nils-herrmann/clean_scirepeval huggingface dataset:
The “ Semiotic Animal ” in Roland Barthes : A Reflection on Calculating the Self as “ Difference in Man ”

The animal metaphor in poststructuralists thinkers like Roland Barthes and Jacques Derrida, offers an understanding into the human self through the relational modes of being and co-being. The present study focuses on the concept of “semiotic animal” proposed by John Deely with reference to Roland Barthes. Human beings are often considered as “rational animal” (Descartes) capable of reason and thinking. By analyzing the “semiotic animal” in Roland Barthes, the intention is to study him as a “mind-dependent” being who discovers the contrast between ens reale and ens rationis through his writing. For Barthes “it is the intimate which seeks utterance” in one and makes “it cry, heard, confronting generality, confronting science.” Roland Barthes attempts to read “his body” from the “tissues of signs” that is driven by the unconscious desires. The study is an attempt to explore the semiological underpinnings in Barthes which are found in the form of rhetorical tropes of cats and dogs and the way he relates it with the ‘self’.
Real-time multiple speaker DOA estimation in a circular microphone array based on Matching Pursuit

We recently proposed an approach inspired by Sparse Component Analysis for real-time localisation of multiple sound sources using a circular microphone array. The method was based on identifying time-frequency zones where only one source is active, reducing the problem to single-source localisation in these zones. A histogram of estimated Directions of Arrival (DOAs) was formed and then processed to obtain improved DOA estimates, assuming that the number of sources was known. In this paper, we extend our previous work by proposing a new method for the final DOA estimations, that outperforms our previous method at lower SNRs and in the case of six simultaneous speakers. In keeping with the spirit of our previous work, the new method is very computationally efficient, facilitating its use in real-time systems.
Corticostriatal Projection Neurons – Dichotomous Types and Dichotomous Functions

The mammalian striatum receives its main excitatory input from the two types of cortical pyramidal neurons of layer 5 of the cerebral cortex - those with only intratelencephalic connections (IT-type) and those sending their main axon to the brainstem via the pyramidal tract (PT-type). These two neurons types are present in layer 5 of all cortical regions, and thus they appear to project together to all parts of striatum. These two neuron types, however, differ genetically, morphologically, and functionally, with IT-type neurons conveying sensory and motor planning information to striatum and PT-type neurons conveying an efference copy of motor commands (for motor cortex at least). Anatomical and physiological data for rats, and more recent data for primates, indicate that these two cortical neuron types also differ in their targeting of the two main types of striatal projection neurons, with the IT-type input preferentially innervating direct pathway neurons and the PT-type input preferentially innervating indirect pathway striatal neurons. These findings have implications for understanding how the direct and indirect pathways carry out their respective roles in movement facilitation and movement suppression, and they have implications for understanding the role of corticostriatal synaptic plasticity in adaptive motor control by the basal ganglia.
An anatomical study of the indirect tendon of the rectus femoris using ultrasonography

The aim of this study was to demonstrate that ultrasound can allow a precise assessment of the indirect tendon of the rectus femoris using a new lateral approach. Four hips were dissected for the anatomical study of the proximal rectus femoris insertions. Under ultrasonographic guidance, spinal needles piercing the direct tendon were placed in the indirect tendon, following by dissection. Then, high-resolution ultrasound was performed in 20 volunteers with assessment of the indirect tendon of the rectus femoris. At dissection, the spinal needles were located in or immediately adjacent to the indirect tendon, thus confirming that it was correctly depicted by ultrasound. The indirect tendon could be identified in each cadaver and each volunteer with ultrasound. The optimal position of the probe to allow assessment of the indirect tendon could be defined. No significant changes in the appearance or thickness of the tendon could be observed. The results of our study showed that the indirect tendon of the rectus femoris muscle can be clearly depicted by sonography in healthy adult subjects. The potential applications of this new use of sonography must now be confirmed by clinical studies. • The anatomy of the proximal rectus femoris is reviewed • Until now, sonography was limited for assessing direct and conjoined tendons • The indirect tendon can be clearly depicted by sonography • A new lateral approach for studying the indirect tendon is described
UML-Based integration testing

Increasing numbers of software developers are using the Unified Modeling Language (UML) and associated visual modeling tools as a basis for the design and implementation of their distributed, component-based applications. At the same time, it is necessary to test these components, especially during unit and integration testing.
At Siemens Corporate Research, we have addressed the issue of testing components by integrating test generation and test execution technology with commercial UML modeling tools such as Rational Rose; the goal being a design-based testing environment. 
In order to generate test cases automatically, developers first define the dynamic behavior of their components via UML Statecharts, specify the interactions amongst them and finally annotate them with test requirements. Test cases are then derived from these annotated Statecharts using our test generation engine and executed with the help of our test execution tool. The latter tool was developed specifically for interfacing to components based on COM/DCOM and CORBA middleware.
In this paper, we present our approach to modeling components and their interactions, describe how test cases are derived from these component models and then executed to verify their conformant behavior. We outline the implementation strategy of our TnT environment and use it to evaluate our approach by means of a simple example.
Application development of virtual metrology in semiconductor industry

Daily wafer fabrication in semiconductor foundry depends on considerable metrology operations for tool-quality and process-quality assurance. The metrology operations required a lot of metrology tools, which increase FAB's investment. Also, these metrology operations will increase cycle time of wafer process. Metrology operations do not bring any value added to wafer but only quality assurance. This article provides a new method denoted virtual metrology (VM) to utilize sensor data collected from 300 mm FAB's tools to forecast quality data of wafers and tools. This proposed method designs key steps to establish a VM control model based on neural networks and to develop and deploy applications following SEMI EDA (equipment data acquisition) standards.
Towards a general theory of driver behaviour.

Taylor [Taylor, D.H., 1964. Drivers' galvanic skin response and the risk of accident. Ergonomics 7, 439-451] argued that drivers attempt to maintain a constant level of anxiety when driving which Wilde [Wilde, G.J.S., 1982. The theory of risk homeostasis: implications for safety and health.  interpreted to be coupled to subjective estimates of the probability of collision. This theoretical paper argues that what drivers attempt to maintain is a level of task difficulty. Naatanen and Summala [Naatanen, R., Summala, H., 1976. Road User Behaviour and Traffic Accidents. North Holland/Elsevier, Amsterdam, New York] similarly rejected the concept of statistical risk as a determinant of driver behaviour, but in so doing fell back on the learning process to generate a largely automatised selection of appropriate safety margins. However it is argued here that driver behaviour cannot be acquired and executed principally in such S-R terms. The concept of task difficulty is elaborated within the framework of the task-capability interface (TCI) model, which describes the dynamic interaction between the determinants of task demand and driver capability. It is this interaction which produces different levels of task difficulty. Implications of the model are discussed regarding variation in performance, resource allocation, hierarchical decision-making and the interdependence of demand and capability. Task difficulty homeostasis is proposed as a key sub-goal in driving and speed choice is argued to be the primary solution to the problem of keeping task difficulty within selected boundaries. The relationship between task difficulty and mental workload and calibration is clarified. Evidence is cited in support of the TCI model, which clearly distinguishes task difficulty from estimates of statistical risk. However, contrary to expectation, ratings of perceived risk depart from ratings of statistical risk but track difficulty ratings almost perfectly. It now appears that feelings of risk may inform driver decision making, as Taylor originally suggested, but not in terms of risk of collision, but rather in terms of task difficulty. Finally risk homeostasis is presented as a special case of task difficulty homeostasis.
A Review of Mobile HCI Research Methods

This paper examines and reviews research methods applied within the field of mobile human-computer interaction. The purpose is to provide a snapshot of current practice for studying mobile HCI to identify shortcomings in the way research is conducted and to propose opportunities for future approaches. 102 publications on mobile human-computer interaction research were categorized in a matrix relating their research methods and purpose. The matrix revealed a number of significant trends with a clear bias towards building systems and evaluating them only in laboratory settings, if at all. Also, gaps in the distribution of research approaches and purposes were identified; action research, case studies, field studies and basic research being applied very infrequently. Consequently, we argue that the bias towards building systems and a lack of research for understanding design and use limits the development of cumulative knowledge on mobile human computer interaction. This in turn inhibits future development of the research field as a whole.
Soft switching interleaved active clamp flyback inverter for a photovoltaic AC module system

A soft switching interleaved active clamp flyback inverter for a photovoltaic AC module system is proposed. The proposed system includes an interleaved active clamp flyback converter and an unfolding bridge inverter. The interleaved active clamp flyback converter has the advantages of zero voltage switching (ZVS) performance for the primary switches, reverse-recovery problem alleviation for the secondary output diodes and small input current ripple. Furthermore, each switch in the flyback converter operates with a high efficiency point along with an output power using a new optimal control method. Therefore, high efficiency and high power density conversion can be achieved in a with input voltage range by employing the proposed system. In order to verify these, theoretical analysis and simulation are performed.
A Power-Efficient Accelerator Based on FPGAs for LSTM Network

Today, artificial neural networks (ANNs) are widely used in a variety of applications, including speech recognition, face detection, disease diagnosis, etc. And as the emerging field of ANNs, Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) which contains complex computational logic. To achieve high accuracy, researchers always build large-scale LSTM networks which are time-consuming and power-consuming. In this paper, we present a hardware accelerator for the LSTM neural network layer based on FPGA Zedboard and use pipeline methods to parallelize the forward computing process. We also implement a sparse LSTM hidden layer, which consumes fewer storage resources than the dense network. Our accelerator is power-efficient and has a higher speed than ARM Cortex-A9 processor.
Bayesian GAN

Generative adversarial networks (GANs) can implicitly learn rich distributions over images, audio, and data which are hard to model with an explicit likelihood. We present a practical Bayesian formulation for unsupervised and semi-supervised learning with GANs, in conjunction with stochastic gradient Hamiltonian Monte Carlo to marginalize the weights of the generator and discriminator networks. The resulting approach is straightforward and obtains good performance without any standard interventions such as feature matching, or mini-batch discrimination. By exploring an expressive posterior over the parameters of the generator, the Bayesian GAN avoids mode-collapse, produces interpretable candidate samples with notable variability, and in particular provides state-of-the-art quantitative results for semi-supervised learning on benchmarks including SVHN, CelebA, and CIFAR-10, outperforming DCGAN, Wasserstein GANs, and DCGAN ensembles.
Concurrent Replicating Garbage Collection

We have implemented a concurrent copying garbage collector that uses replicating garbage collection. In our design, the client can continuously access the heap during garbage collection. No low-level synchronization between the client and the garbage collector is required on individual object operations. The garbage collector replicates live heap objects and periodically synchronizes with the client to obtain the client's current root set and mutation log. An experimental implementation using the Standard ML of New Jersey system on a shared-memory multiprocessor demonstrates excellent pause time performance and moderate execution time speedups.
Neuroprotection by MKK7 inhibition in excitotoxicity

MicroRNAs (miRNAs) are short, 22–25 nucleotide long transcripts that may suppress entire signaling pathways by interacting with the 3’-untranslated region (3’-UTR) of coding mRNA targets, interrupting translation and inducing degradation of these targets. The long 3’-UTRs of brain transcripts compared to other tissues predict important roles for brain miRNAs. Supporting this notion, we found that brain miRNAs co-evolved with their target transcripts, that non-coding pseudogenes with miRNA recognition elements compete with brain coding mRNAs on their miRNA interactions, and that Single Nucleotide Polymorphisms (SNPs) on such pseudogenes are enriched in mental diseases including autism and schizophrenia, but not Alzheimer’s disease (AD). Focusing on evolutionarily conserved and primate-specifi c miRNA controllers of cholinergic signaling (‘CholinomiRs’), we fi nd modifi ed CholinomiR levels in the brain and/or nucleated blood cells of patients with AD and Parkinson’s disease, with treatment-related diff erences in their levels and prominent impact on the cognitive and anti-infl ammatory consequences of cholinergic signals. Examples include the acetylcholinesterase (AChE)-targeted evolutionarily conserved miR-132, whose levels decline drastically in the AD brain. Furthermore, we found that interruption of AChE mRNA’s interaction with the primatespecifi c CholinomiR-608 in carriers of a SNP in the AChE’s miR-608 binding site induces domino-like eff ects that reduce the levels of many other miR-608 targets. Young, healthy carriers of this SNP express 40% higher brain AChE activity than others, potentially aff ecting the responsiveness to AD’s anti-AChE therapeutics, and show elevated trait anxiety, infl ammation and hypertension. Non-coding regions aff ecting miRNA-target interactions in neurodegenerative brains thus merit special attention.
Diverse Routing in Networks With Probabilistic Failures

We develop diverse routing schemes for dealing with multiple, possibly correlated, failures. While disjoint path protection can effectively deal with isolated single link failures, recovering from multiple failures is not guaranteed. In particular, events such as natural disasters or intentional attacks can lead to multiple correlated failures, for which recovery mechanisms are not well understood. We take a probabilistic view of network failures where multiple failure events can occur simultaneously, and develop algorithms for finding diverse routes with minimum joint failure probability. Moreover, we develop a novel Probabilistic Shared Risk Link Group (PSRLG) framework for modeling correlated failures. In this context, we formulate the problem of finding two paths with minimum joint failure probability as an integer nonlinear program (INLP) and develop approximations and linear relaxations that can find nearly optimal solutions in most cases.
Bayesian Sets

Inspired by “GoogleTM Sets”, we consider the problem of retrieving items from a concept or cluster, given a query consisting of a few items from that cluster. We formulate this as a Bayesian inference problem and describe a very simple algorithm for solving it. Our algorithm uses a modelbased concept of a cluster and ranks items using a score which evaluates the marginal probability that each item belongs to a cluster containing the query items. For exponential family models with conjugate priors this marginal probability is a simple function of sufficient statistics. We focus on sparse binary data and show that our score can be evaluated exactly using a single sparse matrix multiplication, making it possible to apply our algorithm to very large datasets. We evaluate our algorithm on three datasets: retrieving movies from EachMovie, finding completions of author sets from the NIPS dataset, and finding completions of sets of words appearing in the Grolier encyclopedia. We compare to Google TM Sets and show that Bayesian Sets gives very reasonable set completions.
Supervised Opinion Aspect Extraction by Exploiting Past Extraction Results

One of the key tasks of sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. In this work, we focus on using supervised sequence labeling as the base approach to performing the task. Although several extraction methods using sequence labeling methods such as Conditional Random Fields (CRF) and Hidden Markov Models (HMM) have been proposed, we show that this supervised approach can be significantly improved by exploiting the idea of concept sharing across multiple domains. For example, “screen” is an aspect in iPhone, but not only iPhone has a screen, many electronic devices have screens too. When “screen” appears in a review of a new domain (or product), it is likely to be an aspect too. Knowing this information enables us to do much better extraction in the new domain. This paper proposes a novel extraction method exploiting this idea in the context of supervised sequence labeling. Experimental results show that it produces markedly better results than without using the past information.
Searchlight: won't you be my neighbor?

The rapid deployment of millions of mobile sensors and smartphones has resulted in a demand for opportunistic encounter-based networking to support mobile social networking applications and proximity-based gaming. However, the success of these emerging networks is limited by the lack of effective and energy efficient neighbor discovery protocols. While probabilistic approaches perform well for the average case, they exhibit long tails resulting in high upper bounds on neighbor discovery time. Recent deterministic protocols, which allow nodes to wake up at specific timeslots according to a particular pattern, improve on the worst case bound, but do so by sacrificing average case performance. In response to these limitations, we have designed Searchlight, a highly effective asynchronous discovery protocol that is built on three basic ideas. First, it leverages the constant offset between periodic awake slots to design a simple probing-based approach to ensure discovery. Second, it allows awake slots to cover larger sections of time, which ultimately reduces total awake time drastically. Finally, Searchlight has the option to employ probabilistic techniques with its deterministic approach that can considerably improve its performance in the average case when all nodes have the same duty cycle. We validate Searchlight through analysis and real-world experiments on smartphones that show considerable improvement (up to 50%) in worst-case discovery latency over existing approaches in almost all cases, irrespective of duty cycle symmetry.
Classical deterministic complexity of Edmonds' Problem and quantum entanglement

Generalizing a decision problem for bipartite perfect matching, J. Edmonds introduced in  the problem (now known as the Edmonds Problem) of deciding if a given linear subspace of M(N) contains a nonsingular matrix, where M(N) stands for the linear space of complex NxN matrices. This problem led to many fundamental developments in matroid theory etc.Classical matching theory can be defined in terms of matrices with nonnegative entries. The notion of Positive operator, central in Quantum Theory, is a natural generalization of matrices with nonnegative entries. (Here operator refers to maps from matrices to matrices.) First, we reformulate the Edmonds Problem in terms of of completely positive operators, or equivalently, in terms of bipartite density matrices. It turns out that one of the most important cases when Edmonds' problem can be solved in polynomial deterministic time, i.e. an intersection of two geometric matroids, corresponds to unentangled (aka separable) bipartite density matrices. We introduce a very general class (or promise) of linear subspaces of M(N) on which there exists a polynomial deterministic time algorithm to solve Edmonds' problem. The algorithm is a thoroughgoing generalization of algorithms in [23], [26], and its analysis benefits from an operator analog of permanents, so called Quantum Permanents. Finally, we prove that the weak membership problem for the convex set of separable normalized bipartite density matrices is NP-HARD.
Pulse Width Modulation for Current Source Converters – A Detailed Concept

The current source converter represents the dual counter part to the better known and more frequently used voltage source converter. As such due to duality rules many offthe-shelf solutions concerning the VSC can be used in a modified manner to handle the CSC. This work gives a comprehensive overview of CSC PWM methods and shows how they are to be distinguished. Then based on duality considerations a complete recipe is given on how to utilise usual micro controller peripherals along with some logic elements to achieve the most significant PWM methods with little effort.