Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Guaranteed-Performance Consensus Control for Multiagent Systems With an Adjustable Convergence Speed

In this paper, adaptive guaranteed-performance consensus control problems for multiagent systems with an adjustable convergence speed are investigated. A novel adaptive guaranteed-performance consensus protocol is proposed, where the communication weights can be adaptively regulated. By the state space decomposition method and the stability theory, sufficient conditions for guaranteed-performance consensus are obtained and the guaranteed-performance cost is determined. Moreover, the lower bound of the convergence coefficient for multiagent systems is deduced, which is linearly adjustable approximately by changing the adaptive control gain. Finally, simulation examples are introduced to demonstrate theoretical results.
Differentiable Approximation Bridges For Training Networks Containing Non-Differentiable Functions

Modern neural network training relies on piece-wise (sub-)differentiable functions in order to use backpropation for efficient calculation of gradients. In this work, we introduce a novel method to allow for non-differentiable functions at intermediary layers of deep neural networks. We do so through the introduction of a differentiable approximation bridge (DAB) neural network which provides smooth approximations to the gradient of the non-differentiable function. We present strong empirical results (performing over 600 experiments) in three different domains: unsupervised (image) representation learning, image classification, and sequence sorting to demonstrate that our proposed method improves state of the art performance. We demonstrate that utilizing non-differentiable functions in unsupervised (image) representation learning improves reconstruction quality and posterior linear separability by 10%. We also observe an accuracy improvement of 77% in neural sequence sorting and a 25% improvement against the straight-through estimator [3] in an image classification setting with the sort non-linearity. This work enables the usage of functions that were previously not usable in neural networks.
Sound texture synthesis using convolutional neural networks

The following article introduces a new parametric synthesis algorithm for sound textures inspired by existing methods used for visual textures. Using a 2D Convolutional Neural Network (CNN), a sound signal is modified until the temporal cross-correlations of the feature maps of its log-spectrogram resemble those of a target texture. We show that the resulting synthesized sound signal is both different from the original and of high quality, while being able to reproduce singular events appearing in the original. This process is performed in the time domain, discarding the harmful phase recovery step which usually concludes synthesis performed in the time-frequency domain. It is also straightforward and flexible, as it does not require any fine tuning between several losses when synthesizing diverse sound textures. A way of extending the synthesis in order to produce a sound of any length is also presented, after which synthesized spectrograms and sound signals are showcased. We also discuss on the choice of CNN, on border effects in our synthesized signals and on possible ways of modifying the algorithm in order to improve its current long computation time.
Vascular development: cellular and molecular regulation

The vascular system forms through a combination of vasculogenesis and angiogenesis. In vasculogenesis, vessels form de novo via the assembly of endothelial precursors called angioblasts, whereas in angiogenesis new vessels arise by migration and proliferation of endothelial cells from preexisting vessels. Although the two processes are distinct in some respects, recent evidence suggests that they share a number of regulatory mechanisms. The identification of a number of defined growth factors, observations of genetically manipulated mice, and the recognition of the importance of cell‐cell interactions have greatly expanded our understanding of the regulation of vascularization. The paracrine actions of a variety of polypeptide growth factors, including platelet‐derived growth factor, vascular endothelial growth factor, transforming growth factor‐beta, and the angiopoietins, appear to be orchestrated in a complex sequence of steps that lead to the development of the adult vascular system. Thus, communication between the forming vasculature and the tissue parenchyma, as well as interactions among cells of the vascular wall, all appear to influence vascular development and growth. —Beck, L. , Jr., D'Amore, P. A. Vascular development: cellular and molecular regulation. FASEB J. 11,365‐373(1997)
CD31-positive microvessel density within adenomas of Lynch Syndrome patients is similar compared to adenomas of non-Lynch patients

Abstract Background and study aims Microsatellite instability accelerates colorectal cancer development in patients with Lynch syndrome (LS). Previous research showed that virtual chromoendoscopy increases detection of adenomas during colonoscopy surveillance of patients with LS. Because previous research revealed that Lynch patients have an increased vascular network in the oral mucosa, we hypothesized that increased vascularization of LS-associated adenomas is the cause of better detection with virtual chromoendoscopy. Patients and methods In this pilot study, patients with LS having a proven germline mutation were selected from two tertiary referral hospitals and non-LS patients from an outpatient colonoscopy center. Adenomas from patients with LS were exactly matched in size and histology with adenomas from non-LS patients. Initial adenoma diagnosis was confirmed by a specialist pathologist. All adenomas were stained with CD31 and adenomatous tissue was annotated by the specialist pathologist. Image analysis of CD31-positive microvessel density was conducted using FIJI software. Results Colonoscopy of 63 patients with LS and 24 non-LS patients provided 40 adenomas that could be exactly matched in size and histology. In image-analysis, the CD31-positive microvessel density (2.49 % vs. 2.47 %, P = 0.96), the average size of CD31-positive structures (514 μm2 vs. 523 μm2, P = 0.26) nor the amount of vascular structures per mm2 (183 vs. 176, P = 0.50) differed between adenomas of LS patients and non-Lynch patients. Conclusion The outcomes of this pilot case-control study did not provide further insights into the mechanism of increased adenoma detection in LS patients using virtual chromoendoscopy techniques.
A Note on Application of the Method of Approximation of Iterated Stochastic Ito integrals Based on Generalized Multiple Fourier Series to the Numerical Integration of Stochastic Partial Differential Equations

We consider a way for approximation of iterated stochastic Ito integrals with respect to infinite-dimensional Wiener process using the mean-square approximation method of iterated stochastic Ito integrals with respect to finite-dimensional Wiener process based on multiple Fourier-Legendre series. The results of the article can be applied to numerical integration of stochastic partial differential equations.
Line-Triangle Test for Collision Detection and Response in Soft Bodies

Soft-body models are common in games to simulate cloth or elastic objects. To realistically simulate soft-body objects, collision detection and response is required. In addition, soft-body models must re-arrange their internal structure to react to the collision. This paper presents a new collision detection and response algorithm which can simulate a variety of soft-body material behaviors ranging from stiff to elastic. In this approach, a line-triangle intersection test is used for collision detection and force propagation is used for collision response. Implementation and experiments using the algorithm show that complex deformable objects composed of thousands vertices can be animated at interactive speeds.
YZ Phoenicis: a very short period K-type contact binary with variation of the O’Connell effect and orbital period change

YZ Phe is a very short-period contact binary (Sp. = K2 V) with an orbital period of 0.2347 d near the short period limit (0.22 d). We present the complete light curves which photometric data were obtained from the 60 cm telescope of PROMPT-8 at CTIO in Chile during 2016 June to October and 2017 August. The photometric solutions were determined by using the Wilson & Devinney code and the results reveal that YZ Phe is a W-subtype shallow contact binary ($f\sim 10\,$, q = 2.635, or 1/q = 0.379 for W subtype) with rotational motion of a large hot spot on the more massive component, showing a strong O’Connell effect with variation of maxima in photometric time series at period of 4.20 yr and stellar cycle at period of 1.28 yr. By compiling all available eclipse times, the result shows a long-term period decrease at a rate of dP/dt = −2.64(±0.02) × 10−8 d yr−1, superimposed on a cyclic variation (A3 = 0.0081 d and P3 = 40.76 yr). This variation cannot be explained by the Applegate mechanism. Thus, the cyclic change may be interpreted as the light-travel time effect via the presence of a cool third body. Based on photometric solutions, the third light was detected as $2\,$ of the total light in V and I bands. These results support the existence of a third body. The long-term period decrease can be explained by mass transfer from the more massive component ($M_2 \sim 0.74\, M_{\odot }$) to the less massive one ($M_1 \sim 0.28\, M_{\odot }$) or plus angular momentum loss (AML) via magnetic braking. With 1/q < 0.4 and long-term period decrease, all factors suggest that YZ Phe is on the AML-controlled state and its fill-out factor will increase, as well as the system evolving into a deeper normal contact binary.
The Continuum Reprocessing E ciency of Be StarCircumstellar DisksC

We have calculated the total ux emitted in H, P, and Br by the circumstellar envelope of both an early Be star, Cas, and a late Be star, 1 Delphini, assuming the central star is the only source of energy input into the circumstellar envelope. These estimates are based on the Be star models of Millar & Marlborough (1998, 1999a&b) which have self-consistent temperature distributions determined by equating the local rates of energy gain and energy loss in the envelopes. We nd that an additional source of ionizing photons, as argued by Apparao (1998), is not necessary to account for the observed emission.
Palm vein recognition using curvelet transform

Palm vein is a promising personal identification method for human recognition. To improve the accuracy and efficiency of the existing palm vein systems, a novel algorithm based on curvelet transform is proposed. The technique utilizes the curvelet transform to extract curve-like features from vein patterns and provides optimally sparse representations of the patterns. For evaluation, HK Poly University multispectral palmprint database is used. The experimental results reveal that the system is robust and encouraging.
Attenuated acute cardiac rejection in NOS2 -/- recipients correlates with reduced apoptosis.

BACKGROUND The mechanisms through which NOS2-mediated pathways regulate graft failure in acute cardiac rejection are ill defined. To determine whether apoptosis promoted by NOS2 may contribute, we used a heterotopic transplant model to study mouse cardiac allografts placed in recipients with targeted gene deletion of NOS2. METHODS AND RESULTS Using 5 different indexes of apoptosis, we showed that mouse cardiac allografts placed in NOS2 -/- recipients (n=7) had reduced apoptotic activity compared with those in NOS2 +/+ controls (n=8). There were significantly fewer TUNEL-positive nuclei per high-powered field (P<0.01), less DNA fragmentation (antinucleosome ELISA; P<0.05), lower corrected transcript levels for caspase-1 and -3 (32P reverse transcriptase-polymerase chain reaction; P<0.01), and reduced caspase-3 activity (cleavage of DEVD-pNA [P<0.001] and poly [ADP-ribose] polymerase) in grafts from NOS2 -/- recipients. This concordant reduction in apoptotic indexes paralleled the improved histological outcome of grafts transplanted into NOS2 -/- recipients (assessed as rejection scores; P=0.012). To identify pathways controlled by NOS2, we compared intragraft transcript levels of potential triggers and regulators. Whereas Fas ligand/Fas and tumor necrosis factor (TNF)-alpha/TNF receptor-1 levels were not altered by NOS2 deficiency, transcript levels for p53 were significantly lower in grafts from NOS2 -/- recipients, coinciding with a significant increase in the antiapoptotic Bcl-2/Bax balance and decrease in Bcl-Xl levels. CONCLUSIONS Using NOS2 knockout mice, we demonstrated that NOS2-mediated pathways can promote acute rejection, at least in part, by inducing apoptotic cell death. When NOS2 is present, p53 might control NOS2-mediated apoptosis by stimulating Bax and repressing Bcl-2 and Bcl-Xl expression, which may activate the cell death program in the rejecting heart.
Minimally Naturalistic Artificial Intelligence

The rapid advancement of machine learning techniques has re-energized research into general artificial intelligence. While the idea of domain-agnostic meta-learning is appealing, this emerging field must come to terms with its relationship to human cognition and the statistics and structure of the tasks humans perform. The position of this article is that only by aligning our agents' abilities and environments with those of humans do we stand a chance at developing general artificial intelligence (GAI). A broad reading of the famous 'No Free Lunch' theorem is that there is no universally optimal inductive bias or, equivalently, bias-free learning is impossible. This follows from the fact that there are an infinite number of ways to extrapolate data, any of which might be the one used by the data generating environment; an inductive bias prefers some of these extrapolations to others, which lowers performance in environments using these adversarial extrapolations. We may posit that the optimal GAI is the one that maximally exploits the statistics of its environment to create its inductive bias; accepting the fact that this agent is guaranteed to be extremely sub-optimal for some alternative environments. This trade-off appears benign when thinking about the environment as being the physical universe, as performance on any fictive universe is obviously irrelevant. But, we should expect a sharper inductive bias if we further constrain our environment. Indeed, we implicitly do so by defining GAI in terms of accomplishing that humans consider useful. One common version of this is need the for 'common-sense reasoning', which implicitly appeals to the statistics of physical universe as perceived by humans.
AES Key Agility Issues in High-Speed IPsec Implementations

Some high-speed IPsec hardware systems need to support many thousands of security associations. The cost of switching among different encryption keys can dramatically affect throughput, particularly for the very common case of small packets. Three of the AES finalists (Rijndael, Serpent, and Twofish) provide very high key agility, as is required for such applications. The other two candidates (MARS, RC6) exhibit low key agility and may not be appropriate for use in such equipment.
Automatic Domain Model Creation Using Pattern-Based Fact Extraction

This paper describes a minimally guided approach to automatic domain model creation. The first step is to carve an area of interest out of the Wikipedia hierarchy based on a simple query or other starting point. The second step is to connect the concepts in this domain hierarchy with named relationships. A starting point is provided by Linked Open Data, such as DBPedia. Based on these community-generated facts we train a pattern-based fact-extraction algorithm to augment a domain hierarchy with previously unknown relationship occurrences. Pattern vectors are learned that represent occurrences of relationships between concepts. The process described can be fully automated and the number of relationships that can be learned grows as the community adds more information. Unlike approaches that are aimed at finding single, highly indicative patterns, we use the cumulative score of many pattern occurrences to increase extraction recall. The relationship identification process itself is based on positiveonly classification of training facts.
Dynamical cluster approximation: Nonlocal dynamics of correlated electron systems

We recently introduced the dynamical cluster approximation (DCA), a technique that includes short-ranged dynamical correlations in addition to the local dynamics of the dynamical mean-field approximation while preserving causality. The technique is based on an iterative self-consistency scheme on a finite-size periodic cluster. The dynamical mean-field approximation (exact result) is obtained by taking the cluster to a single site (the thermodynamic limit). Here, we provide details of our method, explicitly show that it is causal, systematic, Phi derivable, and that it becomes conserving as the cluster size increases. We demonstrate the DCA by applying it to a quantum Monte Carlo and exact enumeration study of the two-dimensional Falicov-Kimball model. The resulting spectral functions preserve causality, and the spectra and the charge-density-wave transition temperature converge quickly and systematically to the thermodynamic limit as the cluster size increases.
Stability Analysis of Markovian Jump System With Multi-Time-Varying Disturbances Based on Improved Interactive Convex Inequality and Positive Definite Condition

Time-varying disturbances not only exist in the external delay of Markovian jump system but also may exist in the transfer rate of internal probability transfer. This paper put forward an improved interactive convex inequality method for Markovian jump system (MJS) with multi-time-varying disturbances: time-varying delays and time-varying transition rates of probability transfer. On this basis, an improved Lyapunov function is quoted to more approximate the actual system, and the optimization of positive definite conditions of the matrix in Lyapunov functional is discussed, the specific positive definite condition is improved. At the end of this paper, the effectiveness of the above methods is verified by several simulations.
Event detection for broadcast tennis videos based on trajectory analysis

This paper presents an effective and efficient semantic event detection method for broadcast tennis videos. We proposed an effective player tracking and trajectory modification technique, and then event detection based on trajectory analysis is developed accordingly. In our work, the playfield and court line are extracted from a court view. Then, we detect player objects and utilize adaptive Kalman filter to improve the tracking accuracy. In event detection phase, we refine the unreasonable trajectory to avoid the noise interference especially in upper court, and eliminate the minor clip which can't be a meaningful event. Finally, we analyze the players' trajectory combined with tennis rules to extract semantic events. The experiments indicate that the proposed method achieves satisfactory performance with 80% of precision rate and 89% of recall rate.
Twistor-Like Formulation of Heterotic Strings

In this talk new formulations of the Green--Schwarz heterotic strings in $D$ dimensions that involve commuting spinors, are reviewed. These models are invariant under $n$--extended, world sheet supersymmetry as well as under $N=1$, target space supersymmetry where $n\leq D-2$ and $D=3,4,6,10$. The world sheet supersymmetry replaces $n$ components (and provides a geometrical meaning) of the $\kappa$--symmetry in the Green--Schwarz approach. The models in $D=10$ for $n=1,2,8$ are discussed explicitly.
Exploring Growing Information Spaces

Growing information spaces bring not only informational challenges, but also new opportunities for exploration. Drawing from maps as an analogy, we conceptualize information seekers as explorers of expanding information landscapes. While text has been the dominant frame of reference for information-seeking interfaces, visualization has the potential to expose faceted overviews and provide many ways of seeing information. Considering recent technological trends on the Web, many new forms of information seeking are feasible that utilize rich semantics and interactive graphics. With these developments on our side, we advocate a shift from information needs and relevance to more open-ended exploration and discovery of growing information spaces.
Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics

In an era when most of our life activities are digitized and recorded, opportunities abound to gain insights about population health. Online product reviews present a unique data source that is currently underexplored. Health-related information, although scarce, can be systematically mined in online product reviews. Leveraging natural language processing and machine learning tools, we were able to mine 1.3 million grocery product reviews for health-related information. The objectives of the study were as follows: (1) conduct quantitative and qualitative analysis on the types of health issues found in consumer product reviews; (2) develop a machine learning classifier to detect reviews that contain health-related issues; and (3) gain insights about the task characteristics and challenges for text analytics to guide future research.
A Lexical Resource-Constrained Topic Model for Word Relatedness

Word relatedness computation is an important supporting technology for many tasks in natural language processing. Traditionally, there have been two distinct strategies for word relatedness measurement: one utilizes corpus-based models, whereas the other leverages external lexical resources. However, either solution has its strengths and weaknesses. In this paper, we propose a lexical resource-constrained topic model to integrate the two complementary strategies effectively. Our model is an extension of probabilistic latent semantic analysis, which automatically learns word-level distributed representations forward relatedness measurement. Furthermore, we introduce generalized expectation maximization (GEM) algorithm for statistical estimation. The proposed model not merely inherit the advantage of conventional topic models in dimension reduction, but it also refines parameter estimation by using word pairs that are known to be related. The experimental results in different languages demonstrate the effectiveness of our model in topic extraction and word relatedness measurement.
Monocular urban localization using street view

This paper presents a metric global localization in the urban environment only with a monocular camera and the Google Street View database. We fully leverage the abundant sources from the Street View and benefits from its topo-metric structure to build a coarse-to-fine positioning, namely a topological place recognition process and then a metric pose estimation by local bundle adjustment. Our method is tested on a 3 km urban environment and demonstrates both sub-meter accuracy and robustness to viewpoint changes, illumination and occlusion. To our knowledge, this is the first work that studies the global urban localization simply with a single camera and Street View.