nils-herrmann/clean_scirepeval huggingface dataset:
What's in a Name? First Names as Facial Attributes

This paper introduces a new idea in describing people using their first names, i.e., the name assigned at birth. We show that describing people in terms of similarity to a vector of possible first names is a powerful description of facial appearance that can be used for face naming and building facial attribute classifiers. We build models for 100 common first names used in the United States and for each pair, construct a pair wise first-name classifier. These classifiers are built using training images downloaded from the Internet, with no additional user interaction. This gives our approach important advantages in building practical systems that do not require additional human intervention for labeling. We use the scores from each pair wise name classifier as a set of facial attributes. We show several surprising results. Our name attributes predict the correct first names of test faces at rates far greater than chance. The name attributes are applied to gender recognition and to age classification, outperforming state-of-the-art methods with all training images automatically gathered from the Internet.
Closed-form dynamic model of PUMA 560 robot arm

This paper investigates an explicit dynamic model of the PUMA 560 robot manipulators, based on standard Denavit-Hartenberg approach and without any mathematical simplifications. The presented model obviates the existing shortcomings in reference model in MATLAB robotic toolbox and it can be an appropriate substitution for robotic toolbox. A numerical comparison, employing different inputs, is utilized to illustrate the accuracy of the mentioned model.
Improved upper bounds in the moving sofa problem

The moving sofa problem, posed by L. Moser in 1966, asks for the planar shape of maximal area that can move around a right-angled corner in a hallway of unit width. It is known that a maximal area shape exists, and that its area is at least 2.2195 . . .—the area of an explicit construction found by Gerver in 1992—and at most 2 √ 2 ≈ 2.82, with the lower bound being conjectured as the true value. We prove a new and improved upper bound of 2.37. The method involves a computer-assisted proof scheme that can be used to rigorously derive further improved upper bounds that converge to the correct value.
Coregulation, dysregulation, self-regulation: an integrative analysis and empirical agenda for understanding adult attachment, separation, loss, and recovery.

An integrative framework is proposed for understanding how multiple biological and psychological systems are regulated in the context of adult attachment relationships, dysregulated by separation and loss experiences, and, potentially, re-regulated through individual recovery efforts. Evidence is reviewed for a coregulatory model of normative attachment, defined as a pattern of interwoven physiology between romantic partners that results from the conditioning of biological reward systems and the emergence of felt security within adult pair bonds. The loss of coregulation can portend a state of biobehavioral dysregulation, ranging from diffuse psychophysiological arousal and disorganization to a full-blown (and highly organized) stress response. The major task for successful recovery is adopting a self-regulatory strategy that attenuates the dysregulating effects of the attachment disruption. Research evidence is reviewed across multiple levels of analysis, and the article concludes with a series of testable research questions on the interconnected nature of attachment, loss, and recovery processes.
The mortality of childhood falls.

Falls accounted for 5.9% of the childhood deaths due to trauma in a review of the medical examiner's files in a large urban county. Falls represented the seventh leading cause of traumatic death in all children 15 years of age or younger, but the third leading cause of death in children 1 to 4 years old. The mean age of those with accidental falls was 2.3 years, which is markedly younger than that seen in hospital admission series, suggesting that infants are much more likely to die from a fall than older children. Forty-one per cent of the deaths occurred from "minor" falls such as falls from furniture or while playing; 50% were falls from a height of one story or greater; the remainder were falls down stairs. Of children falling from less than five stories, death was due to a lethal head injury in 86%. Additionally, 61.3% of the children with head injuries had mass lesions which would have required acute neurosurgical intervention. The need for an organized pediatric trauma system is demonstrated as more than one third of the children were transferred to another hospital, with more than half of these deteriorating during the delay. Of the patients with "minor" falls, 38% had parental delay in seeking medical attention, with deterioration of all. The trauma system must also incorporate the education of parents and medical personnel to the potential lethality of "minor" falls in infants and must legislate injury prevention programs.
Deep Neural Network for PM2.5 Pollution Forecasting Based on Manifold Learning

Accurately forecasting pollution concentration of PM2.5 can provide early warning for the government to alert the persons suffering from air pollution. Many existing approaches fail at providing favorable results duo to shallow architecture in forecasting model that can not learn suitable features. In addition, multiple meteorological factors increase the difficulty for understanding the influence of the PM2.5 concentration. In this paper, a deep neural network is proposed for accurately forecasting PM2.5 pollution concentration based on manifold learning. Firstly, meteorological factors are specified by the manifold learning method, reducing the dimension without any expert knowledge. Secondly, a deep belief network (DBN) is developed to learn the features of the input candidates obtained by the manifold learning and the one-day ahead PM2.5 concentration. Finally, the deep features are modeled by a regression neural network, and the local PM2.5 forecast is yielded. The addressed model is evaluated by the dataset in the period of 28/10/2013 to 31/3/2017 in Chongqing municipality of China. The study suggests that deep learning is a promising technique in PM2.5 concentration forecasting based on the manifold learning.
The Audacity of Fiber-Wireless (FiWi) Networks

A plethora of enabling optical and wireless technologies have been emerging that can be used to build future-proof bimodal fiber-wireless (FiWi) broadband access networks. After overviewing key enabling radio-over-fiber (RoF) and radio-andfiber (R&F) technologies and briefly surveying the state of the art of FiWi networks, we introduce an Ethernet-based access-metro FiWi network, called SuperMAN, that integrates next-generation WiFi and WiMAX networks with WDM-enhanced EPON and RPR networks. Throughout the paper we pay close attention to the technical challenges and opportunities of FiWi networks, but also elaborate on their societal benefits and potential to shift the current research focus from optical-wireless networking to the exploitation of personal and in-home computing facilities to create new unforeseen services and applications as we are about to enter the Petabyte age.
R2 CNN: Rotational Region CNN for Arbitrarily-Oriented Scene Text Detection

Scene text detection is challenging as the input may have different orientations, sizes, font styles, lighting conditions, perspective distortions and languages. This paper addresses the problem by designing a Rotational Region CNN (R2CNN). R2CNN includes a Text Region Proposal Network (Text-RPN) to estimate approximate text regions and a multitask refinement network to get the precise inclined box. Our work has the following features. First, we use a novel multi-task regression method to support arbitrarily-oriented scene text detection. Second, we introduce multiple ROIPoolings to address the scene text detection problem for the first time. Third, we use an inclined Non-Maximum Suppression (NMS) to post-process the detection candidates. Experiments show that our method outperforms the state-of-the-art on standard benchmarks: ICDAR 2013, ICDAR 2015, COCO-Text and MSRA-TD500.
Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery

Recovering a low-rank tensor from incomplete information is a recurring problem in signal processing and machine learning. The most popular convex relaxation of this problem minimizes the sum of the nuclear norms of the unfoldings of the tensor. We show that this approach can be substantially suboptimal: reliably recovering a K-way tensor of length n and Tucker rank r from Gaussian measurements requires Ω(rnK−1) observations. In contrast, a certain (intractable) nonconvex formulation needs only O(r +nrK) observations. We introduce a very simple, new convex relaxation, which partially bridges this gap. Our new formulation succeeds with O(rbK/2cndK/2e) observations. While these results pertain to Gaussian measurements, simulations strongly suggest that the new norm also outperforms the sum of nuclear norms for tensor completion from a random subset of entries. Our lower bound for the sum-of-nuclear-norms model follows from a new result on recovering signals with multiple sparse structures (e.g. sparse, low rank), which perhaps surprisingly demonstrates the significant suboptimality of the commonly used recovery approach via minimizing the sum of individual sparsity inducing norms (e.g. l1, nuclear norm). Our new formulation for low-rank tensor recovery however opens the possibility in reducing the sample complexity by exploiting several structures jointly.
Cultural humility versus cultural competence: a critical distinction in defining physician training outcomes in multicultural education.

Researchers and program developers in medical education presently face the challenge of implementing and evaluating curricula that teach medical students and house staff how to effectively and respectfully deliver health care to the increasingly diverse populations of the United States. Inherent in this challenge is clearly defining educational and training outcomes consistent with this imperative. The traditional notion of competence in clinical training as a detached mastery of a theoretically finite body of knowledge may not be appropriate for this area of physician education. Cultural humility is proposed as a more suitable goal in multicultural medical education. Cultural humility incorporates a lifelong commitment to self-evaluation and self-critique, to redressing the power imbalances in the patient-physician dynamic, and to developing mutually beneficial and nonpaternalistic clinical and advocacy partnerships with communities on behalf of individuals and defined populations.
ROS+unity: An efficient high-fidelity 3D multi-UAV navigation and control simulator in GPS-denied environments

In this paper, we will introduce our newly developed 3D simulation system for miniature unmanned aerial vehicles (UAVs) navigation and control in GPS-denied environments. As we know, simulation technologies can verify the algorithms and identify potential problems before the actual flight test and to make the physical implementation smoothly and successfully. To enhance the capability of state-of-the-art of research-oriented UAV simulation system, we develop a 3D simulator based on robot operation system (ROS) and a game engine, Unity3D. Unity3D has powerful graphics and can support high-fidelity 3D environments and sensor modeling which is important when we simulate sensing technologies in cluttered and harsh environments. On the other hand, ROS can provide clear software structure and simultaneous operation between hardware devices for actual UAVs. By developing data transmitting interface and necessary sensor modeling techniques, we have successfully glued ROS and Unity together. The integrated simulator can handle real-time multi-UAV navigation and control algorithms, including online processing of a large number of sensor data.
Use of Yokukansan (TJ-54) in the treatment of neurological disorders: a review.

Kampo herbal remedies are reported to have a wide range of indications and have attracted attention due to reports suggesting that these remedies are effective when used in disease treatment while maintaining a favourable quality of life. Yokukansan, also known as TJ-54, is composed of seven herbs; Angelica acutiloba, Atractylodes lancea, Bupleurum falcatum, Poria cocos, Glycyrrhiza uralensis, Cnidium officinale and Uncaria rhynchophylla. Yokukansan is used to treat insomnia and irritability as well as screaming attacks, sleep tremors and hypnic myoclonia, and neurological disorders which include dementia and Alzheimer's disease - the focus of this article. It is concluded that Yokukansan is a versatile herbal remedy with a variety of effects on various neurological states, without reported adverse effects. Traditional herbal medicines consist of a combination of constituents which account for the clinical effect seen. Likewise, the benefits of Yokukansan are probably attributable to the preparation as a whole, rather than to individual compounds.
Mining Change Patterns in AspectJ Software Evolution

Understanding software change patterns during evolution is important for researchers concerned with alleviating change impacts. It can provide insight to understand the software evolution, predict future changes, and develop new refactoring algorithms. However, most of the current research focus on the procedural programs like C, or object-oriented programs like Java; seldom effort has been made for aspect-oriented software. In this paper, we propose an approach for mining change patterns in AspectJ software evolution. Our approach first decomposes the software changes into a set of atomic change representations, then employs the apriori data mining algorithm to generate the most frequent itemsets. The patterns we found reveal multiple properties of software changes, including their kind, frequency, and correlation with other changes. In our empirical evaluation on several non-trivial AspectJ benchmarks, we demonstrate that those change patterns can be used as measurement aid and fault predication for AspectJ software evolution analysis.
On minimum inter-event times in event-triggered control

In this paper we study fundamental properties of minimum inter-event times in event-triggered control systems, both in the absence and presence of external disturbances. This analysis reveals, amongst others, that for several popular event-triggering mechanisms no positive minimum inter-event time can be guaranteed in the presence of arbitrary small external disturbances. This clearly shows that it is essential to include the effects of external disturbances in the analysis of the computation/communication properties of event-triggered control systems. In fact, this paper also identifies event-triggering mechanisms that do exhibit these important event-separation properties.
Safety and effect of high dose allopurinol in patients with severe left ventricular systolic dysfunction

Allopurinol used in the treatment of gout has been shown to improve the vascular endothelial dysfunction and reduce the dysfunction of the failing heart. This study was done to evaluate the effect and safety of allopurinol in non-hyperuricemic patients with chronic severe left ventricular (LV) dysfunction.  In this study, 35 consecutive cases of non-hyperuricemic patients with chronic heart failure who had severe LV systolic dysfunction (ejection fraction of less than 35%) and were on optimal guideline directed medical therapies for at least 3 months were included. Allopurinol was administered with the dose of 300 mg po daily for 1 week and then it was up-titrated to a dose of 600 mg po daily for 3 months. Six minute walk test, strain imaging, laboratory testing were done for every patient at baseline and after 3 months treatment with allopurinol.  In this study 30 heart failure (HF) patients with a mean age of 49.3 ± 14.4 years old were evaluated. No adverse effects were reported except for one case of skin rash after 4 days treatment which was excluded from the study. Study showed significant improvement of six minute walk test of the patients from 384.5 ± 81.5 meters to 402.8 ± 89.6 meters and the global longitudinal peak strain (P < 0.001). There was also significant decrease in the level of erythrocyte sedimentation rate and N-terminal pro-brain natriuretic peptide (NT-proBNP) after 3 months.  Allopurinol could be of benefit in non-hyperuricemic patients with severe LV systolic dysfunction without significant adverse effects. Randomized clinical trials are needed in future to confirm the results.
Knowledge of First Aid Skills Among Students of a Medical College in Mangalore City of South India

The adequate knowledge required for handling an emergency without hospital setting at the site of the accident or emergency may not be sufficient as most medical schools do not have formal first aid training in the teaching curriculum.



The aim of this study is to assess the level of knowledge of medical students in providing first aid care.



This cross-sectional study was conducted during May 2011 among 152 medical students. Data was collected using a self-administered questionnaire. Based on the scores obtained in each condition requiring first aid, the overall knowledge was graded as good, moderate and poor.



Only 11.2% (17/152) of the total student participants had previous exposure to first aid training. Good knowledge about first aid was observed in 13.8% (21/152), moderate knowledge in 68.4% (104/152) and poor knowledge in 17.8% (27/152) participants. Analysis of knowledge about first aid management in select conditions found that 21% (32/152) had poor knowledge regarding first aid management for shock and for gastro esophageal reflux disease and 20.4% (31/152) for epistaxis and foreign body in eyes. All students felt that first aid skills need to be taught from the school level onwards and all of them were willing to enroll in any formal first aid training sessions.



The level of knowledge about first aid was not good among majority of the students. The study also identified the key areas in which first aid knowledge was lacking. There is thus a need for formal first aid training to be introduced in the medical curriculum.
Efficient analysis of event processing applications

Complex event processing (CEP) middleware systems are increasingly adopted to implement distributed applications: they not only dispatch events across components, but also embed part of the application logic into declarative rules that detect situations of interest from the occurrence of specific pattern of events. While this approach simplifies the development of large scale event processing applications, writing the rules that correctly capture the application domain arguably remains a difficult and error prone task, which fundamentally lacks consolidated tool support.
 Moving from these premises, this paper introduces CAVE, an efficient approach and tool to support developers in analyzing the behavior of an event processing application. CAVE verifies properties based on the adopted CEP ruleset and on the environmental conditions, and outputs sequences of events that prove the satisfiability or unsatisfiability of each property. The key idea that contributes to the efficiency of CAVE is the translation of the property checking task into a set of constraint solving problems. The paper presents the CAVE approach in detail, describes its prototype implementation and evaluates its performance in a wide range of scenarios.
Intranasal drug delivery in neuropsychiatry: focus on intranasal ketamine for refractory depression.

Intranasal drug delivery (INDD) systems offer a route to the brain that bypasses problems related to gastrointestinal absorption, first-pass metabolism, and the blood-brain barrier; onset of therapeutic action is rapid, and the inconvenience and discomfort of parenteral administration are avoided. INDD has found several applications in neuropsychiatry, such as to treat migraine, acute and chronic pain, Parkinson disease, disorders of cognition, autism, schizophrenia, social phobia, and depression. INDD has also been used to test experimental drugs, such as peptides, for neuropsychiatric indications; these drugs cannot easily be administered by other routes. This article examines the advantages and applications of INDD in neuropsychiatry; provides examples of test, experimental, and approved INDD treatments; and focuses especially on the potential of intranasal ketamine for the acute and maintenance therapy of refractory depression.
Oreo: detection of clones in the twilight zone

Source code clones are categorized into four types of increasing difficulty of detection, ranging from purely textual (Type-1) to purely semantic (Type-4). Most clone detectors reported in the literature work well up to Type-3, which accounts for syntactic differences. In between Type-3 and Type-4, however, there lies a spectrum of clones that, although still exhibiting some syntactic similarities, are extremely hard to detect – the Twilight Zone. Most clone detectors reported in the literature fail to operate in this zone. We present Oreo, a novel approach to source code clone detection that not only detects Type-1 to Type-3 clones accurately, but is also capable of detecting harder-to-detect clones in the Twilight Zone. Oreo is built using a combination of machine learning, information retrieval, and software metrics. We evaluate the recall of Oreo on BigCloneBench, and perform manual evaluation for precision. Oreo has both high recall and precision. More importantly, it pushes the boundary in detection of clones with moderate to weak syntactic similarity in a scalable manner
On the Ergodic Capacity of MIMO NOMA Systems

Non-orthogonal multiple access (NOMA) is expected to be a promising multiple access technique for 5G networks due to its superior spectral efficiency. In this letter, the ergodic capacity maximization problem is first studied for the Rayleigh fading multiple-input multiple-output (MIMO) NOMA systems with statistical channel state information at the transmitter (CSIT). We propose both optimal and low complexity suboptimal power allocation schemes to maximize the ergodic capacity of MIMO NOMA system with total transmit power constraint and minimum rate constraint of the weak user. Numerical results show that the proposed NOMA schemes significantly outperform the traditional orthogonal multiple access scheme.
On the regularization of image semantics by modal expansion

Recent research efforts in semantic representations and context modeling are based on the principle of task expansion: that vision problems such as object recognition, scene classification, or retrieval (RCR) cannot be solved in isolation. The extended principle of modality expansion (that RCR problems cannot be solved from visual information alone) is investigated in this work. A semantic image labeling system is augmented with text. Pairs of images and text are mapped to a semantic space, and the text features used to regularize their image counterparts. This is done with a new cross-modal regularizer, which learns the mapping of the image features that maximizes their average similarity to those derived from text. The proposed regularizer is class-sensitive, combining a set of class-specific denoising transformations and nearest neighbor interpolation of text-based class assignments. Regularization of a state-of-the-art approach to image retrieval is then shown to produce substantial gains in retrieval accuracy, outperforming recent image retrieval approaches.