Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Signal Processing Methods in Scattering and Imaging

This Ph.D. dissertation addresses two related topics in wave-based signal processing: 1) Cramer-Rao bound (CRB) analysis of scattering systems formed by pointlike scatterers in one-dimensional (1D) and three-dimensional (3D) spaces. 2) Compressive optical coherent imaging, based on the incorporation of sparsity priors in the reconstructions. The first topic addresses for wave scattering systems in 1D and 3D spaces the information content about scattering parameters, in particular, the targets’ positions and strengths, and derived quantities, that is contained in scattering data corresponding to reflective, transmissive, and more general sensing modalities. This part of the dissertation derives the Cramer-Rao bound (CRB) for the estimation of parameters of scalar wave scattering systems formed by point scatterers. The results shed light on the fundamental difference between the approximate Born approximation model for weak scatterers and the more general multiple scattering model, and facilitate the identification of regions in parameter space where multiple scattering facilitates or obstructs the estimation of parameters from scattering data, as well as of sensing configurations giving maximal or minimal information about the parameters. In the 1D case we consider first (in Chapter 1) a number of sensing scenarios and of a priori data, which simulate different applications in the estimation of parameters of pointlike scatterers. Later (in Chapter 2) we pay particular attention to elastic scatterers, which simplifies the signal model (relative to more general scattering systems), thereby allowing closed-form expressions for the CRB for special cases. In these chapters the derived CRB results quantify the effect of multiple scattering, be it in enhancing or in diminishing imaging capabilities, relative to the Born approximation model
SRv6 vSID: Network Programming extension for variable length SIDs

This document proposes an extension to Segment Routing IPv6 (SRv6) Network Programming to allow for SRv6 Segment Identifier (SID) of smaller variable length. The use of smaller SRv6 SID reduces the size the SRv6 Header (SRH). This reduces the overhead for both the traffic volume and the network processor. It is a straightforward extension to the SRv6 Network Programming model and its SRH encapsulation.
Trimming and Washing of Beef Carcasses as a Method of Improving the Microbiological Quality of Meat.

A study to compare procedures and interventions for removing physical and bacterial contamination from beef carcasses was conducted in six carcass conversion operations that were representative of modern, high-volume plants and located in five different states. Treatment procedures included trimming, washing, and the current industry practice of trimming followed by washing. In addition, hot (74 to 87.8°C at the pipe) water washing and rinsing with ozone (0.3 to 2.3 ppm) or hydrogen peroxide (5%) were applied as intervention treatments. Beef carcasses were deliberately contaminated with bovine fecal material at >4.0 log colony-forming units (CFU)/cm2 in order to be better able to observe the decontaminating effects of the treatments. Carcasses were visually scored by 2 to 3 trained personnel for the level of gross contamination before and after treatment. Samples (10 by 15 cm, 0.3 to 0.5 cm thick) for microbiological testing were excised as controls or after application of each procedure or intervention and analyzed for aerobic mesophilic plate counts, Escherichia coli Biotype I counts, and presence or absence of Listeria spp. , Salmonella spp. , and Escherichia coli O157:H7. Average reductions in aerobic plate counts were 1.85 and 2.00 log CFU/cm2 for the treatments of trimming-washing and hot-water washing, respectively. Hydrogen peroxide and ozone reduced aerobic plate counts by 1.14 and 1.30 log CFU/cm2, respectively. In general, trimming and washing of beef carcasses consistently resulted in low bacterial populations and scores for visible contamination. However, the data also indicated that hot- (74 to 87.8°C at the pipe) water washing was an effective intervention that reduced bacterial and fecal contamination in a consistent manner.
BIOPHYSICAL MODELS OF TRANSCRIPTIONAL REGULATION FROM SEQUENCE DATA

In the post-genomics era, DNA sequence itself is becoming a medium by which to probe biological phenomena. With the advent of microarray technology, and ultrahigh- throughput sequencing more recently, large sequence data sets are becoming standard products of day-to-day research. Yet as software for analyzing such data proliferates, a fundamental understanding of how DNA sequence should be used to gain biological insight is missing from the literature. The focus of this thesis is on developing tools for characterizing the biophysical interactions underlying transcriptional regulation { the ability of cells to control which genes they transcribe to mRNA, and thus express as protein. We begin by presenting basic principles for the analysis of DNA sequence data { specically, data for which each sequence � is accompanied by a (perhaps very noisy) measurement z of biophysical functionality. A salient feature of experiments which produce such �z data is the di�culty of characterizing experimental noise a priori. We overcome this obstacle by introducing error-model-averaged (EMA) likelihood, which allows biophysical models of arbitrary functional form to be rigorously t to �z data. EMA likelihood is closely related to mutual information, but its probabilistic interpretation provides some advantages. We demonstrate EMA likelihood's utility on previously published microarray data, using Metropolis Monte Carlo sampling to infer models for the DNA-binding energy of transcription factor proteins. The ability to properly analyze �z data leads us to propose a new experimental assay, called Sort-Seq. This technique uses ultra-high-throughput sequencing to probe the protein-DNA and protein-protein interactions underlying transcriptional regulation at specific genomic loci. We present data from a proof-of-principle Sort- Seq experiment probing the lacZ promoter of E. coli, data we use to characterize the sequence-dependent binding energy of the transcription factor CRP. We then discuss what one can, in principle, infer from large Sort-Seq data sets. We show that, with enough �z data probing the binding of multiple proteins per sequence, one should be able to infer both protein-DNA and protein-protein interaction energies in absolute thermal units. We conclude that, with the advent of ultra-highthroughput sequencing, DNA sequence itself might provide a very sensitive means by which to probe in vivo biophysics.
Exploiting De-Noising Convolutional Neural Networks DnCNNs for an Efficient Watermarking Scheme: A Case for Information Retrieval

ABSTRACT Digital watermarking has various applications in which it plays an important role; these include document security and multimedia vendor copyrights. This paper presents an efficient watermarking technique called ‘modified selective embedding in low frequency’ (M-SELF) implementing a Fast Fourier Transform (FFT) for frequency domain analysis where FFT shift brings down lower-frequency components to the center of the cover image. Embedding of the watermark is done by optimal radial implementation after coordinates computation via phase and magnitude separation. Furthermore, the watermark is protected by the convolutional code (1/2) and Advanced Encryption Standard (AES-128) algorithm providing a double layer of security. Moreover, the exploitation of de-noising convolutional neural networks (DnCNNs) is another contribution of the proposed work. The network is utilized as a de-noiser in an integrated manner for the proposed watermarking technique on blind level of additive white Gaussian noise (AWGN) to check the robustness of the technique. Performance evaluation of the proposed integrated watermarking technique is done in terms of percentage of bits being retrieved as a success rate at each level of noise and perceived visual quality by using full-reference image quality assessment (IQA) metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).
Multicenter phase II study of pembrolizumab (P) in advanced soft tissue (STS) and bone sarcomas (BS): Final results of SARC028 and biomarker analyses.

11008Background: SARC028 is the first multicenter Phase II study of P monotherapy in patients (pts) with STS and BS. Designed to detect clinical efficacy signals in multiple histologies, the study collected blood & tissue samples on all pts. We report extended clinical follow-up and in-depth biomarker correlates of response. Methods: The primary endpoint was objective response rate (ORR) by RECIST 1.1. Secondary endpoints were safety, 12 wk progression-free survival (PFS), and overall survival (OS). The STS arm had 10 pts in each of 4 cohorts: undifferentiated pleomorphic sarcoma (UPS), dedifferentiated liposarcoma (DDLPS), synovial sarcoma (SS) and leiomyosarcoma (LMS). The BS arm included 40 pts with osteosarcoma (OS), Ewing sarcoma (ES) or dedifferentiated chondrosarcoma (CS). Pre- and on-P biopsies were required as well as blood at multiple time points. Tumor was assessed for PD-L1 expression (clone 22C3) and immune infiltrates by multi-color IHC (Vectra). Ongoing analyses include circulating cytokine.
An improved method of Tiny YOLOV3

Target detection is the basic technology of self-driving system. In this paper, the problem of high detection rate of pedestrians and other small targets is studied in real-time detection of Tiny YOLOV3 target detection algorithm, and the network structure of Tiny YOLOV3 algorithm is improved. 2-step convolutional layers are added to the network, and deep separable convolution constructs are used to replace the traditional convolutions. On the basis of the original two-scales prediction target of the network, a scale is added to form a three-scales prediction, which can makes the detection of small targets such as pedestrians more accurate. The experimental results show that the average accuracy of the improved target detection algorithm is 8.6% higher than that of Tiny YOLOV3, and it meets the real-time requirements and has certain robustness.
Robust chest x-ray quality assessment using convolutional neural networks and atlas regularization

The quality of chest radiographs is a practical issue because deviations from quality standards cost radiologists' time, may lead to misdiagnosis and hold legal risks. Automatic and reproducible assessment of the most important quality figures on every acquisition can enable a radiology department to measure, maintain, and improve quality rates on an everyday basis. A method is proposed here to automatically quantify the quality according to the aspects of (i) collimation, (ii) patient rotation, and (iii) inhalation state of a chest PA radiograph by localizing a number of anatomical features and calculating some quality figures in accordance with international standards. The anatomical features related to these quality aspects are robustly detected by a combination of three convolutional neural networks and two probabilistic anatomical atlases. An error analysis demonstrates the accuracy and robustness of the method. The implementation proposed here works in real time (less than a second) on a CPU without any GPU support.
Wind power forecasting accuracy and uncertainty in Finland

Wind power cannot be dispatched so the production levels need to be forecasted for electricity market trading. Lower prediction errors mean lower regulation balancing costs, since relatively less energy needs to go through balance settlement. From the power system operator point of view, wind power forecast errors will impact the system net imbalances when the share of wind power increases, and more accurate forecasts mean less regulating capacity will be activated from the real time Regulating Power Market. In this publication short term forecasting of wind power is studied mainly from a wind power producer point of view. The forecast errors and imbalance costs from the day-ahead Nordic electricity markets are calculated based on real data from distributed wind power plants. Improvements to forecasting accuracy are presented using several wind forecast providers, and measures for uncertainty of the forecast are presented. Aggregation of sites lowers relative share of prediction errors considerably, up to 60%. The balancing costs were also reduced up to 60%, from 3 €/MWh for one site to 1–1.4 €/MWh to aggregate 24 sites. Pooling wind power production for balance settlement will be very beneficial, and larger producers who can have sites from larger geographical area will benefit in lower imbalance costs. The aggregation benefits were already significant for smaller areas, resulting in 30–40% decrease in forecast errors and 13–36% decrease in unit balancing costs, depending on the year. The resulting costs are strongly dependent on Regulating Market prices that determine the prices for the imbalances. Similar level of forecast errors resulted in 40% higher imbalance costs for 2012 compared with 2011. Combining wind forecasts from different Numerical Weather Prediction providers was studied with different combination methods for 6 sites. Averaging different providers’ forecasts will lower the forecast errors by 6% for day-ahead purposes. When combining forecasts for short horizons like the following hour, more advanced combining techniques than simple average, such as Kalmar filtering or recursive least squares provided better results. Two different uncertainty quantification methods, based on empirical cumulative density function and kernel densities, were analysed for 3 sites. Aggregation of wind power production will not only decrease relative prediction errors, but also decreases the variation and uncertainty of prediction errors.
Assessing the contribution of ART, HIV and host factors to adipose tissue changes occurring in HIV-infected individuals: Risk profile for lipoatrophy

Objectives: For HIV-infected individuals receiving antiretroviral therapy (ART), the development of lipoatrophy significantly impacts clinical management. To identify critical risk factors for lipoatrophy, we assessed in vivo adipose tissue changes occurring longitudinally in HIV-infected patients. Methods: Subcutaneous fat (n=68 biopsies; n=32 individuals) was paraffin processed and stained by immunohistochemistry for scoring of macrophage numbers and adipocyte-specific protein expression of cytokines (IL-18, IL-6, TNFa, IL-8, IL-12). Data was analysed cross-sectionally and for changes over time (median=7.0, IQR=5.9–10.1 months). Data included samples from individuals initiating [zidovudine (AZT) n=6; stavudine (d4T) n=7; abacavir (ABC)/non-thymidine n=6], switching (d4T onto AZT/ABC n=8; AZT onto ABC n=2) or continuing therapy (n=7). Mitochondrial DNA (mtDNA) content and relative transcriptional expression of caspase I, IL-18, IL-6, TNFa, adiponectin and PPAR-g were also measured in frozen biopsies (AZT n=6; d4T n=10; naive n=14; ABC n=13). The influence of ART, disease and host-related factors on these endpoints was investigated. Results: Cross-sectional analysis revealed protein expression of all cytokines correlated with current ART (all P 0.2). No significant effect of protease inhibitor therapy was detected. Switching from d4T or AZT was associated with modest improvements in tissue morphology and improvements in mtDNA depletion (P=0.008), macrophage numbers and expression of all cytokines, but significance was restricted to IL-8 (P=0.008). Fat gain (positive changes in % leg fat/BMI) was undetectable. Gene expression data failed to show significant correlations with protein expression (all P>0.05). Dublin, Ireland 13–16 November 2005 Conclusions: Profiling of adipose tissue morphology, cytokine protein expression and mtDNA depletion identified ART [d4T>AZT>ABC (without d4t/AZT)= naive], but not viral or host variables, as risk factors for lipoatrophy. Results: Cross-sectional analysis revealed protein expression of all cytokines correlated with current ART (all P 0.2). No significant effect of protease inhibitor therapy was detected. Switching from d4T or AZT was associated with modest improvements in tissue morphology and improvements in mtDNA depletion (P=0.008), macrophage numbers and expression of all cytokines, but significance was restricted to IL-8 (P=0.008). Fat gain (positive changes in % leg fat/BMI) was undetectable. Gene expression data failed to show significant correlations with protein expression (all P>0.05). Conclusions: Profiling of adipose tissue morphology, cytokine protein expression and mtDNA depletion identified ART [d4T>AZT>ABC (without d4t/AZT)= naive], but not viral or host variables, as risk factors for lipoatrophy.
D3.1 – User Requirements & Scenarios - Alpha

This deliverable reports on the user needs and requirements for EMOTIVE experiences, focusing on both main user groups targeted by the project, namely authors and visitors. Visitor groups have been identified for EMOTIVE cultural partner sites (The Hunterian Museum’s Antonine Wall site display in Scotland and the archaeological site of Catalhoyuk in Turkey) and presented in the form of personas. The second part of the document presents selected interpretation material and proposed experience concepts that will lead to the creation of EMOTIVE experiences for both sites. An accompanying D3.1 Annex includes supplementary information on the studies and events carried out in support of user needs elicitation and scenario development.
Automatic Boolean Query Formulation for Systematic Review Literature Search

Formulating Boolean queries for systematic review literature search is a challenging task. Commonly, queries are formulated by information specialists using the protocol specified in the review and interactions with the research team. Information specialists have in-depth experience on how to formulate queries in this domain, but may not have in-depth knowledge about the reviews’ topics. Query formulation requires a significant amount of time and effort, and is performed interactively; specialists repeatedly formulate queries, attempt to validate their results, and reformulate specific Boolean clauses. In this paper, we investigate the possibility of automatically formulating a Boolean query from the systematic review protocol. We propose a novel five-step approach to automatic query formulation, specific to Boolean queries in this domain, which approximates the process by which information specialists formulate queries. In this process, we use syntax parsing to derive the logical structure of high-level concepts in a query, automatically extract and map concepts to entities in order to perform entity expansion, and finally apply post-processing operations (such as stemming and search filters). Automatic query formulation for systematic review literature search has several benefits: (i) it can provide reviewers with an indication of the types of studies that will be retrieved, without the involvement of an information specialist, (ii) it can provide information specialists with an initial query to begin the formulation process, (iii) it can provide researchers that perform rapid reviews with a method to quickly perform searches.
Lagged encoding for image‐based time series classification using convolutional neural networks

Time series classification is a thriving area of research in machine learning. Among many applications, it is frequently applied to human activity analysis. Time series describing a human in motion are ubiquitously collected via omnipresent mobile devices and can be subjected to further processing. In this paper, we propose a novel, deep learning approach to time series classification. It is based on a lagged time series representation stored as images and Convolutional Neural Network used to image classification. We present a comparative study on different variants of lagged time series representation and we evaluate their effectiveness in a series of empirical experiments. We show that the developed method provides satisfying classification accuracy. The proposed image‐based time series encoding is less resource‐consuming than encodings used in other image‐based approaches to time series classification. It is worth to emphasize that the proposed time series encoding conceals original time series values. Images are saved without scales and the order of observations cannot be reconstructed. Thus, the method is particularly suitable for systems that need to store sensitive information.
Response of Indigenous Rhizobia to the Inoculation of Soybean [Glycine max (L.) Merrill] Varieties Cultivated under Controlled Conditions in Côte d’Ivoire

Soybean [Glycine max (L.) Merrill] is an important crop known to improve population nutritional status and increase soil fertility and its productivity through biological nitrogen fixation. In Cote d’Ivoire, the introduced Bradyrhizobium japonicum used as inoculum had slight compatibility to several soybean varieties compromising their vulgarization. Therefore, the present study was conducted to examine the infectiveness and the effectiveness of indigenous rhizobial isolates on three soybean varieties (Canarana, Doko and Piramana) cultivated in Cote d’Ivoire. The experiment was conducted with potted plant filled with sterilized sand and was statistically laid in Completely Randomized Design (CRD) with sixteen (16) natives rhizobia, one (01) reference strain and uninoculated control (with or without nitrogen) with three replications. The results showed that inoculation significantly improved nodule number, nodule dry weight, plant height and total dry matter of soybean over the negative control treatment. Among the inoculated treatments, five indigenous rhizobia RSC119, RSC324, RSC502, RSC504 and RSC508 significantly (P -1) compared to IRAT FA3 strain (95 mg·plant-1) on Doko. The higher effectiveness was recorded with RSC119, RSC504 and RSC502 with 206.73%; 201.79% and 200.45% respectively compared to TN (100%). The correlation analysis indicated significant association of nodule number and total biomass indicating the importance of symbiotic nitrogen fixation. Based on their infectiveness and their effectiveness, the isolates RSC119, RSC324, RSC502, RSC504 and RSC508 could be used as elite local rhizobia and tested in field conditions to establish their potential contribution on soybean productivity.
Overcoming the Barriers That Obscure the Interlinking and Analysis of Clinical Data Through Harmonization and Incremental Learning

Goal: To present a framework for data sharing, curation, harmonization and federated data analytics to solve open issues in healthcare, such as, the development of robust disease prediction models. Methods: Data curation is applied to remove data inconsistencies. Lexical and semantic matching methods are used to align the structure of the heterogeneous, curated cohort data along with incremental learning algorithms including class imbalance handling and hyperparameter optimization to enable the development of disease prediction models. Results: The applicability of the framework is demonstrated in a case study of primary Sjögren's Syndrome, yielding harmonized data with increased quality and more than 85% agreement, along with lymphoma prediction models with more than 80% sensitivity and specificity. Conclusions: The framework provides data quality, harmonization and analytics workflows that can enhance the statistical power of heterogeneous clinical data and enables the development of robust models for disease prediction.