nils-herrmann/clean_scirepeval huggingface dataset:
Assessment of the appearance, location and morphology of mandibular lingual foramina using cone beam computed tomography.

To investigate the appearance, location and morphology of mandibular lingual foramina (MLF) in the Chinese Han population using cone beam computed tomography (CBCT).



CBCT images of the mandibular body in 200 patients (103 female patients and 97 male patients, age range 10-70 years) were retrospectively analysed to identify MLF. The canal number, location and direction were assessed. Additionally, the diameter of the lingual foramen, the distance between the alveolar crest and the lingual foramen, the distance between the tooth apex and the lingual foramen and the distance from the mandibular border to the lingual foramen were examined to describe the MLF characteristics. Gender and age differences with respect to foramina were also studied.



CBCT can be utilized to visualise lingual foramina. In this study, 683 lingual foramina were detected in 200 CBCT scans, with 538 (78.77%) being ≤1 mm in diameter and 145 (21.23%) being >1 mm. In total, 85.07% of MLF are median lingual canals (MLC) and 14.93% are lateral lingual canals (LLC). Two typical types of lingual foramina were identified according to their relationship with the tooth apex. Most lingual foramina (74.08%) were found below the tooth apex, and those above the tooth apex were much smaller in diameter. Male patients had statistically larger lingual foramina. The distance between the lingual foramen and the tooth apex changed with increasing age.



Determination of the presence, position and size of lingual foramina is important before performing a surgical procedure. Careful implant-prosthetic treatment planning is particularly important in male and/or elderly patients because of the structural characteristics of their lingual foramina.
Connectivity reflects coding: a model of voltage-based STDP with homeostasis

Electrophysiological connectivity patterns in cortex often have a few strong connections, which are sometimes bidirectional, among a lot of weak connections. To explain these connectivity patterns, we created a model of spike timing–dependent plasticity (STDP) in which synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane potential, filtered with two different time constants. Our model describes several nonlinear effects that are observed in STDP experiments, as well as the voltage dependence of plasticity. We found that, in a simulated recurrent network of spiking neurons, our plasticity rule led not only to development of localized receptive fields but also to connectivity patterns that reflect the neural code. For temporal coding procedures with spatio-temporal input correlations, strong connections were predominantly unidirectional, whereas they were bidirectional under rate-coded input with spatial correlations only. Thus, variable connectivity patterns in the brain could reflect different coding principles across brain areas; moreover, our simulations suggested that plasticity is fast.
Objective assessment of depressive symptoms with machine learning and wearable sensors data

Depression is the major cause of years lived in disability world-wide; however, its diagnosis and tracking methods still rely mainly on assessing self-reported depressive symptoms, methods that originated more than fifty years ago. These methods, which usually involve filling out surveys or engaging in face-to-face interviews, provide limited accuracy and reliability and are costly to track and scale. In this paper, we develop and test the efficacy of machine learning techniques applied to objective data captured passively and continuously from E4 wearable wristbands and from sensors in an Android phone for predicting the Hamilton Depression Rating Scale (HDRS). Input data include electrodermal activity (EDA), sleep behavior, motion, phone-based communication, location changes, and phone usage patterns. We introduce our feature generation and transformation process, imputing missing clinical scores from self-reported measures, and predicting depression severity from continuous sensor measurements. While HDRS ranges between 0 and 52, we were able to impute it with 2.8 RMSE and predict it with 4.5 RMSE which are low relative errors. Analyzing the features and their relation to depressive symptoms, we found that poor mental health was accompanied by more irregular sleep, less motion, fewer incoming messages, less variability in location patterns, and higher asymmetry of EDA between the right and the left wrists.
Cloudlet Mesh for Securing Mobile Clouds from Intrusions and Network Attacks

This paper presents a new cloudlet mesh architecture for security enforcement to establish trusted mobile cloud computing. The cloudlet mesh is WiFi-or mobile-connected to the Internet. This security framework establishes a cyber trust shield to fight against intrusions to distance clouds, prevent spam/virus/worm attacks on mobile cloud resources, and stop unauthorized access of shared datasets in offloading the cloud. We have specified a sequence of authentication, authorization, and encryption protocols for securing communications among mobile devices, cloudlet servers, and distance clouds. Some analytical and experimental results prove the effectiveness of this new security infrastructure to safeguard mobile cloud services.
Maternal psychosocial stress and children's ADHD diagnosis: a prospective birth cohort study.

Examine the association of mothers' psychosocial stressors before and during pregnancy with their children's diagnosis of attention deficit hyperactivity disorder (ADHD).



This study included 2140 mother-child pairs who had at least one postnatal pediatric visit at the Boston Medical Center between 2003 and 2015. Child ADHD was determined via International Classification of Diseases, Ninth Revision (ICD-9) codes documented in electronic medical records. Latent factors of maternal stress and social support and measures of the physical home environment and psychosocial adversities were constructed using exploratory factor analysis. The association between the latent factors and child ADHD diagnosis was examined using multiple logistic regression, controlling for known risk factors for ADHD.



Children were 1.45 (95% CI: 1.06, 1.99) and 3.03 (95% CI: 2.19, 4.20) times more likely to receive an ADHD diagnosis if their mother experienced a major stressful event during pregnancy or reported a high level of perceived stress, respectively. The number of family adversities increases the risk of ADHD diagnosis [second quartile: OR = 1.90; CI (1.31, 2.77); third quartile: OR = 1.96 CI (1.34, 2.88); fourth quartile: OR = 2.89 CI (2.01, 4.16)] compared to first quartile.



In this prospective, predominantly urban, low-income, minority birth cohort, mothers' psychosocial stress before and during pregnancy appears to be an independent risk factor for the development of ADHD in their children.
Mind Over Matter : Mental Training Increases Physical Strength

This study tested whether mental training alone can produce a gain in muscular strength. Thirty male university athletes, including football, basketball and rugby players, were randomly assigned to perform mental training of their hip flexor muscles, to use weight machines to physically exercise their hip flexors, or to form a control group which received neither mental nor physical training. The hip strength of each group was measured before and after training. Physical strength was increased by 24% through mental practice (p = .008). Strength was also increased through physical training, by 28%, but did not change significantly in the control condition. The strength gain was greatest among the football players given mental training. Mental and physical training produced similar decreases in heart rate, and both yielded a marginal reduction in systolic blood pressure. The results support the related findings of Ranganathan, Siemionow, Liu, Sahgal, and Yue (2004).
MoireGraphs: radial focus+context visualization and interaction for graphs with visual nodes

Graph and tree visualization techniques enable interactive exploration of complex relations while communicating topology. However, most existing techniques have not been designed for situations where visual information such as images is also present at each node and must be displayed. This paper presents MoireGraphs to address this need. MoireGraphs combine a new focus+context radial graph layout with a suite of interaction techniques (focus strength changing, radial rotation, level highlighting, secondary foci, animated transitions and node information) to assist in the exploration of graphs with visual nodes. The method is scalable to hundreds of displayed visual nodes.
Published as a conference paper at ICLR 2018 O N THE CONVERGENCE OF A DAM AND B EYOND

Several recently proposed stochastic optimization methods that have been successfully used in training deep networks such as RMSPROP, ADAM, ADADELTA, NADAM are based on using gradient updates scaled by square roots of exponential moving averages of squared past gradients. In many applications, e.g. learning with large output spaces, it has been empirically observed that these algorithms fail to converge to an optimal solution (or a critical point in nonconvex settings). We show that one cause for such failures is the exponential moving average used in the algorithms. We provide an explicit example of a simple convex optimization setting where ADAM does not converge to the optimal solution, and describe the precise problems with the previous analysis of ADAM algorithm. Our analysis suggests that the convergence issues can be fixed by endowing such algorithms with “long-term memory” of past gradients, and propose new variants of the ADAM algorithm which not only fix the convergence issues but often also lead to improved empirical performance.
Multimodal summarization of complex sentences

In this paper, we introduce the idea of automatically illustrating complex sentences as multimodal summaries that combine pictures, structure and simplified compressed text. By including text and structure in addition to pictures, multimodal summaries provide additional clues of what happened, who did it, to whom and how, to people who may have difficulty reading or who are looking to skim quickly. We present ROC-MMS, a system for automatically creating multimodal summaries (MMS) of complex sentences by generating pictures, textual summaries and structure. We show that pictures alone are insufficient to help people understand most sentences, especially for readers who are unfamiliar with the domain. An evaluation of ROC-MMS in the Wikipedia domain illustrates both the promise and challenge of automatically creating multimodal summaries.
An overview of Internet of Vehicles

The new era of the Internet of Things is driving the evolution of conventional Vehicle Ad-hoc Networks into the Internet of Vehicles (IoV). With the rapid development of computation and communication technologies, IoV promises huge commercial interest and research value, thereby attracting a large number of companies and researchers. This paper proposes an abstract network model of the IoV, discusses the technologies required to create the IoV, presents different applications based on certain currently existing technologies, provides several open research challenges and describes essential future research in the area of IoV.
Leveraging semantic signatures for bug search in binary programs

Software vulnerabilities still constitute a high security risk and there is an ongoing race to patch known bugs. However, especially in closed-source software, there is no straightforward way (in contrast to source code analysis) to find buggy code parts, even if the bug was publicly disclosed.
 To tackle this problem, we propose a method called Tree Edit Distance Based Equational Matching (TEDEM) to automatically identify binary code regions that are "similar" to code regions containing a reference bug. We aim to find bugs both in the same binary as the reference bug and in completely unrelated binaries (even compiled for different operating systems). Our method even works on proprietary software systems, which lack source code and symbols.
 The analysis task is split into two phases. In a preprocessing phase, we condense the semantics of a given binary executable by symbolic simplification to make our approach robust against syntactic changes across different binaries. Second, we use tree edit distances as a basic block-centric metric for code similarity. This allows us to find instances of the same bug in different binaries and even spotting its variants (a concept called vulnerability extrapolation). To demonstrate the practical feasibility of the proposed method, we implemented a prototype of TEDEM that can find real-world security bugs across binaries and even across OS boundaries, such as in MS Word and the popular messengers Pidgin (Linux) and Adium (Mac OS).
Genetic relationships in the olive (Olea europaea L.) reflect multilocal selection of cultivars

One hundred and two olive RAPD profiles were sampled from all around the Mediterranean Basin. Twenty four clusters of RAPD profiles were shown in the dendrogram based on the Ward’s minimum variance algorithm using chi-square distances. Factorial discriminant analyses showed that RAPD profiles were correlated with the use of the fruits and the country or region of origin of the cultivars. This suggests that cultivar selection has occurred in different genetic pools and in different areas. Mitochondrial DNA RFLP analyses were also performed. These mitotypes supported the conclusion also that multilocal olive selection has occurred. This prediction for the use of cultivars will help olive growers to choose new foreign cultivars for testing them before an eventual introduction if they are well adapted to local conditions.
Inborn errors of sterol biosynthesis.

The known disorders of cholesterol biosynthesis have expanded rapidly since the discovery that Smith-Lemli-Opitz syndrome is caused by a deficiency of 7-dehydrocholesterol. Each of the six now recognized sterol disorders-mevalonic aciduria, Smith-Lemli-Opitz syndrome, desmosterolosis, Conradi-Hünermann syndrome, CHILD syndrome, and Greenberg dysplasia-has added to our knowledge of the relationship between cholesterol metabolism and embryogenesis. One of the most important lessons learned from the study of these disorders is that abnormal cholesterol metabolism impairs the function of the hedgehog class of embryonic signaling proteins, which help execute the vertebrate body plan during the earliest weeks of gestation. The study of the enzymes and genes in these several syndromes has also expanded and better delineated an important class of enzymes and proteins with diverse structural functions and metabolic actions that include sterol biosynthesis, nuclear transcriptional signaling, regulation of meiosis, and even behavioral modulation.
Analysis of transmission characteristics for twisted pair cables using the RLGC parameters of the cable

As automotive electronics have increased, models for predicting the transmission characteristics of wiring harnesses, suitable for the automotive EMC tests, are needed. In this paper, the repetitive structures of the cross-sectional shape of the twisted pair cable is focused on. By taking account of RLGC parameters, a theoretical analysis modeling for whole cables, based on multi-conductor transmission line theory, is proposed. Furthermore, the theoretical values are compared with measured values and a full-wave simulator. In case that a twisted pitch, a length of the cable, and a height of reference ground plane are changed, the validity of the proposed model is confirmed.
STICK-BREAKING VARIATIONAL AUTOENCODERS

We extend Stochastic Gradient Variational Bayes to perform posterior inference for the weights of Stick-Breaking processes. This development allows us to define a Stick-Breaking Variational Autoencoder (SB-VAE), a Bayesian nonparametric version of the variational autoencoder that has a latent representation with stochastic dimensionality. We experimentally demonstrate that the SB-VAE, and a semisupervised variant, learn highly discriminative latent representations that often outperform the Gaussian VAE’s.
Outcome-Oriented Predictive Process Monitoring: Review and Benchmark

Predictive business process monitoring refers to the act of making predictions about the future state of ongoing cases of a business process, based on their incomplete execution traces and logs of historical (completed) traces. Motivated by the increasingly pervasive availability of fine-grained event data about business process executions, the problem of predictive process monitoring has received substantial attention in the past years. In particular, a considerable number of methods have been put forward to address the problem of outcome-oriented predictive process monitoring, which refers to classifying each ongoing case of a process according to a given set of possible categorical outcomes—e.g., Will the customer complain or not? Will an order be delivered, canceled, or withdrawn? Unfortunately, different authors have used different datasets, experimental settings, evaluation measures, and baselines to assess their proposals, resulting in poor comparability and an unclear picture of the relative merits and applicability of different methods. To address this gap, this article presents a systematic review and taxonomy of outcome-oriented predictive process monitoring methods, and a comparative experimental evaluation of eleven representative methods using a benchmark covering 24 predictive process monitoring tasks based on nine real-life event logs.
Cephalophagus Non Janiceps : An Extremely Rare Variant of Conjoined Twins : A Case Report

Conjoined twins, popularly known as Siamese twins, result from aberrant embryogenesis . It is a rare presentation with an incidence of 1 in 50,000 births. Since 60% of these cases are still births, so the true incidence is estimated to be approximately 1 in 200,000 births [2-4]. This disorder is more common in females with female to male ratio of 3:1 . Conjoined twins are classified based on their site of attachment with a suffix ‘pagus’ which is a Greek term meaning “fixed”. The main types of conjoined twins are omphalopagus (abdomen), thoracopagus (thorax), cephalopagus (ventrally head to umbilicus), ischipagus (pelvis), parapagus (laterally body side), craniopagus (head), pygopagus (sacrum) and rachipagus (vertebral column) . Cephalophagus is an extremely rare variant of conjoined twins with an incidence of 11% among all cases. These types of twins are fused at head, thorax and upper abdominal cavity. They are pre-dominantly of two types: Janiceps (two faces are on the either side of the head) or non Janiceps type (normal single head and face). We hereby report a case of non janiceps cephalopagus conjoined twin, which was diagnosed after delivery.
Diffusion generated motion using signed distance functions

We describe a new class of algorithms for generating a variety of geometric interfacial motions by alternating two steps: Construction of the signed distance function (i.e. redistancing) to the interface, and convolution with a suitable kernel. These algorithms can be seen as variants of Merriman, Bence, and Osher’s threshold dynamics . The new algorithms proposed here preserve the computational efficiency of the original threshold dynamics algorithm. However, unlike threshold dynamics, the new algorithms also allow attaining high accuracy on uniform grids, without adaptive refinement.
Monitoring Vital Signs: Development of a Modified Early Warning Scoring (Mews) System for General Wards in a Developing Country

The aim of the study was to develop and validate, by consensus, the construct and content of an observations chart for nurses incorporating a modified early warning scoring (MEWS) system for physiological parameters to be used for bedside monitoring on general wards in a public hospital in South Africa.



Delphi and modified face-to-face nominal group consensus methods were used to develop and validate a prototype observations chart that incorporated an existing UK MEWS. This informed the development of the Cape Town ward MEWS chart.



One specialist anaesthesiologist, one emergency medicine specialist, two critical care nurses and eight senior ward nurses with expertise in bedside monitoring (N = 12) were purposively sampled for consensus development of the MEWS. One general surgeon declined and one neurosurgeon replaced the emergency medicine specialist in the final round.



Five consensus rounds achieved ≥70% agreement for cut points in five of seven physiological parameters respiratory and heart rates, systolic BP, temperature and urine output. For conscious level and oxygen saturation a relaxed rule of <70% agreement was applied. A reporting algorithm was established and incorporated in the MEWS chart representing decision rules determining the degree of urgency. Parameters and cut points differed from those in MEWS used in developed countries.



A MEWS for developing countries should record at least seven parameters. Experts from developing countries are best placed to stipulate cut points in physiological parameters. Further research is needed to explore the ability of the MEWS chart to identify physiological and clinical deterioration.
A Fast CUDA-Based Implementation for the Euclidean Distance Transform

In Image Processing efficient algorithms are always pursued for applications that use the most advanced hardware architectures. Distance Transform is a classic operation for blurring effects, skeletonizing, segmentation and various other purposes. This article presents two implementations of the Euclidean Distance Transform using CUDA (Compute Unified Device Architecture) in GPU (Graphics Process Unit): of the Meijster's Sequential Algorithm and another is a very efficient algorithm of simple structure. Both using only shared memory. The results presented herein used images of various types and sizes to show a faster run time compared with the best-known implementations in CPU.
Geckos: combining magnets and pressure images to enable new tangible-object design and interaction

In this paper we present Geckos, a new type of tangible objects which are tracked using a Force-Sensitive Resistance sensor. Geckos are based on low-cost permanent magnets and can also be used on non-horizontal surfaces. Unique pressure footprints are used to identify each tangible Gecko. Two types of tangible object designs are presented: Using a single magnet in combination with felt pads provides new pressure-based interaction modalities. Using multiple separate magnets it is possible to change the marker footprint dynamically and create new haptic experiences. The tangible object design and interaction are illustrated with example applications. We also give details on the feasibility and benefits of our tracking approach and show compatibility with other tracking technologies.