nils-herrmann/clean_scirepeval huggingface dataset:
Synthesizing Training Images for Boosting Human 3D Pose Estimation

Human 3D pose estimation from a single image is a challenging task with numerous applications. Convolutional Neural Networks (CNNs) have recently achieved superior performance on the task of 2D pose estimation from a single image, by training on images with 2D annotations collected by crowd sourcing. This suggests that similar success could be achieved for direct estimation of 3D poses. However, 3D poses are much harder to annotate, and the lack of suitable annotated training images hinders attempts towards end-to-end solutions. To address this issue, we opt to automatically synthesize training images with ground truth pose annotations. Our work is a systematic study along this road. We find that pose space coverage and texture diversity are the key ingredients for the effectiveness of synthetic training data. We present a fully automatic, scalable approach that samples the human pose space for guiding the synthesis procedure and extracts clothing textures from real images. Furthermore, we explore domain adaptation for bridging the gap between our synthetic training images and real testing photos. We demonstrate that CNNs trained with our synthetic images out-perform those trained with real photos on 3D pose estimation tasks.
Planar MIMO antennas for IoT and CR applications

In this paper, a planar, compact, single-substrate, multiband 2 sets of 2-elements each multiple-input-multiple-output (MIMO) antenna system is presented. The MIMO antenna system consists of a tunable 2-element meandered and folded MIMO antenna to cover the LTE Band (698 MHz–813 MHz) and a compact 2-element modified truncated cube wideband antenna to cover 754 MHz–971 MHz, 1.65–1.83 GHz and 2–3.66 GHz, respectively. The ground plane of this antenna behaves as a sensing antenna operating in 0.76–1.92 GHz, and 3.0–5.2 GHz. The upper band antennas operate in 0.728–1.08 GHz, 1.64–1.84 GHz, 2.1–3.69 GHz, and 5.01–5.55 GHz range to develop a complete antenna platform for cognitive radios (CR) and Internet of Things (IoT) applications. The antenna is fabricated on a low cost FR-4 substrate (ε<inf>r</inf>=4.4 tanδ=0.02) of dimensions 65 ×120 ×1.56 mm<sup>3</sup>.
Learning Disentangled Representations with Semi-Supervised Deep Generative Models

Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the data into a single variable. Here we are interested in learning disentangled representations that encode distinct aspects of the data into separate variables. We propose to learn such representations using model architectures that generalize from standard VAEs, employing a general graphical model structure in the encoder and decoder. This allows us to train partially-specified models that make relatively strong assumptions about a subset of interpretable variables and rely on the flexibility of neural networks to learn representations for the remaining variables. We further define a general objective for semi-supervised learning in this model class, which can be approximated using an importance sampling procedure. We evaluate our framework’s ability to learn disentangled representations, both by qualitative exploration of its generative capacity, and quantitative evaluation of its discriminative ability on a variety of models and datasets.
Studying Complex Adaptive Systems

Complex adaptive systems (cas) – systems that involve many components that adapt or learn as they interact – are at the heart of important contemporary problems. The study of cas poses unique challenges: Some of our most powerful mathematical tools, particularly methods involving fixed points, attractors, and the like, are of limited help in understanding the development of cas. This paper suggests ways to modify research methods and tools, with an emphasis on the role of computer-based models, to increase our understanding of cas.
On Advances in Statistical Modeling of Natural Images

Statistical analysis of images reveals two interesting properties: (i) invariance of image statistics to scaling of images, and (ii) non-Gaussian behavior of image statistics, i.e. high kurtosis, heavy tails, and sharp central cusps. In this paper we review some recent results in statistical modeling of natural images that attempt to explain these patterns. Two categories of results are considered: (i) studies of probability models of images or image decompositions (such as Fourier or wavelet decompositions), and (ii) discoveries of underlying image manifolds while restricting to natural images. Applications of these models in areas such as texture analysis, image classification, compression, and denoising are also considered.
Interprofessional education: effects on professional practice and health care outcomes.

As patient care becomes more complex, effective collaboration between health and social care professionals is required. However, evidence suggests that these professionals do not collaborate well together. Interprofessional education (IPE) offers a possible way forward in this area.



To assess the usefulness of IPE interventions compared to education in which the same professions were learning separately from one another.


SEARCH STRATEGY
We searched the Cochrane Effective Practice and Organisation of Care Group specialised register, MEDLINE (1968 to 1998) and Cinahl (1982 to 1998). We also hand searched the Journal of Interprofessional Care (1992 to 1998), the Centre for the Advancement of Interprofessional Education Bulletin (1987 to 1998), conference proceedings, the 'grey literature' held by relevant organisations, and reference lists of articles.



Randomised trials, controlled before and after studies and interrupted time series studies of IPE interventions designed to improve collaborative practice between health/social care practitioners and/or the health/well being of patients/clients. The participants included chiropodists/podiatrists, complementary therapists, dentists, dietitians, doctors/physicians, hygienists, psychologists, psychotherapists, midwives, nurses, pharmacists, physiotherapists, occupational therapists, radiographers, speech therapists and/or social workers. The outcomes included objectively measured or self reported (validated instrument) patient/client outcomes and reliable (objective or validated subjective) health care process measures.



Two reviewers independently assessed the eligibility of potentially relevant studies.



The total yield from the search strategy was 1042, of which 89 were retained for further consideration. However none of these studies met the inclusion criteria.



Despite finding a large body of literature on the evaluation of IPE, these studies lacked the methodological rigour needed to begin to convincingly understand the impact of IPE on professional practice and/or health care outcomes.
Web-of-Objects (WoO)-Based Context Aware Emergency Fire Management Systems for the Internet of Things

Recent advancements in the Internet of Things (IoT) and the Web of Things (WoT) accompany a smart life where real world objects, including sensing devices, are interconnected with each other. The Web representation of smart objects empowers innovative applications and services for various domains. To accelerate this approach, Web of Objects (WoO) focuses on the implementation aspects of bringing the assorted real world objects to the Web applications. In this paper; we propose an emergency fire management system in the WoO infrastructure. Consequently, we integrate the formation and management of Virtual Objects (ViO) which are derived from real world physical objects and are virtually connected with each other into the semantic ontology model. The charm of using the semantic ontology is that it allows information reusability, extensibility and interoperability, which enable ViOs to uphold orchestration, federation, collaboration and harmonization. Our system is context aware, as it receives contextual environmental information from distributed sensors and detects emergency situations. To handle a fire emergency, we present a decision support tool for the emergency fire management team. The previous fire incident log is the basis of the decision support system. A log repository collects all the emergency fire incident logs from ViOs and stores them in a repository.
Performance evaluation of a MongoDB and hadoop platform for scientific data analysis

Scientific facilities such as the Advanced Light Source (ALS) and Joint Genome Institute and projects such as the Materials Project have an increasing need to capture, store, and analyze dynamic semi-structured data and metadata. A similar growth of semi-structured data within large Internet service providers has led to the creation of NoSQL data stores for scalable indexing and MapReduce for scalable parallel analysis. MapReduce and NoSQL stores have been applied to scientific data. Hadoop, the most popular open source implementation of MapReduce, has been evaluated, utilized and modified for addressing the needs of different scientific analysis problems. ALS and the Materials Project are using MongoDB, a document oriented NoSQL store. However, there is a limited understanding of the performance trade-offs of using these two technologies together.In this paper we evaluate the performance, scalability and fault-tolerance of using MongoDB with Hadoop, towards the goal of identifying the right software environment for scientific data analysis.
Analyzing user-generated youtube videos to understand touchscreen use by people with motor impairments

Most work on the usability of touchscreen interaction for people with motor impairments has focused on lab studies with relatively few participants and small cross-sections of the population. To develop a richer characterization of use, we turned to a previously untapped source of data: YouTube videos. We collected and analyzed 187 non-commercial videos uploaded to YouTube that depicted a person with a physical disability interacting with a mainstream mobile touchscreen device. We coded the videos along a range of dimensions to characterize the interaction, the challenges encountered, and the adaptations being adopted in daily use. To complement the video data, we also invited the video uploaders to complete a survey on their ongoing use of touchscreen technology. Our findings show that, while many people with motor impairments find these devices empowering, accessibility issues still exist. In addition to providing implications for more accessible touchscreen design, we reflect on the application of user-generated content to study user interface design.
Age-related differences in performance with touchscreens compared to traditional mouse input

Despite the apparent popularity of touchscreens for older adults, little is known about the psychomotor performance of these devices. We compared performance between older adults and younger adults on four desktop and touchscreen tasks: pointing, dragging, crossing and steering. On the touchscreen, we also examined pinch-to-zoom. Our results show that while older adults were significantly slower than younger adults in general, the touchscreen reduced this performance gap relative to the desktop and mouse. Indeed, the touchscreen resulted in a significant movement time reduction of 35% over the mouse for older adults, compared to only 16% for younger adults. Error rates also decreased.
A Hexanucleotide Repeat Expansion in C9ORF72 Is the Cause of Chromosome 9p21-Linked ALS-FTD

The chromosome 9p21 amyotrophic lateral sclerosis-frontotemporal dementia (ALS-FTD) locus contains one of the last major unidentified autosomal-dominant genes underlying these common neurodegenerative diseases. We have previously shown that a founder haplotype, covering the MOBKL2b, IFNK, and C9ORF72 genes, is present in the majority of cases linked to this region. Here we show that there is a large hexanucleotide (GGGGCC) repeat expansion in the first intron of C9ORF72 on the affected haplotype. This repeat expansion segregates perfectly with disease in the Finnish population, underlying 46.0% of familial ALS and 21.1% of sporadic ALS in that population. Taken together with the D90A SOD1 mutation, 87% of familial ALS in Finland is now explained by a simple monogenic cause. The repeat expansion is also present in one-third of familial ALS cases of outbred European descent, making it the most common genetic cause of these fatal neurodegenerative diseases identified to date.
Lower Extremity Exoskeletons and Active Orthoses: Challenges and State-of-the-Art

In the nearly six decades since researchers began to explore methods of creating them, exoskeletons have progressed from the stuff of science fiction to nearly commercialized products. While there are still many challenges associated with exoskeleton development that have yet to be perfected, the advances in the field have been enormous. In this paper, we review the history and discuss the state-of-the-art of lower limb exoskeletons and active orthoses. We provide a design overview of hardware, actuation, sensory, and control systems for most of the devices that have been described in the literature, and end with a discussion of the major advances that have been made and hurdles yet to be overcome.
Solving Sudoku with genetic operations that preserve building blocks

Genetic operations that consider effective building blocks are proposed for using genetic algorithms to solve Sudoku puzzles. A stronger local search function is also proposed. Evaluation of the proposed techniques using commercial Sudoku puzzle sets and three puzzles ranked as super difficult compared with previously reported examples show that the rate of optimum solutions can be greatly improved. It is demonstrated that even further improvement in accuracy is expected from a correction technique based on disparity hypothesis.
A first look at traffic on smartphones

Using data from 43 users across two platforms, we present a detailed look at smartphone traffic. We find that browsing contributes over half of the traffic, while each of email, media, and maps contribute roughly 10%. We also find that the overhead of lower layer protocols is high because of small transfer sizes. For half of the transfers that use transport-level security, header bytes correspond to 40% of the total. We show that while packet loss is the main factor that limits the throughput of smartphone traffic, larger send buffers at Internet servers can improve the throughput of a quarter of the transfers. Finally, by studying the interaction between smartphone traffic and the radio power management policy, we find that the power consumption of the radio can be reduced by 35% with minimal impact on the performance of packet exchanges.
Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudo-Random Number Generator

A new algorithm called Mersenne Twister (MT) is proposed for generating uniform pseudorandom numbers. For a particular choice of parameters, the algorithm provides a super astronomical period of 2<supscrpt>19937</supscrpt> −1 and 623-dimensional equidistribution up to 32-bit accuracy, while using a working area of only 624 words. This is a new variant of the previously proposed generators, TGFSR, modified so as to admit a Mersenne-prime period. The characteristic polynomial has many terms. The distribution up to <italic>v</italic> bits accuracy for 1 ≤ <italic>v</italic> ≤ 32 is also shown to be good. An algorithm is also given that checks the primitivity of the characteristic polynomial of MT with computational complexity <italic>O(p<supscrpt>2</supscrpt>)</italic> where  <italic>p</italic> is the degree of the polynomial.
We implemented this generator in portable C-code. It passed several stringent statistical tests, including diehard. Its speed is comparable to other modern generators. Its merits are due to the efficient algorithms that are unique to polynomial calculations over the two-element field.
Factors Affecting Repurchase Intention of Smartphone : A Case Study of Huawei Smartphone in Yangon , Myanmar

This study investigates the relationship between functional value, price consciousness, word of mouth, brand image, attitude towards product and repurchases intention of a smartphone brand. To do so, a survey was conducted by distributing 420 questionnaires in 7 different shopping malls in Yangon, Myanmar. The data collected was analyzed using SPSS and hypotheses were examined by employing the Pearson correlation and multiple linear regression. The results show that there is a positive and significant relationship among functional value, word of mouth, price consciousness towards attitude towards product and brand image, attitude towards product, word of mouth influences repurchase intention. Based on these results, it seems that the smart phone company needs to develop marketing strategy to increase repurchase intention.
On the Performance Comparison between IEEE 802.11p and LTE-Based Vehicular Networks

There has been a growing need to integrate communication technologies with transport infrastructure and vehicles. The proposed approach would spur an array of vehicular safety, traffic efficiency and infotainment applications. To address this, majority of the research has been focusing on IEEE 802.11p based vehicular communication. However, despite the efforts from standardization bodies and research consortium development and large-scale deployment of an effective framework still remained a challenge. Given the diverse performance requirements from wide spectrum of vehicular networking applications, several advocate the feasibility of LTE technology as an emerging solution. The goal of this paper is to evaluate the performance of the IEEE 802.11p standard and LTE technology for vehicular networks under various network conditions and parameter values. The results indicate that LTE outperforms IEEE 802.11p in terms of delay, reliability, and scalability, whereas IEEE 802.11p offers acceptable performance for sparse network topologies.
Yield Trends Are Insufficient to Double Global Crop Production by 2050

Several studies have shown that global crop production needs to double by 2050 to meet the projected demands from rising population, diet shifts, and increasing biofuels consumption. Boosting crop yields to meet these rising demands, rather than clearing more land for agriculture has been highlighted as a preferred solution to meet this goal. However, we first need to understand how crop yields are changing globally, and whether we are on track to double production by 2050. Using ∼2.5 million agricultural statistics, collected for ∼13,500 political units across the world, we track four key global crops-maize, rice, wheat, and soybean-that currently produce nearly two-thirds of global agricultural calories. We find that yields in these top four crops are increasing at 1.6%, 1.0%, 0.9%, and 1.3% per year, non-compounding rates, respectively, which is less than the 2.4% per year rate required to double global production by 2050. At these rates global production in these crops would increase by ∼67%, ∼42%, ∼38%, and ∼55%, respectively, which is far below what is needed to meet projected demands in 2050. We present detailed maps to identify where rates must be increased to boost crop production and meet rising demands.
Suitability of soccer training drills for endurance training.

Recent evidence suggests that certain soccer drills produce exercise intensities suitable for physical conditioning. However, it remains debatable whether soccer drills can provide a sufficiently unified exercise intensity among different players and on repetition of a drill, because movement patterns cannot be externally controlled during soccer drills. Good reliability and low variability of exercise intensity would enable all players to receive an appropriate training stimulus. The purpose of this study was to investigate intersubject variability and intrasubject reliability in exercise intensity during soccer drills. It was hypothesized that soccer drills that involve the highest exercise intensities would demonstrate the lowest intersubject variability and the highest intrasubject reliability. Heart rates of 23 professional soccer players were recorded during a range of soccer training drills. The drills consisted of 2 vs. 2 to 8 vs. 8 normal scoring games and 2 further possession games. Heart rate responses were examined for variability, reliability, and suitability for soccer endurance training. Coefficients of variation across players were less than 3% for all drills. Paired t-tests showed no significant differences in heart rate on repetition of the drills and 95% ratio limits of agreement were 1.8-3.8%. There were no significant correlations between exercise intensity and the statistical measures of variability and reliability. Several drills produced exercise intensities suitable for soccer endurance training with mean heart rate responses ranging from 87-91% HRmax. Soccer drills such as those used in the present study appear to be an adequate substitute for physical training without the ball and thus provide simultaneous skill and fitness training. The increase in training time spent developing technical ability and/or a reduction in total training time required may be useful for soccer teams.
Individual differences in reasoning: implications for the rationality debate?

Much research in the last two decades has demonstrated that human responses deviate from the performance deemed normative according to various models of decision making and rational judgment (e.g., the basic axioms of utility theory). This gap between the normative and the descriptive can be interpreted as indicating systematic irrationalities in human cognition. However, four alternative interpretations preserve the assumption that human behavior and cognition is largely rational. These posit that the gap is due to (1) performance errors, (2) computational limitations, (3) the wrong norm being applied by the experimenter, and (4) a different construal of the task by the subject. In the debates about the viability of these alternative explanations, attention has been focused too narrowly on the model response. In a series of experiments involving most of the classic tasks in the heuristics and biases literature, we have examined the implications of individual differences in performance for each of the four explanations of the normative/descriptive gap. Performance errors are a minor factor in the gap; computational limitations underlie non-normative responding on several tasks, particularly those that involve some type of cognitive decontextualization. Unexpected patterns of covariance can suggest when the wrong norm is being applied to a task or when an alternative construal of the task should be considered appropriate.
Training Feedforward Neural Networks Using Genetic Algorithms

Multilayered feedforward neural networks possess a number of properties which make them particularly suited to complex pattern classification problems. However, their application to some realworld problems has been hampered by the lack of a training algonthm which reliably finds a nearly globally optimal set of weights in a relatively short time. Genetic algorithms are a class of optimization procedures which are good at exploring a large and complex space in an intelligent way to find values close to the global optimum. Hence, they are well suited to the problem of training feedforward networks. In this paper, we describe a set of experiments performed on data from a sonar image classification problem. These experiments both 1) illustrate the improvements gained by using a genetic algorithm rather than backpropagation and 2) chronicle the evolution of the performance of the genetic algorithm as we added more and more domain-specific knowledge into it.