Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of time gating and polarization discrimination of propagating light in turbid media during angular domain imaging (ADI)

Angular Domain Imaging (ADI) employs an angular filter array to accept photons within a small acceptance angle along the axis of an aligned laser light source and preferentially reject scattered light. Simulations show that the accepted photons travel the shortest paths between source and detector and are therefore the earliest to arrive. We fabricated angular filter arrays using silicon bulk micromachining and found that an array of 60 μm square shape microtunnels 1 cm in length accepted photons within 0.48 degree of axis of the micro-tunnels. This small acceptance angle rejected most of the scattered light and sub-millimeter resolution targets could be resolved in a few centimeters of turbid medium with at least six times reduced mean free path. ADI through media with higher scattering coefficients was not achievable due to unwanted acceptance of late arriving scattered photons. To reject the late arriving photons, we added time-domain filtration and linear polarization to ADI. The implementation of a time-gated camera, a 780 nm femtosecond pulsed laser, and linear polarization to our ADI system resulted in improved image contrast. The use of ADI with time-gating (gate width 250 ps) and linear polarization enabled visualization of sub-millimeter absorbing objects with approximately eight times higher image contrast compared to ADI in a scattering medium equivalent to six times reduced mean free path.
Compressed channel sensing

Reliable wireless communications often requires accurate knowledge of the underlying multipath channel. This typically involves probing of the channel with a known training waveform and linear processing of the input probe and channel output to estimate the impulse response. Many real-world channels of practical interest tend to exhibit impulse responses characterized by a relatively small number of nonzero channel coefficients. Conventional linear channel estimation strategies, such as the least squares, are ill-suited to fully exploiting the inherent low-dimensionality of these sparse channels. In contrast, this paper proposes sparse channel estimation methods based on convex/linear programming. Quantitative error bounds for the proposed schemes are derived by adapting recent advances from the theory of compressed sensing. The bounds come within a logarithmic factor of the performance of an ideal channel estimator and reveal significant advantages of the proposed methods over the conventional channel estimation schemes.
Non linear space-time equalizer for single-user hybrid mmWave massive MIMO systems

The aim of this manuscript is to propose a new hybrid iterative block space-time receiver structure for millimeter wave (mmW) systems, to efficiently separate the spatial streams. We consider that both the transmitter and the received are equipped with a large antenna array and the number of radio frequency (RF) chains is lower that the number of antennas. The analog and digital parts of the equalizer are jointly optimized using as a metric the mean square error (MSE) between the transmitted data vector and its estimate after the digital equalizer. The specificities of the analog domain impose several constraints in the joint optimization. To efficiently deal with the constraints the analog part is selected from a dictionary based on the array response vectors. Our performance results have shown that the performance of the hybrid iterative space equalizer is close to the fully digital counterpart after only a few iterations. Moreover, it clearly outperforms the linear receivers recently proposed for hybrid mmW massive MIMO architectures.
On the privacy risks of sharing clinical proteomics data

Although the privacy issues in human genomic studies are well known, the privacy risks in clinical proteomic data have not been thoroughly studied. As a proof of concept, we reported a comprehensive analysis of the privacy risks in clinical proteomic data. It showed that a small number of peptides carrying the minor alleles (referred to as the minor allelic peptides) at non-synonymous single nucleotide polymorphism (nsSNP) sites can be identified in typical clinical proteomic datasets acquired from the blood/serum samples of individual patient, from which the patient can be identified with high confidence. Our results suggested the presence of significant privacy risks in raw clinical proteomic data. However, these risks can be mitigated by a straightforward pre-processing step of the raw data that removing a very small fraction (0.1%, 7.14 out of 7,504 spectra on average) of MS/MS spectra identified as the minor allelic peptides, which has little or no impact on the subsequent analysis (and re-use) of these datasets.
No Spillover Effect of the Foreclosure Crisis on Weight Change: The Diabetes Study of Northern California (DISTANCE)

The emerging body of research suggests the unprecedented increase in housing foreclosures and unemployment between 2007 and 2009 had detrimental effects on health. Using data from electronic health records of 105,919 patients with diabetes in Northern California, this study examined how increases in foreclosure rates from 2006 to 2010 affected weight change. We anticipated that two of the pathways that explain how the spike in foreclosure rates affects weight gain—increasing stress and declining salutary health behaviors- would be acute in a population with diabetes because of metabolic sensitivity to stressors and health behaviors. Controlling for unemployment, housing prices, temporal trends, and time-invariant confounders with individual fixed effects, we found no evidence of an association between the foreclosure rate in each patient's census block of residence and body mass index. Our results suggest, although more than half of the population was exposed to at least one foreclosure within their census block, the foreclosure crisis did not independently impact weight change.
Web crippling and bending moment failure of first-generation trapezoidal sheeting, Experiments, Finite Element Models, Mechanical Models

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Radiation Treatment of Lymph Node Recurrence from Prostate Cancer: Is 11C-Choline PET/CT Predictive of Survival Outcomes?

PET/CT is a valuable tool to detect lymph node (LN) metastases in patients with biochemical failure after primary treatment for prostate cancer (PCa). The aim was to assess the predictive role of imaging parameters derived by 11C-choline PET/CT on survival outcomes—overall survival, locoregional relapse-free survival, clinical relapse-free survival (cRFS), and biochemical relapse-free survival (bRFS)—in patients treated with helical tomotherapy (HTT) for LN recurrence. Methods: This retrospective study included 68 patients affected by PCa (mean age, 68 y; age range, 51–81 y) with biochemical recurrence after primary treatment (median prostate-specific antigen values obtained at the time of PET/CT scan, 2.42 ng/mL; range, 0.61–27.56 ng/mL) who underwent 11C-choline PET/CT from January 2005 to January 2013 and were treated with HTT in correspondence of the pathologic choline LN uptake. PET-derived parameters, including maximum/mean standardized uptake value (SUVmax and SUVmean, respectively) and metabolic tumor volume (MTV) with a threshold of 40%, 50%, and 60% were calculated. The best cutoff values of PET-derived parameters discriminating between patients with and without relapse, after treatment guided by PET, were assessed by receiver-operating-characteristic (ROC) curve analysis. Univariate and multivariate Cox regression analysis including the most predictive PET-derived parameters and survival outcomes were performed. Results: The median follow-up was 20 mo (mean, 26 mo; range, 3–97 mo). 11C-choline PET/CT showed pathologic LN uptake in 4 patients at the pelvic level, in 5 at the abdominal level, in 13 at both the pelvic and the abdominal level, and in 46 at the abdominal or pelvic or other sites. The 2-y overall survival, locoregional relapse-free survival, cRFS, and bRFS were 87%, 91%, 51%, and 40%, respectively. On the basis of ROC curves, the most discriminative cutoff value for MTV values was an MTV threshold of 60% (MTV60) of greater than 0.64 cm3. No significant cutoff values were found for SUVmax or SUVmean at univariate analysis, whereas MTV60 was confirmed as an independent predictor in multivariate analysis and significantly correlated with bRFS and cRFS. MTV60 and extrapelvic disease well predict the risk of cRFS. Conclusion: 11C-choline PET/CT performed as a guide for HTT on LN recurrence is predictive of survival. In particular, MTV60 and extrapelvic disease were the best predictors of tumor response for bRFS and cRFS in PCa patients with LN recurrence after primary treatment. This information may be useful in emerging treatment strategies.
Adaptive Approximate Data Collection for Wireless Sensor Networks

Data collection is a fundamental task in wireless sensor networks. In many applications of wireless sensor networks, approximate data collection is a wise choice due to the constraints in communication bandwidth and energy budget. In this paper, we focus on efficient approximate data collection with prespecified error bounds in wireless sensor networks. The key idea of our data collection approach ADC (Approximate Data Collection) is to divide a sensor network into clusters, discover local data correlations on each cluster head, and perform global approximate data collection on the sink node according to model parameters uploaded by cluster heads. Specifically, we propose a local estimation model to approximate the readings of sensor nodes in subsets, and prove rated error-bounds of data collection using this model. In the process of model-based data collection, we formulate the problem of selecting the minimum subset of sensor nodes into a minimum dominating set problem which is known to be NP-hard, and propose a greedy heuristic algorithm to find an approximate solution. We further propose a monitoring algorithm to adaptively adjust the composition of node subsets according to changes of sensor readings. Our trace-driven simulations demonstrate that ADC remarkably reduces communication cost of data collection with guaranteed error bounds.
A highly reliable multi-path routing scheme for ad hoc wireless networks

This paper presents a new routing scheme for ad hoc wireless networks that provides fresh routing information along active routes with affordable cost. The proposed routing mechanism, called proactive route maintenance (PRM), is used to replace the naive route mechanism in existing reactive (on-demand) routing protocols to enhance route reliability and reduce the frequency of expensive route discoveries. The assumption behind PRM is the communication locality in ad hoc wireless networks. That is, most data packets are transported along a few active routes. Data packets are forwarded via multiple optimal paths to meet certain QoS requirements, such as fault tolerance and load balance. Routing information is disseminated along active routes and advertised only by active nodes that forward data packets. Alternative paths are dynamically discovered and maintained by active nodes and their 1-hop neighbors (called passive nodes). The routing overhead in passive nodes is light. PRM maintains reliable end-to-end connections even in dynamic networks with relatively low overhead, and has desirable properties including high delivery ratio, low latency, fair load distribution, self-healing and self-optimization.
On the comparison between performance of DHT-based protocols for opportunistic networks

Opportunistic networks represent a new frontier for networking research as due to node mobility the network might become disconnected. Such intermittent connectivity imposes challenges to protocol design, especially when information access might require the availability of updated information about resources shared by mobile nodes. An opportunistic network can be seen as a peer-to-peer network where resources should be located in a distributed way. Numerous solutions for P2P resource management have been proposed in the last years. Among the different approaches being considered, Distributed Hash Table (DHT) based schemes offer the advantages of a distributed approach which can be tuned to network scalability. In this paper we consider two well known P2P DHT-based solutions for wireless networks denoted as Bamboo and Georoy, and compare their performance in a multihop wireless scenario. We evaluate scalability and key lookup behavior for different network sizes. The results allow us to gain insights into protocol behavior which allows to select for a given network configuration the appropriate scheme.
Methyl N-[1-(Benzoylamino)-2-methoxy-2-oxoethyl]-tryptophanate

The title compound, methyl N-[1-(benzoylamino)-2-methoxy-2-oxoethyl]tryptophanate 2, was synthesized in high yield, via N-alkylation reaction of methyl 2-azido-2-benzamidoacetate with methyl 2-amino-3-(1H-indol-3-yl)propanoate in acetone, with the presence of diisopropylethylamine as a base. The structure of the prepared compound was characterized by 1H, 13C NMR in addition to MS, X-Ray diffraction data, and elemental analysis. This compound was tested in vitro for its antibacterial activity against Gram-positive bacteria, Bacillus subtilis and Staphylococcus aureus, and Gram-negative bacteria, Escherichia coli, Pseudomonas aeruginosa, and Salmonella enteric. The MIC values showed that the synthesized compound had a bactericidal effect against the strains tested.
Purification of snRNPs U1, U2, U4, U5 and U6 with 2,2,7‐trimethylguanosine‐specific antibody and definition of their constituent proteins reacting with anti‐Sm and anti‐(U1)RNP antisera.

Small nuclear ribonucleoprotein particles (snRNPs) of the U‐snRNP class from Ehrlich ascites tumor cells were purified in a one‐step procedure by affinity chromatography with antibodies specific for 2,2,7‐trimethylguanosine (m23.2.7G), which is part of the 5′‐terminal cap structure of snRNAs U1‐U5. Antibody‐bound snRNPs are desorbed from the affinity column by elution with excess nucleoside m23.2. 7G; this guarantees maintenance of their native structure. The snRNPs U1, U2, U4, U5 and U6 can be recovered quantitatively from nuclear extracts by this procedure. Co‐isolation of U6 snRNP must be due to interactions between this and other snRNPs, as anti‐m23.2. 7G antibodies do not react with deproteinized U6 snRNA. We have so far defined nine proteins of approximate mol. wts. 10 000, 12 000, 13 000, 16 000, 21 000, 28 000, 32 000, 34 000 and 75 000. Purified snRNPs react with anti‐(U1)RNP and with anti‐Sm antisera from patients with mixed connective tissue disease and from MRL/l mice. As determined by the protein blotting technique, six of the snRNP polypeptides, characterized by apparent mol. wts. 13 000, 16 000, 21 000, 28 000, 34 000 and 75 000, bear antigenic determinants for one or the other of the above autoantibody classes. This suggests strongly that the U‐snRNPs produced by the procedure described here are indeed representative of the snRNPs in the cell. With highly purified snRNPs available, investigation of possible enzymic functions of the particles may now be undertaken.
New Light Field Image Dataset

Recently, an emerging light field imaging technology, which enables capturing full light information in a scene, has gained a lot of interest. To design, develop, implement, and test novel algorithms in light field image processing and compression, the availability of suitable light field image datasets is essential. In this paper, a publicly available light field image dataset is introduced and described in details. The proposed dataset contains 118 light field images captured by using a Lytro Illum light field camera. Based on their content, acquired light field images were classified into ten different categories with various features covering wide range of potential usage, such as image compression and quality evaluation.
Polylogarithmic Independence Can Fool DNF Formulas

We show that any k-wise independent probability measure on {0, 1}n can O(m2ldr2ldr2-radick/10)-fool any boolean function computable by an rn-clauses DNF (or CNF) formula on n variables. Thus, for each constant c > 0. there is a constant e > 0 such that any boolean function computable by an m-clauses DNF (or CNF) formula can be in m-e-fooled by any clog in-wise probability measure. This resolves, asymptotically and up to a logm factor, the depth-2 circuits case of a conjecture due to Linial and Nisan (1990). The result is equivalent to a new characterization of DNF (or CNF) formulas by low degree polynomials. It implies a similar statement for probability measures with the small bias property. Using known explicit constructions of small probability spaces having the limited independence property or the small bias property, we. directly obtain a large class of explicit PRG's ofO(log2 m log n)-seed length for m-clauses DNF (or CNF) formulas on n variables, improving previously known seed lengths.
A Survey of Model-Based and Model-Free Methods for Resolving Perceptual Aliasing

1 Abstract We focus our attention on agents learning to act in an unknown domain using noisy sensors. Such domains may be modeled by a Partially Observable Markov Decision Process (POMDP) that can be solved optimally. However, when the model of the environment is unknown, most research in the area studies model-free methods | methods that learn to act without learning a model. When the agents’ sensors provide deterministic output, model-free methods produce close to optimal results. However, as sensor noise increases, the accuracy of such methods decreases. Another, less explored, option is the model-based approach | learning a POMDP model of the world, and computing an optimal solution using the learned model. In this survey we explore model-based and model-free techniques for handling perceptual aliasing.
Circadian Rhythm and Stress Response in Droppings of Serinus canaria

Serinus canaria is a widespread domestic ornamental songbird, whose limited knowledge of biology make compelling studies aimed to monitor stress. Here, a commercial enzyme immunoassay was adopted to measure immunoreactive corticosterone (CORT) in single Serinus canaria dropping sample, to monitor the daily fecal excretion of CORT in birds bred singly or in-group and to detect the effect promoted by aviary or small transport cage restraint. A robust daily rhythm of CORT was recorded in animals held on short-day light cycle, independent of bred conditions (single or group), which persisted when space availability was modified in single bred animal (transfer in aviary and transport cages). By contrast, a significant change in CORT excretion was recorded when group bred animals are restrained in a smaller cage. The daily rhythm in CORT excretion in response to manipulation showed the greatest response at the beginning of the light period, followed by the absence of the peak usually recorded at the end of the dark phase. These data indicated that EIA could be used as a reliable noninvasive approach to monitor the stress induced by restraint conditions in Serinus canaria.
Electron diffusion in 0.18 μm MOS transistors at 200 μC

A 0.18 mum CMOS technology has been characterized and modeled from room temperature (20degC) up to 200degC. The carrier mobility, the surface potential, the gradient of inversion charges, and the electric field are calculated by solving the Poisson and the continuity equations. The drift of charges rather than the charge diffusion is found to be responsible for the temperature-independent drain current for the case of a 0.18 mum technology.
In Vivo Bioluminescent Imaging (BLI): Noninvasive Visualization and Interrogation of Biological Processes in Living Animals

In vivo bioluminescent imaging (BLI) is increasingly being utilized as a method for modern biological research. This process, which involves the noninvasive interrogation of living animals using light emitted from luciferase-expressing bioreporter cells, has been applied to study a wide range of biomolecular functions such as gene function, drug discovery and development, cellular trafficking, protein-protein interactions, and especially tumorigenesis, cancer treatment, and disease progression. This article will review the various bioreporter/biosensor integrations of BLI and discuss how BLI is being applied towards a new visual understanding of biological processes within the living organism.
The Volume-of-Tube Formula: Perturbation Tests, Mixture Models and Scan Statistics

This article creates a general class of perturbation models which are described by an underlying null model that accounts for most of the structure in data while a perturbation accounts for possible small localized departures. The goal is to develop theory and inferential methods for fitting the perturbation models including the general case when the null model contains a set of nuisance parameters. Applications of the perturbation models include finite mixture models and spatial scan process. In the context of finite mixture models, the null model represents a mixture with m components and the perturbation model represents additional components. In the spatial scan process context, the null density accounts for the background or noise whereas the perturbation searches for an unusual region such as a tumorous tissue in mammography or a target in an automatic target recognition problem. In this research, (1) a test statistic for detecting the presence of perturbation is proposed and is shown to be equivalent to the likelihood ratio test in certain cases, (2) a general theory for the derivation of the limiting distribution of the test statistic for a class of non-regular problems is developed using the Hotelling-Weyl volume-of-tube formula, (3) the resulting theory is applied to the problem of testing for the order of a mixture model; in particular, the limiting distribution is derived for a general family of mixture models including the multivariate mixture case and (4) explicit determination of the geometric constants appearing in the tube formula is developed.
4T DRAM based on Self-controllable Voltage Level technique for low leakage power in VLSI

In present trend of integration will continue in the projected future. When comparing the integration density of integrated circuits, a clear distinction must be made between the memory chips and logic chips. Memory circuits are highly regular. Increase of chip complexity is consistently higher for memory circuits. The salient features such as low power, reliable performance, circuit techniques for high speed such as using dynamic circuits, and low leakage current, most of these have get give a better advantage. This paper gives an idea about 4T DRAM (dynamic random access memory) which is implemented by using a self controllable voltage level technique. In this technique reduces the leakage current and produce the high performance. Simulation is done by using a microwind 3.1 and DSCH3. Using a 90 nm technology to implemented a 4T DRAM. By using this give the benefit of 67% reduction of leakage current. This reduction gives a advantage of high performance and low leakage current, by controlling these parameter speed is automatically increase.