Miracle-dz/UniFAR-FTUs huggingface dataset:
A Noise Robust Face Hallucination Framework Via Cascaded Model of Deep Convolutional Networks and Manifold Learning

Face hallucination technique generates high-resolution clean faces from low-resolution ones. Traditional technique generates facial features by incorporating manifold structure into patch representation. In recent years, deep learning techniques have achieved great success on the topic. These deep learning based methods can well maintain the middle and low frequency information. However, they still cannot well recover the high-frequency facial features, especially when the input is contaminated by noise. To address this problem, we propose a novel noise robust face hallucination framework via cascaded model of deep convolutional networks and manifold learning. In general, we utilize convolutional network to remove the noise and generate medium and low frequency facial information; then, we further utilize another convolutional network to compensate the lost high frequency with the help of personalized manifold learning method. Experimental results on public dataset show the superiority of our method compared with state-of-the-art methods.
Dalvik Opcode Graph Based Android Malware Variants Detection Using Global Topology Features

Since Android has become the dominator of smartphone operating system market with a share of 86.8%, the number of Android malicious applications are increasing rapidly as well. Such a large volume of diversified malware variants has forced researchers to investigate new methods by using machine learning since it provides a powerful ability for variants detection. Since the static analysis of malware plays an important role in system security and the opcode has been shown as an effective representation of malware, some of them use the Dalvik opcodes as features of malware and adopt machine learning to detect Android malware. However, current opcode-based methods are also facing some problems, such as considering both of accuracy and time cost, selection of features, and the lack of understanding or description of the characteristics of malware. To overcome the existing challenges, we propose a novel method to build a graph of Dalvik opcode and analyze its global topology properties, which will first construct a weighted probability graph of operations, and then we use information entropy to prune this graph while retaining information as more as possible, the next we extract several global topology features of the graph to represent malware, finally search the similarities with these features between programs. These global topology features formulate the high-level characteristics of malware. Our approach provides a light weight framework to detect Android malware variants based on graph theory and information theory. Theoretical analysis and real-life experimental results show the effectiveness, efficiency, and robustness of our approach, which achieves high detection accuracy and cost little training and detection time.
EndoCAT: An EtherCAT-based Articulated Rear View Endoscope for Single Port Surgery

Single port and natural orifice surgeries gain popularity compared to multi-port ones. The main reason is that single port surgeries are less invasive and thus less harmful to the patient than multiport surgeries. However, one of the drawbacks of devices for single port surgery is their limited access to regions proximate to the point of entrance. Moreover, they are strongly limited in choosing (controlling) the end-effector's orientation. Thus, inspecting a point of interest inside the endoscope's workspace from different angles is challenging. To overcome the existing limitations, we propose an EtherCAT-based master-slave robotic system. This master-slave system consists of an articulated endoscope with two work channels that can be manipulated by a force-sensing joystick. The articulated endoscope is capable of realizing a large range of orientations $\pmb{(\pm 220^{\text{o}})}$ throughout the workspace, can turn back, and look upon itself within a radius of 40 mm. The suggested robotic system will be useful in single port interventions such as inspections and treatments of the cardiac sphincter, the fundus in the stomach, or the neck of the bladder. In this paper, we present the design of the endoscope, analyze its kinematics, and present the workspace including the feasible orientations. Finally, the independent tele-operated joint control of the master-slave system EndoCAT is demonstrated in two experiments on a mockup stomach.
Association of breast cancer with human papillomavirus (HPV) infection in Northeast Brazil: molecular evidence

OBJECTIVE: The aim of this study is to investigate the presence of human papillomavirus DNA and genotypes in breast cancer and normal breast tissue samples obtained from women from the northeast region of Brazil. METHOD: One hundred three breast cancer samples and 95 normal breast samples, as the non-malignant controls, were studied. DNA extraction was verified by human beta-globin gene amplification, and polymerase chain reaction was conducted based on HPV L1-specific consensus primers MY09/MY11 and GP5+/GP6+, followed by nested multiplex polymerase chain reaction with type-specific primers for the E6/E7 consensus region. RESULTS: Human papillomavirus DNA was detected in 51 (49.5%) breast carcinoma samples and 15 (15.8%) normal breast samples (p<0.0001). Human papillomavirus genotypes 6 and 11 were identified in 15.2% of all samples. CONCLUSIONS: The high frequency of human papillomavirus infection in breast cancer samples indicates a potential role of this virus in breast carcinogenesis in the studied participants.
Structure-Texture Decomposition via Joint Structure Discovery and Texture Smoothing

Structure-texture decomposition from an image (a.k.a. structure-preserving image smoothing) is important for a variety of multimedia, computer vision and graphics tasks. Its performance heavily depends on the precision of indicating where are structural edges to maintain and where are textures to remove. An intuitive thought for constructing indication is to directly execute edge detection on the input image, which however would suffer from rich textures. Feeding inaccurate or erroneous indications into the smoother is at high risk of generating unsatisfactory results. It is almost sure that edge detectors can do a better job on inputs with textures removed. The above two components, say the smoother and the indicator, turn out to be in a chicken-egg situation. To address this issue, we propose a method to jointly detect structural edges and remove textures, by iteratively smoothing the input based on the edges detected from the previous smoothed result and refining the edges based on the newly processed image. Experiments on a number of challenging cases are conducted to show that the edge detection task and the smoothing task can benefit from each other, and reveal the superiority of our method over other state-of-the-art alternatives. Our code is publicly available at https://sites.google. com/view/xjguo/sdts.
On Scalable Service Function Chaining with $\mathcal{O}(1)$ Flowtable Entries

The emergence of Network Function Virtualization (NFV) enables flexible and agile service function chaining in a Software Defined Network (SDN). While this virtualization technology efficiently offers customization capability, it however comes with a cost of consuming precious TCAM resources. Due to this, the number of service chains that an SDN can support is limited by the flowtable size of a switch. To break this limitation, this paper presents CRT-Chain, a service chain forwarding protocol that requires only constant flowtable entries, regardless of the number of service chain requests. The core of CRT-Chain is an encoding mechanism that leverages Chinese Remainder Theorem (CRT) to compress the forwarding information into small labels. A switch does not need to insert forwarding rules for every service chain request, but only needs to conduct very simple modular arithmetic to extract the forwarding rules directly from CRT-Chain's labels attached in the header. We further incorporate prime reuse and path segmentation in CRT-Chain to reduce the header size and, hence, save bandwidth consumption. Our evaluation results show that, when a chain consists of no more than 5 functions, CRT-Chain actually generates a header smaller than the legacy 32-bit header defined in IETF. By enabling prime reuse and segmentation, CRT-Chain further reduces the total signaling overhead to a level lower than the conventional scheme, showing that CRT-Chain not only enables scalable flowtable-free chaining but also improves network efficiency.
Video Stereo Matching with Temporally Consistent Belief Propagation

The belief propagation (BP) technique is successful in image stereo matching problem. However, when we consider stereo matching for videos, directly applying the BP algorithm frame by frame results in unsatisfactory temporally inconsistent disparity maps. In this paper, we present the temporally consistent belief propagation for video stereo matching. We introduce a temporal term in traditional BP objective function and propose an adaptive weighting scheme to account for this temporal term. We show that the proposed algorithm performs favorably against previous methods in the stereo video datasets. Furthermore, the proposed method can solve problems induced by previous methods like error propagation from previously occluded regions.
Contributions to robust chromatic digital image processing

AbstractThis Thesis deals with digital image segmentation starting from a physical analysis of image formation under the Dichromatic Reflection Model (DRM), pursuing color constancy (CC) properties such as those shown by the human vision system, as psychology vision research has demonstrated. In order to provide robustness regarding illumination changes, first we perform illumination correction. By estimating the illumination chromaticity is possible to normalize the image respect to the illumination accomplishing some degree of CC in image processing. Afterwards, it is possible to separate the diffuse and specular image components. Once the image is normalized, we proceed to the image segmentation, where the key innovative tools introduced in this Thesis are a chromatic gradient and a hybrid gradient defined over a new chromatic distance. Most of this work is based on the identification of Spherical coordinates representation of the colors in the RGB cube, which allow to work with image chromaticity in straightforward manner. Segmentation methods proposed in this Thesis work in the image domain. Specifically, this Thesis proposes two approaches, one is a region growing and the other is a Watershed Transformation. First half of this thesis is focused on RGB images whereas the second one is devoted to the extrapolation of the segmentation methods to hyperspectral images through the use of hyperspherical coordinates.ResumenEsta Tesis trata sobre la segmentacion de imagenes digitales. Comienza con una reflexion sobre la formacion de la imagenes desde un punto de vista fisico a traves de analisis de reflectancia encuadrandolo dentro del modelo de reflexion dicromatica (DRM). Tambien hacemos una reflexion de la interpretacion de la imagenes desde el punto de vista psicologico, centrandonos en la constancia de color (CC). Con el fin de proveer robustez a los metodos de segmentacion, el primer paso es correccion de la iluminacion. Estimando la cromacidad de la iluminacion es posible normalizar la imagen respecto a las propiedades de la iluminacion, de hecho, esta es la forma de aplicar CC en el procesamiento de imagenes digitales. Despues de este paso es posible separar los componentes difusos y especulares de la imagen. Una vez que la imagen esta normalizada, procedemos con la segmentacion de la imagen, donde dos herramientas claves son el gradiente cromatico y el hibrido o sus respectivas inducidas distancias. La parte mas importante de la segmentacion esta implementada con Coordenadas Esfericas, que nos permite trabajar con la cromacidad de la imagen de una manera sencilla. Los metodos de segmentacion que se proponen utilizan la informacion espacial y espectral de la imagen, y siguen diferentes aproximaciones, una por crecimiento de regiones y otra por variaciones de la transformacion ? Watershed? La primera parte de la tesis esta enfocada en las imagenes RGB mientras que la segunda parte se centra en imagenes hiperespectrales. En la segunda parte tratamos de portar algunas ideas aplicadas en imagenes RGB a imagenes hiperespectrales a traves de las coordenadas hiperesfericas.
Deep Point Convolutional Approach for 3D Model Retrieval

With the increasing popularity of 3D models, retrieving deformable 3D objects is becoming a crucial task. The state-of-the-art methods use complex deep neural networks to address this problem, which require lots of computational resources. In this paper, we develop a more effective solution by using point convolution. Our algorithm takes local point descriptors as the input and produces a global vector for shape retrieval. To save the efforts of designing complex deep convolutional neural network (CNN), we first use intrinsic point descriptors to describe the shape deformations. Then, a simple but effective point CNN network is developed to integrate the local shape information by performing subspace compression and fusion, which depends on an end-to-end learning process to link the local and global information for discriminative shape representation. The experimental results on popular benchmarks have verified that our algorithm is able to outperform the state-of-the-art methods.
Seizure pathways: A model-based investigation

We present the results of a model inversion algorithm for electrocorticography (ECoG) data recorded during epileptic seizures. The states and parameters of neural mass models were tracked during a total of over 3000 seizures from twelve patients with focal epilepsy. These models provide an estimate of the effective connectivity within intracortical circuits over the time course of seizures. Observing the dynamics of effective connectivity provides insight into mechanisms of seizures. Estimation of patients seizure dynamics revealed: 1) a highly stereotyped pattern of evolution for each patient, 2) distinct sub-groups of onset mechanisms amongst patients, and 3) different offset mechanisms for long and short seizures. Stereotypical dynamics suggest that, once initiated, seizures follow a deterministic path through the parameter space of a neural model. Furthermore, distinct sub-populations of patients were identified based on characteristic motifs in the dynamics at seizure onset. There were also distinct patterns between long and short duration seizures that were related to seizure offset. Understanding how these different patterns of seizure evolution arise may provide new insights into brain function and guide treatment for epilepsy, since specific therapies may have preferential effects on the various parameters that could potentially be individualized. Methods that unite computational models with data provide a powerful means to generate testable hypotheses for further experimental research. This work provides a demonstration that the hidden connectivity parameters of a neural mass model can be dynamically inferred from data. Our results underscore the power of theoretical models to inform epilepsy management. It is our hope that this work guides further efforts to apply computational models to clinical data.
An Active Android Application Repacking Detection Approach

Repackaging applications as the main carrier of Android malware have caused huge losses to users. In addition, the third-party application market that Android applications rely on is characterized by missing audits and lax supervision, which further encourages the distribution of repackaged applications. Most of the traditional repackaging detection approaches need to rely on a third-party detection platform to passively determine whether or not the Android application is repackaged, which has a high false negative rate. In order to solve the problem, this paper proposes an active detection approach for Android code repacking. The approach embeds code watermarking with the detection code into the appropriate conditional branch code block by means of dynamic loading to achieve the hidden purpose. Then, the active detection approach compares the consistency of the runtime application signature and the original code watermarking signature to realize the code repackaging recognition. Finally, this work takes eight different types of Android applications from Github on three different mobile phones to verify the validity of the approach. Experimental results show that an Android application containing a selfdetecting code watermarking can effectively perform repackaging detection without relying on third parties.
ZC3H13 suppresses colorectal cancer proliferation and invasion via inactivating Ras–ERK signaling

ZC3H13 is a canonical CCCH zinc finger protein, which harbors a somatic frame‐shift mutation in colorectal cancer (CRC). However, its expression and biological function were still uncertain. In the current study, we found that ZC3H13 was served as a tumor suppressor in CRC cells, which decreased the expression of Snail, Cyclin D1, and Cyclin E1, and increased the expression of Occludin and Zo‐1 through inactivating Ras–ERK signaling pathway. Furthermore, reduction of ZC3H13 associated with advanced TNM stage (p = 0.02), positive regional lymph node metastasis ( p = 0.01). Taken together, the current study indicated that ZC3H13 may be an upstream regulator of Ras–ERK signaling pathway and suppressed invasion and proliferation of CRC.
CX3CL1 involves in breast cancer metastasizing to the spine via the Src/FAK signaling pathway

C-X3-C chemokine ligand 1 (CX3CL1) has been shown to be involved in the development of multiple tumors. Our previous study demonstrated that CX3CL1 may be involved in the process of metastasis of various malignant tumors to the spine, including breast cancer, but the molecular mechanism was still unknown. In the present study, we found that the receptor CX3CR1 was overexpressed in the spinal metastases of breast cancer than in para-tumor tissue. In terms of CX3CL1, it was significantly more expressed in normal spinal cancellous bone than in limbs. However, CX3CR1 was not expressed at a high level in every breast cancer cell compared with the human mammary epithelial cell line MCF-10A. In addition, CX3CL1 did promote the migration and invasion abilities of MDA-MB-231 cells. However, CX3CL1 has no obvious effect on cell growth. Furthermore, CX3CL1 induced chemotaxis of tumor cells via the Src/FAK signaling pathway. The migration index enhanced by CX3CL1 was dramatically declined using Bosutinib and PF-00562271, which are the inhibitors of Src and FAK signaling pathways, respectively. Therefore, CX3CL1 in spinal cancellous bone attracts CX3CR1-expressing tumor cells to the spine and enhances their migration and invasion abilities through the Src/FAK signaling pathway.
Routing Strategy Based on Network Coding Under Energy Constraint in MDTN

The mobile delay tolerant network has some features results in transmit message is limited in one touch. In order to transmit more messages in one touch, network encoding technology is introduced. It effectively improves this problem. The mobile devices in the mobile delay tolerant network have the advantages of mobility, portability, but there are also problems, such as: limited energy, node sociality. When the energy of the node in the network is exhausted, it will affect the performance of the network. Therefore, the introduction of energy management strategies into the network coding routing strategy is of great significance to the performance improvement of the mobile delay tolerant network. In this paper, through the ONE to achieve the routing strategy proposed in this paper, and then compared with some existing routing strategy. Experimental results show that the routing strategy proposed in this paper can effectively improve the rate of message delivery, reduce delay, and reduce network overhead.
Bloom Filter with a False Positive Free Zone

Bloom filters and their variants are widely used as space efficient probabilistic data structures for representing set systems and are very popular in networking applications. They support fast element insertion and deletion, along with membership queries with the drawback of false positives. Bloom filters can be designed to match the false positive rates that are acceptable for the application domain. However, in many applications a common engineering solution is to set the false positive rate very small, and ignore the existence of the very unlikely false positive answers. This paper is devoted to close the gap between the two design concepts of unlikely and not having false positives. We propose a data structure, called EGH filter, that supports the Bloom filter operations and besides it can guarantee false positive free operations for a finite universe and a restricted number of elements stored in the filter. We refer to the limited universe and filter size as the false positive free zone of the filter. We describe necessary conditions for the false positive free zone of a filter and generalize the filter to support listing of the elements. We evaluate the performance of the filter in comparison with the traditional Bloom filters. Our data structure is based on recently developed combinatorial group testing techniques.
Chemical vapor deposition and characterization of two-dimensional molybdenum dioxide (MoO2) nanoplatelets

We report on the chemical vapor deposition synthesis of MoO2 nanoplatelets by sublimation of MoO3 and its reduction in a hydrogen atmosphere at 750 °C. When grown on Si/SiO2 substrates, the platelets primarily assume a rhomboidal shape and have thicknesses ranging from several to tens of nm. The morphology of MoO2 crystals was found to depend on the chemical nature of substrates. MoO2 platelets on Si/SiO2 were characterized by a number of microscopic and spectroscopic techniques, and the electrical measurements revealed the metallic nature of their conductivity averaging at 2400 ± 1000 S cm−1. Raman spectroscopy of MoO2 platelets on graphene indicates their strong hole injection property. Small thickness, planar morphology, high chemical stability and metallic conductivity of ultrathin MoO2 platelets make them potentially interesting for integration different other two-dimensional materials in a variety of electronic structures and devices.
Using Ontologies to Drive the Creation of High-Quality Metadata in CEDAR

The Center for Expanded Data Annotation and Retrieval (CEDAR) developed a suite of tools3⁄4the CEDAR Workbench3⁄4that allows users to build metadata templates using ontologies to annotate template fields and to constrain the options available to metadata authors for specific fields; to fill in those templates with metadata; to upload data and their metadata to online repositories; and to perform searches over the metadata stored in CEDAR’s metadata repository. The CEDAR Workbench is released under a BSD 2-Clause opensource license, and it is freely available at https://metadatacenter.org.