Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust minimax MMSE for sparse signal recovery against system perturbations

In this work, we develop a minimum mean square error (MMSE) estimator for the underdetermined systems when the signal of interest is sparse. To address the uncertainty issue introduced in the measurement system, robust approaches are developed based on stochastic and worst case optimization techniques under the minimax framework. To solve the optimization problem, different constraints on the unknown signal of interest are considered to transform the minimax optimization into semidefinite programming problem (SDP), which can be efficiently solved. Numerical studies are provided to demonstrate utilizing sparsity and robust approaches indeed improve MMSE estimator when the sparsity of the signal of interest is utilized and the system considered is underdetermined.
A method of anaesthesia for caesarean section. Evolution of a technique suitable for use in a country hospital.

T H E practice of modern obstetrics has reached a high level of efficiency and requires equally efficient anaesthesia. Caesarean section is often performed to increase the baby's chance of survival and the anaesthetic should not appreciably decrease this chance. A maternal death due to anaesthesia is an unacceptable tragedy. Since there is no generally accepted "best" method of anaesthesia for this common operation, it may be of value to record an individual attempt made by the author, working single-handed in a country hospital, to devise a technique suitable for both mother and baby. The following observations are offered for the consideration of anaesthetists who may find themselves working in similar circumstances. The two most important considerations in the design of a suitable technique are to reduce (1) the risk of maternal vomiting and (2) the effects of the anaesthetic on the foetus. In addition, however, several possible complicating factors may arise. For example, the patient may be toxaemic, shocked, or exhausted by a long unsuccessful labour. Sometimes, too, uterine relaxation may be necessary. Or again, for obstetric reasons, the operation may have to be performed speedily. Finally the obstetrician may use drugs which influence or limit the choice of agents available to the anaesthetist. Most patients in this country prefer to be unconscious during the operation of Caesarean section so that, while many advantages have been claimed for conduction techniques, only general anaesthesia will be discussed here. It is widely accepted that a skilled and experienced anaesthetist should be available for every obstetric anaesthetic, and in considering technique this is presumed to be the case.
NHS76/PEP2, a Fully Human Vasopermeability-Enhancing Agent to Increase The Uptake and Efficacy of Cancer Chemotherapy

Purpose: Previously, we have shown that the attachment of interleukin 2 (IL-2) to a tumor-targeting antibody can produce a 4-fold enhancement in the uptake of antibodies and drugs in tumors. More recently, we discovered that a 37-amino-acid linear sequence of IL-2 designated vasopermeability-enhancing peptide (PEP), contained the vasopermeability activity of IL-2, and could be used after linkage to tumor-targeting antibodies to produce the same enhancement of drugs and antibodies in tumors. We now describe the generation of a fully human antibody fusion protein, designated NHS76/PEP2, which can be used in patients to enhance the therapeutic potential of chemotherapy. Methods: NHS76/PEP2 was expressed in NS0 cells using the glutamine synthetase gene amplification system. To show its clinical potential as a pretreatment to chemotherapy, NHS76/PEP2 was given i.v. 2 hours before the injection of suboptimal doses of etoposide, doxorubicin, Taxol, Taxotere, 5-fluorouracil, or vinblastine in mice bearing established solid tumors. Results were recorded by measuring tumor volumes thrice per week. Results: Compared with drug treatment alone, NHS76/PEP2 pretreatment substantially improved the effectiveness of chemotherapeutic agents in solid tumor models. Tumor suppression was most pronounced in those groups of mice bearing tumors known to be sensitive to the specific drug under study. However, in certain instances, tumors previously known to be resistant to specific single chemotherapeutic agents were shown to respond by the addition of NHS76/PEP2 pretreatment. Conclusions: NHS76/PEP2 seems an excellent candidate to improve the value of standard chemotherapy drug treatment by virtue of its ability to increase the uptake of drugs in solid tumors selectively.
Hanbury Brown Twiss Effect for Ultracold Quantum Gases

We have studied two-body correlations of atoms in an expanding cloud above and below the Bose-Einstein condensation threshold. The observed correlation function for a thermal cloud shows a bunching behavior, whereas the correlation is flat for a coherent sample. These quantum correlations are the atomic analog of the Hanbury Brown Twiss effect. We observed the effect in three dimensions and studied its dependence on cloud size.
The Use of Plasma Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry Proteomic Patterns for Detection of Head and Neck Squamous Cell Cancers

Purpose: Our study was undertaken to determine the utility of plasma proteomic profiling using surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry for the detection of head and neck squamous cell carcinomas (HNSCCs). Experimental Design: Pretreatment plasma samples from HNSCC patients or controls without known neoplastic disease were analyzed on the Protein Biology System IIc SELDI-TOF mass spectrometer (Ciphergen Biosystems, Fremont, CA). Proteomic spectra of mass:charge ratio (m/z) were generated by the application of plasma to immobilized metal-affinity-capture (IMAC) ProteinChip arrays activated with copper. A total of 37,356 data points were generated for each sample. A training set of spectra from 56 cancer patients and 52 controls were applied to the “Lasso” technique to identify protein profiles that can distinguish cancer from noncancer, and cross-validation was used to determine test errors in this training set. The discovery pattern was then used to classify a separate masked test set of 57 cancer and 52 controls. In total, we analyzed the proteomic spectra of 113 cancer patients and 104 controls. Results: The Lasso approach identified 65 significant data points for the discrimination of normal from cancer profiles. The discriminatory pattern correctly identified 39 of 57 HNSCC patients and 40 of 52 noncancer controls in the masked test set. These results yielded a sensitivity of 68% and specificity of 73%. Subgroup analyses in the test set of four different demographic factors (age, gender, and cigarette and alcohol use) that can potentially confound the interpretation of the results suggest that this model tended to overpredict cancer in control smokers. Conclusions: Plasma proteomic profiling with SELDI-TOF mass spectrometry provides moderate sensitivity and specificity in discriminating HNSCC. Further improvement and validation of this approach is needed to determine its usefulness in screening for this disease.
The effect of receiver coil orientations on the imaging performance of magnetic induction tomography

Magnetic induction tomography is an imaging modality which aims to reconstruct the conductivity distribution of the human body. It uses magnetic induction to excite the body and an array of sensor coils to detect the perturbations in the magnetic field. Up to now, much effort has been expended with the aim of finding an efficient coil configuration to extend the dynamic range of the measured signal. However, the merits of different sensor orientations on the imaging performance have not been studied in great detail so far. Therefore, the aim of the study is to fill the void of a systematic investigation of coil orientations on the reconstruction quality of the designs. To this end, a number of alternative receiver array designs with different coil orientations were suggested and the evaluations of the designs were performed based on the singular value decomposition. A generalized class of quality measures, the subclasses of which are linked to both the spatial resolution and uncertainty measures, was used to assess the performance on the radial and axial axes of a cylindrical phantom. The detectability of local conductivity perturbations in the phantom was explored using the reconstructed images. It is possible to draw the conclusion that the proper choice of the coil orientations significantly influences the number of usable singular vectors and accordingly the stability of image reconstruction, although the effect of increased stability on the quality of the reconstructed images was not of paramount importance due to the reduced independent information content of the associated singular vectors.
A Simulation Study of Three Related Causal Data Mining Algorithms

In all scienti c domains causality plays a signi cant role. This study focused on evaluating and re ning e cient algorithms to learn causal relationships from observational data. Evaluation of learned causal output is di cult, due to lack of a gold standard in real-world domains. Therefore, we used simulated data from a known causal network in a medical domain|the Alarm network. For causal discovery we used three variants of the Local Causal Discovery (LCD) algorithms, that are referred to as LCDa, LCDb and LCDc. These algorithms use the framework of causal Bayesian Networks to represent causal relationships among model variables. LCDa, LCDb and LCDc take as input a dataset and a partial node ordering, and output purported causes of the form variable Y causally in uences variable Z. Using the simulated Alarm dataset as input, LCDa had a false positive rate of 0.09, LCDb 0.08, and LCDc 0.04. All the algorithms had a true positive rate of about 0.27. Most of the false positives occurred when a causal relationship was confounded. LCDc output as causal only those causally confounded pairs that had very weak confounding. We identify and discuss the causally confounded relationships that often seem to induce false positive results.
A new trust evaluation approach for cloud computing environments

This paper introduces a novel trust evaluation approach for the cloud computing domain. The wide majority of the existing trust systems are either limited to the qualitative or the quantitative assessment of trust. Even those that combine qualitative and quantitative assessment, present several limitations. For instance, they do not take into account the user's technical requirements. They do not either apply serious measures to detect malicious feedbacks. These restrictions have a detrimental effect on trust establishment in today's cloud computing environments, which are characterized by heterogeneity in providers, services and users. This heterogeneity is also hidden by evaluating trust similarly for all cloud services. In this paper, we designed a new trust evaluation approach that leverages the correlation between QoS attributes. Our approach takes into account all the relevant features of cloud environments and shortcomings of prior trust models. A case study reveals that our proposed approach performs better than similar trust models and capable of deterring unreasonable feedbacks inhibiting the accuracy of the calculated trust values.
Frequency-domain delexicalization using surrogate vowels

We propose a delexicalization algorithm that renders the lexical content of an utterance unintelligible, while preserving important acoustic prosodic cues, as well as naturalness and speaker identity. This is achieved by replacing voiced regions by spectral slices from a surrogate vowel, and by averaging the magnitude spectrum during unvoiced regions. Perceptual tests were carried out comparing sentences that were either unprocessed or delexicalized, using a baseline or the proposed method. An intelligibility test resulted in a keyword recall rate of 92% for the unprocessed sentences, and near complete unintelligibility for both delexicalization methods. Affect recognition was at 65% for unprocessed sentences, and 46% and 49% for the baseline and the proposed method, respectively. Preference tests showed that the proposed method preserved drastically more speaker identity, and sounded more natural than the baseline.
Disparity Estimation in Stereo Sequences using Scene Flow

Fang.F. Liu@philips.comPhilips Germany Vasanth Philomin Vasanth.Philomin@philips.comPhilips Germany Abstract This paper presents a method for estimating disparity images from a stereo image sequence. While many existing stereo algorithms work well on a single pair of stereo images, it is not sufficient to simply apply them to temporal frames independently without considering the temporal consistency between adjacent frames. Our method integrates the state-of-the-art stereo algorithm with the scene flow concept in order to capture the temporal correspondences. It computes the dense disparity images and scene flow in a practical and unified process: the disparity is initialized by a hybrid stereo approach which employs the over-segmentation based stereo and pixelwise iterative stereo; then the scene flow, estimated via a variational approach, is used to predict the disparity image and to compute its confidence map for the next frame. The prediction is modeled as a prior probability distribution and is built into an energy function defined for stereo matching on the next frame. The disparity can be estimated by minimizing this energy function. Experimental results show that the algorithm is able to estimate the disparity images in an accurate and temporally consistent fashion.
A nested linear codes approach to distributed function computation over subspaces

In this paper, we consider a distributed function computation setting, where there are m distributed but correlated sources X1, …, Xm and a receiver interested in computing an s-dimensional subspace generated by [X1, …, Xm]T for some (to x s) matrix Γ of rank s. We construct a scheme based on nested linear codes and characterize the achievable rates obtained using the scheme. The proposed nested-linear-code approach performs at least as well as the Slepian-Wolf scheme in terms of sum-rate performance for all subspaces and source distributions. In addition, for a large class of distributions and subspaces, the scheme improves upon the Slepian-Wolf approach. The nested-linear-code scheme may be viewed as uniting under a common framework, both the Korner-Marton approach of using a common linear encoder as well as the Slepian-Wolf approach of employing different encoders at each source. Along the way, we prove an interesting and fundamental structural result on the nature of subspaces of an m-dimensional vector space V with respect to a normalized measure of entropy. Here, each element in V corresponds to a distinct linear combination of a set {Xi}mi=1 of m random variables whose joint probability distribution function is given.
Understanding Neuronal Architecture in Obesity through Analysis of White Matter Connection Strength

Despite the prevalence of obesity, our understanding of its neurobiological underpinnings is insufficient. Diffusion weighted imaging and calculation of white matter connection strength are methods to describe the architecture of anatomical white matter tracts. This study is aimed to characterize white matter architecture within taste-reward circuitry in a population of obese individuals. Obese (n = 18, age = 28.7 ± 8.3 years) and healthy control (n = 24, age = 27.4 ± 6.3 years) women underwent diffusion weighted imaging. Using probabilistic fiber tractography (FSL PROBTRACKX2 toolbox) we calculated connection strength within 138 anatomical white matter tracts. Obese women (OB) displayed lower and greater connectivity within taste-reward circuitry compared to controls (Wilks’ λ < 0.001; p < 0.001). Connectivity was lower in white matter tracts connecting insula, amygdala, prefrontal cortex (PFC), orbitofrontal cortex (OFC) and striatum. Connectivity was greater between the amygdala and anterior cingulate cortex (ACC). This study indicates that lower white matter connectivity within white matter tracts of insula-fronto-striatal taste-reward circuitry are associated with obesity as well as greater connectivity within white matter tracts connecting the amygdala and ACC. The specificity of regions suggests sensory integration and reward processing are key associations that are altered in and might contribute to obesity.
G ROUPING O BJECTS BASED ON THEIR A PPEARANCE

The use of clustering algorithms for partition to establish a hierarchical structure in a library of object models based on appearance is deployed. The main contribution corresponds to a novel and intuitive algorithm for clustering of models based on their appearance, closer to “human behavior”. This divides the complete set into subclasses. Immediately, divides each of these in a number of predefined groups to complete the levels of hierarchy that the user wants. Whose main purpose is to obtain a competitive classification compared to what a human would perform.
Style-based similarity search for office XML documents

Recent office documents follow an XML archive format, so they consist of multiple XML files. XML files in office documents include information about page structures and styles such as font, color and position. But, existing text-based search engines do not focus on structure and style of documents. By utilizing them, we can achieve similarity search for office documents based on structures and styles. We propose SOS, a similarity search method based on structures and styles of office documents. To compute a similarity value between office documents, we have to compute similarity values between multiple pairs of XML files in the documents. We also propose LAX+, which is an algorithm to calculate a similarity value for a pair of XML files, by extending existing XML leaf node clustering algorithm. In our experiments, we use docx, xlsx and pptx files and evaluate SOS and LAX+ by precision and recall.
Enhancing Relay-based Resource Allocation With Adaptive Amplify-and-Forward Relaying

This study considers the resource allocation problem in relay networks for downlink transmission where base station (BS) communicates with multiple mobile stations (MSs) via relay stations (RSs) operating in either half-duplex (HD) or full-duplex (FD) mode. Relay gain factors for Amplify and Forward (AF) protocol are studied and a new one called adaptive AF (AAF) relaying is proposed. The AAF scheme attempts to equalize the signal-to-noise ratio (SNR) across subcarriers, thus providing increased throughputs and proportional fairness in resource distribution. Simulation results of comparative analysis of the AAF relaying scheme and the two basic relaying protocols namely AF and Decode and Forward (DF) are presented in terms of average system throughput, fairness and percentage of call outage under maximum-sum rate (MSR) resource allocation technique. The results show that the AAF scheme achieves superior performances compared to the other ones.
Extraction of User Profile Based on the Hadoop Framework

With the rapid development of Internet, the web information dramatically increases, the users are often involved in voluminous information to feel lose, Distributed processing of mass data through a cluster composed by many machines and personalized search services based on the user profile have been the hotspots of research and development. This paper firstly studies the operation mechanism of Hadoop, which is a typical distributed processing framework of Apache, then realizes extraction of user profile from a large number of web log data and through comparison experiment with single machine to verify its efficiency. Keywords-Hadoop framework; user profile; distributed computing; WEB data mining; MapReduce; HDFS
Strong lensing statistics in large, z 0.2 surveys: bias in the lens galaxy population

Large current and future surveys, like the Two-degree Field Survey (2dF), the Sloan Digital Sky Survey (SDSS) and the proposed Kilo-Degree Survey (KIDS), are likely to provide us with many new strong gravitational lenses. Taking cosmological parameters as known, we calculate the expected lensing statistics of the galaxy population in large, low-redshift surveys. Galaxies are modelled using realistic, multiple components: a dark matter halo, a bulge component and disc. We use semi-analytic models of galaxies coupled with dark matter haloes in the Millennium Run to model the lens galaxy population. Replicating the selection criteria of the 2dF, we create a mock galaxy catalogue. We predict that a fraction of 7.5 ± 0.2 x 10 -5 of radio sources will be lensed by galaxies within a survey like the 2dF below z < 0.2. We find that proper inclusion of the baryonic component is crucial for calculating lensing statistics - pure dark matter haloes produce lensing cross-sections several orders of magnitude lower. With a simulated sample of lensed radio sources, the predicted lensing galaxy population consists mainly of ellipticals (∼80 per cent) with an average lens velocity dispersion of 191 ± 3 km s -1 , producing typical image separations of ∼1.5 arcsec. The lens galaxy population lies on the fundamental plane but its velocity dispersion distribution is shifted to higher values compared to all early-type galaxies. We show that magnification bias affects lens statistics very strongly and increases the 4:2 image ratio drastically. Taking this effect into account, we predict that the ratio of 4:2 image systems is 18 ± 5 per cent, marginally consistent with the observed ratio found in the Cosmic Lens All-Sky Survey (CLASS). We also find that the population of four-image lens galaxies differs markedly from the population of lens galaxies in two-image systems. We find that while most lenses tend to be ellipticals, galaxies that produce four-image systems preferentially tend to be lower velocity dispersion systems with more pronounced disc components. Our key result is the explicit demonstration that the population of lens galaxies differs markedly from the galaxy population as a whole: lens galaxies have a higher average luminosity and, for a given luminosity, they reside in more massive haloes than the overall sample of ellipticals. This bias restricts our ability to infer galaxy evolution parameters from a sample of lensing galaxies.
Does the D.C. Response of Memristors Allow Robotic Short-Term Memory and a Possible Route to Artificial Time Perception?

Time perception is essential for task switching, and in the mammalian brain appears alongside other processes. Memristors are electronic components used as synapses and as models for neurons. The d.c. response of memristors can be considered as a type of short-term memory. Interactions of the memristor d.c. response within networks of memristors leads to the emergence of oscillatory dynamics and intermittent spike trains, which are similar to neural dynamics. Based on this data, the structure of a memristor network control for a robot as it undergoes task switching is discussed and it is suggested that these emergent network dynamics could improve the performance of role switching and learning in an artificial intelligence and perhaps create artificial time perception.
Software Analysis for the Web: Achievements and Prospects

The web has had a significant impact on our lives. A technology that was initially created for sharing documents across the network has evolved into a strong medium for developing and distributing software applications. In this paper, we first provide a concise overview of the evolution of the web itself. We then focus on some of the main industrial and research achievements in software analysis and testing techniques geared toward web apps, in the past two decades. We discuss static, dynamic, and hybrid analyses approaches, software testing and test adequacy techniques, as well as techniques that help developers write, analyze and maintain their code. Finally, we present some of the current and future challenges and research opportunities ahead in this field.