Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint embeddings of shapes and images via CNN image purification

Both 3D models and 2D images contain a wealth of information about everyday objects in our environment. However, it is difficult to semantically link together these two media forms, even when they feature identical or very similar objects. We propose a joint embedding space populated by both 3D shapes and 2D images of objects, where the distances between embedded entities reflect similarity between the underlying objects. This joint embedding space facilitates comparison between entities of either form, and allows for cross-modality retrieval. We construct the embedding space using 3D shape similarity measure, as 3D shapes are more pure and complete than their appearance in images, leading to more robust distance metrics. We then employ a Convolutional Neural Network (CNN) to "purify" images by muting distracting factors. The CNN is trained to map an image to a point in the embedding space, so that it is close to a point attributed to a 3D model of a similar object to the one depicted in the image. This purifying capability of the CNN is accomplished with the help of a large amount of training data consisting of images synthesized from 3D shapes. Our joint embedding allows cross-view image retrieval, image-based shape retrieval, as well as shape-based image retrieval. We evaluate our method on these retrieval tasks and show that it consistently out-performs state-of-the-art methods, and demonstrate the usability of a joint embedding in a number of additional applications.
SIFT in perception-based color space

Scale Invariant Feature Transform (SIFT) has been proven to be the most robust local invariant feature descriptor. However, SIFT is designed mainly for grayscale images. Many local features can be misclassified if their color information is ignored. Motivated by perceptual principles, this paper addresses a new color space, called perception-based color space, in which the associated metric approximates perceived distances and color displacements and captures illumination invariant relationship. Instead of using grayscale values to represent the input image, the proposed approach builds the SIFT descriptors in the new color space, resulting in a descriptor that is more robust than the standard SIFT with respect to color and illumination variations. The evaluation results support the potential of the proposed approach.
Cache Transition Systems for Graph Parsing

Motivated by the task of semantic parsing, we describe a transition system that generalizes standard transition-based dependency parsing techniques to generate a graph rather than a tree. Our system includes a cache with fixed size m, and we characterize the relationship between the parameter m and the class of graphs that can be produced through the graph-theoretic concept of tree decomposition. We find empirically that small cache sizes cover a high percentage of sentences in existing semantic corpora.
Synthesizing HDL to memristor technology: A generic framework

Memristors are emerging devices with huge potentials. It has been shown that they can be used not only to design non-volatile memories, but also logic circuits. In the latter, memristor devices are stacked on a CMOS circuit which generates the required control signals needed by the memristors to perform the required functionality. This paper sets a step towards automating this process; it proposes a generic synthesis framework to map logic circuits on memristor crossbar. The framework takes HDL descriptions as input and generates both its memristor circuitry and its associated CMOS control. The framework consists of three phases: (i) netlist generation, (ii) partition and mapping, and (iii) placement and routing. To illustrate the framework, a combinational and a sequential circuit are investigated. The results are validated using HSPICE simulations.
MicroRNA-454-3p inhibits cervical cancer cell invasion and migration by targeting c-Met

Increasing evidence has demonstrated that microRNAs (miRNAs) have a crucial role in the initiation and progression of tumors. The present study aimed to investigate the expression and the role of miRNA-454-3p in human cervical cancer. Human cervical cancer cells were transfected with miRNA-454-3p mimics or negative control miRNA. MTT, Transwell and wound healing assays were performed to investigate the effects of miRNA-454-3p overexpression on cell proliferation, invasion and migration, respectively. The results indicated that miRNA-454-3p was down-regulated in human cervical cancer cell lines, while its ectopic overexpression significantly inhibited their proliferation, migration and invasion. Furthermore, a luciferase reporter assay confirmed that c-met was a novel target of miRNA-454-3p in HeLa cells. In conclusion, the results of the present study suggested that miRNA-454-3p exhibits significant tumor-suppressive effects in cervical cancer by targeting c-met, and may be a potential means of treating cervical cancer.
VeritasDB: High Throughput Key-Value Store with Integrity

While businesses shift their databases to the cloud, they continue to depend on them to operate correctly. Alarmingly, cloud services constantly face threats from exploits in the privileged computing layers (e.g. OS, Hypervisor) and attacks from rogue datacenter administrators, which tamper with the database’s storage and cause it to produce incorrect results. Although integrity veriﬁcation of outsourced storage and ﬁle systems is a well-studied problem, prior techniques impose prohibitive overheads (up to 30x in throughput) and place additional responsibility on clients. We present VeritasDB , a key-value store that guarantees data integrity to the client in the presence of exploits or implementation bugs in the database server. VeritasDB is implemented as a network proxy that mediates communication be-tween the unmodiﬁed client(s) and the unmodiﬁed database server, which can be any oﬀ-the-shelf database engine (e.g., Redis, RocksDB, Apache Cassandra). The proxy transforms each client request before forwarding it to the server and checks the correctness of the server’s response before forwarding it to the client. To ensure the proxy is trusted, we use the protections of modern trusted hardware platforms, such as Intel SGX, to host the proxy’s code and trusted state, thus completely eliminating trust on the cloud provider. To maintain high performance in VeritasDB while scaling to large databases, we design an authenticated Merkle B+-tree that leverages features of SGX (modest amount of protected RAM, direct access to large unprotected RAM, and CPU parallelism) to implement several novel optimizations based on caching, concurrency, and compression. On standard YCSB and Visa transaction work-loads, we observe an average overhead of 2.8x in throughput and 2.5x in latency, compared to the (insecure) system with no integrity checks — using CPU parallelism, we bring the throughput overhead down to 1.05x.
Model and SOA solutions for traceability in logistic chains

This paper addresses the problem of traceability in the context of a complex supply chain by proposing a model that captures the main elements necessary to follow a product throughout its lifecycle, from manufacturing to the end consumer. The model is general enough to be applied for several types of supply chains and focuses on external traceability, but also enables the retrieval of internal traceability data. We also present a broker-based service oriented architecture that provides logistics and traceability support according to the model. The traceability operations are performed on the products that travel along supply chains. The architecture targets a distributed environment, with the broker gathering and processing traceability data stored at different sites. We describe two approaches for traceability data retrieval, one for the logistic chains created and managed by the broker, and another one for the supply chains discovered in the EPCglobal Network.
Analysis of the effects of redundancy on the performance of relational database systems

Redundancy in user queries reduce the performance of database management system software. A remedy is to simplify queries before their processing. This paper, presents a tool that simplifies queries in the initial phase of their processing. Simplification use idempotent and equivalence rules for simplification. Also, the tool automatically performs normalization of complex and redundant queries, and as a result provides a simplified query along with the cost of simplification process. Experimental results show that the queries involving redundancy and complexity take more time and resources as compared to executing simple and non-redundant queries.
Acceptance factors of pull requests in open-source projects

Distributed version control systems provide support for pull request strategy, which is used to register external contributions in collaborative software projects. The data present on a pull request can provide insights of factors that have influence on the acceptance or rejection of contributions in open source projects. Furthermore, the discovery of knowledge about pull requests allows confirming or denying existing hypotheses and helps software developers and project managers to guide their actions. This work proposes the use of data mining, more specifically, the extraction of association rules, to find patterns that exert influence on the acceptance (merge) of a pull request. The results suggest that: (i) the use of association rules allows to identify which factors increase the likelihood of a pull request merge; (ii) the identification of attributes that influence the merge reveals important knowledge about the pull request model; and (iii) with the use of association rules, it is possible to determine which factors contribute to a faster merge.
Development of a microfluidic device for partial cell separation

In this study we present a continuous microfluidic device for partial extraction of red blood cells (RBCs) and subsequent measurement of RBC deformability. For this purpose, we use polydimethylsiloxane (PDMS) microchannels having different constrictions (25% and 75%) and investigate their effect on the thickness of the cell-free layer (CFL). By using a combination of image analysis techniques we are able to automatically measure the CFL thickness before and after an artificial contraction. The results suggest that the CFL thickness increases for larger contraction ratio. As a result we expect to use these results to design and optimize a biochip able to perform in one single channel both separation and deformation of cells within the CFL.
A hybrid 3DoF pose estimation method based on camera and lidar data

Despite a large number of specialty mobile manipulators have been deployed in specially designed warehouses, it is still a challenge to accurately estimate the position and orientation of randomly deposited goods and articles. In this paper, we present a hybrid 3DoF pose estimation method, in which camera and Lidar data is fused to accurately estimate the pose of target cubic parcels. We firstly developed a vision-based and a Lidar-based pose estimation method, respectively. Then a hybrid method based on the vision and Lidar methods is proposed to enhance the accuracy and robustness of the pose estimation process. The hybrid method fuses the two outputs from the vision and Lidar methods, employing Kalman filter. In real-world experiments, our real-time system achieves some 1cm mean position error and < 1° mean angle error, and shows its significant robustness when applied in our mobile manipulation platform1.
A time delay integration CMOS image sensor with online deblurring algorithm

This paper presents an online deblurring (ODB) algorithm for time delay integration (TDI) CMOS image sensor (CIS) used in small remote imaging systems, where image quality is degraded due to vibration caused by different factors. The ODB algorithm can detect and compensate the image shift so as to produce sharp TDI images. A 256×8-pixel prototype chip was fabricated using a 0. 18μm CIS technology, which contains 8 TDI stages, column-parallel TDI accumulation and online deblurring circuits. The 4-transistor active pixel sensor (4T-APS) is applied, with the pixel size of 6. 5μm×6. 5μm and the fill factor of 28%.
Thymidine Kinase 2 Deficiency-Induced Mitochondrial DNA Depletion Causes Abnormal Development of Adipose Tissues and Adipokine Levels in Mice

Mammal adipose tissues require mitochondrial activity for proper development and differentiation. The components of the mitochondrial respiratory chain/oxidative phosphorylation system (OXPHOS) are encoded by both mitochondrial and nuclear genomes. The maintenance of mitochondrial DNA (mtDNA) is a key element for a functional mitochondrial oxidative activity in mammalian cells. To ascertain the role of mtDNA levels in adipose tissue, we have analyzed the alterations in white (WAT) and brown (BAT) adipose tissues in thymidine kinase 2 (Tk2) H126N knockin mice, a model of TK2 deficiency-induced mtDNA depletion. We observed respectively severe and moderate mtDNA depletion in TK2-deficient BAT and WAT, showing both tissues moderate hypotrophy and reduced fat accumulation. Electron microscopy revealed altered mitochondrial morphology in brown but not in white adipocytes from TK2-deficient mice. Although significant reduction in mtDNA-encoded transcripts was observed both in WAT and BAT, protein levels from distinct OXPHOS complexes were significantly reduced only in TK2-deficient BAT. Accordingly, the activity of cytochrome c oxidase was significantly lowered only in BAT from TK2-deficient mice. The analysis of transcripts encoding up to fourteen components of specific adipose tissue functions revealed that, in both TK2-deficient WAT and BAT, there was a consistent reduction of thermogenesis related gene expression and a severe reduction in leptin mRNA. Reduced levels of resistin mRNA were found in BAT from TK2-deficient mice. Analysis of serum indicated a dramatic reduction in circulating levels of leptin and resistin. In summary, our present study establishes that mtDNA depletion leads to a moderate impairment in mitochondrial respiratory function, especially in BAT, causes substantial alterations in WAT and BAT development, and has a profound impact in the endocrine properties of adipose tissues.
Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis

The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is “mediation” by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are “cis-mediators” of trans-eQTLs, including those “cis-hubs” involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multi-tissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.
Research on Internal Force Redistribution of Prestressed Continuous Steel-concrete Composite Beam

The redistribution of internal force is an important part of the research on prestressed continuous steel-concrete composite beams,which is the basis of calculating the ultimate bending capacity.This paper indicates that the nonlinear factors such as formation of plastic hinges,crack of concrete and failure of connectors are the main reason to casuse the redistribution of internal force.Some factors which can influence the internal force redistribution of the beams were analyzed,and the combined force ratio R in negative-moment region is considered to be the most important factor to effect the rotation capacity of bearing′s plastic hinge and the redistribution of moment.The paper presents the moment modification coefficients of three pieces of continuous composite beam and analyzes why the values are lager than the test results.
Model-free Dense Stereo Reconstruction for Creating Realistic 3 D City Models Georg

In this paper we describe a framework for fully automatic and model-free generation of accurate and realistic 3D city models using multiple overlapping aerial images. The underlying DSM is computed by dense image matching, using a robustified Census transform as cost function. To further reduce the noise of mismatches, we afterwards minimize a global energy functional incorporating local smoothness constraints using variational methods. Due to the convexity of the framed problem, the solution is guaranteed to converge towards the global energy minimum and can be efficiently implemented on GPU using primal-dual algorithms. The resulting point cloud is then being triangulated, local planarity constraints are exploited to reduce the number of vertices and finally a multi-view texturing is applied. The quality of the DSM and the 3D Model is evaluated on a complex urban environment, using reference data generated by laser scanning (LiDAR).
Behavioural Model of Integrated Circuits for EMC Prediction

This paper deals with the modeling of integrated circuits for electromagnetic compatibility (EMC) analysis. With reference to complex integrated circuits that usually include huge core logic blocks, analog and mixed signal circuits as well as power sections (smart-power system-on-chip), the paper shows a new behavioral model whose parameters can be derived on the basis of scattering parameters and switching noise measurements. Through this new model, the electromagnetic emissions arising from such system-on-chip in application boards can be evaluated and mitigated before fabrication.
Rheological, thermal, and mechanical properties of poly(ethylene naphthalate)/poly(ethylene terephthalate) blends

Abstract Structural, Theological, thermal, and mechanical properties of blends of poly(ethylene naphthalate) (PEN) and poly(ethylene terephthalate) (PET) obtained by melt blending were investigated using capillary rheometry, differential scanning calorimetry (DSC), scanning electron microscopic (SEM) observation, tensile testing. X-ray diffraction, and 1H nuclear magnetic resonance (NMR) measurements. The melt Theological behavior of the PEN/PET blends was very similar to that of the two parent polymers. The melt viscosity of the blends was between that of PEN and that of PET. Thermal properties and NMR measurement of the blends revealed that PEN is partially miscible with PET in the as molded blends, indicating that an interchange reaction occurs to some extent on melt processing. The blend of 50/50 PEN/PET was more difficult to crystallize compared with blends of other PEN/PET ratios. The blends, once melted during DSC measurements, almost never showed cold crystallization and subsequent melting and def.
Development of New Placental and Fetal Expressed Sequence Tags (EST) for Gene Discovery in Pig Reproduction

and Implications One major problem that has high economic impact on pig reproduction is the unexplained loss of potential porcine conceptuses during the first month of gestation. To better understand when and how these losses occur, it is imperative to investigate the underlying genetic regulatory mechanisms. We have recently initiated a large-scale cDNA sequencing project to provide molecular information regarding the genes expressed in female reproductive tissues. cDNA libraries are planned for ovary, hypothalamus, pituitary, placenta, uterus, and several stages of embryonic development. Sequence information will also be highly useful in developing sequence-tagged sites for physical mapping and developing comparative links between the human, mouse, and pig genome maps. We have previously reported the creation of two cDNA libraries, porcine fetal (day 20), and conceptus (day 17). Sequencing of these libraries produced 220 Expressed Sequence Tags (ESTs), with 180 sequences analyzed by clustering algorithms, and 139 clusters identified within these sequences. We now report the creation of two more libraries from porcine fetal (day 45) and placental tissues. The day 45 fetal library has 971,150 independent clones (average insert: 1.4 kb), whereas the placental library has 1,320,000 independent clones. Initial sequencing of the fetal library has produced 119 ESTs (81 clusters), whereas we have obtained 1411 ESTs (1056 clusters) from the placental library. After clustering all sequences thus far obtained, we have identified 1,233 unique clusters. Sequences obtained in this project will be deposited into Genbank dbEST, and all comparative homology information will be summarized on a public Website.
Leveraging deep learning representation for search-based image annotation

Image annotation aims to assign some tags to an image such that these tags provide a textual description for the content of image. Search-based methods extract relevant tags for an image based on the tags of nearest neighbor images in the training set. In these methods, similarity of two images is determined based on the distance between feature vectors of the images. Thus, it is essential to extract informative feature vectors from images. In this paper, we propose a framework that utilize deep learning to obtain visual representation of images. We apply different architectures of convolutional neural networks (CNN) to the input image and obtain a single feature vector that is a rich representation for visual content of the image. In this way, we eliminate the usage of multiple feature vectors used in the state-of-the-art annotation methods. We also integrate our feature extractors with a nearest neighbors approach to obtain relevant tags of an image. Our experiments on the standard datasets of image annotation (including Corel5k, ESP Game, IAPR) demonstrate that our approach reaches higher precision, recall and F1 than the state-of-the-art methods such as 2PKNN, TagProp, NMF-KNN and etc.
On the impact of entity linking in microblog real-time filtering

Microblogging is a model of content sharing in which the temporal locality of posts with respect to important events, either of foreseeable or unforeseeable nature, makes applications of real-time filtering of great practical interest. We propose the use of Entity Linking (EL) in order to improve the retrieval effectiveness, by enriching the representation of microblog posts and filtering queries. EL is the process of recognizing in an unstructured text the mention of relevant entities described in a knowledge base. EL of short pieces of text is a difficult task, but it is also a scenario in which the information EL adds to the text can have a substantial impact on the retrieval process. We implement a start-of-the-art filtering method, based on the best systems from the TREC Microblog track real-time adhoc retrieval and filtering tasks, and extend it with a Wikipedia-based EL method. Results show that the use of EL significantly improves over non-EL based versions of the filtering methods.