Miracle-dz/UniFAR-FTUs huggingface dataset:
The Potential Use of Multi-Band SAR Data for Soil Moisture Retrieval over Bare Agricultural Areas: Hebei, China

The potential use of TerraSAR-X and Radarsat-2 data for soil moisture retrieval over bare agricultural areas was investigated using both empirical and semi-empirical approaches. For the empirical approach, the Support Vector Regression (SVR) model was used with two cases: (1) using only one C-band or X-band image; and (2) using a pair of C-band and X-band images jointly. For the semi-empirical approach, the modified Dubois model based on C-band and X-band SAR data was developed to estimate soil moisture content. The experiments were implemented over two bare agricultural areas, and in-situ measurements were carried out to assess the methods. The results showed that the TerraSAR-X and Radarsat-2 are suitable remote sensing tools for the estimation of surface soil moisture, with an accuracy of about 3 vol % (root mean square error, RMSE) over bare agricultural areas. Compared with the results obtained by Radarsat-2 data, TerraSAR-X data gives a slight improvement in estimating soil moisture. The accuracy of the soil moisture estimation was improved further when the two bands SAR data were used (RMSE of about 2.2 vol %) instead of only one. Moreover, the modified Dubois model showed comparable accuracy to the empirical model independent of the surface roughness.
A multi-role cellular PSO for dynamic environments

In real world, optimization problems are usually dynamic in which local optima of the problem change. Hence, in these optimization problems goal is not only to find global optimum but also to track its changes. In this paper, we propose a variant of cellular PSO, a new hybrid model of particle swarm optimization and cellular automata, which addresses dynamic optimization. In the proposed model, population is split among cells of cellular automata embedded in the search space. Each cell of cellular automata can contain a specified number of particles in order to keep the diversity of swarm. Moreover, we utilize the exploration capability of quantum particles in order to find position of new local optima quickly. To do so, after a change in environment is detected, some of the particles in the cell change their role from standard particles to quantum for few iterations. Experimental results on moving peaks benchmark show that the proposed algorithm outperforms mQSO, a well-known multi swarm model for dynamic optimization, in many environments.
Applying an attachment and microanalytic lens to “embodied mentalization”: Commentary on “Mentalizing homeostasis: the social origins of interoceptive inference” by Fotopoulou and Tsakiris

Fotopoulou and Tsakiris in their target paper offer a theoretical and methodological corrective in their argument for the need to reclaim the physical body of the individual for a fuller understanding of the origins and maintenance of the sense of a unified, regulated, relational self. While we share their injunctive to mindfully not throw out the baby (or body of the baby) with the ‘cognitive/ symbolic/ representational’ bath water, nevertheless we also argue for the inclusion of all that has been gained from the study of representational processes in the parent’s mind, including mentalization or reflective functioning, and the intergenerational data this work has spawned. We also argue for the relevance of careful microanaltyic observations of young infants and their caregivers, and attention to what has been gained from decades of research on this interactive process which is crucial to the development of the self and the emergence of infant-caregiver attachment patterns. In brief, three topics occupy this commentary as foci for future research: (1) links between embodied mentalization and its connection to mental representations of attachment; (2) implications for the intergenerational transmission of body representations; and (3) the specificity afforded by including the microanalytic literature on mother-infant interaction, permitting an enriched view of the origins of embodied mentalization.
An overview of robust compressive sensing of sparse signals in impulsive noise

While compressive sensing (CS) has traditionally relied on l2 as an error norm, a broad spectrum of applications has emerged where robust estimators are required. Among those, applications where the sampling process is performed in the presence of impulsive noise, or where the sampling of the high-dimensional sparse signals requires the preservation of a distance different than l2. This article overviews robust sampling and nonlinear reconstruction strategies for sparse signals based on the Cauchy distribution and the Lorentzian norm for the data fidelity. The derived methods outperform existing compressed sensing techniques in impulsive environments, thus offering a robust framework for CS.
Fast decompression for web-based view-dependent 3D rendering

Efficient transmission of 3D data to Web clients and mobile applications remains a challenge due to limited bandwidth. Most of the research focus in the context of mesh compression has been on improving compression ratio. However, in this context the use of Javascript on the Web and low power CPUS in mobile applications led to critical computational costs. Progressive decoding improves the user experience by providing a simplified version of the model that refines with time, and it's able to mask latency. Current approaches do so at very poor compression rates or at additional computational cost. The need for better performing algorithms is especially evident with this class of methods where Limper [Limper et al. 2013b] demonstrated how decoding time becomes a limiting factor even at moderately low bandwidths. In this paper we present a novel multi-resolution WebGL based rendering algorithm which combines progressive loading, view-dependent resolution and mesh compression, providing high frame rates and a decoding speed of million of triangles per second in Javascript. This method is parallelizable, robust to non-manifold meshes, and scalable to very large models.
Analysis of Layered Hierarchy for Necklaces

Molecular configurations can be modeled as sets of spheres in 3D. By imposing geometric constraints on the spheres based on molecular biology one can apply efficient techniques [3], for example, to analyze the complexity of molecular surface [1]. Guibas et al. [2] introduced a model called necklace that finds applications in computer graphics, computer vision, robotics, geographic information systems and molecular biology. They studied two data structures, wrapped hierarchy and layered hierarchy, for representing necklaces and for performing collision tests. Guibas et al. [2] proved that the wrapped hierarchy admits a separating family of size O(n2−2/d). This is the first subquadratic bound proved for collision detection using predefined hierarchies. Although the layered hierarchy can be used for collision detection, “no subquadratic bound on the size of a separating family based on the layered hierarchy is currently known” [2]. The main result of this paper is that the same upper bound holds for the layered hierarchy. One of the advantages of layered hierarchy over wrapped hierarchy is the “local” definition of the cages. We propose a modification of the layered hierarchy so that some deformations of a necklace can be maintained efficiently. In particular, we show that rigid-body conformational changes can be handled in O(log n) time only.
The Retiming and Routing of VLSI Circuits

In this thesis, we explore three problems arising during the logic synthesis and physical design stages of VLSI circuit design. We first present a new formulation for the retiming of single-phase clocked circuits containing latches. Then, we discuss crosstalk optimization in channel-based routings, and finally present a new performance-driven algorithm for the layer assignment of critical global nets. Although single-phase clocked circuits containing latches are in widespread use, there is no existing practical formulation for such circuits that allows retiming-based optimizations. We present a novel, ILP-based formulation for the retiming of such circuits. This formulation can be used to optimize any linearizable objective function. As examples, we discuss the optimization of the clock period and the area of such circuits. Our experiments demonstrate that our approach is efficient and generates ILPs that are easy to solve. We address the increased importance of crosstalk avoidance in deep submicron technologies by presenting a new postprocessing algorithm to minimize the peak crosstalk in the nets routed in a gridless channel. We study the variation of the crosstalk in a net and its neighbors when some trunk belonging to the net is perturbed, proving that this variation is well-behaved. This allows us to determine the optimal location for the trunk without needing to solve any non-linear equations. Our experimental results verify that our approach is very effective. Multi-layer technologies present additional challenges to the routing of the nets because the layer assignment can have a large impact on the net delays. Traditional approaches cause the first few nets to monopolize the “good” layers. Therefore, they perform poorly under the metric of minimizing the maximum net delay. We propose the use of dynamic area quotas to remedy this problem. Our approach is independent of the routing model and the router used, and works very well in practice.
Developing Cognition with Collaborative Robotic Activities

Cognition, faculty related to perception, imagination, memory, and problem solving, refers to internal mental processes through which sensorial input is acquired, elaborated, used, and stored. One of its importances relies on the fact that it affects in a direct way the learning potential. It has been shown that, even thou cognitive processes develop side by side with biological maturity, this cognitive development can be enhanced by means of mediated learning as signaled by Feuerstein’s Mediated Learning theory. Based on this theory is that we propose an intervention model that addresses school academic issues using technologically assisted small group collaboration, pursuing a dual academic objective: to thrive students’ cognitive processes while addressing school curriculum topics. The purpose, therefore, is to balance the students’ cognitive differences by means of in-school content-filled classroom activities. Our aim is to make use of peer mediation in a real world setting with a virtual construction of it. In this paper, we describe this novel intervention model along with an in-school usage experience. For this, we present an activity designed for high school students, specifically aimed to assist the learning of kinematics, graph interpretation, and graph plotting. In this activity the students work in groups of three, using a robot and wirelessly interconnected Personal Digital Assistants (PDA). By means of a controlled experiment, we show how technologically-supported peer mediation promotes the students’ enrichment of their cognitive processes in each of the different stages of the mental act (input–elaboration– output), favoring communication skills, insight, and reasoning, while also restraining impulsive conduct and trial-and-error answers.
Local stereo matching using geodesic support weights

Local stereo matching has recently experienced large progress by the introduction of new support aggregation schemes. These approaches estimate a pixel's support region via color segmentation. Our contribution lies in an improved method for accomplishing this segmentation. Inside a square support window, we compute the geodesic distance from all pixels to the window's center pixel. Pixels of low geodesic distance are given high support weights and therefore large influence in the matching process. In contrast to previous work, we enforce connectivity by using the geodesic distance transform. For obtaining a high support weight, a pixel must have a path to the center point along which the color does not change significantly. This connectivity property leads to improved segmentation results and consequently to improved disparity maps. The success of our geodesic approach is demonstrated on the Middlebury images. According to the Middlebury benchmark, the proposed algorithm is the top performer among local stereo methods at the current state-of-the-art.
Parkinson Disease The Frontal Assessment Battery as a Tool for Evaluation of Frontal Lobe Dysfunction in Patients With

Background: Frontal-type cognitive deficits are common in patients with Parkinson disease (PD). The Frontal Assessment Battery (FAB) was developed to assess frontal lobe functions. However, many studies found that it also correlated with a variety of other general neuropsychological tests. Objectives: To evaluate whether the FAB has an added value over the Mini-Mental State Examination (MMSE) and other bedside neuropsychological tests in reflecting cognitive deficits in patients with PD. Methods: Seventy-two consecutive patients with PD underwent cognitive assessment including the FAB, the MMSE, and a variety of other neuropsychological tests. Correlations were examined using the Spearman’s r. Results: Highly significant correlations were found between the total FAB score and tests of attention, executive functions, and memory. To evaluate the contribution of the FAB beyond that of the MMSE, partial correlation was used. Analyses revealed that the FAB still correlated with most of the tests. Dividing the patients according to the median MMSE score revealed that the high correlation between the FAB and the MMSE was preserved in the low MMSE group, while in the high MMSE group the correlation was relatively low. In the high MMSE group, the FAB correlated with 11 tests compared to the MMSE that correlated with one (P < .001), while in the low MMSE group the number of correlations was 13 versus 7, respectively (P 1⁄4 .05). Conclusions: In our sample of patients with PD, the FAB correlated with dysfunction in a variety of cognitive domains including attention, memory, and executive functions. The FAB has an added value over the MMSE, particularly among nondemented patients, an advantage that can be used in clinical practice.
A survey and comparative analysis on different algorithms for Blind Source Separation

Blind separation for speech signal is the original purpose of BSS problem, and becomes the research attention of signal processing in recent years. Separation of speeches has an important theoretical importance in voice communications, acoustic target detection, etc. Blind source separation is a well-established signal processing problem. The sources to be estimated present some diversity in order to be efficiently retrieved. Assuming the transmitted signals to be mutually independent in a linear multiple-input-multiple-output (MIMO) memory-less system, the transmitted signal is subjected to Additive white Gaussian noise. The received signals are, hence, corrupted by inter-user interference (IUI), and we can model them as the outputs of a linear multiple-input-multiple-output (MIMO) memory-less system. In this paper, we have surveyed different techniques of blind signal separation.
Tau-pair Production via Photon-photon Collisions at Lep

We point out that the cross-section for the process e + e − → e + e − τ + τ − at LEP is large enough to allow for a study of the anomalous electromagnetic couplings of the τ lepton. We show that the present bounds on the magnetic dipole moment can be improved and that competitive bounds can be obtained for the electric dipole moment using the data taken from 1992 to 1994. Finally, we briefly discuss the improvements that can be obtained at LEP II.
Reducing feature space dimensionality for image retrieval

In the field of image retrieval, images are represented using a feature vector of high dimensions, in the most of applications. Working with vectors of high dimensions consumes an enormous amount of memory and slows down the process. In this paper, we propose an algorithm for selection of some dimensions from the total set of feature dimensions which not only reduces the memory requirement and speeds up the retrieval process, but increases the precision of retrieval, whereas, in the literature it is perceived that reducing the feature space dimension reduces the precision.
Calcium-Stimulated Adenylyl Cyclases Modulate Ethanol-Induced Neurodegeneration in the Neonatal Brain

Fetal alcohol exposure results in cognitive and neurobehavioral deficits, but the effects of modifying genetic loci on the severity of these sequelas have not been well characterized. Although the cAMP signaling pathway has been shown to be an important modulator of ethanol sensitivity in adult mice, its potential role in modulating ethanol-induced neurodegeneration has not been examined. Adenylyl cyclases (ACs) 1 and 8 produce cAMP in response to intracellular calcium elevation and modulate several aspects of neuronal function, including ethanol sensitivity. AC1 and AC8 are expressed widely throughout the brain of neonatal mice, and genetic deletion of both AC1 and AC8 in double-knock-out (DKO) mice enhances ethanol-induced neurodegeneration in the brains of neonatal mice. In addition, ethanol treatment induces significantly greater levels of caspase-3 activation in the brains of DKO mice compared with wild-type (WT) mice, reflecting higher numbers of apoptotic neurons. Administration of the NMDA receptor antagonist MK801 [(+)-5-methyl-10,11-dihydro-5H-dibenzo [a,d] cyclohepten-5,10-imine hydrogen maleate] or the GABAA receptor potentiator phenobarbital, which mimics components of the effects of ethanol on neurons, results in significantly greater neurodegeneration in the brains of neonatal DKO mice than WT mice. Furthermore, loss of a single calcium-stimulated AC isoform potentiates neurodegeneration after administration of ethanol, MK801, or phenobarbital. In contrast, the levels of physiological cell death, death after hypoxia/ischemia, and excitotoxic cell death are not increased in the brains of DKO mice. Thus, AC1 and AC8 are critical modulators of neurodegeneration induced by activity blockade in the neonatal brain and represent genetic loci that may potentially modify the severity of fetal alcohol syndrome.
Self-calibration technique for reduction of hold failures in low-power nano-scaled SRAM

Increasing source voltage (source-biasing) is an efficient technique for reducing gate and sub-threshold leakage of SRAM arrays. However, due to process variation, a higher source voltage can significantly increase data flipping in standby mode (hold failures) resulting in faulty memories. This imposes serious concerns in reducing standby power with source-bias. In this paper, we analyze the effect of source bias on hold failures under both inter-die and intra-die variations. We propose a self-calibrating SRAM for aggressively reducing leakage while maintaining the hold failures under control
Designing a Context-sensitive Dashboard for an Adaptive Knowledge Worker Assistant

We develop a context-sensitive dashboard to assist knowl- edge workers. The term context in our scenario defines explicit categories used to group observed native operations (NOPs) performed, and these NOPs correspond to actions undertaken during work. The dashboard consists of several small applications, widgets, that permit quick access to such entities as files, bookmarks, persons, and notes as well as to a visual backtrack on a per-context basis. The dashboard can be activated on demand. Meanwhile, in background, the user is constantly monitored: a user observation hub records high level user actions, system hooks take screenshots and keystrokes. These can be used by the visual backtrack widget to give a replay and help users to find a way back into the con- text again after some interruption through evaluating similar situations in the past.
A Parallel Fictitious Domain Method for the Three-Dimensional Helmholtz Equation

The application of a fictitious domain (domain embedding) method to the three-dimensional Helmholtz equation with absorbing boundary conditions is considered. The finite element discretization is performed by using locally fitted meshes, and an algebraic fictitious domain method with a separable preconditioner is used in the iterative solution of the resultant linear systems. Such a method is based on embedding the original domain into a larger one with a simple geometry. With this approach, it is possible to realize the GMRES iterations in a low-dimensional subspace and use the partial solution method to solve the linear systems with the preconditioner. An efficient parallel implementation of the iterative algorithm is introduced. Results of numerical experiments demonstrate good scalability properties on distributed-memory parallel computers and the ability to solve high frequency acoustic scattering problems.
Attribute-based content distribution with hidden policy

Access control in content distribution networks (CDNs) is a long-standing problem and has attracted extensive research. Traditional centralized access control approaches, such as reference monitor based approach, do not suit for CDNs as such networks are of large scale and geographically distributed in nature. Current CDNs usually resort to cryptographic-based distributed approaches for better fulfilling the goal of access control. Hence, it is highly critical to design and adapt appropriate cryptographic primitives for such purpose. In this paper, we propose a novel distributed access control approach for CDNs by exploiting a new cryptographic primitive called Ciphertext Policy Attributed-Based Encryption (CP-ABE). Our approach provides flexible yet fine-grained access control (per file level) so that the contents are available only to the authorized users. We further consider the protection of user privacy and enhance the current design of CP-ABE so that not only the cAccess control in content distribution networks (CDNs) is a long-standing problem and has attracted extensive research. Traditional centralized access control approaches, such as reference monitor based approach, do not suit for CDNs as such networks are of large scale and geographically distributed in nature. Current CDNs usually resort to cryptographic-based distributed approaches for better fulfilling the goal of access control. Hence, it is highly critical to design and adapt appropriate cryptographic primitives for such purpose. In this paper, we propose a novel distributed access control approach for CDNs by exploiting a new cryptographic primitive called ciphertext policy attributed-based encryption (CP-ABE). Our approach provides flexible yet fine-grained access control (per file level) so that the contents are available only to the authorized users. We further consider the protection of user privacy and enhance the current design of CP-ABE so that not only the contents themselves but also the access policies, which could lead to the revelation of sensitive user information, are well protected.ontents themselves but also the access policies, which could lead to the revelation of sensitive user information, are well protected.