Deep Reinforcement Learning for Vehicular Edge Computing: An Intelligent Offloading System

The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system

GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learning

This paper investigates a challenging problem of zero-shot learning in the multi-label scenario (MLZSL), wherein, the model is trained to recognize multiple unseen classes within a sample (e.g., an image) based on seen classes and auxiliary knowledge, e.g., semantic information. Existing methods usually resort to analyzing the relationship of various seen classes residing in a sample from the dimension of spatial or semantic characteristics, and transfer the learned model to unseen ones. But they ignore the effective integration of local and global features. That is, in the process of inferring unseen classes, global features represent the principal direction of the image in the feature space, while local features should maintain uniqueness within a certain range. This integrated neglect will make the model lose its grasp of the main components of the image. Relying only on the local existence of seen classes during the inference stage introduces unavoidable bias. In this paper, we propose a novel and effective group bi-enhancement framework for MLZSL, dubbed GBE-MLZSL, to fully make use of such properties and enable a more accurate and robust visual-semantic projection. Specifically, we split the feature maps into several feature groups, of which each feature group can be trained independently with the Local Information Distinguishing Module (LID) to ensure uniqueness. Meanwhile, a Global Enhancement Module (GEM) is designed to preserve the principal direction. Besides, a static graph structure is designed to construct the correlation of local features. Experiments on large-scale MLZSL benchmark datasets NUS-WIDE and Open-Images-v4 demonstrate that the proposed GBE-MLZSL outperforms other state-of-the-art methods with large margins.Comment: 11 pages, 8 figure

RSFNet: A White-Box Image Retouching Approach using Region-Specific Color Filters

Retouching images is an essential aspect of enhancing the visual appeal of photos. Although users often share common aesthetic preferences, their retouching methods may vary based on their individual preferences. Therefore, there is a need for white-box approaches that produce satisfying results and enable users to conveniently edit their images simultaneously. Recent white-box retouching methods rely on cascaded global filters that provide image-level filter arguments but cannot perform fine-grained retouching. In contrast, colorists typically employ a divide-and-conquer approach, performing a series of region-specific fine-grained enhancements when using traditional tools like Davinci Resolve. We draw on this insight to develop a white-box framework for photo retouching using parallel region-specific filters, called RSFNet. Our model generates filter arguments (e.g., saturation, contrast, hue) and attention maps of regions for each filter simultaneously. Instead of cascading filters, RSFNet employs linear summations of filters, allowing for a more diverse range of filter classes that can be trained more easily. Our experiments demonstrate that RSFNet achieves state-of-the-art results, offering satisfying aesthetic appeal and increased user convenience for editable white-box retouching.Comment: Accepted by ICCV 202

Low alpha-defensin gene copy number increases the risk for IgA nephropathy and renal dysfunction

IgA nephropathy (IgAN) is the most common primary glomerulonephritis worldwide. Although a major source of genetic variation, copy number variations (CNVs) and their involvement in disease development have not been well studied. Here, we performed association analysis of the DEFA1A3 CNV locus in two independent IgAN cohorts of Southern Chinese Han (total1189 cases and 1187 controls). We discovered three independent copy number associations within the locus: DEFA1A3 (P=3.99×10-9, OR=0.88), DEFA3 (P=6.55×10-5, OR=0.82) and a noncoding deletion variant (211bp) (P=3.50×10-16, OR=0.75) (OR per copy, fixed-effects meta-analysis). While showing strong association with increased risk for IgAN (P=9.56×10-20), low total copy numbers of the three variants also showed significant association with renal dysfunction in patients with IgAN (P=0.03, HR=3.69, after controlling for the effects of known prognostic factors) as well as high serum IgA1 (P=0.02) and a high proportion of galactose-deficient IgA1 (P=0.03). For replication, we confirmed the associations of DEFA1A3 (P=4.42×10-4, OR=0.82) and DEFA3 copy numbers (P=4.30×10-3, OR=0.74) with IgAN in a Caucasian cohort (531 cases and 198 controls) and found the 211bp variant to be much rarer in Caucasians. Interestingly, we also observed an association of the 211bp copy number with membranous nephropathy (P=1.11×10-7, OR=0.74 in 493 Chinese cases and 500 matched controls), but not with diabetic kidney disease (in 806 Chinese cases and 786 matched controls). By explaining 4.96% of disease risk and influencing the renal dysfunction in IgAN, the DEFA1A3 CNV locus is a potential candidate for therapeutic target and prognostic marker development

Transcriptome analysis of orange-spotted grouper (Epinephelus coioides) spleen in response to Singapore grouper iridovirus

<p>Abstract</p> <p>Background</p> <p>Orange-spotted grouper (<it>Epinephelus coioides</it>) is an economically important marine fish cultured in China and Southeast Asian countries. The emergence of infectious viral diseases, including iridovirus and betanodavirus, have severely affected food products based on this species, causing heavy economic losses. Limited available information on the genomics of <it>E. coioides </it>has hampered the understanding of the molecular mechanisms that underlie host-virus interactions. In this study, we used a 454 pyrosequencing method to investigate differentially-expressed genes in the spleen of the <it>E. coioides </it>infected with Singapore grouper iridovirus (SGIV).</p> <p>Results</p> <p>Using 454 pyrosequencing, we obtained abundant high-quality ESTs from two spleen-complementary DNA libraries which were constructed from SGIV-infected (V) and PBS-injected fish (used as a control: C). A total of 407,027 and 421,141 ESTs were produced in control and SGIV infected libraries, respectively. Among the assembled ESTs, 9,616 (C) and 10,426 (V) ESTs were successfully matched against known genes in the NCBI non-redundant (nr) database with a cut-off E-value above 10^-5. Gene ontology (GO) analysis indicated that "cell part", "cellular process" and "binding" represented the largest category. Among the 25 clusters of orthologous group (COG) categories, the cluster for "translation, ribosomal structure and biogenesis" represented the largest group in the control (185 ESTs) and infected (172 ESTs) libraries. Further KEGG analysis revealed that pathways, including cellular metabolism and intracellular immune signaling, existed in the control and infected libraries. Comparative expression analysis indicated that certain genes associated with mitogen-activated protein kinase (MAPK), chemokine, toll-like receptor and RIG-I signaling pathway were alternated in response to SGIV infection. Moreover, changes in the pattern of gene expression were validated by qRT-PCR, including cytokines, cytokine receptors, and transcription factors, apoptosis-associated genes, and interferon related genes.</p> <p>Conclusion</p> <p>This study provided abundant ESTs that could contribute greatly to disclosing novel genes in marine fish. Furthermore, the alterations of predicted gene expression patterns reflected possible responses of these fish to the virus infection. Taken together, our data not only provided new information for identification of novel genes from marine vertebrates, but also shed new light on the understanding of defense mechanisms of marine fish to viral pathogens.</p

Sheet Stamping Formability Test System based Servo Crank Press

AbstractProposed the tentative plan that carries on the formability test by simulation practical crank punch press slide speed characteristic, designed the solution to the implementation difficulty, and has carried on the actual attempt. The servo motor drive crank press speed alters at more sects which can get slider speed characteristic coherent with crank press varies. The system can test varies stamping formability that speed changeable based on sine curve. The system is composed of 600kN servo crank press, double action and all-purpose moldbase, date get and inspect analyze system. The moldbase adopted positive direction structural and self-motion, with variable blank holder force and counterforce controlled by hydraulic system with closed loop. The blank holder force can be set up in 5 sects which following with slide position, shortest control sect in 200ms. Appropriate profile of blank holder force can setup with the process needed. Blank holder has quartz force sensor which can inspect blank holder force and the control precision is in 0.1kN

Neural aggregation network for video face recognition

This paper presents a Neural Aggregation Network (NAN) for video face recognition. The network takes a face video or face image set of a person with a variable number of face images as its input, and produces a compact, fixed-dimension feature representation for recognition. The whole network is composed of two modules. The feature embedding module is a deep Convolutional Neural Network (CNN) which maps each face image to a feature vector. The aggregation module consists of two attention blocks which adaptively aggregate the feature vectors to form a single feature inside the convex hull spanned by them. Due to the attention mechanism, the aggregation is invariant to the image order. Our NAN is trained with a standard classification or verification loss without any extra supervision signal, and we found that it automatically learns to advocate high-quality face images while repelling low-quality ones such as blurred, occluded and improperly exposed faces. The experiments on IJB-A, YouTube Face, Celebrity-1000 video face recognition benchmarks show that it consistently outperforms naive aggregation methods and achieves the state-of-the-art accuracy.GH was partly supported by NSFC Grant 61629301. HL’s work was supported in part by Australia ARC Centre of Excellence for Robotic Vision (CE140100016) and by CSIRO Data61

Both‐column Acetabular Fractures with Posterior Wall Involved can be Managed through Single Anterior Approach by Evaluation of Computer‐assisted Virtual Surgery Technique

Objective Posterior wall (PW) fractures were sometimes associated in both‐column acetabular fractures. How to evaluate pre‐operatively the necessity for the performance of the posterior approach was an issue to be solved. In order to solve this issue, the computer‐assisted virtual surgery technique was used to evaluate if the involved PW in both‐column acetabular fractures (BACF) should be managed through posterior approach and verify the feasibility of this method. Methods Data of a consecutive cohort of 72 patients with both‐acetabular fractures from January 2012 to January 2020 was collected for retrospective study, of which 44 patients had concomitant acetabular PW fractures, and patients without PW fractures were labeled as the BCAF group. Computer‐assisted virtual surgery technique was performed pre‐operatively to evaluate the necessity for performance of posterior approach in 44 patients, and posterior approach was required if more than 3 mm of displacement was still present in the reduced 3D model. The 23 patients without treatment through posterior approach were labeled as the BCAF‐PW− group, and the 21 patients with treatment through posterior approach were labeled as the BCAF‐PW+ group. Operation‐related and post‐operative parameters were recorded. The quality of reduction and functional outcomes were assessed by the Matta scoring system and modified Merle d'Aubigné and Postel scoring system. The measurement data were analyzed using the t‐test of independent samples and rank‐sum test of ranked data between every two groups. Also, the one‐way analysis of variance (ANOVA) was used to analyze data between the three groups. Results Comparing operation‐related and post‐operative parameters in the three groups, some PW fractures in both‐column acetabular fractures could be ignored, and which could be evaluated pre‐operatively for necessity of an additional posterior approach. Operative time (271.2 ± 32.8 mins) and intra‐operative blood loss (1176.7 ± 211.1 mL) were significantly higher in the BCAF‐PW+ group. The excellent/good of reduction (25/28 of the BCAF group, 21/23 of the BCAF‐PW− group, 19/21 of the BCAF‐PW+ group) and functional outcomes (24/28 of the BCAF group, 18/23 of the BCAF‐PW− group, 18/21 of the BCAF‐PW+ group) of three groups were similar. The incidence of complications, such as deep vein thrombosis (4/28 of the BCAF group >3/23 of the BCAF‐PW− group >1/21 of the BCAF‐PW+ group) and injury of lateral femoral cutaneous nerve (3/23 of the BCAF‐PW− group >2/28 of the BCAF group >0/21 of the BCAF‐PW+ group), was no significant difference. Conclusion The partial both‐column acetabular fractures with PW involvement could be managed through a single anterior approach without another posterior approach by evaluation of computer‐assisted virtual surgery technique

Synthesis of Amino Acid Schiff Base Nickel (II) Complexes as Potential Anticancer Drugs In Vitro

Three hexacoordinated octahedral nickel (II) complexes, [Ni (Trp-sal) (phen) (CH3OH)] (1), [Ni (Trp-o-van) (phen) (CH3OH)]•2CH3OH (2), and [Ni (Trp-naph) (phen) (CH3OH)] (3) (where Trp-sal = Schiff base derived from tryptophan and salicylaldehyde, Trp-o-van = Schiff base derived from tryptophan and o-vanillin, Trp-naph = Schiff base derived from tryptophan and 2-hydroxy-1-naphthaldehyde, phen = 1, 10-phenanthroline), have been synthesized and characterized as potential anticancer agents. Details of structural study of these complexes using single-crystal X-ray crystallography showed that distorted octahedral environment around nickel (II) ion has been satisfied by three nitrogen atoms and three oxygen atoms. All these complexes displayed moderate cytotoxicity toward esophageal cancer cell line Eca-109 with the IC50 values of 23.95 ± 2.54 μM for 1, 18.14 ± 2.39 μM for 2, and 21.89 ± 3.19 μM for 3. Antitumor mechanism studies showed that complex 2 can increase the autophagy, reactive oxygen species (ROS) levels, and decrease the mitochondrial membrane potential remarkably in a dose-dependent manner in the Eca-109 cells. Complex 2 can cause cell cycle arrest in the G2/M phase. Additionally, complex 2 can regulate the Bcl-2 family and autophagy-related proteins