nils-herrmann/clean_scirepeval huggingface dataset:
Dual-Band Probe-Fed Stacked Patch Antenna for GNSS Applications

A novel proximity-coupled probe-fed stacked patch antenna is proposed for Global Navigation Satellite Systems (GNSS) applications. The antenna has been designed to operate for the satellite navigation frequencies in L-band including GPS, GLONASS, Galileo, and Compass (1164-1239 MHz and 1559-1610 MHz). A key feature of our design is the proximity-coupled probe feeds to increase impedance bandwidth and the integrated 90deg broadband balun to improve polarization purity. The final antenna exhibits broad pattern coverage, high gain at the low angles (more than -5 dBi), and VSWR <1.5 for all the operating bands. The design procedures and employed tuning techniques to achieve the desired performance are presented.
Adaptation of Hybrid ANN/HMM Models Using Linear Hidden Transformations and Conservative Training

A technique is proposed for the adaptation of automatic speech recognition systems using hybrid models combining artificial neural networks with hidden Markov models. The application of linear transformations not only to the input features, but also to the outputs of the internal layers is investigated. The motivation is that the outputs of an internal layer represent a projection of the input pattern into a space where it should be easier to learn the classification or transformation expected at the output of the network. A new solution, called conservative training, is proposed that compensates for the lack of adaptation samples in certain classes. Supervised adaptation experiments with different corpora and for different adaptation types are described. The results show that the proposed approach always outperforms the use of transformations in the feature space and yields even better results when combined with linear input transformations
Action understanding as inverse planning

Humans are adept at inferring the mental states underlying other agents' actions, such as goals, beliefs, desires, emotions and other thoughts. We propose a computational framework based on Bayesian inverse planning for modeling human action understanding. The framework represents an intuitive theory of intentional agents' behavior based on the principle of rationality: the expectation that agents will plan approximately rationally to achieve their goals, given their beliefs about the world. The mental states that caused an agent's behavior are inferred by inverting this model of rational planning using Bayesian inference, integrating the likelihood of the observed actions with the prior over mental states. This approach formalizes in precise probabilistic terms the essence of previous qualitative approaches to action understanding based on an "intentional stance" [Dennett, D. C. (1987). The intentional stance. Cambridge, MA: MIT Press] or a "teleological stance" [Gergely, G., Nádasdy, Z., Csibra, G., & Biró, S. (1995). Taking the intentional stance at 12 months of age. Cognition, 56, 165-193]. In three psychophysical experiments using animated stimuli of agents moving in simple mazes, we assess how well different inverse planning models based on different goal priors can predict human goal inferences. The results provide quantitative evidence for an approximately rational inference mechanism in human goal inference within our simplified stimulus paradigm, and for the flexible nature of goal representations that human observers can adopt. We discuss the implications of our experimental results for human action understanding in real-world contexts, and suggest how our framework might be extended to capture other kinds of mental state inferences, such as inferences about beliefs, or inferring whether an entity is an intentional agent.
The gender identity/gender dysphoria questionnaire for adolescents and adults.

The present study reports on the construction of a dimensional measure of gender identity (gender dysphoria) for adolescents and adults. The 27-item gender identity/gender dysphoria questionnaire for adolescents and adults (GIDYQ-AA) was administered to 389 university students (heterosexual and nonheterosexual) and 73 clinic-referred patients with gender identity disorder. Principal axis factor analysis indicated that a one-factor solution, accounting for 61.3% of the total variance, best fits the data. Factor loadings were all >or= .30 (median, .82; range, .34-.96). A mean total score (Cronbach's alpha, .97) was computed, which showed strong evidence for discriminant validity in that the gender identity patients had significantly more gender dysphoria than both the heterosexual and nonheterosexual university students. Using a cut-point of 3.00, we found the sensitivity was 90.4% for the gender identity patients and specificity was 99.7% for the controls. The utility of the GIDYQ-AA is discussed.
Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder

End-to-end training makes the neural machine translation (NMT) architecture simpler, yet elegant compared to traditional statistical machine translation (SMT). However, little is known about linguistic patterns of morphology, syntax and semantics learned during the training of NMT systems, and more importantly, which parts of the architecture are responsible for learning each of these phenomena. In this paper we i) analyze how much morphology an NMT decoder learns, and ii) investigate whether injecting target morphology into the decoder helps it produce better translations. To this end we present three methods: i) joint generation, ii) joint-data learning, and iii) multi-task learning. Our results show that explicit morphological information helps the decoder learn target language morphology and improves the translation quality by 0.2–0.6 BLEU points.
A 1.25 ps Resolution 8b Cyclic TDC in 0.13 $\mu$m CMOS

This paper describes the first implementation of the well-known cyclic ADC architecture into a time-to-digital converter. With an asynchronous clocking scheme, an all-digital 1.5b time-domain multiplying DAC (MDAC) is repetitively used for 8b conversion. The MDAC is based on a 2 × time amplifier with an offset-compensated gain calibration scheme. The proposed cyclic TDC, fabricated in a 0.13 μm CMOS, shows a resolution of 1.25 ps with a total conversion range of ±160 ps, the maximum operating frequency of 100 MHz, and a power consumption of 4.3 mW at 50 MHz. The measured DNL and INL are ± 0.7 LSB and - 3 to + 1 LSB, respectively.
Sound-source recognition: a theory and computational model

The ability of a normal human listener to recognize objects in the environment from only the sounds they produce is extraordinarily robust with regard to characteristics of the acoustic environment and of other competing sound sources. In contrast, computer systems designed to recognize sound sources function precariously, breaking down whenever the target sound is degraded by reverberation, noise, or competing sounds. Robust listening requires extensive contextual knowledge, but the potential contribution of sound-source recognition to the process of auditory scene analysis has largely been neglected by researchers building computational models of the scene analysis process. This thesis proposes a theory of sound-source recognition, casting recognition as a process of gathering information to enable the listener to make inferences about objects in the environment or to predict their behavior. In order to explore the process, attention is restricted to isolated sounds produced by a small class of sound sources, the non-percussive orchestral musical instruments. Previous research on the perception and production of orchestral instrument sounds is reviewed from a vantage point based on the excitation and resonance structure of the sound-production process, revealing a set of perceptually salient acoustic features. A computer model of the recognition process is developed that is capable of “listening” to a recording of a musical instrument and classifying the instrument as one of 25 possibilities. The model is based on current models of signal processing in the human auditory system. It explicitly extracts salient acoustic features and uses a novel improvisational taxonomic architecture (based on simple statistical pattern-recognition techniques) to classify the sound source. The performance of the model is compared directly to that of skilled human listeners, using
Digital Forensic Investigation of Cloud Storage Services

The demand for cloud computing is increasing because of the popularity of digital devices and the wide use of the Internet. Among cloud computing services, most consumers use cloud storage services that provide mass storage. This is because these services give them various additional functions as well as storage. It is easy to access cloud storage services using smartphones. With increasing utilization, it is possible for malicious users to abuse cloud storage services. Therefore, a study on digital forensic investigation of cloud storage services is necessary. This paper proposes new procedure for investigating and analyzing the artifacts of all accessible devices, such as Windows, Mac, iPhone, and Android smartphone.
3D Human Motion Tracking with a Coordinated Mixture of Factor Analyzers

A major challenge in applying Bayesian tracking methods for tracking 3D human body pose is the high dimensionality of the pose state space. It has been observed that the 3D human body pose parameters typically can be assumed to lie on a low-dimensional manifold embedded in the high-dimensional space. The goal of this work is to approximate the low-dimensional manifold so that a low-dimensional state vector can be obtained for efficient and effective Bayesian tracking. To achieve this goal, a globally coordinated mixture of factor analyzers is learned from motion capture data. Each factor analyzer in the mixture is a “locally linear dimensionality reducer” that approximates a part of the manifold. The global parametrization of the manifold is obtained by aligning these locally linear pieces in a global coordinate system. To enable automatic and optimal selection of the number of factor analyzers and the dimensionality of the manifold, a variational Bayesian formulation of the globally coordinated mixture of factor analyzers is proposed. The advantages of the proposed model are demonstrated in a multiple hypothesis tracker for tracking 3D human body pose. Quantitative comparisons on benchmark datasets show that the proposed method produces more accurate 3D pose estimates over time than those obtained from two previously proposed Bayesian tracking methods.
Scaling up machine learning: parallel and distributed approaches

This tutorial gives a broad view of modern approaches for scaling up machine learning and data mining methods on parallel/distributed platforms. Demand for scaling up machine learning is task-specific: for some tasks it is driven by the enormous dataset sizes, for others by model complexity or by the requirement for real-time prediction. Selecting a task-appropriate parallelization platform and algorithm requires understanding their benefits, trade-offs and constraints. This tutorial focuses on providing an integrated overview of state-of-the-art platforms and algorithm choices. These span a range of hardware options (from FPGAs and GPUs to multi-core systems and commodity clusters), programming frameworks (including CUDA, MPI, MapReduce, and DryadLINQ), and learning settings (e.g., semi-supervised and online learning). The tutorial is example-driven, covering a number of popular algorithms (e.g., boosted trees, spectral clustering, belief propagation) and diverse applications (e.g., recommender systems and object recognition in vision).
 The tutorial is based on (but not limited to) the material from our upcoming Cambridge U. Press edited book which is currently in production.
 Visit the tutorial website at http://hunch.net/~large_scale_survey/
A DUAL ALGORITHM FOR THE SOLUTION OF NONLINEAR VARIATIONAL PROBLEMS VIA FINITE ELEMENT APPROXIMATION

For variational problems of the form we propose a dual method which decouples the difficulties relative to the functionals f and g from the possible ill-conditioning effects of the linear operator A. The approach is based on the use of an Augmented Lagrangian functional and leads to an efficient and simply implementable algorithm. We study also the finite element approximation of such problems, compatible with the use of our algorithm. The method is finally applied to solve several problems of continuum mechanics.
The Embodied Cognition Research Programme

Embodied Cognition is an approach to cognition that departs from traditional cognitive science in its reluctance to conceive of cognition as computational and in its emphasis on the significance of an organism’s body in how and what the organism thinks. Three lines of embodied cognition research are described and some thoughts on the future of embodied cognition offered. The embodied cognition research programme, hereafter EC, departs from more traditional cognitive science in the emphasis it places on the role the body plays in an organism’s cognitive processes. Saying more beyond this vague claim is difficult, but this is perhaps not surprising given the diversity of fields – phenomenology, robotics, ecological psychology, artificial life, ethology – from which EC has emerged. Indeed, the point of labelling EC a research programme, rather than a theory, is to indicate that the commitments and subject matters of EC remain fairly nebulous. Yet, much of the flavour of EC becomes evident when considering three prominent directions that researchers in this programme have taken. Before turning to these lines of research, it pays to have in sight the traditional view of cognitive science against which EC positions itself.  Cognitive Science Unifying traditional cognitive science is the idea that thinking is a process of symbol manipulation, where symbols lead both a syntactic and a semantic life (Haugeland, ‘Semantic Engines’). The syntax of a symbol comprises those properties in virtue of which the symbol undergoes rule-dictated transformations. The semantics of a symbol constitute the symbols’ meaning or representational content. Thought consists in the syntactically determined manipulation of symbols, but in a way that respects their semantics. Thus, for instance, a calculating computer sensitive only to the shape of symbols might produce the symbol ‘5’ in response to the inputs ‘2’, ‘+’, and ‘3’. As far as the computer is concerned, these symbols have no meaning, but because of its programme it will produce outputs that, to the user, ‘make sense’ given the meanings the user attributes to the symbols.
Excess Volatility of Corporate Bonds

This paper examines the connection among corporate bonds, stocks, and Treasury bonds under the Merton model with stochastic interest rate, focusing in particular on the volatility of corporate bonds and its connection to the equity volatility of the same firm and the Treasury bond volatility. For a broad cross-section of corporate bonds from 2002 through 2006, empirical measures of bond volatility are constructed using bond returns over daily, weekly, and monthly horizons. Comparing the empirical volatility with its model-implied counterpart, we find an overwhelming degree of excess volatility that is difficult to be explained by a default-based model. This excess volatility is found to be the strongest at the daily and weekly horizons, indicating a more pronounced liquidity component in corporate bonds at short horizons. At the monthly horizon, the excess volatility tapers off but remains significant. Moreover, we find that variables known to be linked to bond liquidity are important in explaining the cross-sectional variations in excess volatility, providing further evidence of a liquidity problem in corporate bonds. Finally, subtracting the equity and Treasury exposures from corporate bond returns, we find a non-trivial systematic component in the bond residuals that give rise to the excess volatility. ∗Bao is at the MIT Sloan School of Management,  Pan is at the MIT Sloan School of Management and NBER, junpan@mit.edu. We benefit from discussions with Jiang Wang; seminar participants at the MIT Finance Lunch and Boston University (Economics). We thank Duncan Ma for assistance in gathering Bloomberg data, and financial support from the outreach program of J.P. Morgan.
Comprehending Reality - Practical Barriers to Industrial Adoption of Software Maintenance Automation

Recent years have seen many significant advances in program comprehension and software maintenance automation technology. In spite of the enormous potential savings in software maintenance costs, for the most part adoption of these ideas in industry remains at the experimental prototype stage. In this paper I explore some of the practical reasons for industrial resistance to adoption of software maintenance automation. Based on the experience of six years of software maintenance automation services to the financial industry involving more than 4.5 Gloc of code at Legasys Corporation, I discuss some of the social, technical and business realities that lie at the root of this resistance, outline various Legasys attempts overcome these barriers, and suggest some approaches to software maintenance automation that may lead to higher levels of industrial acceptance in the future.
Extended Password Key Exchange Protocols Immune to Dictionary Attacks

Strong password methods verify even small passwords over a network without additional stored keys or certificates with the user, and without fear of network dictionary attack. We describe a new extension to further limit exposure to theft of a stored password-verifier, and apply it to several protocols including the Simple Password Exponential Key Exchange (SPEKE). Alice proves knowledge of a password C to Bob, who has a stored verifier S, where S=g mod p. They perform a SPEKE exchange based on the shared secret S to derive ephemeral shared key K1. Bob chooses a random X and sends g mod p. Alice computes K2=g XC mod p, and proves knowledge of {K1,K2}. Bob verifies this result to confirm that Alice knows C. Implementation issues are summarized, showing the potential for improved performance over Bellovin & Merritt's comparably strong Augmented-Encrypted Key Exchange. These methods make the password a strong independent factor in authentication, and are suitable for both Internet and intranet use.
Variational Lossy Autoencoder

Representation learning seeks to expose certain aspects of observed data in a learned representation that’s amenable to downstream tasks like classification. For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture. In this paper, we present a simple but principled method to learn such global representations by combining Variational Autoencoder (VAE) with neural autoregressive models such as RNN, MADE and PixelRNN/CNN. Our proposed VAE model allows us to have control over what the global latent code can learn and by designing the architecture accordingly, we can force the global latent code to discard irrelevant information such as texture in 2D images, and hence the VAE only “autoencodes” data in a lossy fashion. In addition, by leveraging autoregressive models as both prior distribution p(z) and decoding distribution p(x|z), we can greatly improve generative modeling performance of VAEs, achieving new state-of-the-art results on MNIST, OMNIGLOT and Caltech-101 Silhouettes density estimation tasks as well as competitive results on CIFAR10.
Circuit Analysis and Modeling of a Phase-Shifted Pulsewidth modulation Full-Bridge-Inverter-Fed Ozone Generator With Constant Applied Electrode Voltage

This paper proposes a circuit analysis and system modeling of an ozone generator using a phase-shifted pulsewidth modulation full-bridge inverter connected to two electrodes via a step-up transformer. The circuit operation of the inverter is fully described. Approximate models of the high-frequency transformer and the ozone-generating tube are given. Model parameter values are obtained from electrical characteristic measurement in conjunction with physical dimension calculation. In order to ensure that a zero-voltage soft-switching mode always operates over a certain range of a frequency variation, a series compensated resonant inductor is included. The advantage of the proposed system is a capability of varying ozone gas production quantity by varying the frequency of the inverter while the applied electrode voltage is kept constant, in order to overcome a high-frequency effect on the transformer voltage regulation. As a consequence, the absolute ozone production affected by the frequency is possibly achieved. The correctness and validity of the proposed system are verified by both simulation and experimental results.
Networked Participatory Scholarship: Emergent techno-cultural pressures toward open and digital scholarship in online networks

We examine the relationship between scholarly practice and participatory technologies and explore how such technologies invite and reflect the emergence of a new form of scholarship that we call Networked Participatory Scholarship: scholars’ participation in online social networks to share, reflect upon, critique, improve, validate, and otherwise develop their scholarship. We discuss emergent techno-cultural pressures that may influence higher education scholars to reconsider some of the foundational principles upon which scholarship has been established due to the limitations of a pre-digital world, and delineate how scholarship itself is changing with the emergence of certain tools, social behaviors, and cultural expectations associated with participatory technologies.
BioIE: extracting informative sentences from the biomedical literature

BioIE is a rule-based system that extracts informative sentences relating to protein families, their structures, functions and diseases from the biomedical literaturE. Based on manual definition of templates and rules, it aims at precise sentence extraction rather than wide recall. After uploading source text or retrieving abstracts from MEDLINE, users can extract sentences based on predefined or user-defined template categories. BioIE also provides a brief insight into the syntactic and semantic context of the source-text by looking at word, N-gram and MeSH-term distributions. Important Applications of BioIE are in, for example, annotation of microarray data and of protein databases.


AVAILABILITY
http://umber.sbs.man.ac.uk/dbbrowser/bioie/