Miracle-dz/UniFAR-FTUs huggingface dataset:
Real Time Pose Estimation for Model-based Rigid Object

Object detection and tracking stands as a fundamental but crucial problem in robotics ﬁeld. Applications in various scenarios such as manipulation, autonomous driving, and humanrobot interaction have been widely investigated. In this project, we aim to focus on tracking and 6-DoF pose estimating of a model-based rigid object using low-cost video stream device. More speciﬁcally, we would like to introduce a real-time framework of model-based object recognition and 6-DoF pose tracking in the applications of manipulation and learning-by-showing scenarios.
University of Groningen Social stress during adolescence in Wistar rats induces social anxiety in adulthood without affecting brain monoaminergic content and activity Vidal,

Adolescence has been described as an important period to acquire social competences required for adult life. It has been suggested that early stress experiences could affect the development of the brain at different levels. These changes in the brain during adolescence may be related with the development of psychopathologies such as depression and social anxiety in adulthood. In the first experiment, we examined long-term effects of repeated social stress during adolescence on adult social approach–avoidance behavior. For that purpose, adolescent male Wistar rats were exposed twice at postnatal day (Pnd) 45 and Pnd48 to the resident–intruder paradigm followed by three times psychosocial threat with the same resident. Three weeks after the last psychosocial threat experience the animals were behaviorally tested in a social approach–avoidance test. Socially stressed animals spent less time in the interaction zone with an unfamiliar male adult rat. These data suggest that animals exposed to social stress during adolescence show a higher level of social anxiety in adulthood. In the second experiment, we investigated whether these longterm effects of social stress during adolescence on behavior draw a parallel with changes in brain monoamine content, biosynthesis and turnover. Using the same experimental design as in the first experiment, HPLC analysis of various brain regions showed that there were no differences in monoamine content, monoamine biosynthesis and monoamines activity in the prefrontal cortex, hippocampus, hypothalamus and striatum in adulthood. These results indicate that long-lasting changes in social behavior following social stress during adolescence are not accompanied by changes in brain monoamine content, biosynthesis and turnover. © 2007 Elsevier Inc. All rights reserved.
tRNA-derived fragments: Mechanisms underlying their regulation of gene expression and potential applications as therapeutic targets in cancers and virus infections

tRNA-derived fragments (tRFs) are a new category of regulatory noncoding RNAs with distinct biological functions in cancers and stress-induced diseases. Herein, we first summarize the classification and biogenesis of tRFs. tRFs are produced from pre-tRNAs or mature tRNAs. Based on the incision loci, tRFs are classified into several types: tRF-1, tRF-2, tRF-3, tRF-5, and i-tRF. Some tRFs participate in posttranscriptional regulation through microRNA-like actions or by displacing RNA binding proteins and regulating protein translation by promoting ribosome biogenesis or interfering with translation initiation. Other tRFs prevent cell apoptosis by binding to cytochrome c or promoting virus replication. More importantly, the dysregulation of tRFs has important clinical implications. They are potential diagnostic and prognostic biomarkers of gastric cancer, liver cancer, breast cancer, prostate cancer, and chronic lymphocytic leukemia. tRFs may become new therapeutic targets for the treatment of diseases such as hepatocellular carcinoma and respiratory syncytial virus infection. Finally, we point out the existing problems and future research directions associated with tRFs. In conclusion, the current progress in the research of tRFs reveals that they have important clinical implications and may constitute novel molecular therapeutic targets for modulating pathological processes.
In vitro Evaluation of Various Phytoextract against Detected Seed Mycoflora of Groundnut

The several types of oil including: aromatic roasted peanut oil, refined peanut oil, used in the manufacture of soap, medicinal emulsions, wool and silk, artificial leather. Groundnut oil is composed of mixed glycerides and contains high proportion of unsaturated fatty acids, in particular oleic (50-60 %) and linoleic acid (18-30 %). Different micoflora is causes heavy losses during storage of groundnut, keeping in a view the research were carried out with using different phytoextract against Alternaria alternate, Aspergillus flavus, Aspergillus niger. During the investigations various experiments were conducted at the Department of Plant Pathology, College of Agriculture, VNMKV, Parbhani.
Interactive Optimization with Long-Term Preferences Inference on a Shift Scheduling Problem

In this article we describe a method for enriching interactively an optimization model with long-term preferences and illustrate it by a use case. Interactive enrichment is the process of adjusting an optimization model using the feedback of a user on intermediate results. The integration of user’s preferences allows capturing aspects of the optimization problem that have not been modelled initially. In existing interactive optimization approaches, preferences are typically short-term, i.e. speciﬁc to the problem instance. The proposed approach investigates long-term preferences that can be reused for solving multiple problem instances. The interactive optimization method has been developed for solving a shift scheduling problem. An Iterated Local Search (ILS) metaheuristic provides solutions that have to be evaluated by the user. Based on this feedback of the user, long-term preferences are inferred as conjunctive rule sets. The user can then add some of the proposed conjunctive rule sets to enrich sustainably and transparently the optimization model with new soft constraints. For testing the proposed interactive enrichment method a decision support system prototype has been implemented. Preliminary results allowed identifying interesting perspectives and applications for the approach.
Novel Mechanisms In The Sorting Of Proglucagon To The Secretory Granules Of The Regulated Secretory Pathway

The prohormone proglucagon encodes for multiple peptide hormones, including glucagon, glucagon-like peptide-1 (GLP-1), and GLP-2, produced through tissue-specific processing by prohormone convertase (PC) 1/3 and PC2. In alpha cells, PC2 yields glucagon, the major counter-regulatory hormone to insulin, which together, control glucose homeostasis. In contrast, GLP-1 and GLP2 are mainly produced in intestinal Lcells by PC1/3. GLP-1 stimulates insulin secretion following a meal, and therefore has opposing function to glucagon regulating glucose homeostasis; in contrast, GLP-2 enhances gut nutrient absorption. Efficient sorting of proglucagon to secretory granules is required for nutrient-regulated secretion. The aim of this thesis is to discover the molecular mechanisms by which proglucagon is targeted to secretory granules, which ensures that proglucagon is correctly processed to mature hormones, and is necessary for prompt physiologic response to nutrient status. This thesis identifies several sorting signals contained within the mature hormone domains of proglucagon that encodes its targeting information. Using quantitative immunofluorescence microscopy and colocalization analyses, the molecular mechanism that sorts proglucagon to granules is directed by the domains of glucagon and GLP-1. Despite these two hormones sharing a large degree of structural homology, it is their particular alpha-helix structures that enable the sorting of proglucagon. Further, the evidence suggests that proglucagon is first sorted to granules prior to being processed to active hormones. In alpha cells, carboxypeptidase E is required for the sorting of glucagon to granules. Together, each neuropeptide or peptide hormone carries with it a unique sorting “signature” to efficiently reach its destination, and allows alpha and L-cells to tightly regulate nutrient homeostasis.
A review on life style changes in the prevention of type 2 diabetes mellitus

Diabetes mellitus is endocrine disorder characterized by deficiency secretion of insulin, inadequate secretion of insulin from beta cells of pancreas in human body. Microvasucalr complications are diabetic retinopathy, diabetic nephropathy, diabetic neuropathy. In macro vascular complications such as cardiac disease and cerebral vascular disease like haemorrhage, infract and mental confession. Diabetes mellitus can be manage with regular exercise and diet intake.
Quad Compression Technique Using Parallelization Approach

: Fractal compression is a lossy compression method for digital images, based on fractals. The method is best suited for textures and natural images, relying on the fact that parts of an image often resemble other parts of the same image. Fractal algorithms convert these parts into mathematical data called "fractal codes" which are used to recreate the encoded image. With fractal compression, encoding is extremely computationally expensive because of the search used to find the self-similarities. Decoding however is quite fast. While this asymmetry has so far made it impractical for real time applications, when video is archived for distribution from disk storage or file downloads fractal compression becomes more competitive. In this project, the basic theory of image fractal compression is briefly introduced; some typical fractal compression approaches are discussed. Based on this discussion, according to the problem of long encoding time which exists in the typical fractal compression approaches, a fast image fractal compression approach is proposed based on the combination of adaptive quad tree compression approach and graph-based image segmentation algorithm. The project includes RGB to Grayscale conversion, image encoding, decoding, time elapsed details during encoding and decoding options. The improved approach can valid accelerate the fractal coding speed on the premise of guaranteeing the decoded image’s quality
A consequence of the Proper Forcing Axiom in topology

If 〈L,<〉 is a dense linear order without end-points and if A1, A2⊂ L are disjoint dense subsets of L, then OA1A2 denotes the topology on L generated by the closed intervals [a1, a2], where a1 ∈ A1 and a2 ∈ A2. It is proved that under the Proper Forcing Axiom each two spaces of the form 〈R,OA1A2〉, where A1 and A2 are א1-dense subsets of reals, are homeomorphic.
Estimativa da taxa de turnover e fator de discriminação isotópico em espécies de quelônios

10 LISTA DE FIGURAS . 11 LISTA DE TABELAS . 12 1. INTRODUÇÃO . 13 2. REVISÃO DA LITERATURA. 19 2.1. ORDEM TESTUDINES (CHELONIA). 19 2.2. IMPORTÂNCIA DOS ESTUDOS DE DIETA NAS TARTARUGAS DE ÁGUA DOCE . 23 2.3. UTILIZAÇÃO DE TRAÇADORES (ISÓTOPOS ESTÁVEIS) EM ESTUDOS DE DIETA . 28 3. MATERIAL E MÉTODOS . 31 3.1. REALIZAÇÃO DO EXPERIMENTO . 31 3.2. ELABORAÇÃO E FABRICAÇÃO DA RAÇÃO . 33 3.3. FORMA DE ANÁLISE DOS RESULTADOS. 35 3.4. AUTORIZAÇÕES . 36 4. RESULTADOS . 37 4.1. PODOCNEMIS EXPANSA . 39 4.2. PODOCNEMIS UNIFILIS . 41 4.3. PHRYNOPS GEOFFROANUS. 44 4.4. TRACHEMYS SCRIPTA . 46 4.5. TRACHEMYS DORBIGNI . 49 5. DISCUSSÃO . 52 6. CONSIDERAÇÕES FINAIS . 60 REFERÊNCIAS . 62
The Influence of Glass Content on the Hydraulic Conductance and Tubule Occlusion of Novel Bioactive GlassToothpastes

Dentine Hypersensitivity (DH) is characterized as a sharp dental pain of short duration caused by the reaction of exposed dentine surfaces to stimuli, typically thermal, evaporative, tactile, osmotic or chemical that cannot be associated to any other dental defect of pathology [1]. It is based on the hydrodynamic theory where stimuli applied onto the exposed dentinal tubules cause fluid movement across the dentine, passing them to the intradental pulpal nerves and thereby initiating a painful sensation [2].
IMPACT OF THE EASTERN MEDITERRANEAN TRANSIENT (EMT) ON THE NORTHWESTERN MEDITERRANEAN SEA (NWMS): THE CASE OF THE EXCEPTIONAL 2004-05 CONVECTION EVENT

Spectacular convection occurred in the NWMS during 2004-05: the convection area was exceptionally large, and Western Mediterranean Deep Water (WMDW) formed this winter was warmer and saltier than usually. We perform numerical oceanic simulations to assess the respective contributions of oceanic and atmospheric conditions to this event. Our results suggest that WMDW characteristics are mainly related to the NWMS heat and salt contents before the convection. The increase of those characteristics in 2004-05 is due to an increase of those contents, due to the absence of strong convection during the 90’s. Convection intensity is related both to the winter atmospheric conditions and to the autumn NWMS stratification. The EMT modified this stratification in autumn 2004, potentially doubling the volume of WMDW formed.
Toward a Bias-Aware Future for Mixed-Initiative Visual Analytics

Mixed-initiative visual analytics systems incorporate well-established design principles that improve users' abilities to solve problems. As these systems consider whether to take initiative towards achieving user goals, many current systems address the potential for cognitive bias in human initiatives statically, relying on fixed initiatives they can take instead of identifying, communicating and addressing the bias as it occurs. We argue that mixed-initiative design principles can and should incorporate cognitive bias mitigation strategies directly through development of mitigation techniques embedded in the system to address cognitive biases in situ. We identify domain experts in machine learning adopting visual analytics techniques and systems that incorporate existing mixed-initiative principles and examine their potential to support bias mitigation strategies. This examination considers the unique perspective these experts bring to visual analytics and is situated in existing user-centered systems that make exemplary use of design principles informed by cognitive theory. We then suggest informed opportunities for domain experts to take initiative toward addressing cognitive biases in light of their existing contributions to the field. Finally, we contribute open questions and research directions for designers seeking to adopt visual analytics techniques that incorporate bias-aware initiatives in future systems.
Human versus Machine Attention in Deep Reinforcement Learning Tasks

Deep reinforcement learning (RL) algorithms are powerful tools for solving visuomotor decision tasks. However, the trained models are often difficult to interpret, because they are represented as end-to-end deep neural networks. In this paper, we shed light on the inner workings of such trained models by analyzing the pixels that they attend to during task execution, and comparing them with the pixels attended to by humans executing the same tasks. To this end, we investigate the following two questions that, to the best of our knowledge, have not been previously studied. 1) How similar are the visual features learned by RL agents and humans when performing the same task? and, 2) How do similarities and differences in these learned features correlate with RL agents' performance on these tasks? Specifically, we compare the saliency maps of RL agents against visual attention models of human experts when learning to play Atari games. Further, we analyze how hyperparameters of the deep RL algorithm affect the learned features and saliency maps of the trained agents. The insights provided by our results have the potential to inform novel algorithms for the purpose of closing the performance gap between human experts and deep RL agents.
Leveraging Extracted Model Adversaries for Improved Black Box Attacks

We present a method for adversarial input generation against black box models for reading comprehension based question answering. Our approach is composed of two steps. First, we approximate a victim black box model via model extraction. Second, we use our own white box method to generate input perturbations that cause the approximate model to fail. These perturbed inputs are used against the victim. In experiments we find that our method improves on the efficacy of the ADDANY—a white box attack—performed on the approximate model by 25% F1, and the ADDSENT attack—a black box attack—by 11% F1.
Deep generative LDA

Linear discriminant analysis (LDA) is a popular tool for classification and dimension reduction. Limited by its linear form and the underlying Gaussian assumption, however, LDA is not applicable in situations where the data distribution is complex. Recently, we proposed a discriminative normalization flow (DNF) model. In this study, we reinterpret DNF as a deep generative LDA model, and study its properties in representing complex data. We conducted a simulation experiment and a speaker recognition experiment. The results show that DNF and its subspace version are much more powerful than the conventional LDA in modeling complex data and retrieving low-dimensional representations.
All-Weather Object Recognition Using Radar and Infrared Sensing

Autonomous cars are an emergent technology which has the capacity to change human lives. The current sensor systems which are most capable of perception are based on optical sensors. For example, deep neural networks show outstanding results in recognising objects when used to process data from cameras and Light Detection And Ranging (LiDAR) sensors. However these sensors perform poorly under adverse weather conditions such as rain, fog, and snow due to the sensor wavelengths. This thesis explores new sensing developments based on long wave polarised infrared (IR) imagery and imaging radar to recognise objects. First, we developed a methodology based on Stokes parameters using polarised infrared data to recognise vehicles using deep neural networks. Second, we explored the potential of using only the power spectrum captured by low-THz radar sensors to perform object recognition in a controlled scenario. This latter work is based on a data-driven approach together with the development of a data augmentation method based on attenuation, range and speckle noise. Last, we created a new large-scale dataset in the "wild" with many different weather scenarios (sunny, overcast, night, fog, rain and snow) showing radar robustness to detect vehicles in adverse weather. High resolution radar and polarised IR imagery, combined with a deep learning approach, are shown as a potential alternative to current automotive sensing systems based on visible spectrum optical technology as they are more robust in severe weather and adverse light conditions.
Polarization-density patterns of active particles in motility gradients.

The colocalization of density modulations and particle polarization is a characteristic emergent feature of motile active matter in activity gradients. We employ the active-Brownian-particle model to derive precise analytical expressions for the density and polarization profiles of a single Janus-type swimmer in the vicinity of an abrupt activity step. Our analysis allows for an optional (but not necessary) orientation-dependent propulsion speed, as often employed in force-free particle steering. The results agree well with measurement data for a thermophoretic microswimmer presented in the companion paper [Söker et al., Phys. Rev. Lett. 126, 228001 (2021)10.1103/PhysRevLett.126. 228001], and they can serve as a template for more complex applications, e.g., to motility-induced phase separation or studies of physical boundaries. The essential physics behind our formal results is robustly captured and elucidated by a schematic two-species "run-and-tumble" model.
Compensating data shortages in manufacturing with monotonicity knowledge

Systematic decision making in engineering requires appropriate models. In this article, we introduce a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the form of shape constraints, or more specifically, monotonicity constraints. Incorporating such information is particularly useful when the available datasets are small or do not cover the entire input space, as is often the case in manufacturing applications. We set up the regression subject to the considered monotonicity constraints as a semi-infinite optimization problem, and propose an adaptive solution algorithm. The method is applicable in multiple dimensions and can be extended to more general shape constraints. It was tested and validated on two real-world manufacturing processes, namely, laser glass bending and press hardening of sheet metal. It was found that the resulting models both complied well with the expert’s monotonicity knowledge and predicted the training data accurately. The suggested approach led to lower root-mean-squared errors than comparative methods from the literature for the sparse datasets considered in this work.
Towards making formal methods normal: meeting developers where they are

Formal verification of software is a bit of a niche activity: it is only applied to the most safety-critical or security-critical software and it is typically only performed by specialized verification engineers. This paper considers whether it would be possible to increase adoption of formal methods by integrating formal methods with developers' existing practices and workflows. We do not believe that widespread adoption will follow from making the prevailing formal methods argument that correctness is more important than engineering teams realize. Instead, our focus is on what we would need to do to enable programmers to make effective use of formal verification tools and techniques. We do this by considering how we might make verification tooling that both serves developers' needs and fits into their existing development lifecycle. We propose a target of two orders of magnitude increase in adoption within a decade driven by ensuring a positive `weekly cost-benefit' ratio for developer time invested.
Chance-Constrained OPF in Droop-Controlled Microgrids With Power Flow Routers

High penetration of renewable generation poses challenges to power system operation due to its uncertain nature. In droop-controlled microgrids, the voltage volatility induced by renewable uncertainties is aggravated by the high droop gains. This paper proposes a chance-constrained optimal power flow (CC-OPF) problem with power flow routers (PFRs) to better regulate the voltage profile in microgrids. PFR refers to a general type of network-side controller that brings more flexibility to the power network. Comparing with the normal CC-OPF that relies on node power flexibility only, the proposed model introduces a new dimension of control from power network to enhance system performance under renewable uncertainties. Adopting a partial linearization method and an iterative algorithm allows us to address the CC-OPF problem by iteratively solving a subproblem. Since the inclusion of PFRs complicates the subproblem and makes common solvers no longer applicable directly, a semidefinite programming relaxation is used to transform each subproblem into a convex form. The proposed method is verified on a modified IEEE 33-bus system and the results show that PFRs significantly reduce the standard deviations of voltage magnitudes and contribute to mitigating the voltage volatility, which makes the system operate in a more economic and secure way.