nils-herrmann/clean_scirepeval huggingface dataset:
Vitamin D: A millenium perspective.

Vitamin D is one of the oldest hormones that have been made in the earliest life forms for over 750 million years. Phytoplankton, zooplankton, and most plants and animals that are exposed to sunlight have the capacity to make vitamin D. Vitamin D is critically important for the development, growth, and maintenance of a healthy skeleton from birth until death. The major function of vitamin D is to maintain calcium homeostasis. It accomplishes this by increasing the efficiency of the intestine to absorb dietary calcium. When there is inadequate calcium in the diet to satisfy the body's calcium requirement, vitamin D communicates to the osteoblasts that signal osteoclast precursors to mature and dissolve the calcium stored in the bone. Vitamin D is metabolized in the liver and then in the kidney to 1,25-dihydroxyvitamin D [1,25(OH)(2)D]. 1,25(OH)(2)D receptors (VDR) are present not only in the intestine and bone, but in a wide variety of other tissues, including the brain, heart, stomach, pancreas, activated T and B lymphocytes, skin, gonads, etc. 1,25(OH)(2)D is one of the most potent substances to inhibit proliferation of both normal and hyperproliferative cells and induce them to mature. It is also recognized that a wide variety of tissues, including colon, prostate, breast, and skin have the enzymatic machinery to produce 1,25(OH)(2)D. 1,25(OH)(2)D and its analogs have been developed for treating the hyperproliferative disease psoriasis. Vitamin D deficiency is a major unrecognized health problem. Not only does it cause rickets in children, osteomalacia and osteoporosis in adults, but may have long lasting effects. Chronic vitamin D deficiency may have serious adverse consequences, including increased risk of hypertension, multiple sclerosis, cancers of the colon, prostate, breast, and ovary, and type 1 diabetes. There needs to be a better appreciation of the importance of vitamin D for overall health and well being.
Conditional proxy re-encryption secure against chosen-ciphertext attack

In a proxy re-encryption (PRE) system [4], a proxy, authorized by Alice, can convert a ciphertext for Alice into a ciphertext for Bob without seeing the underlying plaintext. PRE has found many practical applications requiring delegation. However, it is inadequate to handle scenarios where a fine-grained delegation is demanded. To overcome the limitation of existing PRE systems, we introduce the notion of conditional proxy re-encryption (C-PRE), whereby only ci-phertext satisfying a specific condition set by Alice can be transformed by the proxy and then decrypted by Bob. We formalize its security model and propose an efficient C-PRE scheme, whose chosen-ciphertext security is proven under the 3-quotient bilinear Diffie-Hellman assumption. We further extend the construction to allow multiple conditions with a slightly higher overhead.
Cellular IP: a new approach to Internet host mobility

This paper describes a new approach to Internet host mobility. We argue that by separating local and wide area mobility, the performance of existing mobile host protocols (e.g. Mobile IP) can be significantly improved. We propose Cellular IP, a new lightweight and robust protocol that is optimized to support local mobility but efficiently interworks with Mobile IP to provide wide area mobility support. Cellular IP shows great benefit in comparison to existing host mobility proposals for environments where mobile hosts migrate frequently, which we argue, will be the rule rather than the exception as Internet wireless access becomes ubiquitous. Cellular IP maintains distributed cache for location management and routing purposes. Distributed paging cache coarsely maintains the position of 'idle' mobile hosts in a service area. Cellular IP uses this paging cache to quickly and efficiently pinpoint 'idle' mobile hosts that wish to engage in 'active' communications. This approach is beneficial because it can accommodate a large number of users attached to the network without overloading the location management system. Distributed routing cache maintains the position of active mobile hosts in the service area and dynamically refreshes the routing state in response to the handoff of active mobile hosts. These distributed location management and routing algorithms lend themselves to a simple and low cost implementation of Internet host mobility requiring no new packet formats, encapsulations or address space allocation beyond what is present in IP.
A direct approach to estimating surfaces in tomographic data

Under ideal circumstances, the inverse of the radon transform is computable, and sequences of measured projections are sufficient to obtain accurate estimates of volume densities. In situations where the sinogram data is incomplete, the radon transform is noninvertable, and attempts to reconstruct greyscale density values result in reconstruction artifacts that can undermine the effectiveness of subsequent processing. This paper presents a direct approach to the segmentation of incomplete tomographic data. The strategy is to impose a fairly simple model on the data, and treat segmentation as a problem of estimating the interface between two substances of somewhat homogeneous density. The segmentation is achieved by simultaneously deforming a surface model and updating density parameters in order to achieve a best fit between the projected model and the measured sinograms. The deformation is implemented with level-set surface models, calculated at the resolution of the input data. Relative to previous work, this paper makes several contributions. First is a proper derivation of the deformation of surface models that move according to a gradient descent on a likelihood measure. We also present a series of computational innovations that make this direct surface-fitting approach feasible with state-of-the-art computers. Another contribution is the demonstration of the effectiveness of this approach on under-constrained tomographic problems, using both simulated and real datasets.
Mobile Application Security in the Presence of Dynamic Code Updates

The increasing number of repeated malware penetrations into official mobile app markets poses a high security threat to the confidentiality and privacy of end users’ personal and sensitive information. Protecting end user devices from falling victims to adversarial apps presents a technical and research challenge for security researchers/engineers in academia and industry. Despite the security practices and analysis checks deployed at app markets, malware sneak through the defenses and infect user devices. The evolution of malware has seen it become sophisticated and dynamically changing software usually disguised as legitimate apps. Use of highly advanced evasive techniques, such as encrypted code, obfuscation and dynamic code updates, etc., are common practices found in novel malware. With evasive usage of dynamic code updates, a malware pretending as benign app bypasses analysis checks and reveals its malicious functionality only when installed on a user’s device. This dissertation provides a thorough study on the use and the usage manner of dynamic code updates in Android apps. Moreover, we propose a hybrid analysis approach, StaDART, that interleaves static and dynamic analysis to cover the inherent shortcomings of static analysis techniques to analyze apps in the presence of dynamic code updates. Our evaluation results on real world apps demonstrate the effectiveness of StaDART. However, typically dynamic analysis, and hybrid analysis too for that matter, brings the problem of stimulating the app’s behavior which is a non-trivial challenge for automated analysis tools. To this end, we propose a backward slicing based targeted inter component code paths execution technique, TeICC. TeICC leverages a backward slicing mechanism to extract code paths starting from a target point in the app. It makes use of a system dependency graph to extract code paths that involve inter component communication. The extracted code paths are then instrumented and executed inside the app context to capture sensitive dynamic behavior, resolve dynamic code updates and obfuscation. Our evaluation of TeICC shows that it can be effectively used for targeted execution of inter component code paths in obfuscated Android apps. Also, still not ruling out the possibility of adversaries reaching the user devices, we propose an on-phone API hooking
Detecting Topic Drift with Compound Topic Models

The Latent Dirichlet Allocation topic model of Blei, Ng, & Jordan (2003) is well-established as an effective approach to recovering meaningful topics of conversation from a set of documents. However, a useful analysis of user-generated content is concerned not only with the recovery of topics from a static data set, but with the evolution of topics over time. We employ a compound topic model (CTM) to track topics across two distinct data sets (i.e. past and present) and to visualize trends in topics over time; we evaluate several metrics for detecting a change in the distribution of topics within a time-window; and we illustrate how our approach discovers emerging conversation topics related to current events in real data sets.
Executive functioning in children with specific language impairment.

A limited range of evidence suggests that children with specific language impairment (SLI) have difficulties with higher order thinking and reasoning skills (executive functioning, EF). This study involved a comprehensive investigation of EF in this population taking into account the contributions of age, nonverbal IQ and verbal ability.



Ten separate measures of EF were assessed in 160 children: 41 had SLI; 31 had low language/cognitive functioning but did not fulfil the criteria for SLI (low language functioning or LLF); and 88 were typically developing with no language difficulties. Group differences in performance were assessed after controlling for age, nonverbal IQ and verbal ability in a series of regression analyses.



Children with SLI and LLF had significantly lower performance than typical children on 6 of the 10 EF tasks once age and nonverbal IQ had been controlled (verbal and nonverbal executive-loaded working memory, verbal and nonverbal fluency, nonverbal inhibition and nonverbal planning). Performance on these EF tasks remained lower for those in the SLI group even when verbal IQ was entered in the regressions.



Children with language impairments showed marked difficulties on a range of EF tasks. These difficulties were present even when adjustments were made for their verbal abilities.
Coding of pleasant touch by unmyelinated afferents in humans

Pleasant touch sensations may begin with neural coding in the periphery by specific afferents. We found that during soft brush stroking, low-threshold unmyelinated mechanoreceptors (C-tactile), but not myelinated afferents, responded most vigorously at intermediate brushing velocities (1−10 cm s−1), which were perceived by subjects as being the most pleasant. Our results indicate that C-tactile afferents constitute a privileged peripheral pathway for pleasant tactile stimulation that is likely to signal affiliative social body contact.
The Energy Intensity of the Internet: Home and Access Networks

Estimates of the energy intensity of the Internet diverge by several orders of magnitude. We present existing assessments and identify diverging definitions of the system boundary as the main reason for this large spread. The decision of whether or not to include end devices influences the result by 1-2 orders of magnitude. If end devices are excluded, customer premises equipment (CPE) and access networks have a dominant influence. Of less influence is the consideration of cooling equipment and other overhead, redundancy equipment, and the amplifiers in the optical fibers. We argue against the inclusion of end devices when assessing the energy intensity of the Internet, but in favor of including CPE, access networks, redundancy equipment, cooling and other overhead as well as optical fibers. We further show that the intensities of the metro and core network are best modeled as energy per data, while the intensity of CPE and access networks are best modeled as energy per time (i.e., power), making overall assessments challenging. The chapter concludes with a formula for the energy intensity of CPE and access networks. The formula is presented both in generic form as well as with concrete estimates of the average case to be used in quick assessments by practitioners. The following chapter develops a similar formula for the core and edge networks. Taken together, the two chapters provide an assessment method of the Internet’s energy intensity that takes into account different modeling paradigms for different parts of the network.
Wound healing Agents from Medicinal Plants: A Review

Healing of wounds starts from the moment of injury and can continue for varying periods of time depending on the extent of wounding and the process can be broadly categorized into three stages; inflammatory phase, proliferate phase, and finally the remodeling phase which ultimately determines the strength and appearance of the healed tissue . Wounds are the physical injuries that result in an opening or breaking of the skin and appropriate method for healing of wounds is essential for the restoration of disrupted anatomical continuity and disturbed functional status of the skin . Wound healing is a complex series of interrelated events that are mediated through the phases by a wide range of chemically co-ordinate cellular processes as well as hormonal influences . Medicinal plants have been shown to possess wound healing activity in animal studies .
Using Quality Models in Software Package Selection

0 7 4 0 7 4 5 9 / 0 3 / $ 1 9 . 0 0 © 2 0 0 3 I E E E All the methodologies that have been proposed recently for choosing software packages compare user requirements with the packages’ capabilities.3–5 There are different types of requirements, such as managerial, political, and, of course, quality requirements. Quality requirements are often difficult to check. This is partly due to their nature, but there is another reason that can be mitigated, namely the lack of structured and widespread descriptions of package domains (that is, categories of software packages such as ERP systems, graphical or data structure libraries, and so on). This absence hampers the accurate description of software packages and the precise statement of quality requirements, and consequently overall package selection and confidence in the result of the process. Our methodology for building structured quality models helps solve this drawback.  A structured quality model for a given package domain provides a taxonomy of software quality features, and metrics for computing their value. Our approach relies on the International Organization for Standardization and International Electrotechnical Commission 9126-1 quality standard,6 which we selected for the following reasons:
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling

Recurrent neural networks (RNNs) have shown promising performance for language modeling. However, traditional training of RNNs, using back-propagation through time, often suffers from overfitting. One reason for this is that stochastic optimization (used for large training sets) does not provide good estimates of model uncertainty. This paper leverages recent advances in stochastic gradient Markov Chain Monte Carlo (also appropriate for large training sets) to learn weight uncertainty in RNNs. It yields a principled Bayesian learning algorithm, adding gradient noise during training (enhancing exploration of the model-parameter space) and model averaging when testing. Extensive experiments on various RNN models and across a broad range of applications demonstrate the superiority of the proposed approach relative to stochastic optimization.
Action observation and robotic agents: Learning and anthropomorphism

The 'action observation network' (AON), which is thought to translate observed actions into motor codes required for their execution, is biologically tuned: it responds more to observation of human, than non-human, movement. This biological specificity has been taken to support the hypothesis that the AON underlies various social functions, such as theory of mind and action understanding, and that, when it is active during observation of non-human agents like humanoid robots, it is a sign of ascription of human mental states to these agents. This review will outline evidence for biological tuning in the AON, examining the features which generate it, and concluding that there is evidence for tuning to both the form and kinematic profile of observed movements, and little evidence for tuning to belief about stimulus identity. It will propose that a likely reason for biological tuning is that human actions, relative to non-biological movements, have been observed more frequently while executing corresponding actions. If the associative hypothesis of the AON is correct, and the network indeed supports social functioning, sensorimotor experience with non-human agents may help us to predict, and therefore interpret, their movements.
2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text

The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks: a concept extraction task focused on the extraction of medical concepts from patient reports; an assertion classification task focused on assigning assertion types for medical problem concepts; and a relation classification task focused on assigning relation types that hold between medical problems, tests, and treatments. i2b2 and the VA provided an annotated reference standard corpus for the three tasks. Using this reference standard, 22 systems were developed for concept extraction, 21 for assertion classification, and 16 for relation classification. These systems showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations. Depending on the task, the rule-based systems can either provide input for machine learning or post-process the output of machine learning. Ensembles of classifiers, information from unlabeled data, and external knowledge sources can help when the training data are inadequate.
Multiobjective Signal Processing Optimization: The way to balance conflicting metrics in 5G systems

The evolution of cellular networks is driven by the dream of ubiquitous wireless connectivity: any data service is instantly accessible everywhere. With each generation of cellular networks, we have moved closer to this wireless dream; first by delivering wireless access to voice communications, then by providing wireless data services, and recently by delivering a Wi-Fi-like experience with wide-area coverage and user mobility management. The support for high data rates has been the main objective in recent years , as seen from the academic focus on sum-rate optimization and the efforts from standardization bodies to meet the peak rate requirements specified in IMT-Advanced. In contrast, a variety of metrics/objectives are put forward in the technological preparations for fifth-generation (5G) networks: higher peak rates, improved coverage with uniform user experience, higher reliability and lower latency, better energy efficiency (EE), lower-cost user devices and services, better scalability with number of devices, etc. These multiple objectives are coupled, often in a conflicting manner such that improvements in one objective lead to degradation in the other objectives. Hence, the design of future networks calls for new optimization tools that properly handle the existence of multiple objectives and tradeoffs between them.
Model-Based Recognition in Robot Vision

This paper presents a comparative study and survey of model-based object-recognition algorithms for robot vision. The goal of these algorithms is to recognize the identity, position, and orientation of randomly oriented industrial parts. In one form this is commonly referred to as the "bin-picking" problem, in which the parts to be recognized are presented in a jumbled bin. The paper is organized according to 2-D, 2½-D, and 3-D object representations, which are used as the basis for the recognition algorithms. Three central issues common to each category, namely, feature extraction, modeling, and matching, are examined in detail. An evaluation and comparison of existing industrial part-recognition systems and algorithms is given, providing insights for progress toward future robot vision systems.
Dissociation, somatization, and affect dysregulation: the complexity of adaptation of trauma.

A century of clinical research has noted a range of trauma-related psychological problems that are not captured in the DSM-IV framework of posttraumatic stress disorder (PTSD). This study investigated the relationships between exposure to extreme stress, the emergence of PTSD, and symptoms traditionally associated with "hysteria," which can be understood as problems with stimulus discrimination, self-regulation, and cognitive integration of experience.



The DSM-IV field trial for PTSD studied 395 traumatized treatment-seeking subjects and 125 non-treatment-seeking subjects who had also been exposed to traumatic experiences. Data on age at onset, the nature of the trauma, PTSD, dissociation, somatization, and affect dysregulation were collected.



PTSD, dissociation, somatization, and affect dysregulation were highly interrelated. The subjects meeting the criteria for lifetime (but not current) PTSD scored significantly lower on these disorders than those with current PTSD, but significantly higher than those who never had PTSD. Subjects who developed PTSD after interpersonal trauma as adults had significantly fewer symptoms than those with childhood trauma, but significantly more than victims of disasters.



PTSD, dissociation, somatization, and affect dysregulation represent a spectrum of adaptations to trauma. They often occur together, but traumatized individuals may suffer from various combinations of symptoms over time. In treating these patients, it is critical to attend to the relative contributions of loss of stimulus discrimination, self-regulation, and cognitive integration of experience to overall impairment and provide systematic treatment that addresses both unbidden intrusive recollections and these other symptoms associated with having been overwhelmed by exposure to traumatic experiences.
Elimination of Residual Flux in Transformers by the Application of an Alternating Polarity DC Voltage Source

The purpose of core demagnetization is twofold: 1) to reduce the inrush currents when transformers are energized; and 2) to make sure that the frequency-response analysis (FRA) tests are consistent to avoid false diagnoses of damage during transportation. The significance of demagnetizing is presented on field measurements of an 80 MVA unit with FRA measurements. A new demagnetizer device with an alternating polarity dc voltage source is prototyped. Experimental verification of this prototype is presented for the demagnetization of transformers. A nearly complete demagnetization was observed in the laboratory for a small single-phase isolation transformer. The method proposed in this paper is applied to three-phase transformers with different core configurations and connections. Topologically correct modeling and numerical simulations confirm the full demagnetization of all branches of three-phase (three- and five-limb) transformer cores. Inrush current measurements and FRA plots before and after demagnetization confirm the effectiveness of the process.