Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative Pose Refinement for Object Pose Estimation Based on RGBD Data

Accurate estimation of 3D object pose is highly desirable in a wide range of applications, such as robotics and augmented reality. Although significant advancement has been made for pose estimation, there is room for further improvement. Recent pose estimation systems utilize an iterative refinement process to revise the predicted pose to obtain a better final output. However, such refinement process only takes account of geometric features for pose revision during the iteration. Motivated by this approach, this paper designs a novel iterative refinement process that deals with both color and geometric features for object pose refinement. Experiments show that the proposed method is able to reach 94.74% and 93.2% in ADD(-S) metric with only 2 iterations, outperforming the state-of-the-art methods on the LINEMOD and YCB-Video datasets, respectively.
Accelerating Interval Iteration for Expected Rewards in Markov Decision Processes

Reachability probabilities and expected rewards are two important classes of properties that are computed in probabilistic model checking. Iterative numerical methods are used to compute these properties. Interval iteration and sound value iteration are proposed in recent years to guarantee the precision of computed values. These methods consider upper and lower bounds of values and update each bound in every iteration until satisfying the convergence criterion. In this paper, we focus on the computation of the expected rewards of models and propose two heuristics to improve the performance of the interval iteration method. The first heuristic updates the upper and lower bounds separately to avoid redundant updates. The second heuristic uses the computed values of the lower bound to approximate a starting point for the upper bound. We also propose a criterion for the correctness of the approximated upper bound. The experiments show that in most cases, interval iteration with our approaches outperforms the standard interval iteration and sound value iteration
Can mid infrared diffuse reflectance analysis replace soil extractions

Summary. Recent developments in infrared spectroscopy and computer software, together with decreasing spectrometer costs, have resulted in an increase in the potential for soil analysis. Infrared spectroscopy in both the near and mid infrared ranges allows rapid acquisition of soil information at quantitative and qualitative, or indicator, levels for use in agriculture and environmental monitoring. In this paper, we describe how mid infrared diffuse reflectance analysis can provide results comparable in accuracy with many traditional extractive and digestion laboratory methods in soil studies, with the possibility of either replacing or enhancing them. Examples are given for estimation of lime requirement, organic carbon, exchangeable cations, air-dry moisture, clay content and biological indicators. Infrared methodology appears to have advantages in facilitating some soil analyses that are otherwise very time-consuming or expensive, or where spatially dense data is required.
A Comprehensive Quantified Approach for Security Risk Management in e-Health Systems

: As a major advancement technology in healthcare industry, e-health contributes to setting up efﬁcient and highly automated healthcare infrastructures. Internet of things (IoT) holds great promise for healthcare providers as well as for its end users. Internet of Medical Things (IoMT) applications are among the major trends of the moment. Nonetheless, numerous security features remain as main issues towards secure, reliable and privacy-preserving e-health systems. Indeed, the participating nodes in IoMT networking for e-health service delivery; which are heterogeneous and resource-constrained; generate, collect and exchange huge amounts of private and extremely sensitive data. These facts, among others, expand the attack surface and decrease the trustworthiness in e-health systems. In this research work, we propose a framework to en-hance trust and help with making decisions based on a quantiﬁed risk assessment approach. This framework relies on a novel approach/model for improving trust and risk management in an e-health context.
cipherPath: Efficient Traversals over Homomorphically Encrypted Paths

: We propose cipherPath , a novel graph encryption scheme that enables exact shortest distance queries on encrypted graphs. Shortest distance queries are very useful in a vast number of applications, including medical, social or geospatial. Our approach using somewhat homomorphic encryption in combination with structured encryption enables exact shortest distance queries on outsourced and encrypted graph data. Our approach upholds provable security against a semi-honest provider. We demonstrate our framework by means of two different shortest path algorithms on encrypted graphs: Dijkstra and Floyd. Finally, we evaluate the leakage proﬁle of cipherPath .
Genomic Dissection of a Wild Region in a Superior Solanum pennellii Introgression Sub-Line with High Ascorbic Acid Accumulation in Tomato Fruit

The Solanum pennellii introgression lines (ILs) have been exploited to map quantitative trait loci (QTLs) and identify favorable alleles that could improve fruit quality traits in tomato varieties. Over the past few years, ILs exhibiting increased content of ascorbic acid in the fruit have been selected, among which the sub-line R182. The aims of this work were to identify the genes of the wild donor S. pennellii harbored by the sub-line and to detect genes controlling ascorbic acid accumulation by using genomics tools. A Genotyping-By-Sequencing (GBS) approach confirmed that no wild introgressions were present in the sub-line besides one region on chromosome 7. By using a dense single nucleotide polymorphism (SNP) map obtained by RNA sequencing (RNA-Seq), the wild region of the sub-line was finely identified; thus, defining 39 wild genes that replaced 33 genes of the ILs genetic background (cv. M82). The differentially expressed genes mapping in the region and the variants detected among the cultivated and the wild alleles evidenced the potential role of the novel genes present in the wild region. Interestingly, one upregulated gene, annotated as a major facilitator superfamily protein, showed a novel structure in R182, with respect to the parental lines. These genes will be further investigated using gene editing strategies.
Degradation of Bcl10 Induced by T-Cell Activation Negatively Regulates NF-κB Signaling

ABSTRACT Bcl10 is a critical regulator of NF-κB activity in T and B cells, coupling antigen receptor signaling to NF-κB activation via protein kinase C (PKC). Here we show that PKC or T-cell receptor (TCR)/CD28 signaling results in downregulation of Bcl10 protein levels, thereby attenuating NF-κB transcriptional activity. Bcl10 degradation requires an intact caspase recruitment domain and is not observed after stimulation with tumor necrosis factor α or lipopolysaccharides. Bcl10 downregulation is not affected by proteasome inhibitors but is accompanied by transient localization to lysosomal vesicles, suggesting involvement of the lysosomal pathway rather than the proteasome. The HECT domain ubiquitin ligases NEDD4 and Itch promote ubiquitination and degradation of Bcl10, thus downmodulating NF-κB activation. Since CD3/CD28-induced activation of JNK is not affected by the decline of Bcl10, degradation of Bcl10 selectively terminates IKK/NF-κB signaling in response to TCR stimulation. Together, these results suggest a new mechanism of negative signaling in which TCR/PKC signaling initially activates Bcl10 but later promotes its degradation.
An Efficient and Secure Cipher Scheme for Filter Bank Multi-Carrier Systems

Future mobile systems support heterogeneous devices with diverse requirements and hence require flexible and efficient allocation of available time-frequency resources. A promising multi-carrier modulation scheme considered for 5G systems is the filter bank multi-carrier (FBMC) scheme. FBMC offers better spectral characteristics compared to conventional orthogonal frequency division multiplexing (OFDM) by combining individual or groups of sub-carriers and applying a shaping filter to achieve better spectral containment. While current research on FBMC has mainly focused on reducing the signal processing complexity associated with FBMC as well as coping with the issue of the increased symbol period, analysis of the security aspects of FBMC systems remains largely unattempted. In this paper, we take the first step in this direction and propose a new cipher scheme to guard against adversaries, while preserving the promised performance of FBMC. The proposed cipher scheme is based on dynamic permutation of time-domain symbols, and a dynamic key approach that generates session keys by exploiting the randomness of the underlying physical communication channel. The randomness of the channel-dependent dynamic key ensures robustness of the cipher solution. The cipher scheme is shown to be highly efficient since it requires only one iteration with one simple operation. Experimental simulations demonstrate that the proposed scheme strikes a good balance between performance and security.
SENSSE: Simple, Efficient Searchable Symmetric Encryption for Sensor Networks

: In this work, we focus on the problem of forward-private dynamic searchable symmetric encryption (DSSE) in the multi-client setting. In order to achieve forward privacy, efﬁcient DSSE schemes require clients to store local information, such as per-keyword search counters. Such construction choices prevent these schemes from being used in a multi-user scenario. We revisit the concept of forward privacy with a goal to examine the need for client storage. As a result, we propose a new method of realising forward privacy without requiring the clients to keep any state information. Based on this method, we construct a dynamic, forward-private searchable symmetric encryption scheme supporting multiple concurrent clients with minimal overhead. The proposed construction requires no state to be kept by clients, yet provides optimal asymptotic behaviour both in time, storage and communication cost, while having similar leakage proﬁle to other state-of-the-art DSSE schemes.
Evasive Windows Malware: Impact on Antiviruses and Possible Countermeasures

The perpetual opposition between antiviruses and malware leads both parties to evolve continuously. On the one hand, antiviruses put in place solutions that are more and more sophisticated and propose more complex detection techniques in addition to the classic signature analysis. This sophistication leads antiviruses to leave more traces of their presence on the machine they protect. To remain undetected as long as possible, malware can avoid executing within such environments by hunting down the modifications left by the antiviruses. This paper aims at determining the possibilities for malware to detect the antiviruses and then evaluating the efficiency of these techniques on a panel of antiviruses that are the most used nowadays. We then collect samples showing this kind of behavior and propose to evaluate a countermeasure that creates false artifacts, thus forcing malware to evade.
Retinal Vessel Segmentation by Inception-like Convolutional Neural Networks

: Deep learning architectures have been proposed in some neural networks like convolutional neural networks (CNN), recurrent neural networks and deep belief neural networks. Among them, CNNs have been applied in image processing tasks frequently. An important section in intelligent image processing is medical image processing which provides intelligent tools and software for medical applications. Analysis of blood vessels in retinal images would help the physicians to detect some retina diseases like glaucoma or even diabetes. In this paper a new neural network structure is proposed which can process the retinal images and detect vessels apart from retinal background. This neural network consists of convolutional layers, concatenate layers and transpose convolutional layers. The results for DRIVE dataset show acceptable performance regarding to accuracy, recall and F-measure criteria.
Symbiotic bacteria attenuate Drosophila oviposition repellence to alkaline through acidification

Metazoans harbor a wealth of symbionts that are ever‐changing the environment by taking up resources and/or excreting metabolites. One such common environmental modification is a change in pH. Conventional wisdom holds that symbionts facilitate the survival and production of their hosts in the wild, but this notion lacks empirical evidence. Here, we report that symbiotic bacteria in the genus Enterococcus attenuate the oviposition avoidance of alkaline environments in Drosophila. We studied the effects of alkalinity on oviposition preference for the first time, and found that flies are robustly disinclined to oviposit on alkali‐containing substrates. This innate repulsion to alkaline environments is explained, in part, by the fact that alkalinity compromises the health and lifespan of both offspring and parent Drosophila. Enterococcus dramatically diminished or even completely reversed the ovipositional avoidance of alkalinity in Drosophila. Mechanistically, Enterococcus generate abundant lactate during fermentation, which neutralizes the residual alkali in an egg‐laying substrate. In conclusion, Enterococcus protects Drosophila from alkali stress by acidifying the ovipositional substrate, and ultimately improves the fitness of the Drosophila population. Our results demonstrate that symbionts are profound factors in the Drosophila ovipositional decision, and extend our understanding of the intimate interactions between Drosophila and their symbionts.
Coffee Consumption, Genetic Polymorphisms, and the Risk of Type 2 Diabetes Mellitus: A Pooled Analysis of Four Prospective Cohort Studies

The association between coffee consumption and the risk of type 2 diabetes may vary by genetic variants. Our study addresses the question of whether the incidence of type 2 diabetes is related to the consumption of coffee and whether this relationship is modified by polymorphisms related to type 2 diabetes. We performed a pooled analysis of four Korean prospective studies that included 71,527 participants; median follow-up periods ranged between 2 and 13 years. All participants had completed a validated food-frequency questionnaire (FFQ) at baseline. The odds ratios (ORs) and 95% confidence intervals (CIs) for type 2 diabetes were calculated using logistic regression models. The ORs were combined using a fixed or random effects model depending on the heterogeneity across the studies. Compared with 0 to <0.5 cups/day of coffee consumption, the OR for type 2 diabetes was 0.89 (95% CI: 0.80–0.98, p for trend = 0.01) for ≥3 cups/day of coffee consumption. We did not observe significant interactions by five single nucleotide polymorphisms (SNPs) related to type 2 diabetes (CDKAL1 rs7756992, CDKN2A/B rs10811661, KCNJ11 rs5215, KCNQ1 rs163184, and PEPD rs3786897) in the association between coffee and the risk of type 2 diabetes. We found that coffee consumption was inversely associated with the risk of type 2 diabetes.
Towards Self-adaptive Defect Classification in Industrial Monitoring

: The conﬁguration of monitoring applications is usually performed using annotations created by experts. Un-like many industrial products, carbon ﬁber textiles exhibit low rigity. Hence, surface anomalies vary to a great extend which poses challenges to quality monitoring and decision makers. This paper therefore proposes an unsupervised learning approach for carbon ﬁber production. The data consists of images continously acquired using a line scan camera. An image processing pipeline, generated by an evolutionary algorithm is applied to segement regions of interest. We then cluster the incoming defect data with stream clustering algorithms in order to identify structures, tendencies and anomalies. We compare well-known heuristics, based on k-means, hierarchical- and density based clustering and conﬁgure them to work best under the given circumstances. The clustering results are then compared to expert labels. A best-practice approach is presented to analyse the defects and their origin in the given image data. The experiments show promising results for classiﬁcation of highly specialised production processes with low defect rates which do not allow reliable, repeatable manual identiﬁcation of classes. We show that unsupervised learning enables quality managers to gain better insights into measurement data in the context of image classiﬁcation without prior knowledge. In addition, our approach helps to reduce training effort of image based monitoring systems.
GENETIC ANALYSIS OF POTATO TUBER FLESH PIGMENTATION AND SHAPE

24 Introduction 25 Materials and Methods 26 Plant materials 26 Transformation construct 26 Crossing 27 Genotyping 27 Anthocyanin extraction 28 HPLC analysis 28 LC/MS analysis 29 Results 30 Discussion 42 REFERENCES 47 CHAPTER THREE: GENETIC AND HISTOLOGICAL ANALYSIS OF POTATO TUBER SHAPE 50 Abstract 50 Introduction 51 Materials and methods 52 Plant materials 52 Phenotypic data 53 Anatomical analysis 53 AFLP analysis 53 Anchor markers 54 Mapping 54 QTL analysis 54 Interactive QTL marker analysis 54
Predicting species distributions: a critical comparison of the most common statistical models using artificial species

Aim To test statistical models used to predict species distributions under different shapes of occurrence–environment relationship. We addressed three questions: (1) Is there a statistical technique that has a consistently higher predictive ability than others for all kinds of relationships? (2) How does species prevalence influence the relative performance of models? (3) When an automated stepwise selection procedure is used, does it improve predictive modelling, and are the relevant variables being selected?
Immunology of viral disease, how to curb persistent infection

Efficient HIV vaccines have to trigger cell-mediated immunity directed against various viral antigens. However little is known about the breadth of the response induced by vaccines carrying multiple proteins. Here, we report on the immunogenicity of a construct harbouring a fusion of the HIV-1 IIIB gag, pol and nef genes ( gpn) designed for optimal safety and equimolar expression of the HIV proteins. The attenuated poxviruses, MVA and NYVAC, harbouring the gpn construct, induced potent immune responses in conventional mice characterised by stimulation of Gpn -specific IFN-producing cells and cytotoxic T cells. In HLA-A2 transgenic mice, recombinant MVA elicited cytotoxic responses against epitopes recognised in most HLA-A2 + HIV-1-infected individuals. We also found that the MVA vaccine triggered the in vitro expansion of peripheral blood cells isolated from a HIV-1-seropositive patient and with similar specificity as found in immunised HLA-A2 transgenic mice. In conclusion, the synthetic HIV polyantigen Gpn delivered by MVA is immunogenic, efficiently processed and presented by human MHC class I molecules. © 2004 Elsevier Ltd. All rights reserved.
Pattern Detection based Network Diagnostics

One of the most important parts of the network administrators’ work is detecting and correcting errors inside computer networks. This part is also called network diagnostics. The problem is that computer networks are very complex, and there is no single universal approach for diagnosing the errors. In this paper, we propose a new method of diagnostics which utilizes looking for specific patterns inside captured network data. This approach automatically checks for all predefined patterns and generates a report with error descriptions for any detected errors. We have created a proof-of-concept tool and demonstrated its functionality.
Children’s, Parents’ and Healthcare Professionals’ Preferences for Weight-Based Terminology in Health Care

ABSTRACT The current study explored the preferences for and knowledge of weight-based terminology used in healthcare-related conversations, and descriptively compared the preferences of children, parents and healthcare professionals. In total, 86 children with overweight or obesity, 90 parents of children with overweight or obesity and 572 healthcare professionals indicated their preferences for 22 terms. When applicable, children and parents could indicate unfamiliarity with a term. Many children were unfamiliar with terms such as “adiposity“adipositas”” (93%), “BMI” (60%) and “morbid obesity” (53%). Children, parents and healthcare professionals disliked “fatadjective”. All groups liked the terms “healthier weight” and “above a healthy weight”. To conclude, children’s, parents’ and healthcare professionals’ preferences for weight-based terminology are predominately congruent, except for “BMI”. “BMI” is a popular term among healthcare professionals. It is recommended that healthcare professionals use terms that can be perceived as neutral or positive, such as “healthier weight”, as this may contribute to a positive conversation which may lead to better compliance, and to avoid terms that can be perceived as judgmental, such as “fatadjective”, as this may worsen the dialogue and relationship between families and healthcare professionals, and increase weight-based stigma. Healthcare professionals should be aware that children may be unfamiliar with some terms.
Progress in Neoantigen Targeted Cancer Immunotherapies

Immunotherapies that harness the immune system to kill cancer cells have showed significant therapeutic efficacy in many human malignancies. A growing number of studies have highlighted the relevance of neoantigens in recognizing cancer cells by intrinsic T cells. Cancer neoantigens are a direct consequence of somatic mutations presenting on the surface of individual cancer cells. Neoantigens are fully cancer-specific and exempt from central tolerance. In addition, neoantigens are important targets for checkpoint blockade therapy. Recently, technological innovations have made neoantigen discovery possible in a variety of malignancies, thus providing an impetus to develop novel immunotherapies that selectively enhance T cell reactivity for the destruction of cancer cells while leaving normal tissues unharmed. In this review, we aim to introduce the methods of the identification of neoantigens, the mutational patterns of human cancers, related clinical trials, neoantigen burden and sensitivity to immune checkpoint blockade. Moreover, we focus on relevant challenges of targeting neoantigens for cancer treatment.
Parameter Selection for Phase Space Reconstruction in Hydrological Series and Rationality Analysis of its Chaotic Characteristics

This study performed a rationality analysis of the delay time and embedding dimension value during phase space reconstruction in hydrological series and the effect on their chaotic characteristics. Using a monthly average runoff time series from the Ayanqian station (upstream) and the Jiangqiao station (midstream) in the Nen River Basin, we reached the following regularity conclusions. 1 Based on the flood season (4 months) in the Nen River Basin, we can deduce that the correlation sequence length for the runoff is 4~5 months, i.e., the delay time =3 or 4 is a reasonable choice. 2 Learn from the predictability experiment results for the monthly rainfall time series, we know that the calculation results of the G-P algorithm for the dimension of runoff series for the Nen River Basin are reasonable, i.e., the embedding dimension is no more than seven. 3 the most suitable parameters for the phase space reconstruction and its chaotic characteristic index in the Nen River Basin are as follows: delay time = 3~4, embedding dimension = 6~7, correlation dimension = 2.90~3.00, maximum Lyapunov index = 0.24~0.32, and the forecast time is 3~4 months.