Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving and Scaling Trans-dimensional Random Field Language Models

The dominant language models (LMs) such as n-gram and neural network (NN) models represent sentence probabilities in terms of conditionals. In contrast, a new trans-dimensional random field (TRF) LM has been recently introduced to show superior performances, where the whole sentence is modeled as a random field. In this paper, we further develop the TDF LMs with two technical improvements, which are a new method of exploiting Hessian information in parameter optimization to further enhance the convergence of the training algorithm and an enabling method for training the TRF LMs on large corpus which may contain rare very long sentences. Experiments show that the TRF LMs can scale to using training data of up to 32 million words, consistently achieve 10% relative perplexity reductions over 5-gram LMs, and perform as good as NN LMs but with much faster speed in calculating sentence probabilities.
A mixed-kinetic model describes unloaded velocities of smooth, skeletal, and cardiac muscle myosin filaments in vitro

Reconstituted muscle-like assays reveal novel mechanisms that control the speed of muscle contraction. In vitro motility assays, where purified myosin and actin move relative to one another, are used to better understand the mechanochemistry of the actomyosin adenosine triphosphatase (ATPase) cycle. We examined the relationship between the relative velocity (V) of actin and myosin and the number of available myosin heads (N) or [ATP] for smooth (SMM), skeletal (SKM), and cardiac (CMM) muscle myosin filaments moving over actin as well as V from actin filaments moving over a bed of monomeric SKM. These data do not fit well to a widely accepted model that predicts that V is limited by myosin detachment from actin (d/ton), where d equals step size and ton equals time a myosin head remains attached to actin. To account for these data, we have developed a mixed-kinetic model where V is influenced by both attachment and detachment kinetics. The relative contributions at a given V vary with the probability that a head will remain attached to actin long enough to reach the end of its flexible S2 tether. Detachment kinetics are affected by L/ton, where L is related to the tether length. We show that L is relatively long for SMM, SKM, and CMM filaments (59 ± 3 nm, 22 ± 9 nm, and 22 ± 2 nm, respectively). In contrast, L is shorter (8 ± 3 nm) when myosin monomers are attached to a surface. This suggests that the behavior of the S2 domain may be an important mechanical feature of myosin filaments that influences unloaded shortening velocities of muscle.
An integrated assessment of histopathological changes of the enteric neuromuscular compartment in experimental colitis

Bowel inflammatory fibrosis has been largely investigated, but an integrated assessment of remodelling in inflamed colon is lacking. This study evaluated tissue and cellular changes occurring in colonic wall upon induction of colitis, with a focus on neuromuscular compartment. Colitis was elicited in rats by 2,4‐dinitrobenzenesulfonic acid (DNBS). After 6 and 21 days, the following parameters were assessed on paraffin sections from colonic samples: tissue injury and inflammatory infiltration by histology; collagen and elastic fibres by histochemistry; HuC/D, glial fibrillar acidic protein (GFAP), proliferating cell nuclear antigen (PCNA), nestin, substance P (SP), von Willebrand factor, c‐Kit and transmembrane 16A/Anoctamin1 (TMEM16A/ANO1) by immunohistochemistry. TMEM16A/ANO1 was also examined in isolated colonic smooth muscle cells (ICSMCs). On day 6, inflammatory alterations and fibrosis were present in DNBS‐treated rats; colonic wall thickening and fibrotic remodelling were evident on day 21. Colitis was associated with both an increase in collagen fibres and a decrease in elastic fibres. Moreover, the neuromuscular compartment of inflamed colon displayed a significant decrease in neuron density and increase in GFAP/PCNA‐positive glia of myenteric ganglia, enhanced expression of neural SP, blood vessel remodelling, reduced c‐Kit‐ and TMEM16A/ANO1‐positive interstitial cells of Cajal (ICCs), as well as an increase in TMEM16A/ANO1 expression in muscle tissues and ICSMCs. The present findings provide an integrated view of the inflammatory and fibrotic processes occurring in the colonic neuromuscular compartment of rats with DNBS‐induced colitis. These morphological alterations may represent a suitable basis for understanding early pathophysiological events related to bowel inflammatory fibrosis.
Decidability of second-order theories and automata on infinite trees

Introduction. In this paper we solve the decision problem of a certain secondorder mathematical theory and apply it to obtain a large number of decidability results. The method of solution involves the development of a theory of automata on infinite trees—a chapter in combinatorial mathematics which may be of independent interest. Let £ = {0, 1}, and denote by T the set of all words (finite sequences) on 2. Let r0: T^-T and rx: T—>■ T be, respectively, the successor functions ro(x)=x0 and r1(x) = xl, xeT. Our main result is that the (monadic) second-order theory of the structure (T, r0, rxy of two successor functions is decidable. This answers a question raised by Biichi [1]. It turns out that this result is very powerful and many difficult decidability results follow from it by simple reductions. The decision procedures obtained by this method are elementary recursive (in the sense of Kalmar). The applications include the following. (Whenever we refer, in this paper, to second-order theories, we mean monadic second-order; weak second-order means quantification restricted to finite subsets of the domain.) The second-order theory of countable linearly ordered sets is proved decidable. As a corollary we get that the weak second-order theory of arbitrary linearly ordered sets is decidable ; a result due to Läuchli [9] which improves on a result of Ehrenfeucht [5]. In [4] Ehrenfeucht announced the decidability of the first-order theory of a unary function. We prove that the second-order theory of a unary function with a countable domain is decidable. Also, the weak second-order theory of a unary function with an arbitrary domain is decidable. There are also applications to point set topology. Let CD be Cantor's discontinuum (i.e., (0, 1}TM with the product topology). Let Fa be the lattice of all subsets of CD which are denumerable unions of closed sets, and let Lc be the sublattice of all closed subsets of CD. The first-order theory of the lattice Fa, with Lc as a distinguished sublattice, is decidable. Similar results hold for the real line with the usual topology. This answers in the affirmative Grzegorczyk's question [8] whether
Classification using hierarchical mixtures of experts

There has recently been widespread interest in the use of multiple models for classification and regression in the statistics and neural networks communities. The hierarchical mixture of experts (HME) has been successful in a number of regression problems, yielding significantly faster training through the use of the expectation maximisation algorithm. In this paper we extend the HME to classification and results are reported for three common classification benchmark tests: exclusive-OR, N-input parity and two spirals. <<ETX>>
Acute and Intermediate Cardiovascular Responses to Zero Gravity and to Fractional Gravity Levels Induced by Head‐Down or Head‐Up Tilt

Determination of early cardiovascular responses to simulated gravity levels between 0 and 1 G will add knowledge of cardiovascular responses to space flight. Cardiovascular responses to 6 hours in a −5° head‐down bedrest model of weightlessness (0 G) were compared to those in head‐up tilts of +10°, +20°, and +42° (1/6, 1/3, and 2/3 G, respectively). Six healthy young adult males experienced the four angles on separate days. Impedance cardiography was used to measure thoracic fluid index, cardiac output, stroke volume, and peak flow. Although much intersubject variation occurred, the mean thoracic fluid content at −5° decreased during the first hour and remained decreased; 6‐hour values were similar to + 10° and +20°. Heart rate decreased the first 2 hours for all angles, then increased, converging at 3–4 hours, and reached control by hour 6. Stroke volume decreased for the first 3 hours at −5°, +10°, +20°; values at all four angles converged at hour 3 and increased in unison thereafter. Cardiac output and peak aortic flow reflected the angle at start of tilt; values at all angles converged by the second hour, decreased through the third hour, and increased thereafter. Pulse pressure decreased for the first 3 hours for angles −5°, +10°, and +20°, converged at the fourth hour, and returned to control. Peak flow at +42° was constant for the first 3 hours and increased thereafter. Blood pressure decreased for the first 2 hours, although the greatest decrease occurred at −5° and +42°; thereafter, values at all angles increased in unison and converged at the fourth hour. Total peripheral resistance increased during the first hour at −5° and +20° and decreased from hour 3 to hours 5–6 at the +42° angle. Cardiovascular values were related to tilt angle for the first 2 hours of tilt, but after hour 3 values at all four angles began to converge, suggesting that cardiovascular homeostatic mechanisms seek a common adapted state regardless of effective gravity level (tilt angle) up to 2/3 G.
Reasoning about the Consequences of Authorization Policies in a Linear Epistemic Logic

Authorization policies are not stand-alone objects: they are used to selectively permit actions that change the state of a system. Thus, it is desirable to have a framework for reasoning about the semantic consequences of policies. To this end, we extend a rewriting interpretation of linear logic with connectives for modeling affirmation, knowledge, and possession. To cleanly confine semantic effects to the rewrite sequence, we introduce a monad. The result is a richly expressive logic that elegantly integrates policies and their effects. After presenting this logic and its metatheory, we demonstrate its utility by proving properties that relate a simple file system’s policies to their semantic consequences.
Understanding the role of chromatin remodeling in the regulation of circadian transcription in Drosophila

ABSTRACT Circadian clocks enable organisms to anticipate daily changes in the environment and coordinate temporal rhythms in physiology and behavior with the 24-h day-night cycle. The robust cycling of circadian gene expression is critical for proper timekeeping, and is regulated by transcription factor binding, RNA polymerase II (RNAPII) recruitment and elongation, and post-transcriptional mechanisms. Recently, it has become clear that dynamic alterations in chromatin landscape at the level of histone posttranslational modification and nucleosome density facilitate rhythms in transcription factor recruitment and RNAPII activity, and are essential for progression through activating and repressive phases of circadian transcription. Here, we discuss the characterization of the BRAHMA (BRM) chromatin-remodeling protein in Drosophila in the context of circadian clock regulation. By dissecting its catalytic vs. non-catalytic activities, we propose a model in which the non-catalytic activity of BRM functions to recruit repressive factors to limit the transcriptional output of CLOCK (CLK) during the active phase of circadian transcription, while the primary function of the ATP-dependent catalytic activity is to tune and prevent over-recruitment of negative regulators by increasing nucleosome density. Finally, we divulge ongoing efforts and investigative directions toward a deeper mechanistic understanding of transcriptional regulation of circadian gene expression at the chromatin level.
Non-linear Analyses of Cell proliferation in the central nervous system reveal stochastic and deterministic components

This paper analyzes the dynamics of cell proliferation in the developing central nervous system. Three different algorithms, Fano Factor, Allan Factor and Detrended Fluctuations Analysis, are used to estimate de scaling exponent of space numerical series obtained by recording the number and position of proliferating cells along the cephalic-caudal axis of the system. It can be concluded that the dynamics of proliferation involves two component: (a) a random non-correlated stochastic component representing a basal proliferating activity uniformly distributed along the cephalic-caudal axis and (b) a deterministic non-stationary component that imposes a defined global trend to the process. The deterministic non-stationary trend can be interpreted as the effect of a controlling influence operating along the cephalic-caudal axis. This result indicates that the proliferative activity is spatially organized along the cephalic-caudal axis of the system.
DXQ: a distributed XQuery scripting language

We present DXQ, an extension of XQuery to support the effective and efficient development of distributed XML applications. A DXQ program can invoke remote DXQ programs both synchronously and asynchronously and can dynamically ship DXQ code to execute at remote servers. We illustrate the power of the language with two distributed applications: the resolution algorithm of the Domain Name System (DNS) and the Narada overlay-network protocol. Our implementation permits concurrent evaluation of DXQ expressions at each server and can produce results extensionally (as XML values) or intensionally (as DXQ expressions).
Biometric Template Storage With Blockchain: A First Look Into Cost and Performance Tradeoffs

We explore practical tradeoffs in blockchain-based biometric template storage. We first discuss opportunities and challenges in the integration of blockchain and biometrics, with emphasis in biometric template storage and protection, a key problem in biometrics still largely unsolved. Blockchain technologies provide excellent architectures and practical tools for securing and managing the sensitive and private data stored in biometric templates, but at a cost. We explore experimentally the key tradeoffs involved in that integration, namely: latency, processing time, economic cost, and biometric performance. We experimentally study those factors by implementing a smart contract on Ethereum for biometric template storage, whose cost-performance is evaluated by varying the complexity of state-of-the-art schemes for face and handwriting signature biometrics. We report our experiments using popular benchmarks in biometrics research, including deep learning approaches and databases captured in the wild. As a result, we experimentally show that straightforward schemes for data storage in blockchain (i.e., direct and hash-based) may be prohibitive for biometric template storage using state-of-the-art biometric methods. A good cost-performance tradeoff is shown by using a blockchain approach based on Merkle trees.
2D/3D Face Recognition Using Neural Networks Based on Hybrid Taguchi-Particle Swarm Optimization

We present a method of face recognition using facial texture and surface information. We first use Gabor wavelets extracting local features at different scales and orientations by gray facial images, then combine the texture with the surface feature vectors based on principal component analysis (PCA) to obtain feature vectors from gray and facial surface images. We propose a hybrid Taguchi particle swarm optimization (HTPSO) algorithm for face recognition based on multilayer neural networks as an identification model. Experimental results demonstrate the efficiency of our method for different face poses and facial expressions. In addition, our work compared with other proposed approaches such as back-propagation (BP), particle swarm optimization (PSO) and the genetic algorithm (GA). With different data modality the experimental results demonstrated that the proposed HTPSO learning algorithm is better than the other approaches in recognition rates. The texture and shape features can improve the efficiency of face recognition.
Hitting Depth : Investigating Robustness to Adversarial Examples in Deep Convolutional Neural Networks

Machine learning models, including Convolutional Neural Networks (CNN) are susceptible to adversarial examples input images that have been perturbed to deliberately fool a model into an incorrect, high-confidence prediction without a visually perceptible change. Previous work shows that high-dimensional linearities cause these adversarial pockets of space. We first validate assumptions about the generalization of gradient-based and pattern-based adversarial examples using VGGNet. We show a process for visualizing and identifying changes in activations between adversarial images and their regular counterparts. Finally, we leverage information from these two approaches in a novel Bayesian framework to increase l2 robustness to adversarial examples. Using this framework, we successfully improve the prediction accuracy on adversarial examples.
Image processing device, method, and program

There are provided an image processing device, method, and program capable of performing compressing to improve contrast without deteriorating the sharpness. An adder (33) calculates an addition/subtraction remaining amount T(L) of the lighting component by an adder (25) to a multiplier (32). An adder (34) adds T(L) to the original lighting component so as to calculate a gain optimization lighting component T(L)'. An aperture controller (23) performs aperture correction depending on the lighting component level according to the application area decided by a reflectance gain coefficient calculation unit (35). An adder (37) adds the T(L)' to a texture component after the aperture correction. Thus, it is possible to obtain a luminance signal Y2 after the dynamic range compression. An HPF (41) to an adder (43) subject a chroma signal of a low-level portion to the LPF processing. Thus, it is possible to obtain a chroma signal C2 after the dynamic range compression. The present invention can be applied to a digital video camera. ® KIPO & WIPO 2007
Evaluation and improvement of multiple sequence methods for protein secondary structure prediction

A new dataset of 396 protein domains is developed and used to evaluate the performance of the protein secondary structure prediction algorithms DSC, PHD, NNSSP, and PREDATOR. The maximum theoretical Q3 accuracy for combination of these methods is shown to be 78%. A simple consensus prediction on the 396 domains, with automatically generated multiple sequence alignments gives an average Q3 prediction accuracy of 72.9%. This is a 1% improvement over PHD, which was the best single method evaluated. Segment Overlap Accuracy (SOV) is 75.4% for the consensus method on the 396‐protein set. The secondary structure definition method DSSP defines 8 states, but these are reduced by most authors to 3 for prediction. Application of the different published 8‐ to 3‐state reduction methods shows variation of over 3% on apparent prediction accuracy. This suggests that care should be taken to compare methods by the same reduction method. Two new sequence datasets (CB513 and CB251) are derived which are suitable for cross‐validation of secondary structure prediction methods without artifacts due to internal homology. A fully automatic World Wide Web service that predicts protein secondary structure by a combination of methods is available via http://barton.ebi. ac.uk/. Proteins 1999;34:508–519. © 1999 Wiley‐Liss, Inc.
Towards a metamorphic testing methodology for service-oriented software applications

Testing applications in service-oriented architecture (SOA) environments needs to deal with issues like the unknown communication partners until the service discovery, the imprecise black-box information of software components, and the potential existence of non-identical implementations of the same service. In this paper, we exploit the benefits of the SOA environments and metamorphic testing (MT) to alleviate the issues. We propose an MT-oriented testing methodology in this paper. It formulates metamorphic services to encapsulate services as well as the implementations of metamorphic relations. Test cases for the unit test phase is proposed to generate follow-up test cases for the integration test phase. The metamorphic services invoke relevant services to execute test cases and use their metamorphic relations to detect failures. It has potentials to shift the testing effort from the construction of the integration test sets to the development of metamorphic relations.
Editorial: Improving Bayesian Reasoning: What Works and Why?

This edited collection was motivated by an interest in understanding how to improve Bayesian reasoning. In that sense, the book before you is pragmatically and prescriptively oriented. Several of the papers address that challenge and some pick up on the important question of why certain factors work as well as they do. However, Improving Bayesian Reasoning: What Works and Why offers more than its editors had bargained for or its title suggests. Many papers offer methodological and conceptual insights that should help readers understand the psychology of Bayesian reasoning as practiced in cognitive science. The book is comprised of 23 papers by 48 authors. The contributions are ordered by type: 10 original research articles first, followed by three reviews and 10 shorter essays. Foregoing an attempt to summarize each contribution in sufficient detail, let us simply draw out some observations about the collection.
The effect of the cardiac rehabilitation program on obese and non-obese females with coronary heart disease

Introduction: Obesity is strongly associated with coronary heart disease and it is known as an independent risk factor. So, the aim of this study was to investigate the effects of phase II comprehensive cardiac rehabilitation program on obesity indexes, functional capacity, lipid profiles, and fasting blood sugar in obese and non-obese female patients with coronary heart disease and to compare changes in these groups. Materials and Methods: Two hundred and five women with coronary heart disease participated in our study. At the beginning of study, body mass index, functional capacity, and lipid profiles and fasting blood sugar were evaluated; then, these patients were divided into two groups, patients who had BMI≥30 were known as obese and who had BMI<30 were known as non-obese patients. All of them completed the period of cardiac rehabilitation program, and 2 months later, all risk factors were examined for the second time in each group. Data were analyzed with SPSS software version 15. For comparing the mean of outcomes, independent t-tests and paired t-tests were used. Results: Data revealed that unless in weight (P=0.00) and functional capacity (P=0.001), there were no significant differences in obese and non-obese female patients, at baseline. As a result of the cardiac rehabilitation program, both groups had significant improvement in functional capacity (P=0.00), weight reduction (P=0.00), triglyceride (P=0.01 and P=0.02, respectively), low-density lipoprotein cholesterol (P=0.01), and low-density lipoprotein cholesterol/high-density lipoprotein cholesterol ratio (P=0.00 and P=0.003, respectively). As well, significant improvement was observed in high-density lipoprotein (P=0.01) only in obese female, and non-obese female had significant differences in total cholesterol (P=0.003). However, there were not significant changes in total cholesterol (P=0.05) and fasting blood sugar (P=0.09) in obese female. Also, non-obese females didn′t have favorable differences in high-density lipoprotein cholesterol (P=0.23) and fasting blood sugar (P=0.13). In addition, comparing two groups didn′t show any significant differences in each risk factors except BMI (P=0.03). Conclusion: Our study revealed that comprehensive cardiac rehabilitation program results in significant improvement in cardiovascular risk factors and functional capacity at all levels of BMI in female with coronary heart disease.