Miracle-dz/UniFAR-FTUs huggingface dataset:
New Facts in Finance

The last 15 years have seen a revolution in the way financial economists understand the world around us. We once thought that stock and bond returns were essentially unpredictable. Now we recognize that stock and bond returns have a substantial predictable component at long horizons. We once thought the capital asset pricing model (CAPM) provided a good description of why average returns on some stocks, portfolios, funds or strategies were higher than others. Now we recognize that the average returns of many investment opportunities cannot be explained by the CAPM, and multifactor models' have supplanted the CAPM to explain them. We once thought that long-term interest rates reflected expectations of future short term rates and that interest rate differentials across countries reflected expectations of exchange-rate depreciation. Now, we see time-varying risk premia in bond and foreign exchange markets as well as in stock markets. Once, we thought that mutual fund average returns were well explained by the CAPM. Now, we recognize ``value'' and other high return strategies in funds, and slight persistence in fund performance. In this article, I survey these new facts. I show how they are related. Each case uses price variables to infer market expectations of future returns; each case notices that an offsetting adjustment (to dividends, interest rates, or exchange rates) seems to be absent or sluggish. Each case suggests that financial markets offer rewards in the form of average returns for holding risks related to recessions and financial distress, in addition to the risks represented by overall market movements.
A New Color-Texture Approach for Industrial Products Inspection

This work presents an approach for color-texture classification of industrial products. An extension of Gray Level Co-occurrence Matrix (GLCM) to color images is proposed. Statistical features are computed from an isotropic Color Co-occurrence Matrix for classification. The following color spaces are used: RGB, HSL and La*b*. New combination schemes for texture analysis are introduced. A comparison with Local Binary Patterns (LBP) is also performed. The tests were conducted in a variety of industrial samples. The obtained results are promising and show the possibility of efficiently classifying complex industrial products based on color and texture features.
Limits of local algorithms over sparse random graphs

Local algorithms on graphs are algorithms that run in parallel on the nodes of a graph to compute some global structural feature of the graph. Such algorithms use only local information available at nodes to determine local aspects of the global structure, while also potentially using some randomness. Research over the years has shown that such algorithms can be surprisingly powerful in terms of computing structures like large independent sets in graphs locally. These algorithms have also been implicitly considered in the work on graph limits, where a conjecture due to Hatami, Lovász and Szegedy [17] implied that local algorithms may be able to compute near-maximum independent sets in (sparse) random d-regular graphs. In this paper we refute this conjecture and show that every independent set produced by local algorithms is smaller that the largest one by a multiplicative factor of at least 1/2+1/(2√2) ≈ . 853, asymptotically as d → ∞. Our result is based on an important clustering phenomena predicted first in the literature on spin glasses, and recently proved rigorously for a variety of constraint satisfaction problems on random graphs. Such properties suggest that the geometry of the solution space can be quite intricate. The specific clustering property, that we prove and apply in this paper shows that typically every two large independent sets in a random graph either have a significant intersection, or have a nearly empty intersection. As a result, large independent sets are clustered according to the proximity to each other. While the clustering property was postulated earlier as an obstruction for the success of local algorithms, such as for example, the Belief Propagation algorithm, our result is the first one where the clustering property is used to formally prove limits on local algorithms.
Computing Shapley Values, Manipulating Value Division Schemes, and Checking Core Membership in Multi-Issue Domains

Coalition formation is a key problem in automated negotiation among self-interested agents. In order for coalition formation to be successful, a key question that must be answered is how the gains from cooperation are to be distributed. Various solution concepts have been proposed, but the computational questions around these solution concepts have received little attention. We study a concise representation of characteristic functions which allows for the agents to be concerned with a number of independent issues that each coalition of agents can address. For example, there may be a set of tasks that the capacity-unconstrained agents could undertake, where accomplishing a task generates a certain amount of value (possibly depending on how well the task is accomplished). Given this representation, we show how to quickly compute the Shapley value--a seminal value division scheme that distributes the gains from cooperation fairly in a certain sense. We then show that in (distributed) marginal-contribution based value division schemes, which are known to be vulnerable to manipulation of the order in which the agents are added to the coalition, this manipulation is NP-complete. Thus, computational complexity serves as a barrier to manipulating the joining order. Finally, we show that given a value division, determining whether some subcoalition has an incentive to break away (in which case we say the division is not in the core) is NP-complete. So, computational complexity serves to increase the stability of the coalition.
Chiari Malformation Type I: A Case-Control Association Study of 58 Developmental Genes

Chiari malformation type I (CMI) is a disorder characterized by hindbrain overcrowding into an underdeveloped posterior cranial fossa (PCF), often causing progressive neurological symptoms. The etiology of CMI remains unclear and is most likely multifactorial. A putative genetic contribution to CMI is suggested by familial aggregation and twin studies. Experimental models and human morphometric studies have suggested an underlying paraxial mesoderm insufficiency. We performed a case-control association study of 303 tag single nucleotide polymorphisms (SNP) across 58 candidate genes involved in early paraxial mesoderm development in a sample of 415 CMI patients and 524 sex-matched controls. A subgroup of patients diagnosed with classical, small-PCF CMI by means of MRI-based PCF morphometry (n = 186), underwent additional analysis. The genes selected are involved in signalling gradients occurring during segmental patterning of the occipital somites (FGF8, Wnt, and retinoic acid pathways and from bone morphogenetic proteins or BMP, Notch, Cdx and Hox pathways) or in placental angiogenesis, sclerotome development or CMI-associated syndromes. Single-marker analysis identified nominal associations with 18 SNPs in 14 genes (CDX1, FLT1, RARG, NKD2, MSGN1, RBPJ1, FGFR1, RDH10, NOG, RARA, LFNG, KDR, ALDH1A2, BMPR1A) considering the whole CMI sample. None of these overcame corrections for multiple comparisons, in contrast with four SNPs in CDX1, FLT1 and ALDH1A2 in the classical CMI group. Multiple marker analysis identified a risk haplotype for classical CMI in ALDH1A2 and CDX1. Furthermore, we analyzed the possible contributions of the most significantly associated SNPs to different PCF morphometric traits. These findings suggest that common variants in genes involved in somitogenesis and fetal vascular development may confer susceptibility to CMI.
Exactly solvable model of superstring in plane wave Ramond-Ramond background

We describe in detail the solution of type IIB superstring theory in the maximally supersymmetric plane-wave background with constant null Ramond-Ramond 5-form field strength. The corresponding light-cone Green-Schwarz action found by Metsaev is quadratic in both bosonic and fermionic coordinates. We obtain the light-cone Hamiltonian and the string representation of the corresponding supersymmetry algebra. The superstring Hamiltonian has a ``harmonic-oscillator'' form in both the string oscillator and the zero-mode parts and thus has a discrete spectrum. We analyze the structure of the zero-mode sector of the theory, establishing the precise correspondence between the lowest-lying ``massless'' string states and the type IIB supergravity fluctuation modes in the plane-wave background. The zero-mode spectrum has a certain similarity to the supergravity spectrum in ${\mathrm{AdS}}_{5}\ifmmode\times\else\texttimes\fi{}{\mathrm{S}}^{5}$ background of which the plane-wave background is a special limit. We also compare the plane-wave string spectrum with the expected form of the light-cone gauge spectrum of the ${\mathrm{AdS}}_{5}\ifmmode\times\else\texttimes\fi{}{\mathrm{S}}^{5}$ superstring.
Synergistic activation of a Drosophila enhancer by HOM/EXD and DPP signaling

The homeotic proteins encoded by the genes of the Drosophila HOM and the vertebrate HOX complexes do not bind divergent DNA sequences with a high selectivity. In vitro, HOM (HOX) specificity can be increased by the formation of heterodimers with Extradenticle (EXD) or PBX homeodomain proteins. We have identified a single essential Labial (LAB)/EXD‐binding site in a Decapentaplegic (DPP)‐responsive enhancer of the homeotic gene lab which drives expression in the developing midgut. We show that LAB and EXD bind cooperatively to the site in vitro, and that the expression of the enhancer in vivo requires exd and lab function. In addition, point mutations in either the EXD or the LAB subsite compromise enhancer function, strongly suggesting that EXD and LAB bind to this site in vivo. Interestingly, we found that the activity of the enhancer is only stimulated by DPP signaling significantly upon binding of LAB and EXD. Thus, the enhancer appears to integrate positional information via the homeotic gene lab, and spatiotemporal information via DPP signaling; only when these inputs act in concert in an endodermal cell is the enhancer fully active. Our results illustrate how a tissue‐specific response to DPP can be generated through synergistic effects on an enhancer carrying both DPP‐ and HOX‐responsive sequences.
A relativistic jet from Cygnus X-1 in the low/hard X-ray state

We present the detection of a radio-emitting jet from the black hole candidate and X-ray binary source Cygnus X-1. Evidence of a bright core with a slightly extended structure was found on milliarcsecond resolution observations with the Very Long Baseline Array (VLBA) at 15.4 GHz. Later observations with the VLBA [and including the phased-up, Very Large Array (VLA)] at 8.4 GHz show an extended jet-like feature extending to ∼ 15 mas from a core region, with an opening angle of 0.6c) jet during this state.
Nanodelivery Systems as New Tools for Immunostimulant or Vaccine Administration: Targeting the Fish Immune System

Fish disease treatments have progressed significantly over the last few years and have moved from the massive use of antibiotics to the development of vaccines mainly based on inactivated bacteria. Today, the incorporation of immunostimulants and antigens into nanomaterials provide us with new tools to enhance the performance of immunostimulation. Nanoparticles are dispersions or solid particles designed with specific physical properties (size, surface charge, or loading capacity), which allow controlled delivery and therefore improved targeting and stimulation of the immune system. The use of these nanodelivery platforms in fish is in the initial steps of development. Here we review the advances in the application of nanoparticles to fish disease prevention including: the type of biomaterial, the type of immunostimulant or vaccine loaded into the nanoparticles, and how they target the fish immune system.
Seasonal and ontogenetic variation of skin microbial communities and relationships to natural disease dynamics in declining amphibians

Recently, microbiologists have focused on characterizing the probiotic role of skin bacteria for amphibians threatened by the fungal disease chytridiomycosis. However, the specific characteristics of microbial diversity required to maintain health or trigger disease are still not well understood in natural populations. We hypothesized that seasonal and developmental transitions affecting susceptibility to chytridiomycosis could also alter the stability of microbial assemblages. To test our hypothesis, we examined patterns of skin bacterial diversity in two species of declining amphibians (Lithobates yavapaiensis and Eleutherodactylus coqui) affected by the pathogenic fungus Batrachochytrium dendrobatidis (Bd). We focused on two important transitions that affect Bd susceptibility: ontogenetic (from juvenile to adult) shifts in E. coqui and seasonal (from summer to winter) shifts in L. yavapaiensis. We used a combination of community-fingerprinting analyses and 16S rRNA amplicon sequencing to quantify changes in bacterial diversity and assemblage composition between seasons and developmental stages, and to investigate the relationship between bacterial diversity and pathogen load. We found that winter-sampled frogs and juveniles, two states associated with increased Bd susceptibility, exhibited higher diversity compared with summer-sampled frogs and adult individuals. Our findings also revealed that hosts harbouring higher bacterial diversity carried lower Bd infections, providing support for the protective role of bacterial communities. Ongoing work to understand skin microbiome resilience after pathogen disturbance has the potential to identify key taxa involved in disease resistance.
Three-dimensional multi-joint reaching under redundancy of DOFs

A simple control method for 3-dimensional multi-joint reaching movements under redundancy of Degrees-of-Freedom is proposed, which neither need to introduce any performance index to solve inverse kinematics uniquely nor need to calculate pseudo-inverse of the Jacobian matrix of task coordinates with respect to joint coordinates. The proposed control signal is composed of linear superposition of three terms: 1) angular-velocity feedback for damping shaping; 2) task-space position feedback with a single stiffness parameter; and 3) compensation for gravity force on the basis of estimates for uncertain parameters of the potential energy. Through a variety of computer simulations by using a whole arm model with five DOFs, the importance of synergistic adjustments of damping factors as well as its relation to selection of the stiffness parameter is pointed out. It is shown that if damping factors are chosen synergistically corresponding to the inertia matrix at the initial time and the stiffness parameter then the endpoint converges asymptotically to the target position and reaches it smoothly without incurring any self-motion.
Progressive Focusing Search

This paper deals with value ordering heuristics used in a complete tree search algorithm for solving binary constraint satisfaction problems. Their aim is to guide the search towards a solution. First, we show the limits of the traditional prospective approach, which uses the size of the domains of the still unassigned variables. In an advantageous context, where arc consistency is maintained and allows the time spent by the dynamic value ordering to be negligible, the speedup is poor when the problems are hard. Then, we present a new value ordering heuristic based on a learning-from-failure scheme. Instead of making a choice a priori, an interleaving search follows every sub-tree to gather information. After this learning phase, the algorithm focuses on the most promising one. This new algorithm, named Progressive Focusing Search, is compared to Interleaved Depth First Search and appears to be efficient for problems on the phase transition complexity peak.
Generating and ranking diverse multi-character interactions

In many application areas, such as animation for pre-visualizing movie sequences and choreography for dance or other types of performance, only a high-level description of the desired scene is provided as input, either written or verbal. Such sparsity, however, lends itself well to the creative process, as the choreographer, animator or director can be given more choice and control of the final scene. Animating scenes with multi-character interactions can be a particularly complex process, as there are many different constraints to enforce and actions to synchronize. Our novel 'generate-and-rank' approach rapidly and semi-automatically generates data-driven multi-character interaction scenes from high-level graphical descriptions composed of simple clauses and phrases. From a database of captured motions, we generate a multitude of plausible candidate scenes. We then efficiently and intelligently rank these scenes in order to recommend a small but high-quality and diverse selection to the user. This set can then be refined by re-ranking or by generating alternatives to specific interactions. While our approach is applicable to any scenes that depict multi-character interactions, we demonstrate its efficacy for choreographing fighting scenes and evaluate it in terms of performance and the diversity and coverage of the results.
Gender identification in face images using KPCA

The data in face images are distributed in a complex manner due to the variation of light intensity, facial expression and pose. In this paper the Kernel Principal Component Analysis (KPCA) is used to extract the feature set of male and female faces. A Gaussian model of skin segmentation method is applied here to exclude the global features such as beard, eyebrow, moustache, etc. both training and test images are randomly selected from four different data bases to improve the training. The experimental results show that the proposed framework is efficient for recognizing the gender of a face image even though it is an impersonation face.
A Description of the August 2006 XG Demonstrations at Fort A.P. Hill

On Aug 15-17, 2006, Shared Spectrum Company (SSC) and the U.S. Department of Defense's Defense Advanced Research Projects Agency (DARPA) demonstrated, for the first time, a six node network of next generation (XG) radios capable of using spectrum over a wide range of frequencies on a secondary basis. The cognitive radios making up several formations of XG networks sensed radio signals over 225-600 MHz and adapted frequencies automatically to prevent interference to existing military and civilian radio systems. The XG networks were tested on a military test range (Fort A.P. Hill, Bowling Green, VA) in front of an audience of over one hundred military and government spectrum management agency representatives from the Army, Navy, Air Force, Coast Guard, Joint Spectrum Center, the Federal Communications Commission and the National Telecommunications and Information Administration. This paper describes the XG radio system and test scenarios. A companion paper presents a detailed account of the test results.
An efficient location extraction algorithm by leveraging web contextual information

A typical location extraction approach consists of two steps, location name detection and location entity disambiguation. Promising results have been obtained in the last decade based on natural language processing technologies. However, there are still two challenges which requires further investigation: 1)How to leverage the prior and contextual evidence to improve the location extraction performance, and 2) How to utilize the interdependence information between the named entity recognition step and disambiguation step. In this paper, we propose an iterative detection-ranking framework to address these problems as well as a set of novel features to mine contextual information from web resources. Experimental results show that our solution outperforms the state-of-the-art approaches, including Metacarta GeoTagger and Yahoo Placemaker.
Dynamic cooperation link selection for network MIMO systems with limited backhaul capacity

Base-station (BS) cooperation in wireless cellular networks offers a promising approach for interference mitigation. However, the implementation of practical network multi-input multi-output (MIMO) system also faces the challenge of high capacity cost for sharing the user data over the backhaul connections. This paper considers a downlink multi-cell orthogonal frequency-division multiple-access (OFDMA) network where the capacities of the backhaul links between the BSs are limited, and extends the single-antenna BS multi-cell system model considered in our previous work to the multiple-antenna BS case. The BSs use zero-forcing precoding to spatially multiplex multiple users within each cell and to pre-subtract the interference from cooperating BSs that share user data with them. An iterative algorithm that maximizes the downlink network utility is proposed. The algorithm iteratively selects the cooperation links, schedules the users, and optimizes the precoding coefficients and the power spectra for each frequency tone. Numerical results suggest that the use of dynamic cooperation link selection can provide a better trade-off between the downlink sum-rate gain and the backhaul capacity than the earlier fixed link-selection algorithm.
Fast and Robust Multi-Frame Super-Resolution

Abstract from http://citeseerx.ist. psu.edu/viewdoc/download? doi=10.1. 1.15. 2875&rep=rep1&type=pdf In the last two decades, many papers have been published, proposing a variety of methods for multiframe resolution enhancement. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their shortcomings. We propose an alternate approach using L1 norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation, and results in images with sharp edges. Simulation results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods.
Stability analysis of T-S fuzzy-model-based control systems using fuzzy Lyapunov function

This paper investigates the system stability of T-S fuzzy-model-based control systems based on an improved fuzzy Lyapunov function. Various non-PDC (parallel distribution compensation) fuzzy controllers are proposed to close the feedback loop. The characteristic of T-S fuzzy model is considered to facilitate the stability analysis. Under a particular case, the time-derivative information of the membership functions vanishes, which simplifies the stability analysis and leads to relaxed stability analysis results. A general case is then considered. An improved non-PDC fuzzy controller is proposed based on the properties of the T-S fuzzy model. The improved non-PDC fuzzy controller exhibits a favourable property to relax the stability conditions. Based on the fuzzy Lyapunov function, stability conditions in terms of linear matrix inequalities are derived to guarantee the system stability. Simulation examples are given to illustrate the effectiveness of the proposed non-PDC fuzzy control schemes.
Wide band meander line antenna for wireless communication systems

Printed antennas are becoming one of the most popular designs in personal wireless communications systems. In this paper, the design of a novel tapered meander line antenna is presented. The design analysis and characterization of the antenna is performed using the finite difference time domain technique and experimental verifications are performed to ensure the effectiveness of the numerical model. The new design features an operating frequency of 2.55 GHz with a 230 MHz bandwidth, which supports future generations of mobile communication systems.