Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyades Oxygen Abundances from the λ6300 [O I] Line: The Giant-Dwarf Oxygen Discrepancy Revisited

We present the results of our abundance analysis of Fe, Ni, and O in high signal-to-noise ratio, high-resolution Very Large Telescope UVES and McDonald 2dcoudé spectra of nine dwarfs and three giants in the Hyades open cluster. The difference in Fe abundances derived from Fe II and Fe I lines ([Fe /H] - [Fe /H]) and Ni I abundances derived from moderately high-excitation (χ ≈ 4.20 eV) lines is found to increase with decreasing Teff for the dwarfs. Both of these findings are in concordance with previous results of overexcitation/overionization in cool young dwarfs. Oxygen abundances are derived from the [O I] λ6300 line, with careful attention given to the Ni I blend. The dwarf O abundances are in star-to-star agreement within uncertainties, but the abundances of the three coolest dwarfs (4573 K ≤ Teff ≤ 4834 K) evince an increase with decreasing Teff. Possible causes for the apparent trend are considered, including the effects of overdissociation of O-containing molecules. O abundances are derived from the near-UV OH λ3167 line in high-quality Keck HIRES spectra, and no such effects are found; indeed, the OH-based abundances show an increase with decreasing Teff, leaving the nature and reality of the cool dwarf [O I]-based O trend uncertain. The mean relative O abundance of the six warmest dwarfs (5075 K ≤ Teff ≤ 5978 K) is [O/H] = +0.14 ± 0.02, and we find a mean abundance of [O/H] = +0.08 ± 0.02 for the giants. Thus, our updated analysis of the [O I] λ6300 line does not confirm the Hyades giant-dwarf oxygen discrepancy initially reported by King & Hiltgen, suggesting that the discrepancy was a consequence of analysis-related systematic errors. LTE oxygen abundances from the near-IR, high-excitation O I triplet are also derived for the giants, and the resulting abundances are approximately 0.28 dex higher than those derived from the [O I] line, in agreement with non-LTE predictions. Non-LTE corrections from the literature are applied to the giant triplet abundances; the resulting mean abundance is [O/H] = +0.17 ± 0.02, in decent concordance with the giant and dwarf [O I] abundances. Finally, Hyades giant and dwarf O abundances derived from the [O I] λ6300 line and high-excitation triplet, as well as dwarf O abundances derived from the near-UV OH λ3167 line, are compared, and a mean cluster O abundance of [O/H] = +0.12 ± 0.02 is achieved, which represents the best estimate of the Hyades O abundance.
Distributed cooperative spectrum sensing strategy based on the joint use of network coding and transform-domain processing

In cognitive radio, spectrum sensing is used to find spectrum hole and identify the primary users. Cognitive users should continuously sense the spectrum and detect the presence of primary user as soon as possible. When cognitive user is far away from the primary user, the signal received from the primary user may be weak and difficult to be detected, causing the problem of low detection probability and long detection time. To solve such problems, this paper proposes a distributed cooperative spectrum sensing strategy based on the joint use of network coding and transform domain processing. Chirp signals with different parameters are used for cognitive users and conventional communication system is used for primary user. In the receiver end, fractional Fourier transform domain based signal processing method is used to separate cognitive users with different parameters and primary user. Network coding is used in different cognitive users to improve the spectrum efficiency of cognitive frequency bands. Theoretical analyses show that the proposed strategy can increase the detection probability, reduce detection time and improve the agility of the system at the same time.
Effects of swing-leg retraction and mass distribution on energy-loss coefficient in limit cycle walking

Limit cycle walkers utilizing their natural dynamics can achieve energy-efficient dynamic walking. Their heel-strike collision with the ground is generally modeled as an inelastic collision, and the discrete walking dynamics can be specified in the same manner as a rimless wheel by using the energy-loss coefficient and restored mechanical energy. Energy-loss coefficient is especially significant because it controls the gait efficiency and stability, but the value varies significantly according to the swing-leg retraction just prior to impact and robot's mass-distribution. This paper then mathematically investigates how energy-loss coefficient changes with respect to them, and discusses the effect on the gait efficiency and stability.
Neural correlates of sensorimotor gating: a metabolic positron emission tomography study in awake rats

Impaired sensorimotor gating occurs in neuropsychiatric disorders such as schizophrenia and can be measured using the prepulse inhibition (PPI) paradigm of the acoustic startle response. This assay is frequently used to validate animal models of neuropsychiatric disorders and to explore the therapeutic potential of new drugs. The underlying neural network of PPI has been extensively studied with invasive methods and genetic modifications. However, its relevance for healthy untreated animals and the functional interplay between startle- and PPI-related areas during a PPI session is so far unknown. Therefore, we studied awake rats in a PPI paradigm, startle control and background noise control, combined with behavioral [18F]fluoro-2-deoxyglucose positron emission tomography (FDG-PET). Subtractive analyses between conditions were used to identify brain regions involved in startle and PPI processing in well-hearing Black hooded rats. For correlative analysis with regard to the amount of PPI we also included hearing-impaired Lister hooded rats that startled more often, because their hearing threshold was just below the lowest prepulses. Metabolic imaging showed that the brain areas proposed for startle and PPI mediation are active during PPI paradigms in healthy untreated rats. More importantly, we show for the first time that the whole PPI modulation network is active during “passive” PPI sessions, where no selective attention to prepulse or startle stimulus is required. We conclude that this reflects ongoing monitoring of stimulus significance and constant adjustment of sensorimotor gating.
Power Control for Wireless Cellular Systems via D.C. Programming

Power control at the base station is typically used in wireless cellular networks in order to optimize the transmission subject to quality of service (QoS) constraints. It has been shown that the power control problem in the wireless cellular network framework can be efficiently solved using the socalled geometric programming. However, in order to enable the application of geometric programming the signal to interference ratio (SIR) has been considered instead of signal to interference plus noise ratio (SINR). Such problem reformulation is imprecise and might be loose because it does not take into account the noise component, especially for low signal to noise ratio (SNR) operation. In this paper, we show that the power control problem for wireless cellular systems can be efficiently solved via the so-called difference of two convex functions (D.C.) programming. Numerical simulation example demonstrates significant performance advantages of the proposed approach.
Ieee Transactions on Intelligent Transportation Systems 1 Text Detection and Recognition on Traffic Panels from Street-level Imagery Using Visual Appearancé

—Traffic signs detection and recognition has been thoroughly studied for a long time. However, traffic panel detection and recognition still remains a challenge in computer vision due to its different types and the huge variability of the information depicted in them. This paper presents a method to detect traffic panels in street-level images and to recognize the information contained on them, as an application to Intelligent Transportation Systems (ITS). The main purpose can be to make an automatic inventory of the traffic panels located in a road to support road maintenance and to assist drivers. Our proposal extracts local descriptors at some interest keypoints after applying blue and white color segmentation. Then, images are represented as a Bag of Visual Words and classified using Na¨ıve Bayes or SVM. This visual appearance categorization method is a new approach for traffic panel detection in the state-of-the-art. Finally, our own text detection and recognition method is applied on those images where a traffic panel has been detected, in order to automatically read and save the information depicted in the panels. We propose a language model partly based on a dynamic dictionary for a limited geographical area using a reverse geocoding service. Experimental results on real images from Google Street View prove the efficiency of the proposed method and give way to using street-level images for different applications on ITS.
A Bayesian framework for video affective representation

Emotions that are elicited in response to a video scene contain valuable information for multimedia tagging and indexing. The novelty of this paper is to introduce a Bayesian classification framework for affective video tagging that allows taking contextual information into account. A set of 21 full length movies was first segmented and informative content-based features were extracted from each shot and scene. Shots were then emotionally annotated, providing ground truth affect. The arousal of shots was computed using a linear regression on the content-based features. Bayesian classification based on the shots arousal and content-based features allowed tagging these scenes into three affective classes, namely calm, positive excited and negative excited. To improve classification accuracy, two contextual priors have been proposed: the movie genre prior, and the temporal dimension prior consisting of the probability of transition between emotions in consecutive scenes. The f1 classification measure of 54.9% that was obtained on three emotional classes with a naïve Bayes classifier was improved to 63.4% after utilizing all the priors.
Implementing utility-optimal CSMA

Hundreds of papers over the last two decades have studied the theory of distributed scheduling in wireless networks, including a number of them on stability or utility maximizing random access. Several publications in 2008 studied an adaptive CSMA that in theory can approach utility optimality without any message passing under a number of assumptions. This paper reports the results from the first deployment of such random access algorithms through an implementation over conventional 802.11 hardware, an on-going effort that started in summer 2009. It shows both a confirmation that utility optimal CSMA may work well in practice even with an implementation over legacy equipment, and a wide array of gaps between theory and practice in the field of wireless scheduling. This paper therefore also brainstorms the discovery of and bridging over these gaps, and the implementation-inspired questions on modeling and analysis of scheduling algorithms.
Assessing Variations in Areal Organization for the Intrinsic Brain: From Fingerprints to Reliability

Resting state fMRI (R-fMRI) is a powerful in-vivo tool for examining the functional architecture of the human brain. Recent studies have demonstrated the ability to characterize transitions between functionally distinct cortical areas through the mapping of gradients in intrinsic functional connectivity (iFC) profiles. To date, this novel approach has primarily been applied to iFC profiles averaged across groups of individuals, or in one case, a single individual scanned multiple times. Here, we used a publically available R-fMRI dataset, in which 30 healthy participants were scanned 10 times (10 minutes per session), to investigate differences in full-brain transition profiles (i.e., gradient maps, edge maps) across individuals, and their reliability. 10-minute R-fMRI scans were sufficient to achieve high accuracies in efforts to “fingerprint” individuals based upon full-brain transition profiles. Regarding testretest reliability, the image-wise intraclass correlation coefficient (ICC) was moderate, and vertex-level ICC varied depending on region; larger durations of data yielded higher reliability scores universally. Initial application of gradient-based methodologies to a recently published dataset obtained from twins suggested inter-individual variation in areal profiles might have genetic and familial origins. Overall, these results illustrate the utility of gradient-based iFC approaches for studying inter-individual variation in brain function.
Multimodality Therapy: Potentiation of High Linear Energy Transfer Radiation with Paclitaxel for the Treatment of Disseminated Peritoneal Disease

Purpose: Studies herein explore paclitaxel enhancement of the therapeutic efficacy of α-particle-targeted radiation therapy. Experimental Design: Athymic mice bearing 3 day i.p. LS-174T xenografts were treated with 300 or 600 μg paclitaxel at 24 h before, concurrently, or 24 h after [213Bi] or [212Pb]trastuzumab. Results: Paclitaxel (300 or 600 μg) followed 24 h later with [213Bi]trastuzumab (500 μCi) provided no therapeutic enhancement. Paclitaxel (300 μg) administered concurrently with [213Bi]trastuzumab or [213Bi]HuIgG resulted in median survival of 93 and 37 days, respectively; no difference was observed with 600 μg paclitaxel. Mice receiving just [213Bi]trastuzumab or [213Bi]HuIgG or left untreated had a median survival of 31, 21, and 15 days, respectively, 23 days for just either paclitaxel dose alone. Paclitaxel (300 or 600 μg) given 24 h after [213Bi]trastuzumab increased median survival to 100 and 135 days, respectively. The greatest improvement in median survival (198 days) was obtained with two weekly doses of paclitaxel (600 μg) followed by [213Bi]trastuzumab. Studies were also conducted investigating paclitaxel administered 24 h before, concurrently, or 24 h after [212Pb]trastuzumab (10 μCi). The 300 μg paclitaxel 24 h before radioimmunotherapy (RIT) failed to provide benefit, whereas 600 μg extended the median survival from 44 to 171 days. Conclusions: These results suggest that regimens combining chemotherapeutics and high linear energy transfer (LET) RIT may have tremendous potential in the management and treatment of cancer patients. Dose dependency and administration order appear to be critical factors requiring careful investigation.
On Characterizing and Measuring Out-of-Band Covert Channels

A methodology for characterizing and measuring out-of-band covert channels (OOB-CCs) is proposed and used to evaluate covert-acoustic channels (i.e., covert channels established using speakers and microphones). OOB-CCs are low-probability of detection/low-probability of interception channels established using commodity devices that are not traditionally used for communication (e.g., speaker and microphone, display and FM radio, etc.). To date, OOB-CCs have been declared "covert" if the signals used to establish these channels could not be perceived by a human adversary. This work examines OOB-CCs from the perspective of a passive adversary and argues that a different methodology is required in order to effectively assess OOB-CCs. Traditional communication systems are measured by their capacity and bit error rate; while important parameters, they do not capture the key measures of OOB-CCs: namely, the probability of an adversary detecting the channel and the amount of data that two covertly communicating parties can exchange without being detected. As a result, the adoption of the measure steganographic capacity is proposed and used to measure the amount of data (in bits) that can be transferred through an OOB-CC before a passive adversary's probability of detecting the channel reaches a given threshold. The theoretical steganographic capacity for discrete memoryless channels as well as additive white Gaussian noise channels is calculated in this paper and a case study is performed to measure the steganographic capacity of OOB covert-acoustic channels, when a passive adversary uses an energy detector to detect the covert communication. The case study reveals the conditions under which the covertly communicating parties can achieve perfect steganography (i.e., conditions under which data can be communicated without risk of detection).
Deciding Satisfiability of Equality Logic Formulas with Uninterpreted Functions

The logic of equality with uninterpreted functions (EUF) has been proposed for verifying abstract hardware designs. Fast satisfiability checking over this logic is important for such verification to be successful. Our algorithm is based on the Davis-Putman-Logemann-Loveland (DPLL) procedure, one of the most effective methods to check propositional satisfiability. The DPLL procedure returns a satisfying assignment, if one exists. It can be extended to return all satisfying assignment. Being based on the DPLL procedure, our algorithm can adopt heuristics developed for this method.
Neighbourhood consistency based deep domain adaption analysis for multi category object detection

Pattern classification in domains that follow dissimilar distribution and where target domain has insufficient labelled samples, requires transfer of knowledge across domains through a process called domain adaption. Deep learning research demonstrates the transferability of deep convolutional features that are activations of intermediate layers of convolutional neural networks for domain adaption. Traditional clustering based domain adaption approaches are practical to handle knowledge transfer scenario. This paper presents a scheme that uses local neighborhoods based consistency analysis of one supervised and another unsupervised model to effectively transfer knowledge using deep features. Contrasting conventional models this approach uses only two models to classify patterns except hard ones. Neighbourhood consistency analysis identifies the hard samples, and is classified using a third model. Experimental analysis has been carried out focusing change on category variation of different samples for train and test cases. The proposed approach yields encouraging experimental result on benchmark domain adaption dataset compared to a deep feature based single support vector machine classifier in terms of state of the art metrics demonstrating effective generalization of source domain information.
Interactive Perception : Closing the Gap Between Action and Perception

We introduce Interactive Perception as a new perceptual paradigm for autonomous robotics in unstructured environments. Interactive perception augments the process of perception with physical interactions, thus integrating robotics and computer vision. By integrating interactions into the perceptual process, it is possible to manipulate the environment so as to uncover information relevant for the robust and reliable execution of a task. Examples of such interactions include the removal of obstructions or object repositioning to improve lighting conditions. More importantly, forceful interaction can uncover perceptual information that would otherwise be imperceivable. In this paper, we begin to explore the potential of the interactive perception paradigm. We present an interactive perceptual primitive that extracts kinematic models from objects in the environment. Many objects in everyday environments, such as doors, drawers, and hand tools, contain inherent kinematic degrees of freedom. Knowledge of these degrees of freedom is required to use the objects in their intended manner. We demonstrate how a robot is capable of extracting a kinematic model from a variety of tools, using very simple algorithms. We then show how the robot can use the resulting kinematic model to operate the tool. The simplicity of these algorithms and their effectiveness in our experiments indicate that Interactive Perception is a promising perceptual paradigm for autonomous robotics.
Photo-real talking head with deep bidirectional LSTM

Long short-term memory (LSTM) is a specific recurrent neural network (RNN) architecture that is designed to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we propose to use deep bidirectional LSTM (BLSTM) for audio/visual modeling in our photo-real talking head system. An audio/visual database of a subject's talking is firstly recorded as our training data. The audio/visual stereo data are converted into two parallel temporal sequences, i.e., contextual label sequences obtained by forced aligning audio against text, and visual feature sequences by applying active-appearance-model (AAM) on the lower face region among all the training image samples. The deep BLSTM is then trained to learn the regression model by minimizing the sum of square error (SSE) of predicting visual sequence from label sequence. After testing different network topologies, we interestingly found the best network is two BLSTM layers sitting on top of one feed-forward layer on our datasets. Compared with our previous HMM-based system, the newly proposed deep BLSTM-based one is better on both objective measurement and subjective A/B test.
GSpartan: a Geospatio-Temporal Multi-task Learning Framework for Multi-location Prediction

This paper presents a novel geospatio-temporal prediction framework called GSpartan to simultaneously build local regression models at multiple locations. The framework assumes that the local models share a common, low-rank representation, which makes them amenable to multi-task learning. GSpartan learns a set of base models to capture the spatio-temporal variabilities of the data and represents each local model as a linear combination of the base models. A graph Laplacian regularization is used to enforce constraints on the local models based on their spatial autocorrelation. We also introduce sparsity-inducing norms to perform feature selection for the base models and model selection for the local models. Experimental results using historical climate data from 37 weather stations showed that, on average, GSpartan outperforms single-task learning and other existing multi-task learning methods in more than 65% of the stations, which increases to 81% when there are fewer training examples.
Is a stop consonant released when followed by another stop consonant

Phoneticians have generally claimed that, in sequences of two‐stop consonants in English, the first stop is often unreleased. To examine this claim, we recorded sentences, produced by several native speakers of American English at a conversational rate, containing disyllabic words with one of the 24 possible sequences of two nonhomorganic stops across the syllable boundary (e.g., cactus, pigpen). In addition, several sentences containing words with two stops in final position were recorded (e.g., act, sobbed). Oscillograms of the critical words revealed that releases of the first stop were nearly always present, except in those instances where the second stop was bilabial. This is a reasonable result when one considers the articulatory maneuvers involved. Since many of the releases we observed were acoustically rather weak, they may be difficult to detect by ear and thus may escape even the careful listener's attention. [Work supported by NICHD and BRS. ]
Agent-Based Simulation As A Support For Price-Setting In Passenger Transport

Companies operating in passenger transport face the challenge that they cannot easily assess the effects of pricing decisions they take. Particularly, many incumbent European Railways are revisiting their traditional pricing structures based on static distancebased fares and develop new ways for pricing their services. Building on insights of behavioural pricing and Revenue Management, I investigate on alternative, more revenue-efficient pricing structures for a focal railway company given individual passenger price reaction behaviour and intramodal competition. First experiments with the model suggest that a limited quota for permanent special offers is counterproductive, while an increase in fuel price gets relatively quickly absorbed by the market.