nils-herrmann/clean_scirepeval huggingface dataset:
Latent Hinge-Minimax Risk Minimization for Inference from a Small Number of Training Samples

Deep Learning (DL) methods show very good performance when trained on large, balanced data sets. However, many practical problems involve imbalanced data sets, or/and classes with a small number of training samples. The performance of DL methods as well as more traditional classifiers drops significantly in such settings. Most of the existing solutions for imbalanced problems focus on customizing the data for training. A more principled solution is to use mixed Hinge-Minimax risk [19] specifically designed to solve binary problems with imbalanced training sets. Here we propose a Latent Hinge Minimax (LHM) risk and a training algorithm that generalizes this paradigm to an ensemble of hyperplanes that can form arbitrary complex, piecewise linear boundaries. To extract good features, we combine LHM model with CNN via transfer learning. To solve multi-class problem we map pre-trained categoryspecific LHM classifiers to a multi-class neural network and adjust the weights with very fast tuning. LHM classifier enables the use of unlabeled data in its training and the mapping allows for multi-class inference, resulting in a classifier that performs better than alternatives when trained on a small number of training samples.
Cortical neurons gradually attain a post-mitotic state

Once generated, neurons are thought to permanently exit the cell cycle and become irreversibly differentiated. However, neither the precise point at which this post-mitotic state is attained nor the extent of its irreversibility is clearly defined. Here we report that newly born neurons from the upper layers of the mouse cortex, despite initiating axon and dendrite elongation, continue to drive gene expression from the neural progenitor tubulin α1 promoter (Tα1p). These observations suggest an ambiguous post-mitotic neuronal state. Whole transcriptome analysis of sorted upper cortical neurons further revealed that neurons continue to express genes related to cell cycle progression long after mitotic exit until at least post-natal day 3 (P3). These genes are however down-regulated thereafter, associated with a concomitant up-regulation of tumor suppressors at P5. Interestingly, newly born neurons located in the cortical plate (CP) at embryonic day 18-19 (E18-E19) and P3 challenged with calcium influx are found in S/G2/M phases of the cell cycle, and still able to undergo division at E18-E19 but not at P3. At P5 however, calcium influx becomes neurotoxic and leads instead to neuronal loss. Our data delineate an unexpected flexibility of cell cycle control in early born neurons, and describe how neurons transit to a post-mitotic state.
Localisation and functional significance of ryanodine receptors during beta-adrenoceptor stimulation in the guinea-pig sino-atrial node.

Recent evidence shows that calcium released from the sarcoplasmic reticulum (SR) plays an important role in the regulation of heart rate. The aim of this study was to investigate the subcellular distribution of ryanodine receptors in the guinea-pig sino-atrial (SA) node and to determine their functional role in the regulation of pacemaker frequency in response to beta-adrenoceptor stimulation.



Monoclonal antibodies raised against the cardiac ryanodine receptor were used with confocal microscopy to investigate ryanodine receptor distribution in single guinea-pig SA node cells. The functional role of ryanodine receptors was investigated in both multicellular SA node/atrial preparations and in single SA node cells.



Ryanodine receptor labelling was observed in all SA node cells studied and showed both subsarcolemmal and intracellular staining. In the latter, labelling appeared as transverse bands with a regular periodicity of approximately 2 microm. This interval resembled that of the expected sarcomere spacing but did not, however, depend on the presence of transverse tubules. The bands of ryanodine receptors appeared to be located in the region of the Z lines, based on co-distribution studies with antibodies to alpha-actinin, myomesin and binding sites for phalloidin. Functional studies on single SA node cells showed that application of ryanodine (2 micromol/l) reduced the rate of firing of spontaneous action potentials (measured using the perforated patch clamp technique) and this was associated with changes in action potential characteristics. Ryanodine also significantly decreased the positive chronotropic actions of isoprenaline in both multicellular and single cell preparations. In single cells exposed to 100 nmol/l isoprenaline, ryanodine caused a decrease in the rate of firing and this was associated with a decrease in the amplitude of the measured calcium transients.



These findings are the first to show immunocytochemical evidence for the presence and organisation of ryanodine receptor calcium release channels in mammalian SA node cells. This study also provides evidence of a role for ryanodine sensitive sites in the beta-adrenergic modulation of heart rate in this species.
A Divide-and-Conquer Algorithm for Betweenness Centrality

Given a graph G we define the betweenness centrality of a node v in V as the fraction of shortest paths between all node pairs in V that contain v. For this setting we describe Brandes++, a divide-and-conquer algorithm that can efficiently compute the exact values of betweenness scores. Brandes++ uses Brandes– the most widelyused algorithm for betweenness computation – as its subroutine. It achieves the notable faster running times by applying Brandes on significantly smaller networks than the input graph, and many of its computations can be done in parallel. The degree of speedup achieved by Brandes++ depends on the community structure of the input network. Our experiments with real-life networks reveal Brandes++ achieves an average of 10-fold speedup over Brandes, while there are networks where this speedup is 75-fold. We have made our code public to benefit the research community.
Get Your Atoms in Order - An Open-Source Implementation of a Novel and Robust Molecular Canonicalization Algorithm

Finding a canonical ordering of the atoms in a molecule is a prerequisite for generating a unique representation of the molecule. The canonicalization of a molecule is usually accomplished by applying some sort of graph relaxation algorithm, the most common of which is the Morgan algorithm. There are known issues with that algorithm that lead to noncanonical atom orderings as well as problems when it is applied to large molecules like proteins. Furthermore, each cheminformatics toolkit or software provides its own version of a canonical ordering, most based on unpublished algorithms, which also complicates the generation of a universal unique identifier for molecules. We present an alternative canonicalization approach that uses a standard stable-sorting algorithm instead of a Morgan-like index. Two new invariants that allow canonical ordering of molecules with dependent chirality as well as those with highly symmetrical cyclic graphs have been developed. The new approach proved to be robust and fast when tested on the 1.45 million compounds of the ChEMBL 20 data set in different scenarios like random renumbering of input atoms or SMILES round tripping. Our new algorithm is able to generate a canonical order of the atoms of protein molecules within a few milliseconds. The novel algorithm is implemented in the open-source cheminformatics toolkit RDKit. With this paper, we provide a reference Python implementation of the algorithm that could easily be integrated in any cheminformatics toolkit. This provides a first step toward a common standard for canonical atom ordering to generate a universal unique identifier for molecules other than InChI.
Penis deformity after intra-urethral liquid paraffin administration in a young male: a case report

Self-induced injections of liquid substances mainly for penis enlargement is a well-documented but still rather uncommon practice in the western world.



Herein we present the case of a 30-year-old male who self-inflicted, twice in a six-month-period, intra-urethral liquid paraffin and tied up his penis with a cord in order to achieve both enlargement and elongation. He arrived in our emergency department suffering from suprapubic pain; physical examination revealed a rather unique deformity of the penis. He finally refused any treatment and absconded.



Side effects after paraffin administration are various and sometimes severe. Most of the times surgical treatment is needed and radical excision together with follow-up seems the best modality. Such practices emphasize on the public's misbelieves and that some aspects of sexual medicine are yet covered with the veil of misconception.
Load Balancing in Cloud Computing Environment : A Comparative Study of Service Models and Scheduling Algorithms

Navpreet Singh M. tech Scholar, CSE & IT Deptt., BBSB Engineering College, Fatehgarh Sahib, Punjab, India (IKG – Punjab Technical University, Jalandhar) navpreetsaini26@gmail.com Dr. Kanwalvir Singh Dhindsa Professor, CSE & IT Deptt., BBSB Engineering College, Fatehgarh Sahib, Punjab, India (IKG – Punjab Technical University, Jalandhar) kanwalvir.singh@bbsbec.ac.in -------------------------------------------------------------------ABSTRACT--------------------------------------------------------------Load balancing is a computer networking method to distribute workload across multiple computers or a computer cluster, network links, central processing units, disk drives, or other resources, to achieve optimal resource utilization, maximize throughput, minimize response time, and avoid overload. Using multiple components with load balancing, instead of a single component, may increase reliability through redundancy. The load balancing service is usually provided by dedicated software or hardware, such as a multilayer switch or a Domain Name System server. In this paper, the existing static algorithms used for simple cloud load balancing have been identified and also a hybrid algorithm for developments in the future is suggested.
Recognizing Textual Entailment Using Sentence Similarity based on Dependency Tree Skeletons

We present a novel approach to RTE that exploits a structure-oriented sentence representation followed by a similarity function. The structural features are automatically acquired from tree skeletons that are extracted and generalized from dependency trees. Our method makes use of a limited size of training data without any external knowledge bases (e.g. WordNet) or handcrafted inference rules. We have achieved an accuracy of 71.1% on the RTE-3 development set performing a 10-fold cross validation and 66.9% on the RTE-3 test data.
A Validation of Object-Oriented Design Metrics as Quality Indicators

This paper presents the results of a study conducted at the University of Maryland in which we experimentally investigated the suite of Object-Oriented (OO) design metrics introduced by [Chidamber&Kemerer, 1994]. In order to do this, we assessed these metrics as predictors of fault-prone classes. This study is complementary to [Li&Henry, 1993] where the same suite of metrics had been used to assess frequencies of maintenance changes to clas es. To perform our validation accurately, we collected data on the development of eight medium-sized information management systems based on identical requirements. All eight projects were developed using a sequential life cycle model, a well-known OO analysis/design method and the C++ programming language. Based on experimental results, the advantages and drawbacks of these OO metrics are discussed. Several of Chidamber&Kemerer’s OO metrics appear to be useful to predict class fault-proneness during the early phases of the life-cycle. We also showed that they are, on our data set, better predictors than “traditional” code metrics, which can only be collected at a later phase of the software development processes.  Object-Oriented Design Metrics; Error Prediction Model; Object-Oriented Software Development; C++  V. Basili and W. Melo are with the University of Maryland, Institute for Advanced Computer Studies and Computer Science Dept., A. V. Williams Bldg., College Park, MD 20742 USA. {basili | melo}@cs.umd.edu L. Briand is with the CRIM, 1801 McGill College Av., Montréal (Québec), H3A 2N4, Canada.  Technical Report, Univ. of Maryland, Dep. of Computer Science, College Park, MD, 20742 USA. April 1995. CS-TR-3443 2 UMIACS-TR-95-40 1 .
Bursting your (filter) bubble: strategies for promoting diverse exposure

Broadcast media are declining in their power to decide which issues and viewpoints will reach large audiences. But new information filters are appearing, in the guise of recommender systems, aggregators, search engines, feed ranking algorithms, and the sites we bookmark and the people and organizations we choose to follow on Twitter. Sometimes we explicitly choose our filters; some we hardly even notice. Critics worry that, collectively, these filters will isolate people in information bubbles only partly of their own choosing, and that the inaccurate beliefs they form as a result may be difficult to correct. But should we really be worried, and, if so, what can we do about it? Our panelists will review what scholars know about selectivity of exposure preferences and actual exposure and what we in the CSCW field can do to develop and test ways of promoting diverse exposure, openness to the diversity we actually encounter, and deliberative discussion.
Group a Streptococcus causing necrotizing fasciitis and toxic shock syndrome after medical termination of pregnancy.

Group A Streptococcus is an aerobic gram-positive bacteria known to cause cutaneous infections. Invasive infections can lead to toxic shock syndrome with multiorgan failure and mortality rates of 25-48%.



A healthy, young woman developed necrotizing fasciitis, myonecrosis, and toxic shock syndrome after an elective medical termination of pregnancy. This patient had confirmed group A Streptococcus on blood cultures and underwent surgical debridement. After aggressive surgical treatment, below-the-knee amputation, and antibiotic therapy, the patient survived.



This case demonstrates the need for prompt recognition and treatment of necrotizing fasciitis/toxic shock syndrome.
A Formalism for Representing and Reasoning with Temporal Information, Event and Change

In this paper we present a general formalism for representing and reasoning with temporal information, event and change. The temporal framework is a theory of time that takes both points and interval as temporal primitives and where the base logic is that of Kleene’s three-valued logic. Thus, we can avoid the Divided Instant Problem (DIP). We present a three-valued based Temporal First-Order Nonmonotonic Logic (TFONL) that employs an explicit representation of time and events. We may embody default logic into TFONL, which takes into consideration the frame, qualification and ramification problems.
TRANSLATING POLISH TEXTS INTO SIGN LANGUAGE IN THE TGT SYSTEM

In the paper a method of translation applied in a new system TGT is discussed. TGT translates texts written in Polish into corresponding utterances in the Polish sign language. Discussion is focused on text-into-text translation phase. Proper translation is done on the level of a predicative representation of the sentence. The representation is built on the basis of syntactic graph that depicts the composition and mutual connections of syntactic groups, which exist in the sentence and are identified at the syntactic analysis stage. An essential element of translation process is complementing the initial predicative graph with nodes, which correspond to lacking sentence members. The method acts for primitive sentences as well as for compound ones, with some limitations, however. A translation example is given which illustrates main transformations done on the linguistic level. It is complemented by samples of images generated by the animating part of the system.
Measurement of human service staff satisfaction: development of the Job Satisfaction Survey.

The development of the Job Satisfaction Survey (JSS), a nine-subscale measure of employee job satisfaction applicable specifically to human service, public, and nonprofit sector organizations, is described. The item selection, item analysis, and determination of the final 36-item scale are also described, and data on reliability and validity and the instrument's norms are summarized. Included are a multitrait-multimethod analysis of the JSS and the Job Descriptive Index (JDI), factor analysis of the JSS, and scale intercorrelations. Correlation of JSS scores with criteria of employee perceptions and behaviors for multiple samples were consistent with findings involving other satisfaction scales and with findings from the private sector. The strongest correlations were with perceptions of the job and supervisor, intention of quitting, and organizational commitment. More modest correlations were found with salary, age, level, absenteeism, and turnover.
Shadow optimization from structured deep edge detection

Local structures of shadow boundaries as well as complex interactions of image regions remain largely unexploited by previous shadow detection approaches. In this paper, we present a novel learning-based framework for shadow region recovery from a single image. We exploit local structures of shadow edges by using a structured CNN learning framework. We show that using structured label information in classification can improve local consistency over pixel labels and avoid spurious labelling. We further propose and formulate shadow/bright measure to model complex interactions among image regions. The shadow and bright measures of each patch are computed from the shadow edges detected by the proposed CNN. Using the global interaction constraints on patches, we formulate a least-square optimization problem for shadow recovery that can be solved efficiently. Our shadow recovery method achieves state-of-the-art results on major shadow benchmark databases collected under various conditions.
Detection of Browser Fingerprinting by Static JavaScript Code Classification

The fast moving technologies on the Web provide users, developers and researchers with increasing challenges when it comes to user privacy. The domain of browser fingerprinting has grown in response to the limitations in other tracking techniques such as cookies. For the sake of countering browser fingerprinting, detection is an important step. This paper sets out to answer whether the detection of scripts by static code analysis and subsequent classification with machine learning is possible. A Support Vector Machine classifier is used in order to attempt classification and detection of fingerprinting websites. The classifier shows to be able to make a correct distinction most of the time. 0.05 of the non-fingerprinting websites are wrongly classified, while 0.70 of the fingerprinting websites are correctly detected. Other metrics prove the proposed method to indeed be promising for detection. Solving the limitation of size of the current dataset might improve the results.
3D Brain Segmentation Using Dual-Front Active Contours with Optional User Interaction

Important attributes of 3D brain cortex segmentation algorithms include robustness, accuracy, computational efficiency, and facilitation of user interaction, yet few algorithms incorporate all of these traits. Manual segmentation is highly accurate but tedious and laborious. Most automatic techniques, while less demanding on the user, are much less accurate. It would be useful to employ a fast automatic segmentation procedure to do most of the work but still allow an expert user to interactively guide the segmentation to ensure an accurate final result. We propose a novel 3D brain cortex segmentation procedure utilizing dual-front active contours which minimize image-based energies in a manner that yields flexibly global minimizers based on active regions. Region-based information and boundary-based information may be combined flexibly in the evolution potentials for accurate segmentation results. The resulting scheme is not only more robust but much faster and allows the user to guide the final segmentation through simple mouse clicks which add extra seed points. Due to the flexibly global nature of the dual-front evolution model, single mouse clicks yield corrections to the segmentation that extend far beyond their initial locations, thus minimizing the user effort. Results on 15 simulated and 20 real 3D brain images demonstrate the robustness, accuracy, and speed of our scheme compared with other methods.
Antenna Design for Low-Cost Laptop-based Ground Penetrating Radar

Ground penetrating radar (GPR) is non-destructive device used for monitoring underground structures. COST Action TU1208 promoted its use outside the civil engineering applications and provided a lot of free resources to the GPR community. In this paper, we built a low-cost GPR prototype for educational purposes according to the given resources and continued the work with focus on GPR antenna design. According to the required radiation characteristics, some antenna types are thoroughly discussed, fabricated and measured.
Trigeminal trophic syndrome: diagnosis and management difficulties.

Trigeminal trophic syndrome is a rare complication after peripheral or central damage to the trigeminal nerve, characterized by sensorial impairment in the trigeminal nerve territory and self-induced nasal ulceration. Conditions that can affect the trigeminal nerve include brainstem cerebrovascular disease, diabetes, tabes, syringomyelia, and postencephalopathic parkinsonism; it can also occur following the surgical management of trigeminal neuralgia. Trigeminal trophic syndrome may develop months to years after trigeminal nerve insult. Its most common presentation is a crescent-shaped ulceration within the trigeminal sensory territory. The ala nasi is the most frequently affected site. Trigeminal trophic syndrome is notoriously difficult to diagnose and manage. A clear history is of paramount importance, with exclusion of malignant, fungal, granulomatous, vasculitic, or infective causes. We present a case of ulceration of the left ala nasi after brainstem cerebrovascular accident.
An optical SDN Controller for Transport Network virtualization and autonomic operation

This paper proposes an architecture for Software Defined Optical Transport Networks. The SDN Controller includes a network abstraction layer allowing the implementation of cognitive controls and policies for autonomic operation, based on global network view. Additionally, the controller implements a virtualized GMPLS control plane, offloading and simplifying the network elements, while unlocking the implementation of new services such as optical VPNs, optical network slicing, and keeping standard OIF interfaces, such as UNI and NNI. The concepts have been implemented and validated in a real testbed network formed by five DWDM nodes equipped with flexgrid WSS ROADMs.
Simplified Frame Rate Up-conversion algorithm with low computational complexity

This paper proposes the efficient Frame Rate Up-conversion (FRUC) that has low computational complexity. The proposed algorithm consists of motion vector (MV) smoothing, selective average based motion compensation (SAMC) and hole interpolation with different weights. The proposed MV smoothing constructs more smooth interpolated frames by correcting inaccurate MVs. The proposed SAMC and hole interpolation effectively deal with overlaps and holes, and thus, they can efficiently reduce the degradation of the interpolated frames by removing blocking artifacts and blurring. Experimental results show that the proposed algorithm improves the average PSNR of the interpolated frames by 4.15dB than the conventional algorithm using bilateral ME, while it shows the average 0.16dB less PSNR performance than the existing algorithm using unilateral ME. However, it can significantly reduce the computational complexity based on absolute difference by 89.3%.