatial scales from ∼20 nm to ∼2 mm. The drastic reduction in acquisition time and sample irradiation afforded by ANNA-PALM enables faster and gentler high-throughput and live-cell super-resolution imaging.
The Distance-Weighted k-Nearest-Neighbor Rule

Among the simplest and most intuitively appealing classes of nonprobabilistic classification procedures are those that weight the evidence of nearby sample observations most heavily. More specifically, one might wish to weight the evidence of a neighbor close to an unclassified observation more heavily than the evidence of another neighbor which is at a greater distance from the unclassified observation. One such classification rule is described which makes use of a neighbor weighting function for the purpose of assigning a class to an unclassified sample. The admissibility of such a rule is also considered.
A recurrent convolutional neural network approach for sensorless force estimation in robotic surgery

Providing force feedback as relevant information in current Robot-Assisted Minimally Invasive Surgery systems constitutes a technological challenge due to the constraints imposed by the surgical environment. In this context, Sensorless Force Estimation techniques represent a potential solution, enabling to sense the interaction forces between the surgical instruments and soft-tissues. Specifically, if visual feedback is available for observing soft-tissues’ deformation, this feedback can be used to estimate the forces applied to these tissues. To this end, a force estimation model, based on Convolutional Neural Networks and Long-Short Term Memory networks, is proposed in this work. This model is designed to process both, the spatiotemporal information present in video sequences and the temporal structure of tool data (the surgical tool-tip trajectory and its grasping status). A series of analyses are carried out to reveal the advantages of the proposal and the challenges that remain for real applications. This research work focuses on two surgical task scenarios, referred to as pushing and pulling tissue. For these two scenarios, different input data modalities and their effect on the force estimation quality are investigated. These input data modalities are tool data, video sequences and a combination of both. The results suggest that the force estimation quality is better when both, the tool data and video sequences, are processed by the neural network model. Moreover, this study reveals the need for a loss function, designed to promote the modeling of smooth and sharp details found in force signals. Finally, the results show that the modeling of forces due to pulling tasks is more challenging than for the simplest pushing actions.
Game design principles in everyday fitness applications

The global obesity epidemic has prompted our community to explore the potential for technology to play a stronger role in promoting healthier lifestyles. Although there are several examples of successful games based on focused physical interaction, persuasive applications that integrate into everyday life have had more mixed results. This underscores a need for designs that encourage physical activity while addressing fun, sustainability, and behavioral change. This note suggests a new perspective, inspired in part by the social nature of many everyday fitness applications and by the successful encouragement of long term play in massively multiplayer online games. We first examine the game design literature to distill a set of principles for discussing and comparing applications. We then use these principles to analyze an existing application. Finally, we present Kukini, a design for an everyday fitness game.
Biplanar proximal phalanx closing wedge osteotomy for hallux rigidus.

Many procedures have been described for surgical treatment of symptomatic hallux rigidus. Dorsal cheilectomy of the metatarsophalangeal joint combined with a dorsal-based closing wedge osteotomy of the proximal phalanx (i.e., Moberg procedure) has been described as an effective procedure. For patients with hallux rigidus and clinically significant hallux valgus interphalangeus, the authors previously described a dorsal cheilectomy combined with a biplanar closing wedge osteotomy of the proximal phalanx, combining a Moberg osteotomy with an Akin osteotomy. The purpose of this study was to describe the clinical results of this procedure.



This article is a retrospective review of prospectively gathered data that reports the clinical and radiographic results of dorsal cheilectomy combined with a biplanar oblique closing wedge proximal phalanx osteotomy (i.e., Moberg-Akin procedure) for patients with symptomatic hallux rigidus and hallux valgus interphalangeus. Consecutive patients were followed and evaluated for clinical and radiographic healing, satisfaction, and ultimate need for additional procedure(s). Thirty-five feet in 34 patients underwent the procedure.



All osteotomies healed. At an average of 22.5 months of follow-up, 90% of patients reported good or excellent results, with pain relief, improved function, and fewer shoe wear limitations following this procedure. Hallux valgus and hallux interphalangeal angles were radiographically improved. Other than one patient who requested hardware removal, no patients required additional surgical procedures.



Dorsal cheilectomy combined with a Moberg-Akin procedure was an effective and durable procedure with minimal morbidity in patients with hallux rigidus combined with hallux valgus interphalangeus.
Modular Reconfigurable Robots in Space Applications

Robots used for tasks in space have strict requirements. Modular reconfigurable robots have a variety of attributes that are advantageous for these conditions including the ability to serve as many tools at once saving weight, packing into compressed forms saving space and having large redundancy to increase robustness. Self-reconfigurable systems can also self-repair as well as automatically adapt to changing conditions or ones that were not anticipated. PolyBot may serve well in the space manipulation and surface mobility class of space applications.