Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Image Understanding from Deep Compression without Decoding

Motivated by recent work on deep neural network (DNN)-based image compression methods showing potential improvements in image quality, savings in storage, and bandwidth reduction, we propose to perform image understanding tasks such as classification and segmentation directly on the compressed representations produced by these compression methods. Since the encoders and decoders in DNN-based compression methods are neural networks with feature-maps as internal representations of the images, we directly integrate these with architectures for image understanding. This bypasses decoding of the compressed representation into RGB space and reduces computational cost. Our study shows that accuracies comparable to networks that operate on compressed RGB images can be achieved while reducing the computational complexity up to $2\times$. Furthermore, we show that synergies are obtained by jointly training compression networks with classification networks on the compressed representations, improving image quality, classification accuracy, and segmentation performance. We find that inference from compressed representations is particularly advantageous compared to inference from compressed RGB images for aggressive compression rates.
Deploying SDP for machine learning

We discuss the use in machine learning of a general type of convex optimisation problem known as semi-definite programming (SDP) (1). We intend to argue that SDP's arise quite naturally in a variety of situations, accounting for their omnipresence in modern machine learning approaches, and we provide examples in support.
Human sperm accumulation near surfaces: a simulation study

A hybrid boundary integral/slender body algorithm for modelling flagellar cell motility is presented. The algorithm uses the boundary element method to represent the ‘wedge-shaped’ head of the human sperm cell and a slender body theory representation of the flagellum. The head morphology is specified carefully due to its significant effect on the force and torque balance and hence movement of the free-swimming cell. The technique is used to investigate the mechanisms for the accumulation of human spermatozoa near surfaces. Sperm swimming in an infinite fluid, and near a plane boundary, with prescribed planar and three-dimensional flagellar waveforms are simulated. Both planar and ‘elliptical helicoid’ beating cells are predicted to accumulate at distances of approximately 8.5–22 μm from surfaces, for flagellar beating with angular wavenumber of 3π to 4π. Planar beating cells with wavenumber of approximately 2. 4π or greater are predicted to accumulate at a finite distance, while cells with wavenumber of approximately 2π or less are predicted to escape from the surface, likely due to the breakdown of the stable swimming configuration. In the stable swimming trajectory the cell has a small angle of inclination away from the surface, no greater than approximately 0.5°. The trapping effect need not depend on specialized non-planar components of the flagellar beat but rather is a consequence of force and torque balance and the physical effect of the image systems in a no-slip plane boundary. The effect is relatively weak, so that a cell initially one body length from the surface and inclined at an angle of 4°–6° towards the surface will not be trapped but will rather be deflected from the surface. Cells performing rolling motility, where the flagellum sweeps out a ‘conical envelope’, are predicted to align with the surface provided that they approach with sufficiently steep angle. However simulation of cells swimming against a surface in such a configuration is not possible in the present framework. Simulated human sperm cells performing a planar beat with inclination between the beat plane and the plane-of-flattening of the head were not predicted to glide along surfaces, as has been observed in mouse sperm. Instead, cells initially with the head approximately 1.5–3 μm from the surface were predicted to turn away and escape. The simulation model was also used to examine rolling motility due to elliptical helicoid flagellar beating. The head was found to rotate by approximately 240° over one beat cycle and due to the time-varying torques associated with the flagellar beat was found to exhibit ‘looping’ as has been observed in cells swimming against coverslips.
DNA barcodes for soil animal taxonomy

A biodiversidade das comunidades do solo continua muito pouco conhecida e entendida. A biologia do solo e fortemente afetada pela crise taxonomica, e a maior parte dos grupos de animais dessa biota sofre forte impedimento taxonomico. O objetivo deste trabalho foi determinar como o codigo de barras de DNA - um metodo novo que usa uma etiqueta microgenomica para identificacao e discriminacao de especies - permite uma melhor avaliacao da taxonomia da biota edafica. Foram analisadas 1.152 sequencias de codigos de barras de dois grupos principais de animais, colembolos e minhocas, que apresentaram ampla amostragem taxonomica e geografica. Alem de refletir fortemente o impedimento taxonomico de ambos os grupos, com um grande numero de linhagens divergentes no nivel da especie que ainda nao esta descrita, os resultados tambem destacam um alto (15%) nivel de diversidade criptica dentro de especies conhecidas de minhocas e colembolos. Esses resultados apoiam estudos locais recentes que usaram metodos similares. Considerando as dificuldades taxonomicas enfrentadas para identificar os animais de solo, ferramentas de identificacao usando DNA podem facilitar e melhorar a exploracao da biodiversidade e a sua descricao. As campanhas de codigo de barras de DNA estao se desenvolvendo rapidamente com animais do solo, e a comunidade de biologos e incitada a adotar esses metodos.
Universität Des Saarlandes Fachrichtung 6.1 – Mathematik Adaptive Structure Tensors and Their Applications Adaptive Structure Tensors and Their Applications

The structure tensor, also known as second moment matrix or Förstner interest operator, is a very popular tool in image processing. Its purpose is the estimation of orientation and the local analysis of structure in general. It is based on the integration of data from a local neighborhood. Normally, this neighborhood is defined by a Gaussian window function and the structure tensor is computed by the weighted sum within this window. Some recently proposed methods, however, adapt the computation of the structure tensor to the image data. There are several ways how to do that. This article wants to give an overview of the different approaches, whereas the focus lies on the methods based on robust statistics and nonlinear diffusion. Furthermore, the data-adaptive structure tensors are evaluated in some applications. Here the main focus lies on optic flow estimation, but also texture analysis and corner detection are considered.
The dynamics of responsiveness and smoothness in heterogeneous networks

Additive increase/multiplicative decrease-based protocols, including transmission control protocol (TCP), TCP-friendly, and a new generation of rate-based protocols, attempt to control the tradeoff of responsiveness and smoothness. Traditionally, smoothness has not been a main concern since it does not impact the performance of regular Internet applications such as the Web, FTP, or e-mail. However, multimedia-driven protocols attempt to favor smoothness at the cost of responsiveness. In general, smoothness and responsiveness constitute a tradeoff; however, we uncover undesirable dynamics of the protocols in the context of wireless/mobile networks with high-error rate or frequent handoffs: low responsiveness is not counterbalanced by gains in smoothness, but instead, produces a conservative behavior that degrades protocol performance with both delay-tolerant and -sensitive applications. Based on our observations, as well as on further analysis of the impact of the bottleneck queue on channel utilization, we seek an alternative strategy for smooth window adjustments. We introduce a new parameter /spl gamma/, which implements a congestion avoidance tactic and reaches better smoothness without damaging responsiveness.
Automatic Change Detection Approach Based on Fusion of Multi-Temporal Images Over Urban Areas

Urban areas are rapidly changing all over the world and therefore provoke the necessity to update urban maps frequently. Remote sensing has been used for many years to monitor these changes. With the availability of multi-sensor, multi-temporal, multi-resolution and multi-frequency image data from operational Earth observation satellites the fusion of digital image data has become a valuable tool in remote sensing image evaluation. Therefore, the goal of an image fusion algorithm is to integrate the redundant and complementary information obtained from the source images in order to form a new image which provides a better description of the scene for human or machine perception. In this paper, an attempt has been made to design an automatic change detection solution. The approach presented here takes advantage of fusion in two levels. That is, Feature-level fusion, which uses attributes (or features) extracted from the raw data as inputs and uses them into new features, or feature map and Decision-level fusion, that takes the decisions from each image as inputs and fuses them to obtain a global decision. The purpose of this paper is to reveal urban changes. Using multitemporal Landsat MSS and Landsat TM images, changes in New Delhi city in India is detected.
Using Reinforcement Learning in Chess Engines

Up until recently, the use of reinforcement learning (RL) in chess programming has been problematic and failed to yield the expected results. The breakthrough was finally achieved through Gerald’s Tesauros work on backgammon, which resulted in a program that could beat the world champion of backgammon in the majority of the matches they played. Our chess engine proved that reinforcement learning in combination with the classification of board state leads to a notable improvement, when compared with other engines that only use reinforcement learning, such as KnightCap. We extended KnightCap’s learning algorithm by using a bigger and more complete board state database, and adjusting and optimizing the coefficients for each position class individually. A clear enhancement of our engine’s learning and playing skills is reached after only a few trained games.
Flammability of natural plant and animal fibers: a heat release survey

With increased interest in sustainable materials for use in building materials and clothing, there is a renewal in the use of natural fibers (plant or animal‐based) versus synthetic fibers in a variety of applications. However, there is not as much information available on the flammability of these natural fibers especially when they are used in products where purification techniques used in conventional textile processing are not required. The literature to date suggests that all of the fibers can be grouped into two categories: cellulosic and animal, with the assumption that regardless of original species, the flammability is similar for fibers within each category. In this report, we have conducted a survey via microcombustion calorimetry to determine if all cellulose‐based and all protein‐based fibers are the same from a heat release perspective. Our findings show that this is not the case, and there are notable differences in fiber types within each genus. Further, how the natural fiber has been treated prior to use can have some dramatic effects on heat release caused by residual impurity content. The results in this paper suggest that there is more to be learned about these natural fiber types in regards to their inherent flammability. Copyright © 2016 John Wiley & Sons, Ltd.
Reconstruction of Family-Level Phylogenetic Relationships within Demospongiae (Porifera) Using Nuclear Encoded Housekeeping Genes

Background Demosponges are challenging for phylogenetic systematics because of their plastic and relatively simple morphologies and many deep divergences between major clades. To improve understanding of the phylogenetic relationships within Demospongiae, we sequenced and analyzed seven nuclear housekeeping genes involved in a variety of cellular functions from a diverse group of sponges. Methodology/Principal Findings We generated data from each of the four sponge classes (i.e., Calcarea, Demospongiae, Hexactinellida, and Homoscleromorpha), but focused on family-level relationships within demosponges. With data for 21 newly sampled families, our Maximum Likelihood and Bayesian-based approaches recovered previously phylogenetically defined taxa: Keratosap, Myxospongiaep, Spongillidap, Haploscleromorphap (the marine haplosclerids) and Democlaviap. We found conflicting results concerning the relationships of Keratosap and Myxospongiaep to the remaining demosponges, but our results strongly supported a clade of Haploscleromorphap+Spongillidap+Democlaviap. In contrast to hypotheses based on mitochondrial genome and ribosomal data, nuclear housekeeping gene data suggested that freshwater sponges (Spongillidap) are sister to Haploscleromorphap rather than part of Democlaviap. Within Keratosap, we found equivocal results as to the monophyly of Dictyoceratida. Within Myxospongiaep, Chondrosida and Verongida were monophyletic. A well-supported clade within Democlaviap, Tetractinellidap, composed of all sampled members of Astrophorina and Spirophorina (including the only lithistid in our analysis), was consistently revealed as the sister group to all other members of Democlaviap. Within Tetractinellidap, we did not recover monophyletic Astrophorina or Spirophorina. Our results also reaffirmed the monophyly of order Poecilosclerida (excluding Desmacellidae and Raspailiidae), and polyphyly of Hadromerida and Halichondrida. Conclusions/Significance These results, using an independent nuclear gene set, confirmed many hypotheses based on ribosomal and/or mitochondrial genes, and they also identified clades with low statistical support or clades that conflicted with traditional morphological classification. Our results will serve as a basis for future exploration of these outstanding questions using more taxon- and gene-rich datasets.
Intratumoral heterogeneity analysis reveals hidden associations between protein expression losses and patient survival in clear cell renal cell carcinoma

Intratumoral heterogeneity (ITH) is a prominent feature of kidney cancer. It is not known whether it has utility in finding associations between protein expression and clinical parameters. We used ITH that is detected by immunohistochemistry (IHC) to aid the association analysis between the loss of SWI/SNF components and clinical parameters.160 ccRCC tumors (40 per tumor stage) were used to generate tissue microarray (TMA). Four foci from different regions of each tumor were selected. IHC was performed against PBRM1, ARID1A, SETD2, SMARCA4, and SMARCA2. Statistical analyses were performed to correlate biomarker losses with patho-clinical parameters. Categorical variables were compared between groups using Fisher's exact tests. Univariate and multivariable analyses were used to correlate biomarker changes and patient survivals. Multivariable analyses were performed by constructing decision trees using the classification and regression trees (CART) methodology. IHC detected widespread ITH in ccRCC tumors. The statistical analysis of the “Truncal loss” (root loss) found additional correlations between biomarker losses and tumor stages than the traditional “Loss in tumor (total)”. Losses of SMARCA4 or SMARCA2 significantly improved prognosis for overall survival (OS). Losses of PBRM1, ARID1A or SETD2 had the opposite effect. Thus “Truncal Loss” analysis revealed hidden links between protein losses and patient survival in ccRCC.
PGRP-LD mediates A. stephensi vector competency by regulating homeostasis of microbiota-induced peritrophic matrix synthesis

Peptidoglycan recognition proteins (PGRPs) and commensal microbes mediate pathogen infection outcomes in insect disease vectors. Although PGRP-LD is retained in multiple vectors, its role in host defense remains elusive. Here we report that Anopheles stephensi PGRP-LD protects the vector from malaria parasite infection by regulating gut homeostasis. Specifically, knock down of PGRP-LD (dsLD) increased susceptibility to Plasmodium berghei infection, decreased the abundance of gut microbiota and changed their spatial distribution. This outcome resulted from a change in the structural integrity of the peritrophic matrix (PM), which is a chitinous and proteinaceous barrier that lines the midgut lumen. Reduction of microbiota in dsLD mosquitoes due to the upregulation of immune effectors led to dysregulation of PM genes and PM fragmentation. Elimination of gut microbiota in antibiotic treated mosquitoes (Abx) led to PM loss and increased vectorial competence. Recolonization of Abx mosquitoes with indigenous Enterobacter sp. restored PM integrity and decreased mosquito vectorial capacity. Silencing PGRP-LD in mosquitoes without PM didn’t influence their vector competence. Our results indicate that PGPR-LD protects the gut microbiota by preventing hyper-immunity, which in turn promotes PM structurally integrity. The intact PM plays a key role in limiting P. berghei infection.
Reduced-Dose Imageless Needle and Patient Tracking in Interventional CT Procedures

This paper describes a new method for imageless needle and patient tracking in interventional CT procedures based on fractional CT scanning. Our method accurately locates a needle with a spherical marker attached to it at a known distance from the tip with respect to the patient in the CT scanner coordinate frame with online sparse scan sampling and without reconstructing the CT image. The key principle of our method is to detect the needle and attached spherical marker in projection (sinogram) space based on the strongly attenuated X-ray signal due to the metallic composition of the needle and the needle’s thin cylindrical geometry, and based on the marker’s spherical geometry. A transformation from projection space to physical space uniquely determines the location and orientation of the needle and the needle tip position. Our method works directly in projection space and simultaneously performs patient registration and needle localization for every fractional CT scanning acquisition using the same sparse set of views. We performed registration and needle tip localization in five abdomen phantom scans using a rigid needle, and obtained a voxel-size tip localization error. Our experimental results indicate a voxel-sized deviation of the localization from a comparable method in 3-D image space, with the benefit of allowing X-ray dose reduction via fractional scanning at each localization. This benefit enables more frequent tip localizations during needle insertion for a similar total dose, or a reduced total dose for the same frequency of tip localization.
SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection

Sybil attacks are becoming increasingly widespread and pose a significant threat to online social systems; a single adversary can inject multiple colluding identities in the system to compromise security and privacy. Recent works have leveraged social network-based trust relationships to defend against Sybil attacks. However, existing defenses are based on oversimplified assumptions about network structure, which do not necessarily hold in real-world social networks. Recognizing these limitations, we propose SybilFuse, a defense-in-depth framework for Sybil detection when the oversimplified assumptions are relaxed. SybilFuse adopts a collective classification approach by first training local classifiers to compute local trust scores for nodes and edges, and then propagating the local scores through the global network structure via weighted random walk and loopy belief propagation mechanisms. We evaluate our framework on both synthetic and real-world network topologies, including a large-scale, labeled Twitter network comprising 20M nodes and 265M edges, and demonstrate that SybilFuse outperforms state-of-the-art approaches significantly. In particular, SybilFuse achieves 98% of Sybil coverage among top-ranked nodes.
Does ‘altitude training’ increase exercise performance in elite athletes?

The general practice of altitude training is widely accepted as a means to enhance sport performance despite a lack of rigorous scientific studies. For example, the scientific gold-standard design of a double-blind, placebo-controlled, cross-over trial has never been conducted on altitude training. Given that few studies have utilised appropriate controls, there should be more scepticism concerning the effects of altitude training methodologies. In this brief review we aim to point out weaknesses in theories and methodologies of the various altitude training paradigms and to highlight the few well-designed studies to give athletes, coaches and sports medicine professionals the current scientific state of knowledge on common forms of altitude training. Another aim is to encourage investigators to design well-controlled studies that will enhance our understanding of the mechanisms and potential benefits of altitude training.
Face hallucination: How much it can improve face recognition

Face hallucination has been a popular topic in image processing in recent years. Currently the commonly used performance criteria for face hallucination are peak signal noise ratio (PSNR) and the root mean square error (RMSE). Though it is logically believed that hallucinated high-resolution face images should have a better performance in face recognition, we show in this paper that this `the higher resolution, the higher recognition' assumption is not validated systematically by some designed experiments. First, we illustrate this assumption only works when the image solution is sufficiently large. Second, in the case of very extreme low resolutions, the recognition performance of the hallucinated images obtained by some typical existing face hallucination approaches will not improve. Finally, the relationship of the popular evaluation methods in face hallucination, PSNR and RMSE, with the recognition performance are investigated. The findings of this paper can help people design new hallucination approaches with an aim of improving face recognition performance with specified classifiers.
Analysis of a Hybrid Rotor Permanent Magnet Motor Based on Equivalent Magnetic Network

An equivalent magnetic circuit (EMC) model is built for a hybrid rotor permanent magnet (HRPM) motor. The key of the EMC model is that a novel section modeling method for permanent magnet (PM) is proposed to take PM end-leakage and local saturation of magnetic bridge into consideration, and it is used to separate the main magnetic circuit and leakage magnetic circuit of PM. Besides, a generalized equivalent method of winding magnetomotive force is adopted for the modeling of distributed winding. Meanwhile, an improved iterative algorithm is presented to solve the problem that the iteration does not converge when iron core is severely saturated. Then, the accuracy of the proposed EMC model is evaluated by experimental test and finite-element analysis. Finally, frozen permeability method based on the proposed EMC model is utilized to separate the PM torque component and the reluctance torque, which is time saving, and can be used to investigate and optimize the torque-angle characteristic clearly.