nils-herrmann/clean_scirepeval huggingface dataset:
Simpler core vector machines with enclosing balls

The core vector machine (CVM) is a recent approach for scaling up kernel methods based on the notion of minimum enclosing ball (MEB). Though conceptually simple, an efficient implementation still requires a sophisticated numerical solver. In this paper, we introduce the enclosing ball (EB) problem where the ball's radius is fixed and thus does not have to be minimized. We develop efficient (1 + e)-approximation algorithms that are simple to implement and do not require any numerical solver. For the Gaussian kernel in particular, a suitable choice of this (fixed) radius is easy to determine, and the center obtained from the (1 + e)-approximation of this EB problem is close to the center of the corresponding MEB. Experimental results show that the proposed algorithm has accuracies comparable to the other large-scale SVM implementations, but can handle very large data sets and is even faster than the CVM in general.
Current Self-Balance Mechanism in Multiphase Buck Converter

Some of the recent applications in the field of the power supplies use multiphase converters to achieve fast dynamic response, smaller input/output filters, or better packaging. Typically, these converters have several paralleled power stages, with a current loop in each phase and a single voltage loop. The presence of the current loops avoids current imbalance among phases. The purpose of this paper is to demonstrate that, in CCM, with a proper design, there is an intrinsic mechanism of self-balance that reduces the current imbalance. Thus, in the buck converter, if natural zero-voltage switching (ZVS) is achieved in both transitions, the instantaneous inductor current compensates partially the different DC currents through the phases. The need for using n current loops will be finally determined by the application but not by the converter itself. Using the buck converter as a base, a multiphase converter has been developed. Several tests have been carried out in the laboratory and the results show clearly that, when the conditions are met, the phase currents are very well balanced even during transient conditions.
Effect of penile-extender device in increasing penile size in men with shortened penis: preliminary results.

It has been suggested that the application of penile-extender devices increases penile length and circumference. However, there are a few scientific studies in this field.



The aim of this study was to assess the efficacy of a penile-extender (Golden Erect(®) , Ronas Tajhiz Teb, Tehran, Iran) in increasing penile size.



This prospective study was performed on subjects complaining about "short penis" who were presented to our clinic between September 15, 2008 and December 15, 2008. After measuring the penile length in flaccid and stretched forms and penile circumference, patients were instructed to wear Golden Erect(®) , 4-6 hours per day during the first 2 weeks and then 9 hours per day until the end of the third month. The subjects were also trained how to increase the force of the device during determined intervals. The patients were visited at the end of the first and third months, and penile length and circumference were measured and compared with baseline.



The primary end point of the study was changes in flaccid and stretched penile lengths compared with the baseline size during the 3 months follow-up.



Twenty-three cases with a mean age of 26.5 ± 8.1 years entered the study. The mean flaccid penile length increased from 8.8 ± 1.2 cm to 10.1 ± 1.2 cm and 10.5 ± 1.2 cm, respectively, in the first and third months of follow-up, which was statistically significant (P < 0.05). Mean stretched penile length also significantly increased from 11.5 ± 1.0 cm to, respectively, 12.4 ± 1.3 cm and 13.2 ± 1.4 cm during the first and second follow-up (P < 0.05). No significant difference was found regarding proximal penile girth. However, it was not the same regarding the circumference of the glans penis (9.3 ± 0.86 cm vs. 8.8 ± 0.66 cm, P < 0.05).



Our findings supported the efficacy of the device in increasing penile length. Our result also suggested the possibility of glans penis girth enhancement using penile extender. Performing more studies is recommended.
A least-norm approach to flattenable mesh surface processing

Following the definition of developable surface in differential geometry, the flattenable mesh surface, a special type of piecewise- linear surface, inherits the good property of developable surface about having an isometric map from its 3D shape to a corresponding planar region. Different from the developable surfaces, a flattenable mesh surface is more flexible to model objects with complex shapes (e.g., cramped paper or warped leather with wrinkles). Modelling a flattenable mesh from a given input mesh surface can be completed under a constrained nonlinear optimization framework. In this paper, we reformulate the problem in terms of estimation error. Therefore, the shape of a flattenable mesh can be computed by the least-norm solutions faster. Moreover, the method for adding shape constraints to the modelling of flattenable mesh surfaces has been exploited. We show that the proposed method can compute flattenable mesh surfaces from input piecewise linear surfaces successfully and efficiently.
The enzyme-sensitive release of prodigiosin grafted β-cyclodextrin and chitosan magnetic nanoparticles as an anticancer drug delivery system: Synthesis, characterization and cytotoxicity studies.

In present investigation, two glucose based smart tumor-targeted drug delivery systems coupled with enzyme-sensitive release strategy are introduced. Magnetic nanoparticles (Fe3O4) were grafted with carboxymethyl chitosan (CS) and β-cyclodextrin (β-CD) as carriers. Prodigiosin (PG) was used as the model anti-tumor drug, targeting aggressive tumor cells. The morphology, properties and composition and grafting process were characterized by transmission electron microscope (TEM), Fourier transform infrared spectroscopy (FT-IR), vibration sample magnetometer (VSM), X-ray diffraction (XRD) analysis. The results revealed that the core crystal size of the nanoparticles synthesized were 14.2±2.1 and 9.8±1.4nm for β-CD and CS-MNPs respectively when measured using TEM; while dynamic light scattering (DLS) gave diameters of 121.1 and 38.2nm. The saturation magnetization (Ms) of bare magnetic nanoparticles is 50.10emucm-3, while modification with β-CD and CS gave values of 37.48 and 65.01emucm-3, respectively. The anticancer compound, prodigiosin (PG) was loaded into the NPs with an encapsulation efficiency of approximately 81% for the β-CD-MNPs, and 92% for the CS-MNPs. This translates to a drug loading capacity of 56.17 and 59.17mg/100mg MNPs, respectively. Measurement of in vitro release of prodigiosin from the loaded nanocarriers in the presence of the hydrolytic enzymes, alpha-amylase and chitosanase showed that 58.1 and 44.6% of the drug was released after one-hour of incubation. Cytotoxicity studies of PG-loaded nanocarriers on two cancer cell lines, MCF-7 and HepG2, and on a non-cancerous control, NIH/3T3 cells, revealed that the drug loaded nanoparticles had greater efficacy on the cancer cell lines. The selective index (SI) for free PG on MCF-7 and HepG2 cells was 1.54 and 4.42 respectively. This parameter was reduced for PG-loaded β-CD-MNPs to 1.27 and 1.85, while the SI for CS-MNPs improved considerably to 7.03 on MCF-7 cells. Complementary studies by fluorescence and confocal microscopy and flow cytometry confirm specific targeting of the nanocarriers to the cancer cells. The results suggest that CS-MNPs have higher potency and are better able to target the prodigiosin toxicity effect on cancerous cells than β-CD-MNPs.
Common mechanisms of human perceptual and motor learning

The adult mammalian brain has a remarkable capacity to learn in both the perceptual and motor domains through the formation and consolidation of memories. Such practice-enabled procedural learning results in perceptual and motor skill improvements. Here, we examine evidence supporting the notion that perceptual and motor learning in humans exhibit analogous properties, including similarities in temporal dynamics and the interactions between primary cortical and higher-order brain areas. These similarities may point to the existence of a common general mechanism for learning in humans.
Loopy Neural Nets : Imitating Feedback Loops in the Human Brain

Artificial Neural networks purport to be biomimetic, but are by definition acyclic computational graphs. As a corollary, neurons in artificial nets fire only once and have no time-dynamics. Both these properties contrast with what neuroscience has taught us about human brain connectivity, especially with regards to object recognition. We therefore propose a way to simulate feedback loops in the brain by unrolling loopy neural networks several timesteps, and investigate the properties of these networks. We compare different variants of loops, including multiplicative composition of inputs and additive composition of inputs. We demonstrate that loopy networks outperform deep feedforward networks with the same number of parameters on the CIFAR-10 dataset, as well as nonloopy versions of the same network, and perform equally well on the MNIST dataset. In order to further understand our models, we visualize neurons in loop layers with guided backprop, demonstrating that the same filters behave increasingly nonlinearly at higher unrolling levels. Furthermore, we interpret loops as attention mechanisms, and demonstrate that the composition of the loop output with the input image produces images that look qualitatively like attention maps.
State-space models’ dirty little secrets: even simple linear Gaussian models can have estimation problems

State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results.
Particle-based language modelling

This paperinvestigatesthe useof particle (sub-word) -grams for languagemodelling. One linguistics-basedand two datadriven algorithmsare presentedand evaluatedin termsof perplexity for RussianandEnglish. Interpolatingword trigramand particle6-grammodelsgivesup to a 7.5%perplexity reduction over thebaselinewordtrigrammodelfor Russian.Latticerescor ing experimentsarealsoperformedon1997DARPA Hub4evaluationlatticeswheretheinterpolatedmodelgivesa 0.4%absolute reductionin worderrorrateoverthebaselinewordtrigrammodel.
Cognitive neural prosthetics

The cognitive neural prosthetic (CNP) is a very versatile method for assisting paralyzed patients and patients with amputations. The CNP records the cognitive state of the subject, rather than signals strictly related to motor execution or sensation. We review a number of high-level cortical signals and their application for CNPs, including intention, motor imagery, decision making, forward estimation, executive function, attention, learning, and multi-effector movement planning. CNPs are defined by the cognitive function they extract, not the cortical region from which the signals are recorded. However, some cortical areas may be better than others for particular applications. Signals can also be extracted in parallel from multiple cortical areas using multiple implants, which in many circumstances can increase the range of applications of CNPs. The CNP approach relies on scientific understanding of the neural processes involved in cognition, and many of the decoding algorithms it uses also have parallels to underlying neural circuit functions. 169 A nn u. R ev . P sy ch ol . 2 01 0. 61 :1 69 -1 90 . D ow nl oa de d fr om a rj ou rn al s. an nu al re vi ew s. or g by C al if or ni a In st itu te o f T ec hn ol og y on 0 1/ 03 /1 0. F or p er so na l u se o nl y. ANRV398-PS61-07 ARI 17 November 2009 19:51 Cognitive neural prosthetics (CNPs): instruments that consist of an array of electrodes, a decoding algorithm, and an external device controlled by the processed cognitive signal Decoding algorithms: computer algorithms that interpret neural signals for the purposes of understanding their function or for providing control signals to machines
Massive MIMO Antenna Selection: Switching Architectures, Capacity Bounds, and Optimal Antenna Selection Algorithms

Antenna selection is a multiple-input multiple-output (MIMO) technology, which uses radio frequency (RF) switches to select a good subset of antennas. Antenna selection can alleviate the requirement on the number of RF transceivers, thus being attractive for massive MIMO systems. In massive MIMO antenna selection systems, RF switching architectures need to be carefully considered. In this paper, we examine two switching architectures, i.e., full-array and sub-array. By assuming independent and identically distributed Rayleigh flat fading channels, we use asymptotic theory on order statistics to derive the asymptotic upper capacity bounds of massive MIMO channels with antenna selection for the both switching architectures in the large-scale limit. We also use the derived bounds to further derive the upper bounds of the ergodic achievable spectral efficiency considering the channel state information (CSI) acquisition. It is also showed that the ergodic capacity of sub-array antenna selection system scales no faster than double logarithmic rate. In addition, optimal antenna selection algorithms based on branch-and-bound are proposed for both switching architectures. Our results show that the derived asymptotic bounds are effective and also apply to the finite-dimensional MIMO. The CSI acquisition is one of the main limits for the massive MIMO antenna selection systems in the time-variant channels. The proposed optimal antenna selection algorithms are much faster than the exhaustive-search-based antenna selection, e.g., 1000 × speedup observed in the large-scale system. Interestingly, the full-array and sub-array systems have very close performance, which is validated by their exact capacities and their close upper bounds on capacity.
The measurement and meaning of unintended pregnancy.

The concept of unintended pregnancy has been essential to demographers in seeking to understand fertility, to public health practitioners in preventing unwanted childbear-ing and to both groups in promoting a woman's ability to determine whether and when to have children. Accurate measurement of pregnancy intentions is important in understanding fertility-related behaviors, forecasting fertility, estimating unmet need for contraception, understanding the impact of pregnancy intentions on maternal and child health, designing family planning programs and evaluating their effectiveness, and creating and evaluating community-based programs that prevent unintended pregnancy. 1 Pregnancy unintendedness is a complex concept, and has been the subject of recent conceptual and method-ological critiques. 2 Pregnancy intentions are increasingly viewed as encompassing affective, cognitive, cultural and contextual dimensions. Developing a more complete understanding of pregnancy intentions should advance efforts to increase contraceptive use, to prevent unintended pregnancies and to improve the health of women and their children. To provide a scientific foundation for public health efforts to prevent unintended pregnancy, we conducted a review of unintended pregnancy between the fall of 1999 and the spring of 2001 as part of strategic planning activities within the Division of Reproductive Health at the Centers for Disease Control and Prevention (CDC). We reviewed the published and unpublished literature, consulted with experts in reproductive health and held several joint meetings with the Demographic and Behavioral Research Branch of the National Institute of Child Health and Human Development , and the Office of Population Affairs of the Department of Health and Human Services. We used standard scientific search engines, such as Medline, to find relevant articles published since 1975, and identified older references from bibliographies contained in recent articles; academic experts and federal officials helped to identify unpublished reports. This comment summarizes our findings and incorporates insights gained from the joint meetings and the strategic planning process. CURRENT DEFINITIONS AND MEASURES Conventional measures of unintended pregnancy are designed to reflect a woman's intentions before she became pregnant. 3 Unintended pregnancies are pregnancies that are reported to have been either unwanted (i.e., they occurred when no children, or no more children, were desired) or mistimed (i.e., they occurred earlier than desired). In contrast, pregnancies are described as intended if they are reported to have happened at the " right time " 4 or later than desired (because of infertility or difficulties in conceiving). A concept related to unintended pregnancy is unplanned pregnancy—one that occurred when …
Scalable Cache Miss Handling for High Memory-Level Parallelism

Recently-proposed processor microarchitectures for high Memory Level Parallelism (MLP) promise substantial performance gains. Unfortunately, current cache hierarchies have Miss-Handling Architectures (MHAs) that are too limited to support the required MLPthey need to be redesigned to support 1-2 orders of magnitude more outstanding misses. Yet, designing scalable MHAs is challenging: designs must minimize cache lock-up time and deliver high bandwidth while keeping the area consumption reasonable. This paper presents a novel scalable MHA design for high-MLP processors. Our design introduces two main innovations. First, it is hierarchical, with a small MSHR file per cache bank, and a larger MSHR file shared by all banks. Second, it uses a Bloom filter to reduce searches in the larger MSHR file. The result is a highperformance, area-efficient design. Compared to a state-of-the-art MHA on a high-MLP processor, our design speeds-up some SPECint, SPECfp, and multiprogrammed workloads by a geometric mean of 32%, 50%, and 95%, respectively. Moreover, compared to two extrapolations of current MHA designs, namely a large monolithic MSHR file and a large banked MSHR file, all consuming the same area, our design speeds-up the workloads by a geometric mean of 1-18% and 10-21%, respectively. Finally, our design performs very close to an unlimited-size, ideal MHA.
Machine learning for stress detection from ECG signals in automobile drivers

Physiological sensor analytics is becoming an important tool to monitor health as the availability of sensor-enabled portable, wearable, and implantable devices becomes ubiquitous in the growing Internet of Things (IoT). Physiological multi-sensor studies have been conducted previously to detect stress. In this study, we focus on ECG monitoring that can now be performed with minimally invasive wearable patches and sensors, to develop an efficient and robust mechanism for accurate stress identification. A unique aspect of our research is personalized individual stress analysis including three stress levels: low, medium and high. Using machine learning algorithms from the ECG signals alone, we could achieve 88.24% accuracy in detecting the three classes of stress. We also find that high stress can be successfully detected for a person in comparison to his or her rest period with 100% accuracy.
Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element

Using 2 phase-change memory (PCM) devices per synapse, a 3-layer perceptron network with 164,885 synapses is trained on a subset (5000 examples) of the MNIST database of handwritten digits using a backpropagation variant suitable for NVM+selector crossbar arrays, obtaining a training (generalization) accuracy of 82.2% (82.9%). Using a neural network (NN) simulator matched to the experimental demonstrator, extensive tolerancing is performed with respect to NVM variability, yield, and the stochasticity, linearity and asymmetry of NVM-conductance response.
The Query Change Model: Modeling Session Search as a Markov Decision Process

Modern information retrieval (IR) systems exhibit user dynamics through interactivity. These dynamic aspects of IR, including changes found in data, users, and systems, are increasingly being utilized in search engines. Session search is one such IR task—document retrieval within a session. During a session, a user constantly modifies queries to find documents that fulfill an information need. Existing IR techniques for assisting the user in this task are limited in their ability to optimize over changes, learn with a minimal computational footprint, and be responsive. This article proposes a novel query change retrieval model (QCM), which uses syntactic editing changes between consecutive queries, as well as the relationship between query changes and previously retrieved documents, to enhance session search. We propose modeling session search as a Markov decision process (MDP). We consider two agents in this MDP: the user agent and the search engine agent. The user agent’s actions are query changes that we observe, and the search engine agent’s actions are term weight adjustments as proposed in this work. We also investigate multiple query aggregation schemes and their effectiveness on session search. Experiments show that our approach is highly effective and outperforms top session search systems in TREC 2011 and TREC 2012.
Cloud adoption : a goal-oriented requirements engineering approach

We motivate the need for a new requirements engineering methodology for systematically helping businesses and users to adopt cloud services and for mitigating risks in such transition. The methodology is grounded in goal oriented approaches for requirements engineering. We argue that Goal Oriented Requirements Engineering (GORE) is a promising paradigm to adopt for goals that are generic and flexible statements of users' requirements, which could be refined, elaborated, negotiated, mitigated for risks and analysed for economics considerations. We describe the steps of the proposed process and exemplify the use of the methodology through an example. The methodology can be used by small to large scale organisations to inform crucial decisions related to cloud adoption.