nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive optimization for self: reconciling high performance with exploratory programming

iii I certify that I have read this dissertation and that in my opinion it is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. I certify that I have read this dissertation and that in my opinion it is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. I certify that I have read this dissertation and that in my opinion it is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. _______________________________________ L. Peter Deutsch I certify that I have read this dissertation and that in my opinion it is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy.  Object-oriented programming languages confer many benefits, including abstraction, which lets the programmer hide the details of an object's implementation from the object's clients. Unfortunately, crossing abstraction boundaries often incurs a substantial run-time overhead in the form of frequent procedure calls. Thus, pervasive use of abstraction , while desirable from a design standpoint, may be impractical when it leads to inefficient programs. Aggressive compiler optimizations can reduce the overhead of abstraction. However, the long compilation times introduced by optimizing compilers delay the programming environment's responses to changes in the program. Furthermore, optimization also conflicts with source-level debugging. Thus, programmers are caught on the horns of two dilemmas: they have to choose between abstraction and efficiency, and between responsive programming environments and efficiency. This dissertation shows how to reconcile these seemingly contradictory goals by performing optimizations lazily. Four new techniques work together to achieve high performance and high responsiveness: • Type feedback achieves high performance by allowing the compiler to inline message sends based on information extracted from the runtime system. On average, programs run 1.5 times faster than the previous SELF system; compared to a commercial Smalltalk implementation, two medium-sized benchmarks run about three times faster. This level of performance is obtained with a compiler that is both simpler and faster than previous SELF compilers. • Adaptive optimization achieves high responsiveness without sacrificing performance by using a fast non-optimizing compiler to generate initial code while automatically recompiling heavily used parts of the program with an optimizing compiler. On a previous-generation workstation like the SPARCstation-2, fewer than 200 pauses exceeded 200 ms during a 50-minute interaction, and 21 pauses exceeded one second. …
Automated generation of geometric theorems from images of diagrams

We propose an approach to generate geometric theorems from electronic images of diagrams automatically. The approach makes use of techniques of Hough transform to recognize geometric objects and their labels and of numeric verification to mine basic geometric relations. Candidate propositions are generated from the retrieved information by using six strategies and geometric theorems are obtained from the candidates via algebraic computation. Experiments with a preliminary implementation illustrate the effectiveness and efficiency of the proposed approach for generating nontrivial theorems from images of diagrams. This work demonstrates the feasibility of automated discovery of profound geometric knowledge from simple image data and has potential applications in geometric knowledge management and education.
Chronic kidney disease

Chronic kidney disease (CKD) is defined by persistent urine abnormalities, structural abnormalities or impaired excretory renal function suggestive of a loss of functional nephrons. The majority of patients with CKD are at risk of accelerated cardiovascular disease and death. For those who progress to end-stage renal disease, the limited accessibility to renal replacement therapy is a problem in many parts of the world. Risk factors for the development and progression of CKD include low nephron number at birth, nephron loss due to increasing age and acute or chronic kidney injuries caused by toxic exposures or diseases (for example, obesity and type 2 diabetes mellitus). The management of patients with CKD is focused on early detection or prevention, treatment of the underlying cause (if possible) to curb progression and attention to secondary processes that contribute to ongoing nephron loss. Blood pressure control, inhibition of the renin–angiotensin system and disease-specific interventions are the cornerstones of therapy. CKD complications such as anaemia, metabolic acidosis and secondary hyperparathyroidism affect cardiovascular health and quality of life, and require diagnosis and treatment.
Hippotherapy--an intervention to habilitate balance deficits in children with movement disorders: a clinical trial.

Clinical observations have suggested that hippotherapy may be an effective strategy for habilitating balance deficits in children with movement disorders. However, there is limited research to support this notion.



The purposes of this study were to assess the effectiveness of hippotherapy for the management of postural instability in children with mild to moderate balance problems and to determine whether there is a correlation between balance and function.



A repeated-measures design for a cohort of children with documented balance deficits was used.



Sixteen children (9 boys and 7 girls) who were 5 to 16 years of age and had documented balance problems participated in this study. Intervention consisted of 45-minute hippotherapy sessions twice per week for 6 weeks. Two baseline assessments and 1 postintervention assessment of balance, as measured with the Pediatric Balance Scale (PBS), and of function, as measured with the Activities Scale for Kids-Performance (ASKp), were performed.



With the Friedman analysis of variance, the PBS and the ASKp were found to be statistically significant across all measurements (P<.0001 for both measures). Post hoc analysis revealed a statistical difference between baseline and postintervention measures (P≤.017). This degree of difference resulted in large effect sizes for PBS (d=1.59) and ASKp (d=1.51) scores after hippotherapy. A Spearman rho correlation of .700 indicated a statistical association between PBS and ASKp postintervention scores (P=.003). There was no correlation between the change in PBS scores and the change in ASKp scores (r(s)=.13, P>.05).



Lack of a control group and the short duration between baseline assessments are study limitations.



The findings suggest that hippotherapy may be a viable strategy for reducing balance deficits and improving the performance of daily life skills in children with mild to moderate balance problems.
RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation

Indoor semantic segmentation has always been a difficult task in computer vision. In this paper, we propose an RGB-D residual encoder-decoder architecture, named RedNet, for indoor RGB-D semantic segmentation. In RedNet, the residual module is applied to both the encoder and decoder as the basic building block, and the skip-connection is used to bypass the spatial feature between the encoder and decoder. In order to incorporate the depth information of the scene, a fusion structure is constructed, which makes inference on RGB image and depth image separately, and fuses their features over several layers. In order to efficiently optimize the network’s parameters, we propose a ‘pyramid supervision’ training scheme, which applies supervised learning over different layers in the decoder, to cope with the problem of gradients vanishing. Experiment results show that the proposed RedNet(ResNet-50) achieves a state-of-the-art mIoU accuracy of 47.8% on the SUN RGB-D benchmark dataset.
Action Detection From Skeletal Data Using Effecient Linear Search

Sliding window is one direct way to extend a successful recognition system to handle the more challenging detection problem. While action recognition decides only whether or not an action is present in a pre-segmented video sequence, action detection identifies the time interval where the action occurred in an unsegmented video stream. Sliding window approaches for action detection can however be slow as they maximize a classifier score over all possible sub-intervals. Even though new schemes utilize dynamic programming to speed up the search for the optimal sub-interval, they require offline processing on the whole video sequence. In this paper, we propose a novel approach for online action detection based on 3D skeleton sequences extracted from depth data. It identifies the sub-interval with the maximum classifier score in linear time. Furthermore, it is invariant to temporal scale variations and is suitable for real-time applications with low latency.
Is there a semantic system for abstract words?

Two views on the semantics of concrete words are that their core mental representations are feature-based or are reconstructions of sensory experience. We argue that neither of these approaches is capable of representing the semantics of abstract words, which involve the representation of possibly hypothetical physical and mental states, the binding of entities within a structure, and the possible use of embedding (or recursion) in such structures. Brain based evidence in the form of dissociations between deficits related to concrete and abstract semantics corroborates the hypothesis. Neuroimaging evidence suggests that left lateral inferior frontal cortex supports those processes responsible for the representation of abstract words.
Quality of Life Theory II. Quality of Life as the Realization of Life Potential: A Biological Theory of Human Being

This review presents one of the eight theories of the quality of life (QOL) used for making the SEQOL (self-evaluation of quality of life) questionnaire or the quality of life as realizing life potential. This theory is strongly inspired by Maslow and the review furthermore serves as an example on how to fulfill the demand for an overall theory of life (or philosophy of life), which we believe is necessary for global and generic quality-of-life research. Whereas traditional medical science has often been inspired by mechanical models in its attempts to understand human beings, this theory takes an explicitly biological starting point. The purpose is to take a close view of life as a unique entity, which mechanical models are unable to do. This means that things considered to be beyond the individual's purely biological nature, notably the quality of life, meaning in life, and aspirations in life, are included under this wider, biological treatise. Our interpretation of the nature of all living matter is intended as an alternative to medical mechanism, which dates back to the beginning of the 20th century. New ideas such as the notions of the human being as nestled in an evolutionary and ecological context, the spontaneous tendency of self-organizing systems for realization and concord, and the central role of consciousness in interpreting, planning, and expressing human reality are unavoidable today in attempts to scientifically understand all living matter, including human life.
Semi-Supervised Deep Hashing with a Bipartite Graph

Recently, deep learning has been successfully applied to the problem of hashing, yielding remarkable performance compared to traditional methods with hand-crafted features. However, most of existing deep hashing methods are designed for the supervised scenario and require a large number of labeled data. In this paper, we propose a novel semi-supervised hashing method for image retrieval, named Deep Hashing with a Bipartite Graph (BGDH), to simultaneously learn embeddings, features and hash codes. More specifically, we construct a bipartite graph to discover the underlying structure of data, based on which an embedding is generated for each instance. Then, we feed raw pixels as well as embeddings to a deep neural network, and concatenate the resulting features to determine the hash code. Compared to existing methods, BGDH is a universal framework that is able to utilize various types of graphs and losses. Furthermore, we propose an inductive variant of BGDH to support out-of-sample extensions. Experimental results on real datasets show that our BGDH outperforms state-of-the-art hashing methods.
A file is not a file: understanding the I/O behavior of Apple desktop applications

We analyze the I/O behavior of iBench, a new collection of productivity and multimedia application workloads. Our analysis reveals a number of differences between iBench and typical file-system workload studies, including the complex organization of modern files, the lack of pure sequential access, the influence of underlying frameworks on I/O patterns, the widespread use of file synchronization and atomic operations, and the prevalence of threads. Our results have strong ramifications for the design of next generation local and cloud-based storage systems.
Pancreatic Fistula after Pancreatectomy: Definitions, Risk Factors, Preventive Measures, and Management—Review

Resection of pancreas, in particular pancreaticoduodenectomy, is a complex procedure, commonly performed in appropriately selected patients with benign and malignant disease of the pancreas and periampullary region. Despite significant improvements in the safety and efficacy of pancreatic surgery, pancreaticoenteric anastomosis continues to be the "Achilles heel" of pancreaticoduodenectomy, due to its association with a measurable risk of leakage or failure of healing, leading to pancreatic fistula. The morbidity rate after pancreaticoduodenectomy remains high in the range of 30% to 65%, although the mortality has significantly dropped to below 5%. Most of these complications are related to pancreatic fistula, with serious complications of intra-abdominal abscess, postoperative bleeding, and multiorgan failure. Several pharmacological and technical interventions have been suggested to decrease the pancreatic fistula rate, but the results have been controversial. This paper considers definition and classification of pancreatic fistula, risk factors, and preventive approach and offers management strategy when they do occur.
Comparison of Multiple Linear Regressions ( MLR ) and Artificial Neural Network ( ANN ) in Predicting the Yield Using its Components in the Hulless Barley

In this study 40 genotypes in a randomized complete block design with three replications for two years were planted in the region of Ardabil. The yield related data and its components over the years of the analysis of variance were combined.Results showed that there was a significant difference between genotypes and genotype interaction in the environment. MLR and ANN methods were used to predict yield in barley. The fitted model in a yield predicting linear regression method was as follows: Reg = 1.75 + 0.883 X1 + 0.05017X2 +1.984X3 Also, yield prediction based on multi-layer neural network (ANN) using the Matlab Perceptron type software with one hidden layer including 15 neurons and using algorithm after error propagation learning method and hyperbolic tangent function was implemented, in both methods absolute values of relative error as a deviation index in order to estimate and using duad t test of mean deviation index of the two estimates was examined. Results showed that in the ANN technique the mean deviation index of estimation significantly was one-third (1 / 3) of its rate in the MLR, because there was a significant interaction between genotype and environment and its impact on estimation by MLR method.Therefore, when the genotype environment interaction is significant, in the yield prediction in instead of the regression is recommended of a neural network approach due to high yield and more velocity in the estimation to be used.
Dominant Sets and Pairwise Clustering

We develop a new graph-theoretic approach for pairwise data clustering which is motivated by the analogies between the intuitive concept of a cluster and that of a dominant set of vertices, a notion introduced here which generalizes that of a maximal complete subgraph to edge-weighted graphs. We establish a correspondence between dominant sets and the extrema of a quadratic form over the standard simplex, thereby allowing the use of straightforward and easily implementable continuous optimization techniques from evolutionary game theory. Numerical examples on various point-set and image segmentation problems confirm the potential of the proposed approach
Forced Migration , Female Labour Force Participation , and Intra-household Bargaining : Does Conflict Empower Women ?

Civilian displacement is a common phenomenon in developing countries confronted with internal conflict. While displacement directly affects forced migrants, it also contributes to deteriorating labour conditions of vulnerable groups in receiving communities. For the displaced population, the income losses are substantial, and as they migrate to cities, they usually end up joining the informal labour force. Qualitative evidence reveals that displaced women are better suited to compete in urban labour markets, as their labour experience is more relevant with respect to certain urban low-skilled occupations. Our study uses this exogenous change in female labour force participation to test how it affects female bargaining power within the household. Our results show that female displaced women work longer hours, earn similar wages and contribute in larger proportions to household earnings relative to rural women who remain in rural areas. However, as measured by several indicators, their greater contribution to households’ earnings does not strengthen their bargaining power. Most notably, domestic violence have increased among displaced women. The anger and frustration of displaced women also increases the level of violence directed at children. Because the children of displaced families have been the direct victims of conflict and domestic violence, the intragenerational transmission of violence is highly likely.
Robotics in Lower-Limb Rehabilitation after Stroke

With the increase in the elderly, stroke has become a common disease, often leading to motor dysfunction and even permanent disability. Lower-limb rehabilitation robots can help patients to carry out reasonable and effective training to improve the motor function of paralyzed extremity. In this paper, the developments of lower-limb rehabilitation robots in the past decades are reviewed. Specifically, we provide a classification, a comparison, and a design overview of the driving modes, training paradigm, and control strategy of the lower-limb rehabilitation robots in the reviewed literature. A brief review on the gait detection technology of lower-limb rehabilitation robots is also presented. Finally, we discuss the future directions of the lower-limb rehabilitation robots.
The Switch-Mode Flying-Capacitor DC–DC Converters With Improved Natural Balancing

This paper presents investigations of voltage-sharing stabilization with the use of passive RLC circuit in switch-mode flying capacitor DC-DC converters. Practical and simulation results and also a mathematical analysis of the balancing process in boost and buck-boost converters are presented. Analyzed converters use additional capacitors (flying capacitors), charged to proper value, for decreasing the voltage on switches and increasing the inductor-current frequency. Such advantages are achieved under proper voltage sharing on the flying capacitors. The voltages are stabilized in a natural way by the load current and with the use of external RLC circuit to force the current that flows through the converters' capacitors under unbalance state. This paper focuses on the analysis of the balancing phenomenon with the use of the external RLC circuit in these topologies. The balancing booster improves the balancing process in these converters, making it independent of the load. It can also reduce oscillations that arise in the converters in transient states.
Making web applications more energy efficient for OLED smartphones

A smartphone’s display is one of its most energy consuming components. Modern smartphones use OLED displays that consume more energy when displaying light colors as op- posed to dark colors. This is problematic as many popular mobile web applications use large light colored backgrounds. To address this problem we developed an approach for auto- matically rewriting web applications so that they generate more energy efficient web pages. Our approach is based on program analysis of the structure of the web application im- plementation. In the evaluation of our approach we show that it can achieve a 40% reduction in display power con- sumption. A user study indicates that the transformed web pages are acceptable to users with over 60% choosing to use the transformed pages for normal usage.
Pharmacokinetics and pharmacodynamics of insulin glargine given in the evening as compared with in the morning in type 2 diabetes.

To compare pharmacokinetics (PK) and pharmacodynamics (PD) of insulin glargine in type 2 diabetes mellitus (T2DM) after evening versus morning administration.



Ten T2DM insulin-treated persons were studied during 24-h euglycemic glucose clamp, after glargine injection (0.4 units/kg s.c.), either in the evening (2200 h) or the morning (1000 h).



The 24-h glucose infusion rate area under the curve (AUC0-24h) was similar in the evening and morning studies (1,058 ± 571 and 995 ± 691 mg/kg × 24 h, P = 0.503), but the first 12 h (AUC0-12h) was lower with evening versus morning glargine (357 ± 244 vs. 593 ± 374 mg/kg × 12 h, P = 0.004), whereas the opposite occurred for the second 12 h (AUC12-24h 700 ± 396 vs. 403 ± 343 mg/kg × 24 h, P = 0.002). The glucose infusion rate differences were totally accounted for by different rates of endogenous glucose production, not utilization. Plasma insulin and C-peptide levels did not differ in evening versus morning studies. Plasma glucagon levels (AUC0-24h 1,533 ± 656 vs. 1,120 ± 344 ng/L/h, P = 0.027) and lipolysis (free fatty acid AUC0-24h 7.5 ± 1.6 vs. 8.9 ± 1.9 mmol/L/h, P = 0.005; β-OH-butyrate AUC0-24h 6.8 ± 4.7 vs. 17.0 ± 11.9 mmol/L/h, P = 0.005; glycerol, P < 0.020) were overall more suppressed after evening versus morning glargine administration.



The PD of insulin glargine differs depending on time of administration. With morning administration insulin activity is greater in the first 0-12 h, while with evening administration the activity is greater in the 12-24 h period following dosing. However, glargine PK and plasma C-peptide levels were similar, as well as glargine PD when analyzed by 24-h clock time independent of the time of administration. Thus, the results reflect the impact of circadian changes in insulin sensitivity in T2DM (lower in the night-early morning vs. afternoon hours) rather than glargine per se.