Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Host-Plant Phylogenetic Isolation on Species Richness, Composition and Specialization of Insect Herbivores: A Comparison between Native and Exotic Hosts

Understanding the drivers of plant-insect interactions is still a key issue in terrestrial ecology. Here, we used 30 well-defined plant-herbivore assemblages to assess the effects of host plant phylogenetic isolation and origin (native vs. exotic) on the species richness, composition and specialization of the insect herbivore fauna on co-occurring plant species. We also tested for differences in such effects between assemblages composed exclusively of exophagous and endophagous herbivores. We found a consistent negative effect of the phylogenetic isolation of host plants on the richness, similarity and specialization of their insect herbivore faunas. Notably, except for Jaccard dissimilarity, the effect of phylogenetic isolation on the insect herbivore faunas did not vary between native and exotic plants. Our findings show that the phylogenetic isolation of host plants is a key factor that influences the richness, composition and specialization of their local herbivore faunas, regardless of the host plant origin.
Retention of Non-Traditional Students.

ABSTRACT Indicators of persistence of nontraditional students at a large commuter state university were studied, and the literature on persistence was reviewed. The literature revieu covered student variables affecting retention rates, ethnic status and retention, institutional variables affecting persistence, and theoretical models that explain persistence. Fulland part-time students at the commuter university were tracked from fall 1982 through the beginning of the fall 1986 term. Information was gathered on fall 1982 grade point average (GPA) and cumulative GPA in 1986. For students who entered as freshmen, high school GPA and Scholastic Aptitude Test scores were obtained. Transfer GPA and credit hours were obtained for transfer students. Persistence was defined as either obtaining a degree or being registered for the fall 1986 term. The analysis was done by class level, including remedial, freshman, sophomore, junior, and senior levels. Multiple discriminant analysis included the variables of gender and minority status. It was found that academic integration as measured by GPA was by far the best indicator of persistence. This finding was consistent with previous research on nontraditional students. However, retention patterns were somewhat affected by gender and minority status. Five data tables and a two-page list of references conclude the document. (SW)
LASSI: A low-rank and adaptive sparse signal model for highly accelerated dynamic imaging

Sparsity-based approaches have been popular in many applications in image processing and imaging. Recent research has shown the usefulness of sparsity or low-rank techniques for solving inverse problems such as those in dynamic imaging. In particular, the imaged temporal data sequence is modeled as a sum of low-rank and sparse components that are estimated from measurements. In this work, we instead decompose the temporal image sequence into a low-rank component and a component whose spatiotemporal patches are assumed sparse in some adaptive dictionary domain. We present a methodology to jointly estimate the underlying signal components and the spatiotemporal dictionary from highly under-sampled measurements. Our numerical experiments demonstrate the promising performance of our scheme for dynamic magnetic resonance image reconstruction from undersampled k-t space data.
Fast Exact MultiConstraint Shortest Path Algorithms

QoS routing has been shown to be NP-hard. A recent study of its hardness shows that the "worst-case" may not occur in practice (Kuipers mieghem, 2003). This suggests that there may exist fast exact algorithms for the multi-constraint shortest path (MCSP) problem, an instance of QoS routing. Search techniques such as A* and IDA* may solve hard problems exactly in polynomial time. In Li et al. (2005), we deploy the idea of iterative deepening search to design IDA*_MCSP, and show its efficiency by extensive empirical study. In this paper, we show that for infeasible cases, where there is no feasible solution, IDA*_MCSP may not be as efficient as A*Prune. This motivates us to design an algorithm that is efficient in both feasible and infeasible cases. We design an exact MCSP algorithm A*_MCSP, which introduces the state notion and dominance relationship between states. Furthermore, we design an exact MCSP algorithm FringeMCSP. It can be regarded as an integration of IDA*_MCSP and A*_MCSP. Extensive empirical study shows that FringeMCSP has good performance in both feasible and infeasible cases; while IDA*_MCSP still shows its superiority among the proposed MCSP algorithms in feasible cases.
Quarterly intrinsic disorder digest (January-February-March, 2014)

ABSTRACT This is the 5th issue of the Digested Disorder series that represents a reader's digest of the scientific literature on intrinsically disordered proteins. We continue to use only 2 criteria for inclusion of a paper to this digest: The publication date (a paper should be published within the covered time frame) and the topic (a paper should be dedicated to any aspect of protein intrinsic disorder). The current digest issue covers papers published during the first quarter of 2014; i.e., during the period of January, February, and March of 2014. Similar to previous issues, the papers are grouped hierarchically by topics they cover, and for each of the included papers a short description is given on its major findings.
Contributions to the Formal Verification of Arithmetic Algorithms. (Contributions à la vérification formelle d'algorithmes arithmétiques)

L'implantation en Virgule Flottante (VF) d'une fonction a valeurs reelles est realisee avec arrondi correct si le resultat calcule est toujours egal a l'arrondi de la valeur exacte, ce qui presente de nombreux avantages. Mais pour implanter une fonction avec arrondi correct de maniere fiable et efficace, il faut resoudre le «dilemme du fabricant de tables» (TMD en anglais). Deux algorithmes sophistiques (L et SLZ) ont ete concus pour resoudre ce probleme, via des calculs longs et complexes effectues par des implantations largement optimisees. D'ou la motivation d'apporter des garanties fortes sur le resultat de ces pre-calculs couteux. Dans ce but, nous utilisons l'assistant de preuves Coq. Tout d'abord nous developpons une bibliotheque d'«approximation polynomiale rigoureuse», permettant de calculer un polynome d'approximation et un intervalle bornant l'erreur d'approximation a l'interieur de Coq. Cette formalisation est un element cle pour valider la premiere etape de SLZ, ainsi que l'implantation d'une fonction mathematique en general (avec ou sans arrondi correct). Puis nous avons implante en Coq, formellement prouve et rendu effectif 3 verifieurs de certificats, dont la preuve de correction derive du lemme de Hensel que nous avons formalise dans les cas univarie et bivarie. En particulier, notre «verifieur ISValP» est un composant cle pour la certification formelle des resultats generes par SLZ. Ensuite, nous nous sommes interesses a la preuve mathematique d'algorithmes VF en «precision augmentee» pour la racine carre et la norme euclidienne en 2D. Nous donnons des bornes inferieures fines sur la plus petite distance non nulle entre sqrt(x²+y²) et un midpoint, permettant de resoudre le TMD pour cette fonction bivariee. Enfin, lorsque differentes precisions VF sont disponibles, peut survenir le phenomene de «double-arrondi», qui peut changer le comportement de petits algorithmes usuels en arithmetique. Nous avons prouve en Coq un ensemble de theoremes decrivant le comportement de Fast2Sum avec double-arrondis.
Occupation Measure Heuristics for Probabilistic Planning

For the past 25 years, heuristic search has been used to solve domain-independent probabilistic planning problems, but with heuristics that determinise the problem and ignore precious probabilistic information. To remedy this situation, we explore the use of occupation measures, which represent the expected number of times a given action will be executed in a given state of a policy. By relaxing the well-known linear program that computes them, we derive occupation measure heuristics -- the first admissible heuristics for stochastic shortest path problems (SSPs) taking probabilities into account. We show that these heuristics can also be obtained by extending recent operator-counting heuristic formulations used in deterministic planning. Since the heuristics are formulated as linear programs over occupation measures, they can easily be extended to more complex probabilistic planning models, such as constrained SSPs (C-SSPs). Moreover, their formulation can be tightly integrated into i-dual, a recent LP-based heuristic search algorithm for (constrained) SSPs, resulting in a novel probabilistic planning approach in which policy update and heuristic computation work in unison. Our experiments in several domains demonstrate the benefits of these new heuristics and approach.
Error resilient lossless image coding

We present an image compression scheme that is error resilient and offers lossless decompression in the absence of channel noise. Attempts are made to detect transmission errors and data found to be in error is discarded so as not to pollute the decompressed output. This is achieved while still maintaining a level of compression that is competitive with lossless image compression standards.
Handling noise in image deconvolution with local/non-local priors

Non-blind deconvolution consists in recovering a sharp latent image from a blurred image with a known kernel. Deconvolved images usually contain unpleasant artifacts due to the ill-posedness of the problem even when the kernel is known. Making use of natural sparse priors has shown to reduce ringing artifacts but handling noise remains limited. On the other hand, non-local priors have shown to give the best results in image denoising. We propose in this paper to combine both local and non-local priors to handle noise. We show that the blur increases the self-similarity within an image and thus makes non-local priors a good choice for denoising blurred images. However, denoising introduces outliers which are not Gaussian and should be well modeled. Experiments show that our method produces a better image reconstruction both visually and empirically compared to methods some popular methods.
Direct Optimization of the Dictionary Learning Problem

A novel way of solving the dictionary learning problem is proposed in this paper. It is based on a so-called direct optimization as it avoids the usual technique which consists in alternatively optimizing the coefficients of a sparse decomposition and in optimizing dictionary atoms. The algorithm we advocate simply performs a joint proximal gradient descent step over the dictionary atoms and the coefficient matrix. After having derived the algorithm, we also provided in-depth discussions on how the stepsizes of the proximal gradient descent have been chosen. In addition, we uncover the connection between our direct approach and the alternating optimization method for dictionary learning. We have shown that it can be applied to a broader class of non-convex optimization problems than the dictionary learning one. As such, we have denoted the algorithm as a one-step block-coordinate proximal gradient descent. The main advantage of our novel algorithm is that, as suggested by our simulation study, it is more efficient than alternating optimization algorithms.
Cellulose‐Based Diagnostic Devices for Diagnosing Serotype‐2 Dengue Fever in Human Serum

Here, two types of cellulose‐based in vitro diagnostic devices are demonstrated for the diagnosis of dengue virus infection in both buffer system and human serum: 1) paper‐based ELISA for providing the semiquantitative information of the disease activity of serotype‐2 dengue fever to healthcare persons (i.e., monitoring the disease activity with a specific serotype in single patients); 2) lateral flow immunoassays to screen for infection with serotype‐2 dengue fever (i.e., rapid YES or NO diagnosis prepared for large populations, in terms of global public health). Paper‐based ELISA (specific to serotype‐2 dengue fever), which builds off of our previous studies and a revised previous ELISA procedure, owns multiple advantages: 1) high sensitivity (about 40 times higher than the current ELISA‐based approaches, due to our therapeutic‐based monoclonal antibody) and specificity (specific to dengue virus serotype‐2 nonstructural protein‐1 antigens); 2) tiny amount of sample and reagent used for single tests; 3) short operating duration (i.e., rapid diagnostic device); and, 4) inexpensiveness (appropriate for use in all developing and underdeveloped nations of the world). Due to the higher sensitivity and shorter operating duration of paper‐based ELISA (compared with conventional ELISA, and lateral flow immunoassays also performed in this study), this study has not only been able to perform the diagnosis of dengue virus serotype‐2 nonstructural protein‐1 antigens in both buffer system and human serum but also to evaluate dengue virus serotype‐2 envelope proteins in the buffer system, thus successfully achieving the first such use of these proteins as the target antigen for the development of diagnostic tools. These results provide a more comprehensive understanding for the genesis of dengue fever diagnostic tools (through antibody‐antigen recognition).
Frontal cerebral blood flow change associated with infant-directed speech

Objective: To examine the auditory perception of maternal utterances by neonates using near-infrared spectroscopy (NIRS). Methods: Twenty full-term, healthy neonates were included in this study. The neonates were tested in their cribs while they slept in a silent room. First, two probe holders were placed on the left and right sides of the forehead over the eyebrows using double-sided adhesive tape. The neonates were then exposed to auditory stimuli in the form of infant-directed speech (IDS) or adult-directed speech (ADS), sampled from each of the mothers, through an external auditory speaker. Results: A 2 (stimulus: IDS and ADS) × 2 (recording site: channel 1 (right side) and channel 2 (left side)) analysis of variance for these relative oxygenated haemoglobin values showed that IDS (Mean = 0.25) increased brain function significantly (F = 3.51) more than ADS (Mean = −0.26). Conclusions: IDS significantly increased brain function compared with ADS. These results suggest that the emotional tone of maternal utterances could have a role in activating the brains of neonates to attend to the utterances, even while sleeping.
Real-Time Ultrasound-Guided Spinal Anaesthesia: A Prospective Observational Study of a New Approach

Identification of the subarachnoid space has traditionally been achieved by either a blind landmark-guided approach or using prepuncture ultrasound assistance. To assess the feasibility of performing spinal anaesthesia under real-time ultrasound guidance in routine clinical practice we conducted a single center prospective observational study among patients undergoing lower limb orthopaedic surgery. A spinal needle was inserted unassisted within the ultrasound transducer imaging plane using a paramedian approach (i.e., the operator held the transducer in one hand and the spinal needle in the other). The primary outcome measure was the success rate of CSF acquisition under real-time ultrasound guidance with CSF being located in 97 out of 100 consecutive patients within median three needle passes (IQR 1–6). CSF was not acquired in three patients. Subsequent attempts combining landmark palpation and pre-puncture ultrasound scanning resulted in successful spinal anaesthesia in two of these patients with the third patient requiring general anaesthesia. Median time from spinal needle insertion until intrathecal injection completion was 1.2 minutes (IQR 0.83–4.1) demonstrating the feasibility of this technique in routine clinical practice.
Reduced Idle Listening Based Medium Access Control Protocol for Wireless Sensor Networks

In this paper, an energy efficient MAC protocol based on the finite projective plane (FPP) theory is proposed. Our protocol introduces an adaptive energy-efficient duty cycle for the sensor nodes to reduce the idle listening problem. By assigning each node with an FPP-set, we propose to decrease wake-up time and to determine the sleep or wake-up schedules based on the existing number of neighbors of a network coordinator. We have done rigorous simulation to analyze and compare the performance of our MAC protocol with some important and well known MAC protocols of wireless sensor. The simulation results show that our protocol outperforms over other MAC protocols in terms of energy consumption and end-to-end delay.
Identification of Chaos-Periodic Transitions, Band Merging, and Internal Crisis Using Wavelet-DFA Method

Detrended Fluctuation Analysis (DFA) is a scaling analysis method that can identify intrinsic self-similarity in any nonstationary time series. In contrast, Wavelet Transform (WT) method is widely used to investigate the self-similar processes, as the self-similarity properties exist within the subbands. Therefore, a combination of these two approaches, DFA and WPT, is promising for rigorous investigation of such a system. In this paper a new methodology, so-called wavelet DFA, is introduced and interpreted to evaluate this idea. This approach, further than identifying self-similarity properties, enable us to detect and capture the chaos-periodic transitions, band merging, and internal crisis in systems that become chaotic through period-doubling phenomena. Changes of wavelet DFA exponent have been compared with that of Lyapunov and DFA through Logistic, Sine, Gaussian, Cubic, and Quartic Maps. Furthermore, the potential capabilities of this new exponent have been presented.
Cardiac-Specific YAP Activation Improves Cardiac Function and Survival in an Experimental Murine MI Model

Rationale: Yes-associated protein (YAP), the terminal effector of the Hippo signaling pathway, is crucial for regulating embryonic cardiomyocyte proliferation. Objective: We hypothesized that YAP activation after myocardial infarction (MI) would preserve cardiac function and improve survival. Methods and Results: We used a cardiac-specific, inducible expression system to activate YAP in adult mouse heart. Activation of YAP in adult heart promoted cardiomyocyte proliferation and did not deleteriously affect heart function. Furthermore, YAP activation after MI preserved heart function and reduced infarct size. Using adeno-associated virus subtype 9 (AAV9) as a delivery vector, we expressed human YAP (hYAP) in the adult murine myocardium immediately after MI. We found that AAV9:hYAP significantly improved cardiac function and mouse survival. AAV9:hYAP did not exert its salutary effects by reducing cardiomyocyte apoptosis. Rather, AAV9:hYAP stimulated adult cardiomyocyte proliferation. Gene expression profiling indicated that AAV9:hYAP stimulated expression of cell cycle genes and promoted a less mature cardiac gene expression signature. Conclusions: Cardiac-specific YAP activation after MI mitigated myocardial injury, improved cardiac function, and enhanced survival. These findings suggest that therapeutic activation of YAP or its downstream targets, potentially through AAV-mediated gene therapy, may be a strategy to improve outcome after MI.
Analysis of Matched Case–Control Data in Presence of Nonignorable Missing Exposure

Summary. The present article deals with informative missing (IM) exposure data in matched case–control studies. When the missingness mechanism depends on the unobserved exposure values, modeling the missing data mechanism is inevitable. Therefore, a full likelihood‐based approach for handling IM data has been proposed by positing a model for selection probability, and a parametric model for the partially missing exposure variable among the control population along with a disease risk model. We develop an EM algorithm to estimate the model parameters. Three special cases: (a) binary exposure variable, (b) normally distributed exposure variable, and (c) lognormally distributed exposure variable are discussed in detail. The method is illustrated by analyzing a real matched case–control data with missing exposure variable. The performance of the proposed method is evaluated through simulation studies, and the robustness of the proposed method for violation of different types of model assumptions has been considered.
Semantic segmentation for room categorization

Room category recognition is a highly desired functionality in household robots as it enables natural indoor navigation without having to rely solely on range sensors. Significant research has been invested into the problem of image-based room recognition. All approaches assume that the image contains a single category and usually apply holistic scene models. This assumption is often violated in realistic robotic scenarios, in which a robot observes several room categories within its view. We propose an alternative approach for room category recognition, based on per-pixel semantic segmentation. Our approach decomposes the input image into semantic regions, and easily deals with images containing either a single or multiple categories. As a side product, each room category is localized in the image, which makes our approach suitable for robotic exploration and navigation based on visual feedbacks. Our approach is evaluated on a new dataset, annotated with pixel-wise ground-truth labels for eight room categories. Results show that our approach outperforms the state-of-the-art in recognition as well as pixel-wise localization. Our approach exhibits a remarkable robustness to rotation and outperforms the holistic state-of-the-art approach even on examples with a single category.
Time and Spectral Domain Relative Entropy: A New Approach to Multivariate Spectral Estimation

The concept of spectral relative entropy rate is introduced for jointly stationary Gaussian processes. Using classical information-theoretic results, we establish a remarkable connection between time and spectral domain relative entropy rates. This naturally leads to a new spectral estimation technique where a multivariate version of the Itakura-Saito distance is employed. It may be viewed as an extension of the approach, called THREE, introduced by Byrnes, Georgiou, and Lindquist in 2000 which, in turn, followed in the footsteps of the Burg-Jaynes Maximum Entropy Method. Spectral estimation is here recast in the form of a constrained spectrum approximation problem where the distance is equal to the processes relative entropy rate. The corresponding solution entails a complexity upper bound which improves on the one so far available in the multichannel framework. Indeed, it is equal to the one featured by THREE in the scalar case. The solution is computed via a globally convergent matricial Newton-type algorithm. Simulations suggest the effectiveness of the new technique in tackling multivariate spectral estimation tasks, especially in the case of short data records.