Miracle-dz/UniFAR-FTUs huggingface dataset:
A Selective Broth for Clostridium Difficile

Summary A selective broth for the isolation of Clostridium difficile from stool specimens is described. The broth contains gentamicin, cycloserine and cefoxitin (GCC broth) and gives rapid presumptive evidence of the presence of C. difficile using gas‐liquid chromatography. The broth may also be used for the detection of cytotoxin. Final recovery of C. difficile was significantly improved with an increase in isolation rate of 20% in patients in whom fecal cytotoxin could be detected and 125% in patients where fecal cytotoxin could not be detected. Until the pathogenesis of C. difficile‐associated diseases is more clearly defined we would stress the importance of isolating the organism, and we advocate the use of a selective broth such as GCC to improve the isolation rate.
Suture Fixation Technique for Endoscopic Brow Lift

Endoscopic brow lift has become widely accepted as a procedure for restoring a youthful brow, as only three, hardly noticeable incisions of the scalp are needed for this subperiosteal dissection and final repositioning of the brow. It has become an acceptable technique, an alternative to the conventional technique or transcoronal browpexy. Endoscopic brow lift allows separation and repositioning of the periosteum of the orbital rims and zygomaxilla. In a 7-year period beginning September 1999, 350 patients underwent endoscopic brow lift. In these patients, we used our fixation approach, which was accomplished with an absorbable suture subperiosteally. Satisfactory forehead rejuvenation was obtained in all patients with correct eyebrow movement. Long-term results over 7 years confirm the strength and durability of our fixation approach. We consider this approach to be a simple, fast, and reliable fixation technique that establishes precise, secure, and direct brow fixation that allows satisfactory and long-lasting cosmetic results.
Flexible Task Execution and Attentional Regulations in Human-Robot Interaction

A robotic system that interacts with humans is expected to flexibly execute structured cooperative tasks while reacting to unexpected events and behaviors. In this paper, we face these issues presenting a framework that integrates cognitive control, executive attention, and hierarchical plan execution. In the proposed approach, the execution of structured tasks is guided by top-down (task-oriented) and bottom-up (stimuli-driven) attentional processes that affect behavior selection and activation, while resolving conflicts and decisional impasses. Specifically, attention is here deployed to stimulate the activations of multiple hierarchical behaviors orienting them toward the execution of finalized and interactive activities. On the other hand, this framework allows a human to indirectly and smoothly influence the robotic task execution exploiting attention manipulation. We provide an overview of the overall system architecture discussing the framework at work in different case studies. In particular, we show that multiple concurrent tasks can be effectively orchestrated and interleaved in a flexible manner; moreover, in a human-robot interaction setting, we test and assess the effectiveness of attention manipulation for interactive plan guidance.
An Efficient Synthesis of 3,4-Dihydropyrimidin-2(1H)-Ones and Thiones Catalyzed by a Novel Brønsted Acidic Ionic Liquid under Solvent-Free Conditions

We report here an efficient and green method for Biginelli condensation reaction of aldehydes, β-ketoesters and urea or thiourea catalyzed by Brønsted acidic ionic liquid [Btto][p-TSA] under solvent-free conditions. Compared to the classical Biginelli reaction conditions, the present method has the advantages of giving good yields, short reaction times, near room temperature conditions and the avoidance of the use of organic solvents and metal catalyst.
OPTICAL METAMATERIALS AND SUPER-RESOLUTION IMAGING

Metamaterials are specially designed periodic structures (a ) that give rise to extraordinary optical properties that no natural materials can possess. It can have extremely large refractive index or a negative refractive index over a frequency band. The refractive index, n, determines how light travels in a medium and is related to the permittivity, ", and permeability, , by the relation n 1⁄4 ð" Þ1=2. It was ̄rst pointed out by Veselago that if " < 0 and < 0, then n becomes negative. Nevertheless, there are no natural materials that possess negative permeability, though metals possess negative permittivity at subplasma frequency. However it was Pendry's suggestion that negative refraction would make a perfect lens that drawn worldwide interests in this ̄eld. 5 Besides subwavelength di®raction limited imaging, metamaterials have also found applications in areas such as wideband directive antennas, band-stop ̄lters, radar absorbers, broadband phase shifters, cloaking and far̄eld optical microscopy. These can be achieved by engineering the propagation of light in the structure. This list of potential applications is non-exhaustive asmore concentrated e®orts are currently ongoing all over the world to design new structures and improve on old ones. The beauty of using metamaterial structures in devices is that the design can be made much smaller with improved performances, or at least comparable performances with conventionally established techniques. Ernest Abbe (184
Integrating supervised subspace criteria with restricted Boltzmann Machine for feature extraction

Restricted Boltzmann Machine (RBM) is a widely used building-block in deep neural networks. However, RBM is an unsupervised model which can not exploit the rich supervised information of data. Therefore, we consider combining the descriptive (generative) ability of RBM with the discriminative ability of supervised subspace models, i.e., Fisher linear discriminant analysis (FDA), marginal Fisher analysis (MFA), and heat kernel MFA (hkMFA). Specifically, the hidden layer of RBM is regularized by the supervised subspace criteria, and the joint learning model can then be efficiently optimized by gradient descent and graph construction (used to define the scatter matrix in the subspace models) on mini-batch data. Compared with the traditional subspace models (FDA, MFA, hkMFA), the proposed hybrid models are essentially nonlinear and can be optimized by gradient descent instead of eigenvalue decomposition. More importantly, traditional subspace models can only reduce the dimensionality (because of linear transformation), while the proposed models can also increase the dimensionality for better class discrimination. Experiments on three databases demonstrate that the proposed hybrid models outperform both RBM and their counterpart subspace models (FDA, MFA, hkMFA) consistently.
Intrusion detection system based on Multi-Layer Perceptron Neural Networks and Decision Tree

The growth of internet attacks is a major problem for today's computer networks. Hence, implementing security methods to prevent such attacks is crucial for any computer network. With the help of Machine Learning and Data Mining techniques, Intrusion Detection Systems (IDS) are able to diagnose attacks and system anomalies more effectively. Though, most of the studied methods in this field, including Rule-based expert systems, are not able to successfully identify the attacks which have different patterns from expected ones. By using Artificial Neural Networks (ANNs), it is possible to identify the attacks and classify the data, even when the dataset is nonlinear, limited, or incomplete. In this paper, a method based on the combination of Decision Tree (DT) algorithm and Multi-Layer Perceptron (MLP) ANN is proposed which is able to identify attacks with high accuracy and reliability.
Sensitivity Analysis of Feature Set Employed for Anaphora Resolution

Sensitivity analysis is the process of doing a systematic review involving a sequence of parameter, feature set and decisions to calculate the impact of these parameters on the study. It will guide the researchers to evaluate the parameter to consider their relevance in the study. In this paper we consider two features out of seven tags which were employed to resolve the anaphora in Hindi. These tags and their values analyzed empirically for the corpus. We analyzed 165 news items of Ranchi Express from EMILEE corpus of plain text. It consists 1745 sentences. Eight files of dialogue base from the same corpus have been analyzed which will have 1521 sentences. We exploited tag set proposed by different authors and their features. General Terms Natural language Processing, Machine Translation, Issues in machine translation.
Measurement of Neuronal Activity in a Macaque Monkey in Response to Animate Images Using Near-Infrared Spectroscopy

Near-infrared spectroscopy (NIRS) has been used extensively for functional neuroimaging over the past decade, in part because it is considered a powerful tool for investigating brain function in human infants and young children, for whom other neuroimaging techniques are not suitable. In particular, several studies have measured hemodynamic responses in the occipital region in infants upon exposure to visual stimuli. In the present study, we used a multi-channel NIRS to measure neuronal activity in a macaque monkey who was trained to watch videos showing various circus animals performing acrobatic activities without fixing the head position of the monkey. Cortical activity from the occipital region was measured first by placing a probe comprising a 3 × 5 array of emitters and detectors (2 × 4 cm) on the area (area 17), and the robustness and stability of the results were confirmed across sessions. Cortical responses were then measured from the dorsofrontal region. The oxygenated hemoglobin signals increased in area 9 and decreased in area 8b in response to viewing the videos. The results suggest that these regions are involved in cognitive processing of visually presented stimuli. The monkey showed positive responsiveness to the stimuli from the affective standpoint, but its attentional response to them was an inhibitory one.
The Annals

It is therefore no wonder that with such frequent alterations of the system younger naturalists, who may be inclined to regard the system as the foundation of the science, find themselves at first in no small difficulty, until, subsequently, after they have penetrated more deeply into the interior of nature, and recognized the essence of zoology more accurately, they arrive at the right view, that in reality classification or taxonomy is by no means the foundation of the science, but rather only the roofing-in of the structure raised upon the foundation of morphology and embryology. As the main cause of the vacillations nowadays apparently occurring in classification we must undoubtedly regard only
Strawman: Resolving collisions in bursty low-power wireless networks

Low-power wireless networks must leverage radio duty cycling to reduce energy consumption, but duty cycling drastically increases the risk of radio collisions, resulting in power-expensive retransmissions or data loss. We present Strawman, a contention resolution mechanism designed for low-power duty-cycled networks that experience traffic bursts. Strawman efficiently resolves network contention, mitigates the hidden terminal problem, and has zero overhead unless activated to resolve data collisions. Our testbed experiments show that Strawman instantaneously provides increased network capacity when needed, allocates the available bandwidth evenly among contenders, and increases energy efficiency in multihop collection networks compared to the traditionally used random backoff.
Characterizing Steady-State Topologies of SIS Dynamics on Adaptive Networks

Disease awareness in epidemiology can be modelled with adaptive contact networks, where the interplay of disease dynamics and network alteration often adds new phases to the standard models (Gross et al. 2006, Shaw et al. 2008) and, in stochastic simulations, lets network topology settle down to a steady state that can be static (in the frozen phase) or dynamic (in the endemic phase). We show for the SIS model that, in the endemic phase, this steady state does not depend on the initial network topology, only on the disease and rewiring parameters and on the link density of the network, which is conserved. We give an analytic description of the structure of this co-evolving network of infection through its steady-state degree distribution.
A Climatology of the Tropical Tropopause Layer

The tropical tropopause layer (TTL) is a transition region between the troposphere and the stratosphere. In this study the vertical extent of the TTL is diagnosed from radiosonde and ozonesonde profiles in the tropics and a climatology of this layer is presented. The radiative balance in the TTL is also characterized. The TTL is locally defined as extending from the level of the lapse rate minimum at 10-12 km to the cold point tropopause (CPT) at 16-17 km. The minimum in lapse rate represents the level of maximum convective impact on upper tropospheric temperatures, which is found to closely correspond to a minimum in ozone. Variations in this level are correlated with convective activity as measured by satellite brightness temperatures and Outgoing Longwave Radiation (OLR). At the cold point, the TTL height is nearly uniform throughout the tropics, and has a pronounced annual cycle. There are regional variations in the altitude of the lower boundary of the TTL. Interannual variations of the TTL result from changes in the large scale organization of convective activity, such as from the El-Nino Southern Oscillation (ENSO). Over the last 40 years, records indicate an increase (200-400 m) in the height of both the cold point tropopause and the level of minimum lapse rate. To better understand vertical transport in the TTL, the clear sky radiative heating rate is diagnosed using a sophisticated radiative transfer scheme. The level of zero radiative heating occurs roughly 1 km below the CPT, implying that convection needs to loft air 4-5 km above the base of the TTL if the air is to eventually enter the stratosphere.
Toughening through multilayering in TiN–AlTiN films

The damage response of columnar multilayers of TiN and AlTiN to Vickers indentation is studied through focused ion beam machining and elastic modelling. Multilayers display an enhanced resistance, which increases with layer refinement, to the multiple fracture modes that appear at high loads in these materials, including edge (nested) cracks and inclined shear cracks. Measurements of layer thickness reveal that multilayers display additional modes of plastic deformation that lead to permanent compression and bending of the film. An elastic model of contact deformation in a bilayer where plasticity is mimicked by greatly enhanced elastic compliance of the film is used to rationalize the trends in crack resistance. It is shown that the enhanced toughness is not due to any increase in the strain capacity (hardness/modulus) of the film material, brought about by multilayering.
A general framework for content-based medical image retrieval with its application to mammograms

In the field of medical imaging, content-based image retrieval (CBIR) techniques are employed to aid radiologists in the retrieval of images with similar contents. However, CBIR methods are usually developed based on specific features of images so that those methods are not readily inter-applicable among different kinds of medical images. This work proposes a general CBIR framework in attempt to alleviate this limitation. The framework is consisted of two parts: image analysis and image retrieval. In the image analysis part, normal and abnormal regions of interest (ROIs) in a number of images are selected to form a ROI dataset. These two groups of ROIs are used to analyze 11 textural features based on gray level co-occurrence matrices. The multivariate T test is then applied to identify the features with significant discriminating power for inclusion in a feature descriptor. In the image retrieval part, each feature of the descriptor is normalized by clipping the values of the largest 5% of the same feature component, and then projecting each normalized feature onto the unit sphere. The L2 norm is then employed to determine the similarity between the query image and each ROI in the dataset. This system works in the manner of query-by-example (QBE). Query images were selected from different classes of abnormal ROIs. A maximum precision of 51% and a maximum recall of 19% were obtained. The averages of precision and recall are 49% and 18% in this experiment.
Fiscal Spillovers and Trade Relations in Europe 1

This paper explores the impact of openness, as measured by import penetration, on the size and duration of fiscal multipliers and spillovers across European Union members. The analysis is embedded in a macroeconomic model, NiGEM, to capture structural differences across countries, time and policy regimes. Panel estimation with PMG and CCE techniques is used to estimate trade relations. Our analysis indicates that fiscal multipliers and spillovers depend crucially on structural assumptions about policy and the formation of expectations. Fiscal policy has limited effects on the economies of Europe, and that the case for fiscal policy coordination is weak except in the short run. The case for coordination rests on spillovers which are likely to become ever smaller as financially liberalisation continues.
Latency-aware rate adaptation in 802.11n home networks

Latency-sensitive applications (e.g., wireless gaming and TV remote play) are increasingly popular in home WiFi networks. Such millisecond-level latency requirements call for new fine-grained approaches at the link layer. In this paper, we show that current solutions work well for throughput but not for latency due to the long tail of the packet delay distribution. We thus propose LLRA, a new latency-aware rate adaptation scheme that reduces the tail latency for delay-sensitive applications. LLRA takes concerted design in rate control, frame aggregation scheduling and software/hardware retransmission dispatching. Our implementation and evaluation confirm the viability of LLRA in 802.11n home networks.
Multiple Regression of Log Spectra for In-Car Speech Recognition Using Multiple Distributed Microphones

This paper describes a new multi-channel method of noisy speech recognition, which estimates the log spectrum of speech at a close-talking microphone based on the multiple regression of the log spectra (MRLS) of noisy signals captured by distributed microphones. The advantages of the proposed method are as follows: 1) The method does not require a sensitive geometric layout, calibration of the sensors nor additional pre-processing for tracking the speech source; 2) System works in very small computation amounts; and 3) Regression weights can be statistically optimized over the given training data. Once the optimal regression weights are obtained by regression learning, they can be utilized to generate the estimated log spectrum in the recognition phase, where the speech of close-talking is no longer required. The performance of the proposed method is illustrated by speech recognition of real in-car dialogue data. In comparison to the nearest distant microphone and multi-microphone adaptive beamformer, the proposed approach obtains relative word error rate (WER) reductions of 9.8% and 3.6%, respectively.
Orientation of rapid thermally annealed lead zirconate titanate thin films on (111) Pt substrates

The nucleation, growth, and orientation of lead zirconate titanate thin films prepared from organometallic precursor solutions by spin coating on (111) oriented platinum substrates and crystallized by rapid thermal annealing was investigated. The effects of pyrolysis temperature, post-pyrolysis thermal treatments, and excess lead addition are reported. The use of post-pyrolysis oxygen anneals at temperatures in the regime of 350–450 °C was found to strongly affect the kinetics of subsequent amorphous-pyrochlore-perovskite crystallization by rapid thermal annealing. The use of such post-pyrolysis anneals allowed films of reproducible microstructure and textures [both (100) and (111)] to be prepared by rapid thermal annealing. It is proposed that such anneals and pyrolysis temperature affect the oxygen concentration/average Pb valence in the amorphous films prior to annealing. Such changes in the Pb valence state then affect the stability of the transient pyrochlore phase and thus the kinetics of perovskite crystallization.
Bmp signaling regulates a dose-dependent transcriptional program to control facial skeletal development

We performed an in depth analysis of Bmp4, a critical regulator of development, disease, and evolution, in cranial neural crest (CNC). Conditional Bmp4 overexpression, using a tetracycline-regulated Bmp4 gain-of-function allele, resulted in facial skeletal changes that were most dramatic after an E10.5 Bmp4 induction. Expression profiling uncovered a signature of Bmp4-induced genes (BIG) composed predominantly of transcriptional regulators that control self-renewal, osteoblast differentiation and negative Bmp autoregulation. The complimentary experiment, CNC inactivation of Bmp2, Bmp4 and Bmp7, resulted in complete or partial loss of multiple CNC-derived skeletal elements, revealing a crucial requirement for Bmp signaling in membranous bone and cartilage development. Importantly, the BIG signature was reduced in Bmp loss-of-function mutants, indicating Bmp-regulated target genes are modulated by Bmp dose. Chromatin immunoprecipitation (ChIP) revealed a subset of the BIG signature, including Satb2, Smad6, Hand1, Gadd45γ and Gata3, that was bound by Smad1/5 in the developing mandible, revealing direct Smad-mediated regulation. These data support the hypothesis that Bmp signaling regulates craniofacial skeletal development by balancing self-renewal and differentiation pathways in CNC progenitors.
A fixed frequency sampling method for wireless sensors power consumption estimation

Many efforts have been made over the last decade to provide useful simulation tools allowing designers to estimate the power needs of an embedded system. Most of these tools are based on an event driven technique. This way of modeling embedded systems leads to inaccuracies in power estimation. This is especially the case for wireless sensors networks where nodes are running for a long time without changing their activity state, but still consume energy from battery. In this article, we present a new power estimation technique using a fixed-frequency sampling. This technique allows users to improve the accuracy of the power consumption modeling under the OMNeT++ simulator.