Miracle-dz/UniFAR-FTUs huggingface dataset:
Boring sponges, an increasing threat for coral reefs affected by bleaching events

Coral bleaching is a stress response of corals induced by a variety of factors, but these events have become more frequent and intense in response to recent climate-change-related temperature anomalies. We tested the hypothesis that coral reefs affected by bleaching events are currently heavily infested by boring sponges, which are playing a significant role in the destruction of their physical structure. Seventeen reefs that cover the entire distributional range of corals along the Mexican Pacific coast were studied between 2005/2006, and later between 2009/2010. Most of these coral reefs were previously impacted by bleaching events, which resulted in coral mortalities. Sponge abundance and species richness was used as an indicator of bioerosion, and coral cover was used to describe the present condition of coral reefs. Coral reefs are currently highly invaded (46% of the samples examined) by a very high diversity of boring sponges (20 species); being the coral reef framework the substrate most invaded (56%) followed by the rubbles (45%), and the living colonies (36%). The results also indicated that boring sponges are promoting the dislodgment of live colonies and large fragments from the framework. In summary, the eastern coral reefs affected by bleaching phenomena, mainly provoked by El Niño, present a high diversity and abundance of boring sponges, which are weakening the union of the colony with the reef framework and promoting their dislodgment. These phenomena will probably become even more intense and severe, as temperatures are projected to continue to rise under the scenarios for future climate change, which could place many eastern coral reefs beyond their survival threshold.
ZnO Nanoribbon Microcavity Lasers

Exploration of one-dimensional semiconductor nanostruc-tures has led to great progress in the areas of optoelectronics in the past few years. [1] Nanolasers, [2±4] waveguides, [5] frequency converters (second or third harmonic generators), [6] photoconductive optical switches, [7] and sensors [8,9] have been developed based on oxide nanowires. The single crystalline nature of these nanowires makes them ideal candidates for probing size-dependent and dimensionality-controlled physical phenomena. In particular, the transverse nanoscale and longitudinal microscale dimensions (i.e., large aspect ratio) as well as well-defined faceting nature of such nanostructures enable the observation of unique optical confinement and mi-crocavity effects. [2±4] Previously, hexagonal cylindrical ZnO nanowires have been examined as a laser gain medium. These nanocylinders indeed can serve as miniaturized Fabry± Perot optical cavities in the ultraviolet (UV) region with high gain and quality factor. [10] Based on classical waveguide theory , different transverse optical modes can be sustained within waveguides of different cross-sections. [11] It is thus fundamentally interesting to examine the optical cavity effects within nanowires with cross-sections other than hexagonal. Herein, we examine the lasing phenomenon from ZnO nanoribbons with pseudo-rectangular cross-section. Cavity-length dependent optical mode analysis reveals different cavity effects from those of hexagonal nanocylinders. ZnO, being environmentally benign and having a large bandgap (3.37 eV) and exciton binding energy (60 meV), has been considered as a promising candidate for UV light-emitting diodes and laser diodes. It has also displayed an astonishing series of nanostructures with different morphologies. Among many others, the hexagonal nanocylinders, [2±4] nano-ribbons, [5,13] tetrapods, [12] and comb-like nanowire arrays [14] are highly interesting for their fundamental significance in revealing microcavity effects as well as near-field optical coupling phenomena. The ZnO nanoribbons in this work were synthesized using two methodologies. One is a carbon thermal reduction process at 900 C. [12] The other is the high temperature (1350 C) approach developed by Wang and co-workers. [13] The two methods involve different growth mechanisms. The low temperature process utilized Au as the growth initiator. The observation of Au nanoparticles on the nanoribbon tips in the transmission electron microscope (TEM, Figure 1B) suggests that this is a vapor±liquid±solid (VLS) growth process. In contrast , the high-temperature approach is a vapor±solid (VS) growth process without any foreign metal initiation. Regardless of these different growth mechanisms, the growth directions and side facets of the produced nanoribbons are identical for the two sets of the samples. The length of the …
The cubic chessboard

We present a survey of recent results, scattered in a series of papers that have appeared during the past five years, whose common denominator has been the use of cubic relations in various algebraic structures. Cubic (or ternary) relations can represent different symmetries with respect to the permutation group S3, or its cyclic subgroup Z3. Also ordinary or ternary algebras can be divided into different classes with respect to their symmetry properties. We pay special attention to the non-associative ternary algebra of 3-forms (or cubic matrices), and Z3-graded matrix algebras. We also discuss the Z3-graded generalization of Grassmann algebras and their realization in generalized exterior differential forms dξ and d2ξ, with d3ξ=0. A new type of gauge theory based on this differential calculus is presented. Finally, a ternary generalization of Clifford algebras is introduced, and an analogue of Dirac's equation is discussed, which can be diagonalized only after taking the cube of the Z3-graded generalization of Dirac's operator. A possibility of using these ideas for the description of quark fields is suggested and discussed in the last section.
The association between HIV and antituberculosis drug resistance

In the UK, HIV is considered to be a risk factor for antituberculosis drug resistance. Evidence of the association is, however, inconclusive and there are few population-level data. The present study investigated the association in England and Wales during the period 1999–2005. National tuberculosis surveillance data for adults were matched to HIV/AIDS reports. Unmatched cases were assumed to be HIV-negative. Separate analyses were conducted on new tuberculosis cases and those with a previous diagnosis. Logistic regression was used for univariable and multivariable analyses. There were 1,657 previously diagnosed cases (80 HIV-positive) and 18,130 new cases (1,156 HIV-positive). Isoniazid resistance was found in 8.1% of previously diagnosed cases and 6.6% of new cases, and multidrug resistance in 2.8% and 0.7%, respectively. There was no evidence of an association between HIV and antituberculosis drug resistance among previously diagnosed cases. Among new cases, there was no overall association between HIV and isoniazid or multidrug resistance after adjusting for confounding factors. White HIV-positive patients were more likely to have multidrug resistance, but numbers were small. In contrast to some previous studies, this large, up-to-date study provides little evidence that HIV co-infected tuberculosis patients in England and Wales are at increased risk of firstline antituberculosis drug resistance.
A generalized method for computing oscillator phase noise spectra

This paper presents a generalized semi-analytic method for computing oscillator phase noise spectra, including the details very close to the oscillation frequency. The starting point is a general relation between an oscillator's output power spectral density and the characteristics of the input noise processes. For weak input noise processes that vary sufficiently fast over time, this relation reduces to an analytic expression. For cases that do not satisfy these conditions, the relation is, in part, evaluated numerically. This is accomplished using techniques for exponential data fitting. The resulting method is able to compute oscillator phase noise spectra for a wide range of input noise characteristics.
Cross Layer Design For Multiaccess Communication Over Rayleigh Fading Channels

An information theoretic queueing model is proposed in a wireless multiple access communication setup. The proposed symmetric N user model captures physical layer parameters such as the encoding rate, transmit power and medium access control (MAC) layer metrics such as queue stability. Two alternative medium access strategies are considered: centralized scheduling and ALOHA. Next, a cross-layer approach is taken wherein the maximum stable throughput of the system is achieved by a joint optimization over the MAC parameters (viz., scheduling set size with scheduling and transmission probability with ALOHA) and the encoding rate. Performance comparisons with traditional layered designs are given. It is shown that in the low and high SNR regimes, layered designs are close to optimal whereas in the moderate SNR range, cross-layer designs outperform layered schemes. Exact characterizations of the "low" and "high" SNR regimes are given quantitatively. It is also shown that ALOHA with transmission probability one is optimal in the low SNR regime
Using relational knowledge discovery to prevent securities fraud

We describe an application of relational knowledge discovery to a key regulatory mission of the National Association of Securities Dealers (NASD). NASD is the world's largest private-sector securities regulator, with responsibility for preventing and discovering misconduct among securities brokers. Our goal was to help focus NASD's limited regulatory resources on the brokers who are most likely to engage in securities violations. Using statistical relational learning algorithms, we developed models that rank brokers with respect to the probability that they would commit a serious violation of securities regulations in the near future. Our models incorporate organizational relationships among brokers (e.g., past coworker), which domain experts consider important but have not been easily used before now. The learned models were subjected to an extensive evaluation using more than 18 months of data unseen by the model developers and comprising over two person weeks of effort by NASD staff. Model predictions were found to correlate highly with the subjective evaluations of experienced NASD examiners. Furthermore, in all performance measures, our models performed as well as or better than the handcrafted rules that are currently in use at NASD.
Offset-Canceling Current-Sampling Sense Amplifier for Resistive Nonvolatile Memory in 65 nm CMOS

Resistive nonvolatile memory (NVM) is considered to be a leading candidate for next-generation memory. However, maintaining a target sensing margin is a challenge with technology scaling because of the increased process variation and decreased read cell current. This paper proposes an offset-canceling current-sampling sense amplifier (OCCS-SA) that is intended for use in deep submicrometer resistive NVM. The proposed OCCS-SA has the three major advantages of: 1) offset voltage cancellation; 2) double sensing margin structure; and 3) strong positive feedback. The measurement results from a 65 nm test chip show that the proposed OCCS-SA achieves 2.4 times faster sensing time (<inline-formula> <tex-math notation="LaTeX">$t_{\mathrm {\mathbf {SEN}}}$ </tex-math></inline-formula>) at a nominal supply voltage (<inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {\mathbf {DD}}}$ </tex-math></inline-formula>) of 1.0 V and a greater than 20% reduction in <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {\mathbf {DD}}}$ </tex-math></inline-formula> at the same <inline-formula> <tex-math notation="LaTeX">$t_{\mathrm {\mathbf {SEN}}}$ </tex-math></inline-formula>, compared to the state-of-the-art current-sampling-based SA, which features offset voltage cancellation and weak positive feedback.
Primitive accumulation and enclosure of the commons: Genetically engineered seeds and Canadian jurisprudence

Abstract Legal decisions in the case of Percy Schmeiser, who was sued by Monsanto for patent infringement, are related to the attempt by the Organic Agriculture Protection Fund to obtain class certification in its efforts to sue Monsanto and Bayer for genetic contamination of organic canola. Together these two cases establish an unacceptable incongruity at common law between the rights enjoyed by intellectual property owners and any corresponding duties that might attach to their inventions. Marx's concept of primitive accumulation offers a suitable theoretical register for apprehending contemporary erosions of the commons through the enclosure effects that follow in the wake of agricultural biotechnology and these two legal cases.
[Depressives have the better view - the influence of mood on the recognition of emotional expression].

OBJECTIVES There are different approaches to whether depressed people perceive their environment differently than nondepressed.We analyzed whether depressed patients show greater deficits in decoding emotional expressions than nondepressives. METHODS A sample of 52 depressed patients and a sample of 72 nondepressed persons were investigated as to their ability to identify emotionally laden facial expressions (computer-assisted presentation of photos). RESULTS Our results demonstrate significant differences between depressive patients and nondepressive persons in the ability to decode the emotional states of others. In four out of six tests the depressive persons achieved significantly better results. CONCLUSIONS We suggest that these results can be interpreted as further evidence for the concept of depressive emotional realism.
A Study on the Wide-Sense Stationarity of the Underwater Acoustic Channel for Non-coherent Communication Systems

In this paper we study the wide sense stationarity of the energy of the underwater acoustic channel impulse response and we provide an estimate of the interval during which the wide sense stationarity property holds. We analyze the SPACE08 data set, which has been collected near Martha's Vineyard Island during October 2008. In this data set the environmental conditions such as the height of the waves and the wind direction vary significantly in time. We consider the time series recorded in four different fixed positions. This allows us to compare the results between different locations and environmental conditions.
Equilibria and convergence of auctions on networks

First, a quantized progressive second price auction mechanism called UQ-PSP is developed to allocate a divisible resource among arbitrary populations of agents. It is shown that (i) the states (i.e. bid prices and quantities) of the corresponding iterative dynamical auction system converge to a unique quantized (Nash) equilibrium with a common limit price for all agents, and (ii) the dynamics are independent of the initial data. Second, a network based auction is developed where each agent employs a UQ-PSP scheme by observing only their neighbors' bids. The equilibria and convergence properties of this class of distributed auctions are established and are shown to depend on the network topology, and numerical examples are given.
Feature Selection and Classification on Matrix Data: From Large Margins to Small Covering Numbers

We investigate the problem of learning a classification task for datasets which are described by matrices. Rows and columns of these matrices correspond to objects, where row and column objects may belong to different sets, and the entries in the matrix express the relationships between them. We interpret the matrix elements as being produced by an unknown kernel which operates on object pairs and we show that - under mild assumptions - these kernels correspond to dot products in some (unknown) feature space. Minimizing a bound for the generalization error of a linear classifier which has been obtained using covering numbers we derive an objective function for model selection according to the principle of structural risk minimization. The new objective function has the advantage that it allows the analysis of matrices which are not positive definite, and not even symmetric or square. We then consider the case that row objects are interpreted as features. We suggest an additional constraint, which imposes sparseness on the row objects and show, that the method can then be used for feature selection. Finally, we apply this method to data obtained from DNA microar-rays, where "column" objects correspond to samples, "row" objects correspond to genes and matrix elements correspond to expression levels. Benchmarks are conducted using standard one-gene classification and support vector machines and K-nearest neighbors after standard feature selection. Our new method extracts a sparse set of genes and provides superior classification results.
Design and Implementation of a String Matching System for Network Intrusion Detection using FPGA-based low power multiple-hashing Bloom Filters

Modern Network Intrusion Detection Systems (NIDS) inspect the network packet payload to check if it conforms to the security policies of the given network. This process, often referred to as deep packet inspection, involves detection of predefined signature strings or keywords starting at an arbitrary location in the payload. String matching is a computationally intensive task and can become a potential bottleneck without high-speed processing. Since the conventional software-implemented string matching algorithms have not kept pace with the increasing network speeds, special purpose hardware, Field Programmable Gate Arrays (FPGAs), have been introduced. A Bloom filter is a simple spaceefficient randomized data structure for representing a set in order to support string matching of Network Intrusion Detection System (NIDS). Bloom filters allow false positives but the space savings often outweigh this drawback when the probability of an error is controlled. FPGAs have achieved sufficient capability to performing complex network processing in programmable hardware. Network devices utilizing FPGAs show a desirable balance between performance and flexibility, which makes FPGA preferable to pure software and ASIC solutions. We present an implementation of low power multiple hashing bloom filter using FPGAs. We describe how multiple hashing Bloom filters can be implemented feasibly on Xilinx XCV2000E FPGA.
Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

Abstract. Currently, histopathological tissue examination by a pathologist represents the gold standard for breast lesion diagnostics. Automated classification of histopathological whole-slide images (WSIs) is challenging owing to the wide range of appearances of benign lesions and the visual similarity of ductal carcinoma in-situ (DCIS) to invasive lesions at the cellular level. Consequently, analysis of tissue at high resolutions with a large contextual area is necessary. We present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, DCIS, and invasive ductal carcinoma (IDC). We first train a CNN using high pixel resolution to capture cellular level information. The feature responses generated by this model are then fed as input to a second CNN, stacked on top of the first. Training of this stacked architecture with large input patches enables learning of fine-grained (cellular) details and global tissue structures. Our system is trained and evaluated on a dataset containing 221 WSIs of hematoxylin and eosin stained breast tissue specimens. The system achieves an AUC of 0.962 for the binary classification of nonmalignant and malignant slides and obtains a three-class accuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC, demonstrating its potential for routine diagnostics.
A UAV vision system for airborne surveillance

The paper presents a machine vision system for aerial surveillance that can interpret and process data acquired by a UAV on-board infrared camera. System components include noise reduction, feature extraction, classification and decision-making. Decision-making is performed in terms of an alarm signal. The system has been configured for automatic fire detection applications where the alarm is set off in case of fire identification. Real time tests have been performed and the system has been tested producing sets of real images. Finally, a genetic algorithm was used to automatically define some of the system's parameters.
Development of computational models for microstructural features

Microstructural models have been developed to describe static and dynamic ductile or brittle frature, wave propagation in composites, phase transformations, and the detonation of explosives. Here an approach to developing such models is presented. The models are continuum models on a macro level, but have additional internal state variables describing the number, size, and other aspects of inclusions, cracks, voids, or hot spots. The models include rules for modifying the internal state variables: these rules are derived from the physical processes of initiation, nucleation, growth, and coalescence. Stress‐strain relations are constructed which account for the microstructural features. A ductile facture model is used to illustrate the modeling approach. Advantages of the microstructural approach include the ability to represent very complex, nonlinear processes in a physically reasonable way and the close coordination with experimental results which leads to better understanding of the microprocesses.
Trajectory planning in a dynamic workspace: a 'state-time space' approach

This paper addresses trajectory planning in a dynamic workspace, i.e. motion planning for a robot subject to dynamic constraints and moving in a workspace with moving obstacles. First the novel concept of state-time space is introduced, i.e. the state space of the robot augmented of the time dimension. Like configuration space which is a tool to formulate path-planning problems, state-time space is a tool to formulate trajectory planning in dynamic workspace problems. It permits us to study the different aspects of dynamic trajectory planning, i.e. moving obstacles and dynamic constraints, in a unified way. Then this new concept is applied to the case of a car-like robot subject to dynamic constraints and moving along a given path on a dynamic planar workspace. A near-time-optimal approach that searches the solution trajectory over a restricted set of canonical trajectories is presented. These canonical trajectories are defined as having discrete and piecewise constant acceleration. Under these assumption.
Improving speech systems built from very little data

This paper studies two ways for helping non-specialist users develop speech systems from limited data for new languages. Focused web re-crawling finds additional examples of text matching the domain as specified by the user. This improves the language model and cuts word error rate nearly in half. Iterative voice building with interleaved lexicon construction uses the voice from a previous iteration to help construct an improved voice. 4.5 hours of the user’s time reduces transcription error rate from 32% to 4%.