Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditional local approximation in the representation of a discrete process by interpolation polynomials

The organization of calculations or the processing of data on observable samples of a process carried out by means of representation by algebraic polynomials, belongs to the class of methods of analytical representation and signal processing. This paper discusses the structure of devices and the formulas that define them, which are used for local approximation using power algebraic polynomials, when the observed data are known exactly.
Variation Pattern Recognition of the BIW OCMM Online Measurement Data Based on LSTM NN

An accurate recognition of a dimensional variation pattern is very important for producing high-quality body-in-white (BIW). The wide application of optical coordination measurement machines (OCMM) in vehicle factory provided massive online dimensional data for the variation pattern recognition. However, the massive serially correlated or autocorrelated and 100% measurement data generated from the OCMM challenge the traditional statistical process control (SPC) technology and the common variation recognition approaches. This paper presents a novel deep-learning method, long short-term memory neural network (LSTM NN), to recognize the variation pattern of the BIW OCMM online measurement data. A comparative study between the backpropagation neural network (BP NN) and the LSTM NN was implemented, and the practicability of the proposed intelligent method was demonstrated by a case study. With the efficient use of time series information, the LSTM NN has a good performance in variation patterns’ recognition and high practicability in improving the quality of the BIW.
On the Optimality of Simple Han-Kobayashi Schemes for Gaussian Interference Channels

The Generalized Degrees of Freedom (GDoF) region of the 2-user Gaussian Interference Channel (GIC) was derived by Etkin, Tse and Wang. This GDoF region is achieved using the class of Han-Kobayashi (HK) schemes. For $K$-user GICs with $K > 2$, the GDoF region is not known completely. For the $K$-user GIC, Geng et al. derived the channel conditions under which Gaussian signalling and Treating Interference as Noise (TIN) is GDoF optimal. The TIN scheme also belongs to the class of HK schemes. In this paper, we derive conditions under which Simple HK (S-HK) schemes are GDoF optimal for general $K$-user GICs. Simple HK schemes are HK schemes with Gaussian signalling, no time sharing, and no private-common power splitting. The class of simple HK schemes includes the TIN scheme and schemes that involve various levels of interference decoding and cancellation at each receiver.
Effective Filters and Linear Time Verification for Tree Similarity Joins

The tree similarity join computes all similar pairs in a collection of trees. Two trees are similar if their edit distance falls within a user-defined threshold. Previous algorithms, which are based on a filter-verify approach, suffer from the following two issues. First, ineffective filters produce a large number of candidates that must be further verified. Second, the candidates are verified by computing the tree edit distance, which is cubic in the number of tree nodes. Thus, these techniques fail to scale to large tree collections and are infeasible even for small collections when the trees are large. In this paper, we present a scalable solution for the tree similarity join that is based on (1) an effective indexing technique that leverages both the labels and the structure of trees to reduce the number of candidates, (2) an efficient upper bound filter that moves many of the candidates directly to the join result without additional verification, (3) a linear time verification technique for the remaining candidates that avoids the expensive tree edit distance. We are the first to propose an efficient, non-trivial upper bound and linear time verification for the tree edit distance. Unlike previous solutions, our approach scales to collections with millions of trees. We improve the overall join time by up to two orders of magnitude w.r. t. the state of the art.
Polyphosphate kinase is a component of the Escherichia coli RNA degradosome

The Escherichia coli degradosome is a multienzyme complex with four major protein components: the endoribonuclease RNase E, the exoribonuclease PNPase, the RNA helicase RhlB and enolase. The first three of these proteins are known to have important functions in mRNA processing and degradation. In this work, we identify an additional component of the degradosome, polyphosphate kinase (PPK), which catalyses the reversible polymerization of the γ‐phosphate of ATP into polyphosphate (poly(P)). An E. coli strain deleted for the ppk gene showed increased stability of the ompA mRNA. Purified His‐tagged PPK was shown to bind RNA, and RNA binding was prevented by hydrolysable ATP. Chemical modification of RNA by PPK, for example the addition or removal of 3′ or 5′ terminal phosphates, could not be detected. However, polyphosphate was found to inhibit RNA degradation by the degradosome in vitro. This inhibition was overcome by the addition of ADP, required for the degradation of polyphosphate and for the regeneration of ATP by PPK in the degradosome. Thus, PPK in the degradosome appears to maintain an appropriate microenvironment, removing inhibitory polyphosphate and NDPs and regenerating ATP.
Development of an Enzyme-Linked Immunosorbent Assay Using a Recombinant LigA Fragment Comprising Repeat Domains 4 to 7.5 as an Antigen for Diagnosis of Equine Leptospirosis

ABSTRACT Leptospira immunoglobulin (Ig)-like (Lig) proteins are a novel family of surface-associated proteins in which the N-terminal 630 amino acids are conserved. In this study, we truncated the LigA conserved region into 7 fragments comprising the 1st to 3rd (LigACon1-3), 4th to 7.5th (LigACon4-7.5), 4th (LigACon4), 4.5th to 5.5th (LigACon4.5–5.5), 5.5th to 6.5th (LigACon5.5–6.5), 4th to 5th (LigACon4-5), and 6th to 7.5th (LigACon6-7.5) repeat domains. All 7 recombinant Lig proteins were screened using a slot-shaped dot blot assay for the diagnosis of equine leptospirosis. Our results showed that LigACon4-7.5 is the best candidate diagnostic antigen in a slot-shaped dot blot assay. LigACon4-7.5 was further evaluated as an indirect enzyme-linked immunosorbent assay (ELISA) antigen for the detection of Leptospira antibodies in equine sera. This assay was evaluated with equine sera (n = 60) that were microscopic agglutination test (MAT) negative and sera (n = 220) that were MAT positive to the 5 serovars that most commonly cause equine leptospirosis. The indirect ELISA results showed that at a single serum dilution of 1:250, the sensitivity and specificity of ELISA were 80.0% and 87.2%, respectively, compared to those of MAT. In conclusion, an indirect ELISA was developed utilizing a recombinant LigA fragment comprising the 4th to 7.5th repeat domain (LigACon4-7.5) as a diagnostic antigen for equine leptospirosis. This ELISA was found to be sensitive and specific, and it yielded results that concurred with those of the standard MAT.
Volleyball Setting Technique Assessment Using a Single Point Sensor

The correct technique is one of the main aspects for semi-professional athletes training their volleyball skills. Traditional training and movement assessment though, might not yield the best result to improve the capabilities of a player. Hereby problems or sub-optimal executions of a movement from a technical point of view are often not easily detectable by a coach or without technical support. We investigate the usage of an IMU (inertial measurement unit) combined with an EMG sensor in form of a ‘Myo’ Sensor unit [16], to classify the setting action of a volleyball player to afterwards judge the technical qualities of the movement and suggest improvements like a digital coach. We look into the framework to gather a suitable ground truth and detect the sequence of the actual setting in the datasets. This is then used in combination with a machine learning model to classify the movement. Results show that a subjective direct description of the inaccuracies of the movement as a ground truth is sufficient for this approach. An additional scored function is designed to classify allowed setting actions by the international Volleyball rules [6]. The sequence selection shows optimal results for 54.4% of the samples, 26.6% of the selected sequences show minor displacements. The classification of the setting action shows best results for labels with 2, 3 and 4 classes with an F1-score of 0.74, 0.64 and 0.35, respectively. The classification results are overall reasonable and are especially interesting for the scored function, giving feedback for beginner players. Using the classification model, feedback for the player is created directly through the ground truth labeling.
Learning to Restrict Test Range for Compiler Test

it is a tremendous challenge to guarantee the correctness of compilers in a limited time, especially when the compiler product is immature. It is necessary to restrict the test range to avoid over-testing, since our compiler products are usually delivered for specific domain. We perform feature extraction on user code through machine learning and use feature information for fuzzy test case generation. The probability of bugs detected has been improved 3.7 times and case size has been reduced 70%.
A Hardware-Accelerated Solution for Hierarchical Index-Based Merge-Join(Extended Abstract)

Hardware acceleration through field programmable gate arrays (FPGAs) has recently become a technique of growing interest for many data-intensive applications. Join query is one of the most fundamental database query types useful in relational database management systems. However, the available solutions so far have been beset by higher costs in comparison with other query types. In this paper, we develop a novel solution to accelerate the processing of sort-merge join queries with low match rates. Specifically, our solution makes use of hierarchical indexes to identify result-yielding regions in the solution space in order to take advantage of result sparseness. Further, in addition to one-dimensional equi-join query processing, our solution supports processing of multidimensional similarity join queries. Experimental results show that our solution is superior to the best existing method in a low match rate setting; the method achieves a speedup factor of 4.8 for join queries with a match rate of 5%.
Requirements Dependencies-Based Test Case Prioritization for Extra-Functional Properties

The use of requirements' information in testing is a well-recognized practice in the software development life cycle. Literature reveals that existing tests prioritization and selection approaches neglected vital factors affecting tests priorities, like interdependencies between requirement specifications. We believe that models may play a positive role in specifying these inter-dependencies and prioritizing tests based on these inter-dependencies. However, till date, few studies can be found that make use of requirements inter-dependencies for test case prioritization. This paper uses a meta-model to aid modeling requirements, their related tests, and inter-dependencies between them. The instance of this meta-model is then processed by our modified PageRank algorithm to prioritize the requirements. The requirement priorities are then propagated to related test cases in the test model and test cases are selected based on coverage of extra-functional properties. We have demonstrated the applicability of our proposed approach on a small example case.
WheelShare: Crowd-Sensed Surface Classification for Accessible Routing

Accessible path routing for wheeled mobility is an important problem given the permanent and temporary obstacles in the built environment. Existing research works have focused on identifying several obstacles as well as facilities such as crosswalks with traffic signals using smartphone based sensing or crowd-sourcing and used those knowledge to generate accessible routes. In this work, we propose WheelShare which generates an accessible route through the best possible surface depending on user and wheelchair requirements. It is 1) scalable, as it uses crowd-sensing to collect voluminous data, 2) dynamic, as the data gets constantly updated, and 3) objective, as it uses an empirical and data-centric approach.
A 20-GHz Microwave Miniaturized Ring Resonator for nL Microfluidic Sensing Applications

In this article, a miniaturized microwave resonator is designed and fabricated. A loop-mode resonance is created when the permittivity difference between the two materials loaded in the two fluidic channels is sufficiently large. A highly concentrated electrical field is generated in a microfluidic channel loaded with deionized water to achieve high sensing spatial resolution within a sensing volume of 45 pL. On-wafer measurements are performed by keeping one channel unloaded as a reference while loading the other channel with different mixtures of isopropanol and deionized water. The acquired microwave-microfluidic measurements confirm that the proposed resonator can detect changes in the permittivity of the loaded liquid between the values of 3.2 and 42.4, making it a promising low-cost and label-free approach in applications requiring novel sensing techniques such as mixing monitoring and flow cytometry.
An Efficient Parallel Keyword Search Engine on Knowledge Graphs

Keyword search has recently become popular as a way to query relational databases, and even graphs, since it allows users to issue queries without learning a complex query language and data schema. Evaluating a keyword query is usually significantly more expensive than evaluating an equivalent selection query, since the query specification is less complete, and many alternative answers have to be considered by the system, requiring considerable effort to generate and compare. Current interest in big data and AI are putting even more demands on the efficiency of keyword search. In particular, searching of knowledge graphs is gaining popularity. As knowledge graphs often comprise many millions of nodes and edges, performing real-time search on graphs of this size is an open challenge. In this paper, we attempt to address this need by leveraging advances in hardware technologies, e.g. multi-core CPUs and GPUs. Specifically, we implement a parallel keyword search engine for Knowledge Bases (KB). To be able to do so, and to exploit parallelism, we devise a new approach to keyword search, based on a concept we introduce called Central Graph. Unlike the Group Steiner Tree (GST) model, widely used for keyword search, our approach can naturally work in parallel and still return compact answer graphs with rich information. Our approach can work in either multi-core CPUs or a single GPU. In particular, our GPU implementation is two to three orders of magnitudes faster than state-of-the-art keyword search method. We conduct extensive experiments to show that our approach is both efficient and effective.
Deep Generative Adversarial Networks for Thin-Section Infant MR Image Reconstruction

Due to their high spatial resolution, thin-section magnetic resonance (MR) images serve as ideal medical images for brain structure investigation and brain surgery navigation. However, compared with the clinically widely used thick-section MR images, thin-section MR images are less available due to the imaging cost. Thin-section MR images of infants are even scarcer but are quite valuable for the study of human brain development. Therefore, we propose a method for the reconstruction of thin-section MR images from thick-section images. A two-stage reconstruction framework based on generative adversarial networks (GANs) and a convolutional neural network (CNN) is proposed to reconstruct thin-section MR images from thick-section images in the axial and sagittal planes. A 3D-Y-Net-GAN is first proposed to fuse MR images from the axial and sagittal planes and to achieve the first-stage thin-section reconstruction. A 3D-DenseU-Net followed by a stack of enhanced residual blocks is then proposed to provide further detail recalibrations and structural corrections in the sagittal plane. In this method, a comprehensive loss function is also proposed to help the networks capture more structural details. The reconstruction performance of the proposed method is compared with bicubic interpolation, sparse representation, and 3D-SRU-Net. Cross-validation based on 35 cases and independent testing based on two datasets with totally 114 cases reveal that, compared with the other three methods, the proposed method provides an average 23.5% improvement in peak signal-to-noise ratio (PSNR), 90.5% improvement in structural similarity (SSIM), and 21.5% improvement in normalized mutual information (NMI). The quantitative evaluation and visual inspection demonstrate that our proposed method outperforms those methods by reconstructing more realistic results with better structural details.
An MBR-Oriented Approach for Efficient Skyline Query Processing

This research proposes an advanced approach that improves the efficiency of skyline query processing by significantly reducing the computational cost on object comparisons, i.e., dominance tests between objects. Our solutions are based on two novel concepts. The skyline query over Minimum Bounding Rectangles (MBRs) receives a set of MBRs and returns the MBRs that are not dominated by other MBRs. In the dominance test for MBRs, the detailed attribute values of objects in the MBRs are not accessed. Moreover, the dependent group of MBRs reduces the search space for dominance tests. Objects in an MBR are only compared with the ones in the corresponding dependent groups of the MBR rather than with the entire dataset. Our solutions apply the two concepts to the R-tree in order to use its hierarchical structure in which every node is a natural abstraction of an MBR. Specifically, given the R-tree index of an input dataset, we first eliminate unqualified objects by utilizing the skyline query over MBRs (i.e., intermediate nodes in the R-tree). Subsequently, we generate dependent groups for the skyline MBRs. Two dependent group generation methods that rely on either the sorting technique or the R-tree index are developed. Further, we apply an existing skyline algorithm to every dependent group, and the results of the original skyline query are the union of skyline objects in the dependent groups. In addition, we also analyze the cardinality of the two new concepts based on a probabilistic model, which enables us to analyze the computational complexity of the proposed solutions. Our experimental results show that the proposed solutions are clearly more efficient than the state-of-the-art approaches.
GEM^2-Tree: A Gas-Efficient Structure for Authenticated Range Queries in Blockchain

Blockchain technology has attracted much attention due to the great success of the cryptocurrencies. Owing to its immutability property and consensus protocol, blockchain offers a new solution for trusted storage and computation services. To scale up the services, prior research has suggested a hybrid storage architecture, where only small meta-data are stored on-chain and the raw data are outsourced to off-chain storage. To protect data integrity, cryptographic proof can be constructed online for queries over the data stored in the system. However, the previous schemes only support simple key-value queries. In this paper, we take the first step toward studying authenticated range queries in the hybrid-storage blockchain. The key challenge lies in how to design an authenticated data structure (ADS) that can be efficiently maintained by the blockchain, in which a unique gas cost model is employed. By analyzing the performance of the existing techniques, we propose a novel ADS, called GEM^2-tree, which is not only gas-efficient but also effective in supporting authenticated queries. To further reduce the ADS maintenance cost without sacrificing much the query performance, we also propose an optimized structure, GEM^2*-tree, by designing a two-level index structure. Theoretical analysis and empirical evaluation validate the performance of the proposed ADSs.
A multi-exchange heuristic for formation of balanced disjoint rings

A Multi-Exchange Heuristic for Formation of Balanced Disjoint Rings. (August 2005) Sarath K. Sasi Kumar, B.E. , Regional Engineering College, Trichy, India Chair of Advisory Committee: Dr. Halit Uster Telecommunication networks form an integral part of life. Avoiding failures on these networks is always not possible. Designing network structures that survive these failures have become important in ensuring the reliability of these network structures. With the introduction of SONET (Synchronous Optical Network) technology, rings have become the preferred survivable network structure. This network configuration has a set of disjoint rings (each node being a part of single ring), and these disjoint rings are connected via another main ring. In this research, we present a mathematical model for the design of such disjoint rings with node number balance criterion among the rings. When, given a set of nodes and distances between them, the Balanced Disjoint Rings (BDR) problem is the minimum total link length clustering of nodes into a given number of disjoint rings in such a way that there is almost the same number of nodes in each ring. The BDR problem is a class of the standard Traveling Salesman Problem (TSP). It is clear from this observation that the BDR problem becomes a TSP when the number of rings required is set to one. Hence BDR is NP-Hard, and we do not expect to obtain a polynomial time algorithm for its solution. To overcome this problem, we developed a set of construction heuristics (Break-MST, Distance Method, Hybrid Method, GRASP-Based Distance Method) and improvement heuristics (Multi-Exchange, Single Move). Different combinations
Cooperative Motion Planning with Time, Energy and Active Navigation Constraints

One of the key elements for the successful execution of multiple AUV missions is the availability of reliable and efficient cooperative motion planning systems. In a representative mission, this system should have the capability to generate collision-free trajectories from the AUVs’ initial positions to desired goal positions while optimizing some mission specific performance metric. The trajectories must also satisfy different constraints imposed by environmental conditions, AUV dynamics, and underwater communication limitations. This calls for the availability of algorithms that can deal with a large number of constraints and complex cost functions. In this paper we address the cooperative motion planning problem for a group of AUVs with time, energy, and active navigation constraints. We focus on two challenging problems. In the first problem the trajectories should guide the AUVs to a desired formation with minimum energy consumption, while in the second the information content of the path should be maximized for accurate single-beacon AUV positioning. We formulate the problems as optimal control problems to explicitly address the objectives and constraints. We employ the “direct multiple shooting” method along with a structure-exploiting optimization solver to efficiently solve the optimal control problem. Numerical results illustrate the efficacy of the methodology adopted for cooperative motion planning.