Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Commons @ University of Digital Commons @ University of South Florida South Florida

Zooplankton dominate the abundance and biomass of multicellular animals in pelagic marine environments; however, traditional methods to characterize zooplankton communities are invasive and laborious. This study compares zooplankton taxonomic composition revealed through metabarcoding of the cytochrome oxidase I (COI) and 18S rRNA genes to traditional morphological identiﬁcation by microscopy. Trip-licates of three different sample types were collected from three coral reef sites in the Florida Keys National Marine Sanctuary: (1) 1 L surface seawater samples preﬁltered through 3 l m ﬁlters and subsequently collected on 0.22 l m ﬁlters for eDNA (PF-eDNA); (2) 1 L surface seawater samples ﬁltered on 0.22 l m pore-size ﬁlters (environmental DNA; eDNA), and (3) zooplankton tissue samples from 64 l m, 200 l m, and 500 l m mesh size net tows. The zooplankton tissue samples were split, with half identiﬁed morphologically and tissue DNA (T-DNA) extracted from the other half. The COI and 18S rRNA gene metabarcoding of PF-eDNA, eDNA, and T-DNA samples was performed using Illumina MiSeq. Of the families detected with COI and 18S rRNA gene metabarcoding, 40% and 32%, respectively, were also identiﬁed through morphological assessments. Signiﬁcant differences in taxonomic composition were observed between PF-DNA, eDNA, and T-DNA
G-tree: an efficient index for KNN search on road networks

In this paper we study the problem of kNN search on road networks. Given a query location and a set of candidate objects in a road network, the kNN search finds the k nearest objects to the query location. To address this problem, we propose a balanced search tree index, called G-tree. The G-tree of a road network is constructed by recursively partitioning the road network into sub-networks and each G-tree node corresponds to a sub-network. Inspired by classical kNN search on metric space, we introduce a best-first search algorithm on road networks, and propose an elaborately-designed assembly-based method to efficiently compute the minimum distance from a G-tree node to the query location. G-tree only takes O(|V|log|V|) space, where |V| is the number of vertices in a network, and thus can easily scale up to large road networks with more than 20 millions vertices. Experimental results on eight real-world datasets show that our method significantly outperforms state-of-the-art methods, even by 2-3 orders of magnitude.
Capital budgeting practices in the U.S. forest products industry: A reappraisal

This study examined the capital investment practices of publicly owned forest products firms in the United States in 2001 by replicating research reported by Cubbage and Redmond in 1985. The current study utilized a survey instrument to collect data that was identical to the Cubbage and Redmond, thereby allowing for direct comparison of data. Results indicate that discount cash flow techniques are the most preferred capital budgeting decision criteria used in the industry, similar to results reported by Cubbage and Redmond. However, there has been an increase in the use of the net present value methods, and, despite suggestions against its use in the financial literature, widespread use of internal rates of return measures in capital budgeting analysis. Some larger companies indicated frequent use of more sophisticated evaluation methods, such as economic value analysis. Additional questions were added to the 2001 survey to assess perceptions of risk and uncertainty as they pertain to capital budgeting practices and are also reported in this study.
Socially Aware Robot Navigation through Scoring Using Vision-Language Models

—We propose a novel Vision-Language Model (VLM) based navigation approach to compute a robot’s trajectory in human-centered environments. Our goal is to make real-time decisions on robot actions that are socially compliant with human expectations. We utilize the perception model to detect important social entities and prompt a VLM to generate guidance for socially compliant robot behavior. Our approach uses a VLM-based scoring module that computes a cost term that ensures socially appropriate and effective robot actions generated by the underlying planner. Our overall approach reduces reliance on large datasets (for training) and enhances adaptability in decision-making. In practice, it results in improved socially compliant navigation in human-shared environments. We demonstrate and evaluate our system in four different real-world social navigation scenarios with a Turtlebot robot. We observe at least 36 . 37% improvement of success rate in four challenging social navigation scenarios comparing with other state-of-the-art methods.
An auto-configuration of 4M group management using Wireless Sensor Networks for reconfigurable manufacturing system

The 1st tier of automotive manufacturers has faced to pressures about move, modify, updating tasks for manufacturing resources in production processes from demand response of production order sequence for motor company and process innovation purpose for productivity. For meets this requirements, it has to require absolutely lead time to re-wiring of physical interface for production equipment, needs for change existing program and test over again. For prepare this constraints, it needs studying an auto-configuration functions that build for both visibility and flexibility based on the 4M (Man, Machine, Material, Method) group management which is supports from WSN (Wireless Sensor Network) of the open embedded device called M2M (Machine to Machine) and major functions of middleware including point manager for real time device communication, real time data management, Standard API (Application Program Interface) and application template management. To be application system to RMS (Reconfigurable Manufacturing System) for rapidly response from various orders and model from motor company that is beginning to establishing the mapping of manufacturing resources of 4M using WSN.
Eurographics Symposium on Point-based Graphics (2004) M. Shape Segmentation and Matching from Noisy Point Clouds

We present the implementation results of a shape segmentation technique and an associated shape matching method whose input is a point sample from the shape. The sample is allowed to be noisy in the sense that they may scatter around the boundary of the shape instead of lying exactly on it. The algorithm is simple and mostly combinatorial in that it builds a single data structure, the Delaunay triangulation of the point set, and groups the tetrahedra to form the segments. A small set of weighted points are derived from the segments which are used as signatures to match shapes. Experimental results establish the effectiveness of the method in practice.
Diagnostic Accuracy of Fluorine-18-Fluorodeoxyglucose Positron Emission Tomography in the Evaluation of the Primary Tumor in Patients with Cholangiocarcinoma: A Meta-Analysis

Objective. To meta-analyze published data about the diagnostic accuracy of fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) or PET/computed tomography (PET/CT) for primary tumor evaluation in patients with cholangiocarcinoma (CCa). Methods. A comprehensive literature search of studies published through December 31, 2013, was performed. Pooled sensitivity and specificity were calculated on a per patient based analysis. Subgroup analyses considering the device used (PET versus PET/CT) and the localization of the primary tumor (intrahepatic cholangiocarcinoma (IH-CCa), extrahepatic cholangiocarcinoma (EH-CCa), and hilar cholangiocarcinoma (H-CCa)) were carried out. Results. Twenty-three studies including 1232 patients were included in the meta-analysis. Pooled sensitivity and specificity of 18F-FDG-PET or PET/CT were 81% and 82%, respectively. Pooled sensitivity and specificity, respectively, were 80% and 89% for PET, 82% and 75% for PET/CT, 95% and 83% for IH-CCa, 84% and 95% for H-CCa, and 76% and 74% for EH-CCa. Conclusions. 18F-FDG-PET and PET/CT were demonstrated to be accurate diagnostic imaging methods for primary tumor evaluation in patients with CCa. These tools have a better diagnostic accuracy in patients with IH-CCa than in patients with EH-CCa. Further studies are needed to evaluate the accuracy of 18F-FDG-PET or PET/CT in patients with H-CCa.
Expertise Combination with Spectral Clustering in Inverse Reinforcement Learning

Already present in early childhood, humans have a high capacity to learn from each other by observation (Tomasello, 2009). Being able to model this behavior is an active area of artiﬁcial intelligence research. This project explores the question of how can an agent combine their own expertise with what they have learnt from observing another, possibly suboptimal agent in order to perform a task more optimally. Expertise combination with spectral clustering as a method for achieving the combination of the knowledge of two agent is proposed which divides the state space of a simulated environment into regions to aid in choosing in what state is it useful to abide by which agent’s behavior. It is shown that the proposed method of expertise combination is efﬁcient in gridworld environments for creating new policies that outperform the original agents. Ultimately, it is demonstrated that incorporating expertise combination within the inverse reinforcement learning loop is an efﬁcient tactic to learn from agents that do not directly optimise for the target task.
Amplitude and timescale of metacommunity trait-lag response to climate change

Climate change is altering the structure and functioning of communities 1. Trait-based approaches are powerful predictive tools that allow consideration of changes in structure and functioning simultaneously 2, 3. The realised biomass-weighted trait distribution of a community rests on the ecophysiology of individuals, but integrates local species interactions and spatial dynamics that feed back to ecosystem functioning. Consider a response trait that determines species performance (e.g. growth rate) as a function of an environmental variable (e.g. temperature). The change in this response trait’s distribution following directional environmental change integrates all factors contributing to the community’s response and directly reflects the community’s response capacity 3. Here we introduce the average regional community trait-lag (TLMC) as a novel measure of whole-metacommunity response to warming. We show that functional compensation (shifts in resident species relative abundances) confers initial response capacity to communities by reducing and delaying the initial development of a trait-lag. Metacommunity adaptive capacity in the long-term, however, was dependent on dispersal and species tracking of their climate niche by incremental traversal of the landscape. With increasing inter-patch distances, network properties of the functional connectivity network became increasingly more important, and may guide prioritisation of habitat for conservation.
Saliency-Guided Just Noticeable Distortion Estimation Using the Normalized Laplacian Pyramid

The human visual system (HVS), like any other physical system, has limitations. For instance, it is known that the HVS can only sense the content changes that are larger than the so-called just noticeable distortion (JND) threshold. Also, to reduce the computational load on the brain, the visual attention mechanism is deployed such that regions with higher visual saliency are processed with higher priority than other less-salient regions. It is also known that visual saliency has a modulatory effect on JND thresholds. In this letter, we present a novel pixel-wise JND estimation method that considers the interplay between visual saliency and JND thresholds. In the proposed method, the largest JND thresholds of a given image are found such that the perceptual distance between the image and its JND noise-contaminated version is minimized in a perceptual space defined by the coefficients of the image in a normalized Laplacian pyramid. Experimental results indicate that the proposed method outperforms four of the latest JND models for static images.
3-D Facial Landmark Localization With Asymmetry Patterns and Shape Regression from Incomplete Local Features

We present a method for the automatic localization of facial landmarks that integrates nonrigid deformation with the ability to handle missing points. The algorithm generates sets of candidate locations from feature detectors and performs combinatorial search constrained by a flexible shape model. A key assumption of our approach is that for some landmarks there might not be an accurate candidate in the input set. This is tackled by detecting partial subsets of landmarks and inferring those that are missing, so that the probability of the flexible model is maximized. The ability of the model to work with incomplete information makes it possible to limit the number of candidates that need to be retained, drastically reducing the number of combinations to be tested with respect to the alternative of trying to always detect the complete set of landmarks. We demonstrate the accuracy of the proposed method in the face recognition grand challenge database, where we obtain average errors of approximately 3.5 mm when targeting 14 prominent facial landmarks. For the majority of these our method produces the most accurate results reported to date in this database. Handling of occlusions and surfaces with missing parts is demonstrated with tests on the Bosphorus database, where we achieve an overall error of 4.81 and 4.25 mm for data with and without occlusions, respectively. To investigate potential limits in the accuracy that could be reached, we also report experiments on a database of 144 facial scans acquired in the context of clinical research, with manual annotations performed by experts, where we obtain an overall error of 2.3 mm, with averages per landmark below 3.4 mm for all 14 targeted points and within 2 mm for half of them. The coordinates of automatically located landmarks are made available on-line.
Modulation of In Vivo Cardiac Function by Myocyte-Specific Nitric Oxide Synthase-3

Abstract— Nitric oxide (NO) functions principally as a diffusible paracrine effector. The exception is in cardiomyocytes where both NO synthases (NOS) and target proteins coexist, allowing NO to work in an autocrine/intracrine fashion. However, the most abundant myocyte isoform (NOS3) is far more expressed in vascular endothelium; thus, the in vivo contribution of myocyte-NOS3 remains less clear. The present study tested this role by transfecting whole hearts of NOS3-null (NOS3−/−) mice with adenovirus-expressing NOS3 coupled to a &agr;-MHC promoter (AdVNOS3), comparing results to hearts transfected with marker-gene &bgr;-galactosidase (AdV&bgr;gal). Total myocardial NOS3 protein and activity were restored to near wild-type (WT) levels in NOS3−/−+AdVNOS3 hearts, and NOS3 relocalized normally with caveolin-3. Ejection function by pressure-volume analysis was enhanced in NOS3−/−+AdV&bgr;gal over WT or NOS3−/−+AdVNOS3. More prominently, isoproterenol (ISO)-stimulated systolic and diastolic function in WT was amplified in NOS3−/−+AdV&bgr;gal, whereas NOS3−/−+AdVNOS3 returned the response to control. ISO-activated systolic function was inhibited 85% by concomitant muscarinic stimulation (carbachol) in NOS3−/−+AdVNOS3 but not NOS3−/−+AdV&bgr;gal hearts. Lastly, NOS3−/−+AdV&bgr;gal mice displayed enhanced inotropy and lusitropy over WT at slower heart rates but a blunted rate augmentation versus controls. A more positive rate response was restored in NOS3−/−+AdVNOS3 (P <0.001). Thus, myocyte autocrine/intracrine NOS3 regulation in vivo can underlie key roles in &bgr;-adrenergic, muscarinic, and frequency-dependent cardiac regulation.
Petal: distributed virtual disks

The ideal storage system is globally accessible, always available, provides unlimited performance and capacity for a large number of clients, and requires no management. This paper describes the design, implementation, and performance of Petal, a system that attempts to approximate this ideal in practice through a novel combination of features. Petal consists of a collection of network-connected servers that cooperatively manage a pool of physical disks. To a Petal client, this collection appears as a highly available block-level storage system that provides large abstract containers called virtual disks. A virtual disk is globally accessible to all Petal clients on the network. A client can create a virtual disk on demand to tap the entire capacity and performance of the underlying physical resources. Furthermore, additional resources, such as servers and disks, can be automatically incorporated into Petal.We have an initial Petal prototype consisting of four 225 MHz DEC 3000/700 workstations running Digital Unix and connected by a 155 Mbit/s ATM network. The prototype provides clients with virtual disks that tolerate and recover from disk, server, and network failures. Latency is comparable to a locally attached disk, and throughput scales with the number of servers. The prototype can achieve I/O rates of up to 3150 requests/sec and bandwidth up to 43.1 Mbytes/sec.
On restoring band-limited signals

The problem of reconstruction of band-limited signals from discrete and noisy data is studied. The reconstruction schemes employing cardinal expansions are proposed and their asymptotical properties are examined. In particular, the conditions for the convergence of the mean integrated squared error are found and the rate of convergence is evaluated. The main difference between the proposed reconstruction scheme and the classical one is in treating the sampling rate and the reconstruction rate differently. This distinction is necessary to ensure consistency of the reconstruction scheme in the presence of noise
Malware Detection by Data Mining Techniques Based on Positionally Dependent Features

The challenges being thrown to modern world by the need to counteract against malicious software (malware) are going on to increase own importance. This fact stays actual, in spite of obvious great results in improving the efficacy of procedures of malware propagation detection, analysis and updating the bases of signatures and detection rules. The important aspect of this problem is looking for more reliable heuristic detection methods. These methods focus on recognition of new (unknown before) malicious programs which can not be detected by using traditional signature- and rule-based detection techniques, oriented on search for concrete malware samples and families. Virtually, just these heuristic methods provide counteraction against targeted and zero-day attacks, since the rate of detecting such relatively new types of threats by traditional techniques is not enough. The presented paper is devoted to using Data Mining methods for constructing heuristic malware detectors. The approach described below differs from others by focusing on processing static positionally dependent features which consider the specificities of object’s file format of potential malware containers. The paper describes the realization and investigation of the common methodology for design of Data Mining-based malware detectors’ using positionally dependent static information.
Enhanced T2 contrast for MR histology of the mouse brain

A 3D Carr‐Purcell‐Meiboom‐Gill (CPMG) sequence was implemented to obtain enhanced T2 contrast in actively stained (perfusion with fixative and contrast agent) mouse brains at 9.4 T. Short interecho spacing was used to minimize diffusion and susceptibility losses. The sequence produced 16 3D volumes with an interecho spacing of 7 ms for isotropic 43‐μ‐resolution images of the mouse brains in a scan time of 4 hr. To enhance the signal‐to‐noise ratio (SNR) and contrast, the multiecho frequency domain image contrast (MEFIC) method was applied, resulting in a composite image with T2‐weighted contrast. The high SNR and contrast thus achieved revealed aspects of mouse brain morphology, such as multiple cortical layers, groups of thalamic nuclei, layers of the inferior and superior colliculus, and molecular and granular layers of the cerebellum, with a high degree of definition and contrast that was not previously achieved in T2‐weighted acquisitions at high fields. Magn Reson Med, 2006. © 2006 Wiley‐Liss, Inc.
NaviRadar: a novel tactile information display for pedestrian navigation

We introduce NaviRadar: an interaction technique for mobile phones that uses a radar metaphor in order to communicate the user's correct direction for crossings along a desired route. A radar sweep rotates clockwise and tactile feedback is provided where each sweep distinctly conveys the user's current direction and the direction in which the user must travel. In a first study, we evaluated the overall concept and tested five different tactile patterns to communicate the two different directions via a single tactor. The results show that people are able to easily understand the NaviRadar concept and can identify the correct direction with a mean deviation of 37° out of the full 360° provided. A second study shows that NaviRadar achieves similar results in terms of perceived usability and navigation performance when compared with spoken instructions. By using only tactile feedback, NaviRadar provides distinct advantages over current systems. In particular, no visual attention is required to navigate; thus, it can be spent on providing greater awareness of one's surroundings. Moreover, the lack of audio attention enables it to be used in noisy environments or this attention can be better spent on talking with others during navigation.
Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space

This paper explores image caption generation using conditional variational auto-encoders (CVAEs). Standard CVAEs with a fixed Gaussian prior yield descriptions with too little variability. Instead, we propose two models that explicitly structure the latent space around $K$ components corresponding to different types of image content, and combine components to create priors for images that contain multiple types of content simultaneously (e.g., several kinds of objects). Our first model uses a Gaussian Mixture model (GMM) prior, while the second one defines a novel Additive Gaussian (AG) prior that linearly combines component means. We show that both models produce captions that are more diverse and more accurate than a strong LSTM baseline or a "vanilla" CVAE with a fixed Gaussian prior, with AG-CVAE showing particular promise.