Miracle-dz/UniFAR-FTUs huggingface dataset:
Exact algorithms via monotone local search

We give a new general approach for designing exact exponential-time algorithms for subset problems. In a subset problem the input implicitly describes a family of sets over a universe of size n and the task is to determine whether the family contains at least one set. A typical example of a subset problem is Weighted d-SAT. Here, the input is a CNF-formula with clauses of size at most d, and an integer W. The universe is the set of variables and the variables have integer weights. The family contains all the subsets S of variables such that the total weight of the variables in S does not exceed W, and setting the variables in S to 1 and the remaining variables to 0 satisfies the formula. Our approach is based on “monotone local search”, where the goal is to extend a partial solution to a solution by adding as few elements as possible. More formally, in the extension problem we are also given as input a subset X of the universe and an integer k. The task is to determine whether one can add at most k elements to X to obtain a set in the (implicitly defined) family. Our main result is that a cknO(1) time algorithm for the extension problem immediately yields a randomized algorithm for finding a solution of any size with running time O((2−1/c)n). In many cases, the extension problem can be reduced to simply finding a solution of size at most k. Furthermore, efficient algorithms for finding small solutions have been extensively studied in the field of parameterized algorithms. Directly applying these algorithms, our theorem yields in one stroke significant improvements over the best known exponential-time algorithms for several well-studied problems, including d-Hitting Set, Feedback Vertex Set, Node Unique Label Cover, and Weighted d-SAT. Our results demonstrate an interesting and very concrete connection between parameterized algorithms and exact exponential-time algorithms. We also show how to derandomize our algorithms at the cost of a subexponential multiplicative factor in the running time. Our derandomization is based on an efficient construction of a new pseudo-random object that might be of independent interest. Finally, we extend our methods to establish new combinatorial upper bounds and develop enumeration algorithms.
Cost-Algebraic Heuristic Search

Heuristic search is used to efficiently solve the single-node shortest path problem in weighted graphs. In practice, however, one is not only interested in finding a short path, but an optimal path, according to a certain cost notion. We propose an algebraic formalism that captures many cost notions, like typical Quality of Service attributes. We thus generalize A*, the popular heuristic search algorithm. for solving optimal-path problem. The paper provides an answer to a fundamental question for AI search, namely to which general notion of cost, heuristic search algorithms can be applied. We proof correctness of the algorithms and provide experimental results that validate the feasibility of the approach.
Critical collapse of the massless scalar field in axisymmetry

We present the results from a numerical study of critical gravitational collapse of axisymmetric distributions of massless scalar field energy. We find threshold behavior that can be described by the spherically symmetric critical solution with axisymmetric perturbations. However, we see indications of a growing, nonspherical mode about the spherically symmetric critical solution. The effect of this instability is that the small asymmetry present in what would otherwise be a spherically symmetric self-similar solution grows. This growth continues until a bifurcation occurs and two distinct regions form on the axis, each resembling the spherically symmetric self-similar solution. The existence of a nonspherical unstable mode is in conflict with previous perturbative results, and we therefore discuss whether such a mode exists in the continuum limit, or whether we are instead seeing a marginally stable mode that is rendered unstable by numerical approximation.
Validation of PROBA-V GEOV1 and MODIS C5 & C6 fAPAR Products in a Deciduous Beech Forest Site in Italy

The availability of new fAPAR satellite products requires simultaneous efforts in validation to provide users with a better comprehension of product performance and evaluation of uncertainties. This study aimed to validate three fAPAR satellite products, GEOV1, MODIS C5, and MODIS C6, against ground references to determine to what extent the GCOS requirements on accuracy (maximum 10% or 5%) can be met in a deciduous beech forest site in a gently and variably sloped mountain site. Three ground reference fAPAR, differing for temporal (continuous or campaign mode) and spatial sampling (single points or Elementary Sampling Units—ESUs), were collected using different devices: (1) Apogee (defined as benchmark in this study); (2) PASTIS; and (3) Digital cameras for collecting hemispherical photographs (DHP). A bottom-up approach for the upscaling process was used in the present study. Radiometric values of decametric images (Landsat-8) were extracted over the ESUs and used to develop empirical transfer functions for upscaling the ground measurements. The resulting high-resolution ground-based maps were aggregated to the spatial resolution of the satellite product to be validated considering the equivalent point spread function of the satellite sensors, and a correlation analysis was performed to accomplish the accuracy assessment. PASTIS sensors showed good performance as fAPARPASTIS appropriately followed the seasonal trends depicted by fAPARAPOGEE (benchmark) (R2 = 0.84; RMSE = 0.01). Despite small dissimilarities, mainly attributed to different sampling schemes and errors in DHP classification process, the agreement between fAPARPASTIS and fAPARDHP was noticeable considering all the differences between both approaches. The temporal courses of the three satellite products were found to be consistent with both Apogee and PASTIS, except at the end of the summer season when ground data were more affected by senescent leaves, with both MODIS C5 and C6 displaying larger short-term variability due to their shorter temporal composite period. MODIS C5 and C6 retrievals were obtained with the backup algorithm in most cases. The three green fAPAR satellite products under study showed good agreement with ground-based maps of canopy fAPAR at 10 h, with RMSE values lower than 0.06, very low systematic differences, and more than 85% of the pixels within GCOS requirements. Among them, GEOV1 fAPAR showed up to 98% of the points lying within the GCOS requirements, and slightly lower values (mean bias = −0.02) as compared with the ground canopy fAPAR, which is expected to be only slightly higher than green fAPAR in the peak season.
Dynamic community detection based on game theory in social networks

The difficulty of analysis in social networks mainly originates from its huge scale and complicated relations in the network. Social network is a complex system that we can hardly inspect its deep mechanism with only regarding it as a whole. Game theory is a study that focuses on interactions and reactions between intelligent individuals. Recently, game theory based methods are proposed to tackle different kinds of problems in social networks. In this paper, we focus on a tough problem that detect communities in dynamic networks. We present a game theoretic approach for community detection in dynamic networks, in which each node regarded as an intelligent and selfish agent chooses from a set of actions, that is, join, leave or switch a community in order to maximize its utility. The experimental results show the effectiveness and the advantages of our model.
Channel decision in cognitive radio enabled sensor networks: A reinforcement learning approach

Recent advancements in the field of cognitive radio technology have paved way for cognitive radio-based wireless sensor networks. This has been tipped to be the next generation sensor. Spectrum sensing and energy efficient channel access are two important operations in this network. In this paper, we propose the use of machine learning and decision making capability of reinforcement learning to address the problem of energy efficiency associated with channel access in cognitive radio aided sensor networks. A simple learning algorithm was developed to improve network parameters such as secondary user throughput, channel availability in relation to the sensing time. Comparing the results obtained from simulations with other channel access without intelligent learning such as random channel assignment and dynamic channel assignment, the learning algorithm produced better performance in terms of throughput, energy efficiency and other quality of service requirement of the network application.
The Significance of Genetic Polymorphisms within and between Founder Populations of Ceratitis capitata (Wied.) from Argentina

Background The Mediterranean fruit fly Ceratitis Capitata (DIPTERA: Tephritidae) is a major agricultural pest in Argentina. One main cause for the success of non-contaminant control programs based on genetic strategies is compatibility between natural and laboratory germplasms. A comprehensive characterization of the fruit fly based on genetic studies and compatibility analysis was undertaken on two founder populations from the provinces of Buenos Aires and Mendoza, used in pioneering sterile male technique control programmes in our country. The locations are 1,000 km apart from each other. Methodology/Principal Findings We compared the genetic composition of both populations based on cytological, physiological and morphological characterization. Compatibility studies were performed in order to determine the presence of isolation barriers. Results indicate that the Buenos Aires germplasm described previously is partially different from that of the Mendoza population. Both laboratory colonies are a reservoir of mutational and cytological polymorphisms. Some sexual chromosome variants such as the XL and the YL resulting from attachment of a B-chromosome to the X-chromosome or Y-chromosome behave as a lethal sex-linked factor. Our results also show incompatibility between both germplasms and pre-zygotic isolation barriers between them. Our evidence is consistent with the fact that polymorphisms are responsible for the lack of compatibility. Conclusions The genetic control mechanism should be directly produced in the germplasm of the target population in order to favour mating conditions. This is an additional requirement for the biological as well as economic success of control programs based on genetic strategies such as the sterile insect technique. The analysis of representative samples also revealed natural auto-control mechanisms which could be used in modifying pest population dynamics.
Image Processing algorithm for matching horizons across faults in seismic data

1. Abstract An approach to automatically match corresponding horizon segments across single normal faults in 2-D reflection seismic images is presented. The difficulties of this task are due to those types of images which contain only a small amount of local information, furthermore partially disturbed by vague or noisy signals. Our method reduces the uncertainties of a solely local feature based approach by combining both local and global features. To obtain a geologically and geometrically consistent solution, a model is introduced which consists of constraints derived from observations regarding the characteristics of normal faults. The procedure of automatic horizon tracking across faults may be subdivided into two components. The first component consists of the calculation of a measurement for each possible pair of reflectors from either side of the fault which expresses their local similarity. The second component comprises the grouping of reflector-pairs into a geologically consistent combination which is considered as an optimisation problem. The optimisation task cannot be solved exhaustively since it would cause exponential computational cost. Among stochastic methods, a genetic algorithm has been chosen to solve the optimisation problem. Repeated application of the algorithm to four different faults delivered an acceptable solution in 94-100% of the experiments. The global optimum was equal to the solution chosen by a human interpreter in three of the four cases.
Distributed vector decorrelation and anomaly detection using the vector Sparse Matrix Transform

Here, we propose the vector Sparse Matrix Transform (SMT), a novel decorrelating transform suitable for performing distributed processing of high dimensional signals in sensor networks. We assume that each sensor in the network encodes its measurements into vector outputs instead of scalar ones. The proposed transform decorrelates a sequence of pairs of vector sensor outputs, until these vectors are decorrelated. In our experiments, we simulate distributed anomaly detection by a camera network monitoring a spatial region. Each camera records an image of the monitored environment from its particular viewpoint and outputs a vector encoding the image. Results show that the vector SMT effectively decorrelates images from the multiple cameras in the network and significantly improves anomaly detection accuracy while requiring low overall communication energy.
Designing virtual accessing adapter and non-volatile memory management for memory-disk integrated system

This paper presents a new memory hierarchy system using next-generation non-volatile memory devices merging a conventional main memory layer and a disk storage layer into a single memory layer, called a memory-disk integrated system (MDIS). In the MDIS, files are stored in MDIS storage and accessed in place as if they are in conventional DRAM main memory without any duplication between the main memory and secondary storage. The MDIS comprises three components: a virtual accessing adapter (VAA), an array of non-volatile hybrid memory (NVHM), and its associated virtual memory management module. The NVHM can be divided into two spaces-specifically, static and dynamic spaces-to support conventional memory management. Further, the VAA comprises four small buffers that support static and dynamic virtualization according to program execution characteristics. Experimental results show that the read/write load traffics for the proposed MDIS can be reduced by about 73% as compared with the cases of conventional main memory system. In addition, access latency of non-volatile hybrid memory is reduced by 51%.
Modeling and Interpretation of Scattering Mechanisms in Polarimetric Synthetic Aperture Radar: Advances and perspectives

Recent advances in scattering modeling and model-based decomposition theorem were reviewed. The notable achievements include orientation compensation processing, nonnegative eigenvalue constraint, generalized scattering models, complete information utilization, full-parameter inversion strategy, and the polarimetric-interferometric decomposition scheme. These advances contribute to make scattering models more adaptive, better fit observations and guarantee physically meaningful decomposition solutions. The key features of these advances have been summarized. Performance evaluation and further development perspectives were also discussed. One promising way is to fuse multiple data to better model scattering mechanisms, such as the polarimetric-interferometric modeling attempts. Besides, with the progress in PolSAR sensors, imaging modes (e.g., bistatic, hybrid-polarization and multi-incident-angle modes) and application requirements, the development of specific scattering mechanism interpretation techniques, multiangular decomposition, and compact/hybrid decomposition techniques are also highly preferred.
On the Discovery of Evolving Truth

In the era of big data, information regarding the same objects can be collected from increasingly more sources. Unfortunately, there usually exist conflicts among the information coming from different sources. To tackle this challenge, truth discovery, i.e., to integrate multi-source noisy information by estimating the reliability of each source, has emerged as a hot topic. In many real world applications, however, the information may come sequentially, and as a consequence, the truth of objects as well as the reliability of sources may be dynamically evolving. Existing truth discovery methods, unfortunately, cannot handle such scenarios. To address this problem, we investigate the temporal relations among both object truths and source reliability, and propose an incremental truth discovery framework that can dynamically update object truths and source weights upon the arrival of new data. Theoretical analysis is provided to show that the proposed method is guaranteed to converge at a fast rate. The experiments on three real world applications and a set of synthetic data demonstrate the advantages of the proposed method over state-of-the-art truth discovery methods.
Neural network observer-based optimal control for unknown nonlinear systems with control constraints

In this paper, a neural-network (NN) observer-based optimal control solution for unknown nonlinear systems with control constraints using adaptive dynamic programming (ADP) is considered. First, to confront the unknown system, a NN observer is designed to estimate system states. Second, to deal with the control constraints, a quasi-norm performance index function is introduced. Third, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In the design, two NNs are used: a feedforward NN to constitute the NN observer which is applied to obtain the states, and a critic NN to approximate the value function. Finally, by using Lyapunov's direct method, uniform ultimate boundedness (UUB) stability of the NN observer-based control system is proved.
Off-Grid Direction-of-Arrival Estimation Using a Sparse Array Covariance Matrix

An off-grid direction-of-arrival (DOA) estimation method that utilizes a sparse array covariance matrix is proposed. In this method, the array covariance matrix is sparsely represented in the form of a vector and then modified to become an off-grid DOA estimation model according to the first-order Taylor series. By solving for the two sparse vectors in the resulting array covariance matrix, the off-grid DOA estimation can thus be achieved. We present an alternating iterative algorithm that exploits the alternating update of a convex optimization problem and a least-squares problem to solve for these two sparse vectors. Our method also extends the aperture. The effectiveness and efficiency of the proposed method are demonstrated in the simulation results.
Logic Synthesis for a Fine-grain , DynamicallyRecon gurable FPGANan Zhuang

This paper describes an algorithm that combines logic synthesis and technology mapping speciically for Xilinx's XC6200, a new family of ne-grain, dynamically reconngurable FPGA. The algorithm employs a BDD representation of the logic function, and Genetic Algorithm (GA) is used to nd a good decomposition variable ordering. It also exploits the architectural features of the XC6200 to minimise the number of cells required to implement a given function. Results on benchmark circuits show that the new algorithm performs signiicantly better than other synthesis tools in the majority of cases.
Towards a framework for treatment effectiveness in schizophrenia

Introduction Prompt administration of antipsychotic treatment that is adhered to is essential for the optimal treatment of schizophrenia. Many patients have benefited from the advent of second-generation antipsychotics, which can offer good symptomatic control with reduced incidence of extrapyramidal symptoms, although with higher risk of metabolic side effects. It is unsurprising that accounts as to whether first- and second-generation antipsychotics differ in their efficacy vary, since treatment effectiveness is a broad notion and difficult to define. Objectives Numerous factors may be used to gauge treatment effectiveness and, while it has largely been defined in terms of improvements in four domains (symptoms of disease, treatment burden, disease burden, and health and wellness), the real-world clinical utility of this consensus is unclear. Therefore, this article aims to provide a framework that can aid psychiatrists in making assessments about treatment effectiveness. Methods and results A panel of 12 psychiatrists and psychopharmacologists convened to develop and propose an accessible and globally-applicable framework for assessing the effectiveness of antipsychotic treatments in patients with schizophrenia. Following presentation of a preliminary proposal to a wider group of psychiatrists from across Europe, it was refined into a framework comprising five domains: symptomatic remission and retention of treatment; affective symptoms; cognitive functioning; treatment satisfaction; and personal and social functioning – each of which is discussed in this article. Conclusions This article provides a framework that can aid psychiatrists in making assessments about treatment effectiveness. It is anticipated that the framework outlined here may contribute to improving clinical practice through the promotion of a patient-centered approach to the assessment of treatment effectiveness, using five specified domains, in patients with schizophrenia.
Additive genetic variation for tolerance to estrogen pollution in natural populations of Alpine whitefish (Coregonus sp., Salmonidae)

The evolutionary potential of natural populations to adapt to anthropogenic threats critically depends on whether there exists additive genetic variation for tolerance to the threat. A major problem for water‐dwelling organisms is chemical pollution, and among the most common pollutants is 17α‐ethinylestradiol (EE2), the synthetic estrogen that is used in oral contraceptives and that can affect fish at various developmental stages, including embryogenesis. We tested whether there is variation in the tolerance to EE2 within Alpine whitefish. We sampled spawners from two species of different lakes, bred them in vitro in a full‐factorial design each, and studied growth and mortality of embryos. Exposure to EE2 turned out to be toxic in all concentrations we tested (≥1 ng/L). It reduced embryo viability and slowed down embryogenesis. We found significant additive genetic variation in EE2‐induced mortality in both species, that is, genotypes differed in their tolerance to estrogen pollution. We also found maternal effects on embryo development to be influenced by EE2, that is, some maternal sib groups were more susceptible to EE2 than others. In conclusion, the toxic effects of EE2 were strong, but both species demonstrated the kind of additive genetic variation that is necessary for an evolutionary response to this type of pollution.
Direct Urca process in a neutron star mantle

We show that the direct Urca process of neutrino emission is allowed in two possible phases of nonspherical nuclei (inverse cylinders and inverse spheres) in the mantle of a neutron star near the crust-core interface. The process is open because neutrons and protons move in a periodic potential created by inhomogeneous nuclear structures. In this way the nucleons acquire large quasimomenta needed to satisfy momentum-conservation in the neutrino reaction. The appropriate neutrino emissivity in a nonsuperfluid matter is about 2-3 orders of magnitude higher than the emissivity of the modified Urca process in the stellar core. The process may noticeably accelerate the cooling of low-mass neutron stars.