Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards canonical quantum gravity for 3+1 geometries admitting maximally symmetric two-dimensional surfaces

The canonical decomposition of all 3+1 geometries admitting two-dimensional space-like surfaces is exhibited as a generalization of a previous work. A proposal, consisting of a specific renormalization Assumption and an accompanying Requirement, which has been put forward in the 2+1 case is now generalized to 3+1 dimensions. This enables the canonical quantization of these geometries through a generalization of Kuchař's quantization scheme in the case of infinite degrees of freedom. The resulting Wheeler–DeWitt equation is based on a renormalized manifold parameterized by three smooth scalar functionals. The entire space of solutions to this equation is analytically given, a fact that is entirely new to the present case. This is made possible through the exploitation of the residual freedom in the choice of the third functional, which is left by the imposition of the Requirement, and is proven to correspond to a general coordinate transformation in the renormalized manifold.
Accounting for the Great Depression

Economists have offered many theories for the U.S. Great Depression, but no consensus has formed on the main forces behind it. Here we describe and demonstrate a simple methodology for determining which theories are the most promising. We show that a large class of models, including models with various frictions, are equivalent to a prototype growth model with time-varying efficiency, labor, and investment wedges that, at least on face value, look like time-varying productivity, labor taxes, and investment taxes. We use U.S. data to measure these wedges, feed them back into the prototype growth model, and assess the fraction of the fluctuations in 1929? 39 that they account for. We find that the efficiency and labor wedges account for essentially all of the decline and subsequent recovery. Investment wedges play, at best, a minor role. This article originally appeared in the American Economic Review. (c) American Economic Association. ; RELATED PAPER: Staff Report 328 Business Cycle Accounting
Products and help bits in decision trees

We investigate two problems concerning the complexity of evaluating a function f at k-tuple of unrelated inputs by k parallel decision tree algorithms. In the product problem, for some fixed depth bound d, we seek to maximize the fraction of input k-tuples for which all k decision trees are correct. Assume that for a single input to f, the best decision tree algorithm of depth d is correct on a fraction p of inputs. We prove that the maximum fraction of k-tuples on which k depth d algorithms are all correct is at most p/sup k/, which is the trivial lower bound. We show that if we replace the depth d restriction by "expected depth d", then this result fails. In the help-bit problem, we are permitted to ask k-1 arbitrary binary questions about the k-tuple of inputs. For each possible k-1-tuple of answers to these queries we will have a k-tuple of decision trees which are supposed to correctly compute all functions on k-tuples that are consistent with the particular answers. The complexity here is the maximum depth of any of the trees in the algorithm. We show that for all k sufficiently large, this complexity is equal to deg/sup s/(f) which is the minimum degree of a multivariate polynomial whose sign is equal to f. Finally, we give a brief discussion of these problems in the context of other complexity models. <<ETX>>
Robust ISS of uncertain discrete-time singularly perturbed systems with disturbances

ABSTRACT This paper is concerned with robustly input-to-state stable (ISS) and Robust ISS by feedback of uncertain discrete-time singularly perturbed systems (SPSs) with disturbances. Meanwhile, robust stability and stabilisation of uncertain discrete-time SPSs are also obtained as the particular cases of robust ISS and robust ISS by feedback. We first find a sufficient condition by using the fixed-point principle in terms of linear matrix inequalities (LMIs) to guarantee that the considered system is always standard discrete-time SPSs subject to uncertainty and disturbances. Then, the full systems could decompose into the continuous-time uncertain slow subsystem with disturbance and discrete-time uncertain fast subsystems with disturbance, respectively. Based on the two-time-scale decomposition technique, sufficient condition in terms of LMIs is given such that the full systems are uniformly standard and robust ISS simultaneously. In addition, a state feedback controller is constructed by using the LMI approach such that the resulting closed-loop systems are robust ISS. Finally, a numerical example is provided to illustrate the effectiveness of the proposed approach.
Cooperative pose stabilization of an aerial vehicle through physical interaction with a team of ground robots

This work presents the design of a cooperative control policy for a group of heterogeneous vehicles given by a set of ground robots able to physically interact with an aerial robot by applying forces trough tethered cables governed by actuated winches. It is shown how different geometric configurations of the group may lead to possibly different dynamical properties of the overall system which can be exploited to improve the achievable closed-loop performances in certain situations. Suitable control allocation problems are then formulated in which the ground robots are considered as real mobile actuators able to improve the controllability of the aerial vehicle and to help it counteracting external disturbances such as wind.
Structural Biology and Crystallization Communications Expression, Purification and Crystallization of an Atypical Class C Acid Phosphatase from Mycoplasma Bovis

Class C acid phosphatases (CCAPs) are 25–30 kDa bacterial surface proteins that are thought to function as broad-specificity 5 0 ,3 0-nucleotidases. Analysis of the newly published complete genome sequence of Mycoplasma bovis PG45 revealed a putative CCAP with a molecular weight of 49.9 kDa. The expression, purification and crystallization of this new family member are described here. Standard purification procedures involving immobilized metal-ion affinity chromatography and ion-exchange chromatography yielded highly pure and crystallizable protein. Crystals were grown in sitting drops at room temperature in the presence of PEG 3350 and HEPES buffer pH 7.5 and diffracted to 2.3 A ˚ resolution. Analysis of diffraction data suggested a primitive monoclinic space group, with unit-cell parameters a = 78, b = 101, c = 180 A ˚ , = 92. The asymmetric unit is predicted to contain six molecules, which are likely to be arranged as three dimers.
Quantitative variation in the core bacterial community associated with honey bees from Varroa-infested colonies

Infestations of the ectoparasitic mite Varroa destructor cause significant honey bee colony weakening, due to both host-feeding and virus transmission effects. In addition to the natural (i.e., behavioral) and innate (i.e., cellular and humoral response) mechanisms of defense against pathogens and parasites, contribution to maintain good colony health may derive from the resident microbial community. The present study investigated variation in immune-related gene expression levels and in the relative abundance of representative bacterial taxa of the core honey bee microbiota in colonies infested by Varroa mites in comparison with honey bees from non-infested colonies. Significant changes in the target bacterial taxa (Gammaproteobacteria, Betaproteobacteria, Lactobacillus spp., Bifidobacterium spp., Brevibacillus laterosporus) were detected in immune-suppressed adults emerging from Varroa-infested colonies. Insect stages randomly sampled from infested and non-infested hives were characterized by high variability. Emerging adults bearing sucking Varroa mites on their body showed a considerably higher deformed wing virus titer and a greater immune gene downregulation compared with adults without mites. Further, the abundance of the overall bacterial community and of specific bacterial taxa increased significantly in parasitized adults. A possible compensation mechanism involving the core bacterial community in Varroa-infested hives is discussed.
Nonserial dynamic programming and local decomposition algorithms in discrete programming

One of perspective ways to exploit sparsity in the dependency graph of an optimization problem is nonserial dynamic programming (NSDP) which allows to compute solution in stages, each of them uses results from previous stages. The class of discrete optimization problems with the block-tree matrix of constraints is considered. Nonserial dynamic programming (NSDP) algorithm for the solution of this class of problems is proposed. Properties of NSDP algorithm for the solution of the problems of this class are investigated.
Matching Visual and Acoustic Mirror Forms

This paper presents a comparative analysis of the ability to recognize three mirror forms in visual and acoustic tasks: inversion (reflection on a horizontal axis), retrograde (reflection on a vertical axis) and retrograde inversion (reflection on both horizontal and vertical axes). Dynamic patterns consisting of five tones in succession in the acoustic condition and five square dots in succession in the visual condition were presented to 180 non‐musically expert participants. In a yes/no task, they were asked to ascertain whether a comparison stimulus represented the “target” transformation (i.e., inversion, retrograde or retrograde inversion). Three main results emerged. Firstly, the fact that symmetry pertaining to a vertical axis is the most easily perceived does not only apply to static visual configurations (as found in previous literature) but also applies to dynamic visual configurations and acoustic stimuli where it is in fact even more marked. Secondly, however, differences emerged between the facility with which the three mirror forms were recognized in the acoustic and visual tasks. Thirdly, when the five elements in the stimulus were not of the same duration and therefore a rhythmic structure emerged, performance improved not only in the acoustic but also (even more significantly) in the visual task.
Mining in a mobile environment

Distributed PRocessing in Mobile Environments (DPRiME) is a framework for processing large data sets across an ad-hoc network. Developed to address the shortcomings of Google's MapReduce outside of a fully-connected network, DPRiME separates nodes on the network into a master and workers; the master distributes sections of the data to available one-hop workers to process in parallel. Upon returning results to its master, a worker is assigned an unfinished task. Five data mining classifiers were implemented to process the data: decision trees, k-means, k-nearest neighbor, Naïve Bayes, and artificial neural networks. Ensembles were used so the classification tasks could be performed in parallel. This framework is well-suited for many tasks because it handles communications, node movement, node failure, packet loss, data partitioning, and result collection automatically. Therefore, DPRiME allows users with little knowledge of networking or distributed systems to harness the processing power of an entire network of single- and multi-hop nodes.
Virtual Archaeologist: Assembling the Past

We present a complete method, encapsulated in our Virtual Archaeologist system, for the full reconstruction of archaeological finds from 3D scanned fragments. Virtual Archaeologist is designed to assist archaeologists in reconstructing monuments or smaller finds by avoiding unnecessary manual experimentation with fragile and often heavy fragments. An automated procedure cannot completely replace the archaeology expert, but provides a useful estimation of valid fragment combinations, and accurately measures fragment matches. In Virtual Archaeologist, we regard the reconstruction problem from a general, geometric point of view, relying on the broken surface morphology to determine correct matches between fragments.
Gesund unter Freunden

Wenn man ein Buch schreibt, dann ist es unausweichlich, dass es eine Deadline gibt, also einen Redaktionsschluss. Und wenn dann danach noch eine interessante Arbeit erscheint (was naturgemäß immer irgendwann der Fall ist), ertappe ich mich regelmäßig bei dem Gedanken „schade, das hätte noch gut hineingepasst“. Erst kürzlich erschien mein Buch Einsamkeit, in dem ich versucht habe, dem Gesundheitsaspekt unserer sozialen Existenz auf den Grund zu gehen (10). Dieses Feld ist recht neu, und entsprechend dauerte es nicht lange, bis weitere Arbeiten dazu erschienen. Seit knapp 30 Jahren ist bekannt, dass unser Sozialleben unsere Gesundheit beeinflusst (3, 8, 9). Sowohl objektive soziale Isolation als auch subjektiv erlebte Einsamkeit sind nach einer sehr großen epidemiologischen Studie mit einer erhöhten Mortalität verknüpft (7).
Application of Decision Trees for Classifying Astronomical Objects

Data mining techniques used to analyze and discover data and correlations already present in databases, showed to be very reliable and useful especially when large volumes of data are processed. These techniques have been applied to many areas, such as marketing, medicine, diagnosis, business, biology, astronomy and others. In particular, astronomy requires techniques that allow the recognition or classification of astronomical objects, for example galaxies, stars or quasars, from databases that contain millions of objects. Due to this, astronomers often deal with the analysis of large amounts of data obtained from telescopes, seeking for several characteristics for their interpretation. Decision tree is one of the most used techniques in data mining because of its simplicity to explain the results. Besides, there are decision tree algorithms that work with parallel and incremental techniques, which help to process large databases for classifying new objects faster than traditional algorithms. ParDTLT algorithm, which possesses these characteristics, was used in this work in context of astronomical objects catalogue SDSS, with the aim of obtaining decision rules to help astronomers to understand the behavior patterns of different kinds of astronomical objects.
ACCOMPLISHMENTS AND PLANS

In my research I apply tools from model theory, a branch of mathematical logic, to study sets obtained from certain integral manifolds. (A good reference for Model Theory is [8].) This program has two central aspects: (1) I extend results that are known to be true on the ordered field of real numbers R := 〈R, +,×, <, 0, 1〉 to larger classes of real closed fields, and (2) I then use model theoretic methods to obtain uniformity results in expansions of R.
Self-management, satisfaction with family functioning, and the course of psychological symptoms in emerging adults with spina bifida.

OBJECTIVE To explore psychological symptoms in emerging adults with spina bifida (SB) and their association with self-management and satisfaction with family functioning. METHODS Longitudinal data were collected at 2 time points, 15 months apart, in 48 individuals with SB. Reliable change indices and paired samples t-tests assessed change in anxiety and depressive symptoms. Hierarchical regression models explored the contributions of SB severity, family satisfaction, and self-management in explaining change in psychological symptoms. RESULTS No significant group level differences in psychological symptoms were found across time in participants (Mean age 22 years), but significant individual-level change in anxiety symptoms (n = 13) and depressive symptoms (n = 9) was observed. Improved satisfaction with family functioning was associated with decreased anxiety symptoms (b = -0.30, p = .02), and increased SB self-management was related to reduced depressive symptoms (b = -0.63, p = .01). CONCLUSIONS Changes in self-management and satisfaction with family functioning may influence the course of psychological symptoms.
Interactions and Effects of CRM 2.0 in Public Administration: Issues of Interest to IT Professionals

This article analyzes how CRM are affected from technological and social changes that involves the new paradigm of knowledge sharing and collaborative Web 2.0, giving rise to what is defined as CRM 2.0, and how the public administration-institutions that despite economic and social importance have been little studied in relation to the incorporation of technologies-can take advantage of these technologies. This requires provide knowledge to public administrations about the benefits they can get and which difficulties may be encountered in these projects. In this sense, from an exploratory research through the application of a Delphi to senior managers of public administrations, the authors have identified as positive effects of implementing CRM projects with Web 2.0; improved productivity, increased citizen satisfaction, and better integration of the information and as main barriers highlight the measurement of the ROI, security and organizational issues. Questions of interest are to be considered by IT professionals who have to develop these initiatives in the future.
Low-Dimensional Embedding of Functional Connectivity Graphs for Brain State Decoding

Functional connectivity graphs are fully defined by their weighted adjacency matrix. Beyond the computation of graph-theoretical measures, we propose to use these graphs for inter-subject classification. Since they form a class of graphs with undirected edges and fixed number and ordering of vertices, vector space graph embedding techniques can be used to provide good classification performance. We propose a method that represents connectivity graphs in low-dimensional spaces, and we show experimental results hinting that such low-dimensional projections are beneficial for classification performance.
INTEGRATING ARTICULATORY FEATURES INTO ACOUSTIC MODELS FOR SPEECH RECOGNITION

It is often assumed that acoustic-phonetic or articulatory features can be beneficial for automatic speech recognition (ASR), e.g. because of their supposedly greater noise robustness or because they provide a more convenient interface to higher-level components of ASR systems such as pronunciation modeling. However, the success of these features when used as an alternative to standard acoustic speech signal representations (e.g. MFCCs) has so far been demonstrated only for limited domains, such as phone recognition or smallvocabulary speech recognition. On more challenging tasks, e.g. large-vocabulary speech recognition, standard acoustic features have consistently shown a superior performance. This study compares the performance of standard acoustics-based systems to that of articulatory feature-based systems on medium to large vocabulary recognition tasks. Results suggest that, for an optimal recognition performance, it is more advantageous to selectively combine information from both acoustic and articulatory representations than it is to use an articulatory feature-based representation alone. Data-driven techniques are applied to determine what kind of information articulatory features can contribute in addition to standard acoustic speech features.
Reduced complexity tone classifier for automatic tonal speech recognizer

A tone classifier is an essential part of an automatic tonal speech recognizer (ATSR) because tonal languages recognize word meaning by tones. However, many researchers have developed a highly efficient tone recognition by using rich mathematical techniques and used the whole input speech as an input of pitch detection process. This paper proposes a reduced complexity tone classifier for the automatic tonal speech recognizer. The classifier reduces the number of input frames by detecting only the vowel signals as an input of the pitch detection, called vowel-AMDF (V-AMDF). The classifier uses a lower number of floating-point operations (FLOPs) than used in the whole input speech method. Due to the reduced number of FLOPs, this tone classifier can be suitable for portable electronic equipment. In addition, V-AMDF reduces F0 contour errors caused by the influence from neighboring syllables. This proposed classifier was tested and set by 19 Thai words, selected from voice activation for GPS system and phone dialing options. The experimental results show 86.0% recognition accuracy, and 21.8% reduction in the number of FLOPs, compared with using the whole input speech.
Image analysis based on two dimensional Hilbert Huang Transform

Hilbert Huang Transform is a new developed method especially suitable for non-stationary signal processing. In this paper, we propose a two dimensional Hilbert-Huang Transform based on Bidimensional Empirical Mode Decomposition (BEMD) and Bi-orthant analytic signal. Bidimensional Empirical Mode Decomposition is adaptive signal decomposition method and its decomposition results are almost monocomponent. Bi-orthant analytic signal satisfies most of the two dimensional extension properties and is especially suitable for the monocomponent. In detail, the image is first decomposed to several comoponents by Bidimensional Empirical Mode Decomposition. Then we get the two dimensional Bi-orthant analytic signal. Two dimensional Hilbert spectral characters are got based on BEMD and two dimensional Bi-orthant analytic signals. Then we use this method in image analysis, and got the instantaneous amplitude, the instantaneous phase, and the instantaneous frequencies. We illustrate the techniques on natural images, and demonstrate the estimated instantaneous frequencies using the needle program. These features inflect the intrinsic characters of image.
Pseudonymous Broadcast and Secure Computation from Cryptographic Puzzles

In standard models of secure computation, point-to-point channels between parties are assumed to be authenticated by some pre-existing means. In other cases, even stronger pre-existing setup—e.g., a public-key infrastructure (PKI)—is assumed. These assumptions are too strong for open, peer-to-peer networks, where parties do not necessarily have any prior relationships and can come and go as they please. Nevertheless, these assumptions are made due to the prevailing belief that nothing “interesting” can be achieved without them. Taking inspiration from Bitcoin, we show that precise bounds on computational power can be used in place of pre-existing setup to achieve weaker (but nontrivial) notions of security. Specifically, under the assumption that each party can solve cryptographic puzzles only at a bounded rate (and the existence of digital signatures), we show that without prior setup and with no bound on the number of corruptions, a group of parties can agree on a PKI with which they can then realize pseudonymous notions of authenticated communication, broadcast, and secure computation. Roughly, “pseudonymous” here means that inputs/outputs are bound to pseudonyms rather than parties’ true identities. ∗Dept. of Computer Science, University of Maryland. Email: {jkatz,amiller,elaine}@cs.umd. edu. †Work supported by NSF awards #0964541, #1111599, and #1223623. ‡Work supported by NSF award #1314857, a Sloan Research Fellowship, and a Google Faculty Research Award.
Intelligibility of modifications to dysarthric speech

Dysarthria is a motor speech impairment affecting millions of people. Dysarthric speech can be far less intelligible than that of non-dysarthric speakers, causing significant communication difficulties. The goal of our work is to understand the effect that certain modifications have on the intelligibility of dysarthric speech. These modifications are designed to identify aspects of the speech signal or signal processing that may be especially relevant to the effectiveness of a system that transforms dysarthric speech to improve its intelligibility. A result of this study is that dysarthric speech can, in the best case, be modified only at the short-term spectral level to improve intelligibility from 68% to 87%. A baseline transformation system using standard technology, however, does not show improvement in intelligibility. Prosody also has a significant (p<0.05) effect on intelligibility.