nils-herrmann/clean_scirepeval huggingface dataset:
Maps for the Visualization of high-dimensional Data Spaces

-The U-Matrix is a canonical tool for the display of distance structures in data space using emergent SOM (ESOM). The U-Matrix defined originally for planar map spaces is extended in this work to toroid neuron spaces. Embedding the neuron space in a finite but borderless space, such as a torus, avoids border effects of planar spaces. A planar display of a toroid map space disrupts, however, coherent U-Matrix structures. Tiling multiple instances of the U-Matrix solves this problem at the cost of multiple images of data points. The P-Matrix, as defined here, is a display of the density relationships in the data space using Pareto Density Estimation. While the P-Matrix is useful for clustering, it can also be used for a non-ambiguous display of a non planar neuron space. Centering the display for high density regions and removing ambiguous images of data points leads to U-Maps and P-Maps. U-Maps depict the distance structure of a data space as a borderless three dimensional landscape whose floor space is ordered according to the topology preserving features of ESOM. P-Maps display the density structures. Both maps are specially suited for data mining and knowledge discovery.
A wideband ultra-low-current on-chip ammeter

A high-bandwidth ultra-low-current measurement circuit is presented in this paper. The circuit is capable of measuring an on-chip 75 fA current at a bandwidth up to 1 kHz with a noise floor of 0.235 fArms/√Hz. A low leakage reset scheme is utilized to improve the precision. Nested auto-zeroing combined with modified correlated double sampling is employed to mitigate the error due to various circuit imperfections. Noise analysis is carried out and “capacitive noise matching” is proposed to minimize the noise floor. The circuit is also capable of digitizing the measured current and streaming the data through a serial interface. The measurement circuit occupies 0.065 mm2 of active silicon area in a 90 nm CMOS process and consumes 147 μW from 2.5 V and 1V supplies.
Eruptive Vellus Hair Cyst: An Uncommon and Underdiagnosed Entity

Eruptive vellus hair cyst (EVHC) is a rare follicular developmental abnormality of the vellus hair follicles. They are usually seen in children, adolescents, or young adults and manifest as reddish-brown smooth papules most commonly involving the chest, limbs, and abdomen. An 18-year-old male presented with asymptomatic papules on the trunk and flexor aspect of both forearms for the past 2 years. There was no family history of similar lesions. His medical history was also not contributory. A clinical diagnosis of steatocystoma multiplex and chronic folliculitis was given, and a punch biopsy from the papule was performed and sent for histopathological examination. On microscopic examination, a final diagnosis of EVHC was rendered. The patient was advised topical treatment of retinoic acid cream (0.05%) for 6 months, and he is currently under follow-up period. Due to its rarity and resemblance to many similar entities, histopathological examination plays a major role in establishing a definite diagnosis and further proper management of the patient. We report this unusual case to generate awareness about this rarely diagnosed condition.
Development methodology of the HISMM Maturity Model

Maturity Models have been introduced, over the last four decades, as guides and references for Information System management in organizations from different sectors of activity. In the healthcare field, Maturity Models have also been used to deal with the enormous complexity and demand of Hospital Information Systems. This article presents a research project that aimed to develop a new comprehensive model of maturity for a health area. HISMM (Hospital Information System Maturity Model) was developed to address a complexity of SIH and intends to offer a useful tool for the demanding role of its management. The HISMM has the peculiarity of congregating a set of key maturity Influence Factors and respective characteristics, enabling not only the assessment of the global maturity of a HIS but also the individual maturity of its different dimensions. In this article, we present the methodology for the development of Maturity Models adopted for the creation of HISMM and the underlying reasons for its choice.
3 D CAD model generation of mechanical parts using coded-pattern projection and laser triangulation systems

Investigate the use of two imaging-based methods – coded pattern projection and laser-based triangulation – to generate 3D models as input to a rapid prototyping pipeline.  Discusses structured lighting technologies as suitable imaging-based methods. Two approaches, coded-pattern projection and laser-based triangulation, are specifically identified and discussed in detail. Two commercial systems are used to generate experimental results. These systems include the Genex Technologies 3D FaceCam and the Integrated Vision Products Ranger System.  Presents 3D reconstructions of objects from each of the commercial systems.  Provides background in imaging-based methods for 3D data collection and model generation. A practical limitation is that imaging-based systems do not currently meet accuracy requirements, but continued improvements in imaging systems will minimize this limitation.  Imaging-based approaches to 3D model generation offer potential to increase scanning time and reduce scanning complexity.  Introduces imaging-based concepts to the rapid prototyping pipeline.
A Metric Motor in Your Future (?)

Concurrent with the enactment of metric conversion legislation by the U. S. Congress in 1975, the Motor and Generator Section of the National Electrical Manufacturer Association (NEMA) voted to proceed with the development of a Guide for the Development of Metric Standards for Motors and Generators, referred to as the " IMetric Guide" or "the Guide." The first edition was published in 1978, followed by a second, more extensive, edition in November 1980. A summary of the Metric Guide, is given, including comparison with NEMA and International Electrotechnical Commission (IEC) standards.
Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog

One of the first steps in the utterance interpretation pipeline of many task-oriented conversational AI systems is to identify user intents and the corresponding slots. Neural sequence labeling models have achieved very high accuracy on these tasks when trained on large amounts of training data. However, collecting this data is very time-consuming and therefore it is unfeasible to collect large amounts of data for many languages. For this reason, it is desirable to make use of existing data in a high-resource language to train models in low-resource languages. In this paper, we investigate the performance of three different methods for cross-lingual transfer learning, namely (1) translating the training data, (2) using cross-lingual pre-trained embeddings, and (3) a novel method of using a multilingual machine translation encoder as contextual word representations. We find that given several hundred training examples in the the target language, the latter two methods outperform translating the training data. Further, in very low-resource settings, we find that multilingual contextual word representations give better results than using crosslingual static embeddings. We release a dataset of around 57k annotated utterances in English (43k), Spanish (8.6k) and Thai (5k) for three task oriented domains at https://fb.me/multilingual_task_oriented_data.
Depth estimation from single monocular images using deep hybrid network

Depth estimation is a significant task in the robotics vision. In this paper, we address the depth estimation from a single monocular image, which is a challenging problem in automated vision systems since a single image alone does not carry any additional measurements. To tackle our main objective, we design a deep hybrid neural network, which is composed of convolutional and recurrent layers (ReNet), where each ReNet layer is composed of the Long Short-Term Memory unit (LSTM), which is famous for the ability to memorize long-range context. In the proposed network, ReNet layers aim to enrich the features representation by directly capturing global context. The effective integration of ReNet and convolutional layers in the common CNN framework allows us to train the hybrid network in the end-to-end fashion. Experimental evaluation on the benchmarks dataset demonstrated, that hybrid network achieves the state-of-the-art results without any post-processing steps. Moreover, the composition of recurrent and convolutional layers provide more satisfying results.
Study of micro-BGA solder joint reliability

A new re¯ow parameter, heating factor (Q g), which is de®ned as the integral of the measured temperature over the dwell time above liquidus, has been proposed in this report. It can suitably represent the combined eect of both temperature and time in usual re¯ow process. Relationship between reliability of the micro-ball grid array (micro-BGA) package and heating factor has been discussed. The fatigue failure of micro-BGA solder joints re¯owed with dierent heating factor in nitrogen ambient has been investigated using the bending cycle test. The fatigue lifetime of the micro-BGA assemblies ®rstly increases and then decreases with increasing heating factor. The greatest lifetime happens at Q g near 500 s °C. The optimal Q g range is between 300 and 750 s °C. In this range, the lifetime of the micro-BGA assemblies is greater than 4500 cycles. SEM micrographs reveal that cracks always initiate at the point of the acute angle where the solder joint joins the PCB pad.
The influence of tobacco marketing on adolescent smoking intentions via normative beliefs.

Using cross-sectional data from three waves of the Youth Tobacco Policy Study, which examines the impact of the UK's Tobacco Advertising and Promotion Act (TAPA) on adolescent smoking behaviour, we examined normative pathways between tobacco marketing awareness and smoking intentions. The sample comprised 1121 adolescents in Wave 2 (pre-ban), 1123 in Wave 3 (mid-ban) and 1159 in Wave 4 (post-ban). Structural equation modelling was used to assess the direct effect of tobacco advertising and promotion on intentions at each wave, and also the indirect effect, mediated through normative influences. Pre-ban, higher levels of awareness of advertising and promotion were independently associated with higher levels of perceived sibling approval which, in turn, was positively related to intentions. Independent paths from perceived prevalence and benefits fully mediated the effects of advertising and promotion awareness on intentions mid- and post-ban. Advertising awareness indirectly affected intentions via the interaction between perceived prevalence and benefits pre-ban, whereas the indirect effect on intentions of advertising and promotion awareness was mediated by the interaction of perceived prevalence and benefits mid-ban. Our findings indicate that policy measures such as the TAPA can significantly reduce adolescents' smoking intentions by signifying smoking to be less normative and socially unacceptable.
COBANETS: A new paradigm for cognitive communications systems

In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this position paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS we propose to combine the learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared to past and current research efforts in this area, the technical approach depicted in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication networks.
Active learning machine learns to create new quantum experiments

How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments. The artificial intelligence system learns to create a variety of entangled states and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments-a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research.
Tag-Latent Dirichlet Allocation: Understanding Hashtags and Their Relationships

A hash tag is defined to be a word or phrase prefixed with the symbol "#". It is widely used in current social media sites including Twitter and Google+, and serves as a significant meta tag to categorize users' messages, to propagate ideas and topic trends. The use of hash tags has become an integral part of the social media culture. However, the free-form nature and the varied contexts of hash tags bring challenges: how to understand hash tags and discover their relationships? In this paper, we propose Tag-Latent Dirichlet Allocation (TLDA), a new topic modeling approach to bridge hash tags and topics. TLDA extends Latent Dirichlet Allocation by incorporating the observed hash tags in the generative process. In TLDA, a hash tag is mapped into the form of a mixture of shared topics. This representation further enables the analysis of the relationships between the hash tags. Applying our model to tweet data, we first illustrate the ability of our approach to explain hard-to-understand hash tags with topics. We also demonstrate that our approach enables users to further analyze the relationships between the hash tags.
Camouflage Treatment of a Severe Open Bite Case

Skeletal open bite is a complex and multifactorial anomaly. There are many reasons for the occurrence of open bite, including skeletal abnormal growth pattern; dental, respiratory, neurologic, and habitual factors; and tongue posture and function. Anterior open bite is one of the most difficult problems to treat in orthodontics. Various treatment modalities for the correction of an anterior open bite have been proposed such as extrusion of anterior teeth using intermaxillary elastics, multiloop edgewise archwire technique developed by Kim, inhibition of molar eruption using bite blocks and high-pull headgear during growth, and extraction therapy. The most effective treatment option in adult patients is the repositioning of both the maxilla and mandible through a surgical correction. Although satisfactory results can be achieved with orthognathic surgery, the complexity, risks and costs of surgery are disadvantages of the surgical treatment. With the advent of skeletal anchorage, a number of studies were released to introduce the treatment of anterior open bite by intrusion of maxillary posterior teeth. This report presents the treatment and long-term stability of an adult case of a severe anterior open bite treated by means of extraction of the 4 first molars.
Management of children with autism spectrum disorders.

Pediatricians have an important role not only in early recognition and evaluation of autism spectrum disorders but also in chronic management of these disorders. The primary goals of treatment are to maximize the child's ultimate functional independence and quality of life by minimizing the core autism spectrum disorder features, facilitating development and learning, promoting socialization, reducing maladaptive behaviors, and educating and supporting families. To assist pediatricians in educating families and guiding them toward empirically supported interventions for their children, this report reviews the educational strategies and associated therapies that are the primary treatments for children with autism spectrum disorders. Optimization of health care is likely to have a positive effect on habilitative progress, functional outcome, and quality of life; therefore, important issues, such as management of associated medical problems, pharmacologic and nonpharmacologic intervention for challenging behaviors or coexisting mental health conditions, and use of complementary and alternative medical treatments, are also addressed.
Millimeter-wave wideband transition from CPW to substrate integrated waveguide on electrically thick high-permittivity substrates

A wideband transition from coplanar waveguide (CPW) to substrate integrated waveguide (SIW) is proposed and presented in the 50 GHz frequency range. Electrically thick alumina was used in this case, representative for other high-permittivity substrates such as semiconductors. Simulations predict less than -15 dB return loss within a 35 % bandwidth. CPW probe measurements were carried out and 40 % bandwidth were achieved at -0.5 dB insertion loss for a single transition. Modified SIW via configurations being suitable for simplified fabrication on electrically thick substrates in the upper millimeter-wave spectrum are discussed in the second part.
DSD: Regularizing Deep Neural Networks with Dense-Sparse-Dense Training Flow

Modern deep neural networks have a large number of parameters, making them very powerful machine learning systems. A critical issue for training such large networks on large-scale data-sets is to prevent overfitting while at the same time providing enough model capacity. We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural networks. In the first D step, we train a dense network to learn which connections are important. In the S step, we regularize the network by pruning the unimportant connections and retrain the network given the sparsity constraint. In the final D step, we increase the model capacity by freeing the sparsity constraint, re-initializing the pruned parameters, and retraining the whole dense network. Experiments show that DSD training can improve the performance of a wide range of CNN, RNN and LSTMs on the tasks of image classification, caption generation and speech recognition. On the Imagenet dataset, DSD improved the absolute accuracy of AlexNet, GoogleNet, VGG-16, ResNet50, ResNet-152 and SqueezeNet by a geo-mean of 2.1 points (Top-1) and 1.4 points (Top-5). On the WSJ’92 and WSJ’93 dataset, DSD improved DeepSpeech2 WER by 0.53 and 1.08 points. On the Flickr-8K dataset, DSD improved the NeuralTalk BLEU score by 2.0 points. DSD training flow produces the same model architecture and doesn’t incur any inference overhead.
SmartBoxes for interactive urban reconstruction

We introduce an interactive tool which enables a user to quickly assemble an architectural model directly over a 3D point cloud acquired from large-scale scanning of an urban scene. The user loosely defines and manipulates simple building blocks, which we call SmartBoxes, over the point samples. These boxes quickly snap to their proper locations to conform to common architectural structures. The key idea is that the building blocks are smart in the sense that their locations and sizes are automatically adjusted on-the-fly to fit well to the point data, while at the same time respecting contextual relations with nearby similar blocks. SmartBoxes are assembled through a discrete optimization to balance between two snapping forces defined respectively by a data-fitting term and a contextual term, which together assist the user in reconstructing the architectural model from a sparse and noisy point cloud. We show that a combination of the user's interactive guidance and high-level knowledge about the semantics of the underlying model, together with the snapping forces, allows the reconstruction of structures which are partially or even completely missing from the input.
Tear trough deformity: different types of anatomy and treatment options

To explore the efficacy of tear trough deformity treatment with the use of hyaluronic acid gel or autologous fat for soft tissue augmentation and fat repositioning via arcus marginalis release.



Seventy-eight patients with the tear trough were divided into three groups. Class I has tear trough without bulging orbital fat or excess of the lower eyelid skin. Class II is associated with mild to moderate orbital fat bulging, without excess of the lower eyelid skin. Class III is associated with severe orbital fat bulging and excess of the lower eyelid skin. Class I or II was treated using hyaluronic acid gel or autologous fat injections. Class III was treated with fat repositioning via arcus marginalis release. The patients with a deep nasojugal groove of class III were treated with injecting autologous fat into the tear trough during fat repositioning lower blepharoplasty as a way of supplementing the volume added by the repositioned fat.



Seventy-eight patients with tear trough deformity were confirmed from photographs taken before and after surgery. There were some complications, but all had complete resolution.



Patients with mild to moderate peri-orbital volume loss without severe orbital fat bulging may be good candidates for hyaluronic acid filler or fat grafting alone. However, patients with more pronounced deformities, severe orbital fat bulging and excess of the lower eyelid skin are often better served by fat repositioning via arcus marginalis release and fat grafting.
The DSM diagnostic criteria for sexual sadism.

I reviewed the empirical literature for 1900-2008 on the paraphilia of Sexual Sadism for the Sexual and Gender Identity Disorders Workgroup for the forthcoming fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM). The results of this review were tabulated into a general summary of the criticisms relevant to the DSM diagnosis of Sexual Sadism, the assessment of Sexual Sadism utilizing the DSM in samples drawn from forensic populations, and the assessment of Sexual Sadism using the DSM in non-forensic populations. I conclude that the diagnosis of Sexual Sadism should be retained, that minimal modifications of the wording of this diagnosis are warranted, and that there is a need for the development of dimensional and structured diagnostic instruments.