Locally repairable codes (LRCs) are a type of new erasure codes designed for modern distributed storage systems (DSSs). In order to obtain ternary LRCs of distance 6, firstly, we propose constructions with disjoint repair groups and construct several families of LRCs with 1 ≤ r ≤ 6, where codes with 3 ≤ r ≤ 6 are obtained through a search algorithm. Then, we propose a new method to extend the length of codes without changing the distance. By employing the methods such as expansion and deletion, we obtain more LRCs from a known LRC. The resulting LRCs are optimal or near optimal in terms of the Cadambe-Mazumdar (C-M) bound.

In this paper, we propose a real-time BSS (Blind Source Separation) system with two microphones that extracts only desired sound sources. Under the assumption that the desired sound sources are close to the microphones, the proposed BSS system suppresses distant sound sources as undesired sound sources. We previously developed a BSS system that can estimate the distance from a microphone to a sound source and suppress distant sound sources, but it was not a real-time processing system. The proposed BSS system is a real-time version of our previous BSS system. To develop the proposed BSS system, we simplify some BSS procedures of the previous system. Simulation results showed that the proposed system can effectively suppress the distant source signals in real-time and has almost the same capability as the previous system.

Locally repairable codes (LRCs) are implemented in distributed storage systems (DSSs) due to their low repair overhead. The locality of an LRC is the number of nodes in DSSs that participate in the repair of failed nodes, which characterizes the repair cost. An LRC is called optimal if its minimum distance attains the Singleton-type upper bound [1]. In this letter, optimal LRCs are considered. Using the concept of projective code in projective space PG(k, q) and shortening strategy, LRCs with d=3 are proposed. Meantime, derived from an ovoid [q2+1, 4, q2]q code (responding to a maximal (q2+1)-cap in PG(3, q)), optimal LRCs over Fq with d=4 are constructed.

The simultaneous utterances impact the ability of both the hearing-impaired persons and automatic speech recognition systems. Recently, deep neural networks have dramatically improved the speech separation performance. However, most previous works only estimate the speech magnitude and use the mixture phase for speech reconstruction. The use of the mixture phase has become a critical limitation for separation performance. This study proposes a two-stage phase-aware approach for multi-talker speech separation, which integrally recovers the magnitude as well as the phase. For the phase recovery, Multiple Input Spectrogram Inversion (MISI) algorithm is utilized due to its effectiveness and simplicity. The study implements the MISI algorithm based on the mask and gives that the ideal amplitude mask (IAM) is the optimal mask for the mask-based MISI phase recovery, which brings less phase distortion. To compensate for the error of phase recovery and minimize the signal distortion, an advanced mask is proposed for the magnitude estimation. The IAM and the proposed mask are estimated at different stages to recover the phase and the magnitude, respectively. Two frameworks of neural network are evaluated for the magnitude estimation on the second stage, demonstrating the effectiveness and flexibility of the proposed approach. The experimental results demonstrate that the proposed approach significantly minimizes the distortions of the separated speech.

Based on the concept of polynomial operators, this paper explores generalized direct-form II structure and its state-space realization for two-dimensional separable-denominator digital filters of order (m, n) where a structure with 3(m+n)+mn+1 fixed parameters plus m+n free parameters is introduced and analyzed. An l2-scaling method utilizing different coupling coefficients at different branch nodes to avoid overflow is presented. Expressions of evaluating the roundoff noise for the filter structure as well as its state-space realization are derived and investigated. The availability of the m+n free parameters is shown to be beneficial as the roundoff noise measures can be minimized with respect to these free parameters by means of an exhaustive search over a set with finite number of candidate elements. The important role these parameters can play in the endeavors of roundoff noise reduction is demonstrated by numerical experiments.

Due to most of the existing graph repartitioning methods are known for poor efficiency in distributed environments. In this paper, we introduce a new graph repartitioning method with two phases in distributed environments. In the first phase, a local method is designed to identify all the potential candidate vertices that should be moved to the other partitions at once in each partition locally. In the second phase, a streaming graph processing model is adopted to reassign the candidate vertices to achieve lightweight graph repartitioning. During the reassignment of the vertex, we propose an objective function to balance both the load balance and the number of crossing edges among the distributed partitions. The experimental results with a large set of real word and synthetic graph datasets show that the communication cost can be reduced by nearly 1 to 2 orders of magnitude compared with the existing methods.

Locally repairable codes (LRCs) with locality r and availability t are a class of codes which can recover data from erasures by accessing other t disjoint repair groups, that every group contain at most r other code symbols. This letter will investigate constructions of LRCs derived from cyclic codes and generalized quadrangle. On the one hand, two classes of cyclic LRC with given locality m-1 and availability em are proposed via trace function. Our LRCs have the same locality, availability, minimum distance and code rate, but have short length and low dimension. On the other hand, an LRC with $(2,(p+1)lfloorrac{s}{2} floor)$ is presented based on sets of points in PG(k, q) which form generalized quadrangles with order (s, p). For k=3, 4, 5, LRCs with r=2 and different t are determined.

When a unit repeats some works over again and undergoes minimal repairs at failures, it is more practical to replace it preventively at the end of working cycles or at its failure times. In this case, it would be an interesting problem to know which is better to replace the unit at a number of working cycles or at random failures from the point of cost. For this purpose, we give models of the expected cost rates for the following replacement policies: (1) The unit is replaced at a working cycle N and at a failure number K, respectively; (2) Replacement first and last policies with working cycle N and failure number K, respectively; (3) Replacement overtime policies with working cycle N and failure number K, respectively. Optimizations and comparisons of the policies for N and K are made analytically and numerically.

In this letter, we investigate the separating redundancy of binary linear codes. Using analytical techniques, we provide a general lower bound on the first separating redundancy of binary linear codes and show the bound is tight for a particular family of binary linear codes, i.e., cycle codes. In other words, the first separating redundancy of cycle codes can be determined. We also derive a deterministic and constructive upper bound on the second separating redundancy of cycle codes, which is shown to be better than the general deterministic and constructive upper bounds for the codes.

In this letter, a polarization filtering based transmission (PFBT) scheme is proposed to enhance the spectrum efficiency in wireless communications. In such scheme, the information is divided into several parts and each is conveyed by a polarized signal with a unique polarization state (PS). Then, the polarized signals are added up and transmitted by the dual-polarized antenna. At the receiver side, the oblique projection polarization filters (OPPFs) are adopted to separate each polarized signal. Thus, they can be demodulated separately. We mainly focus on the construction methods of the OPPF matrix when the number of the separate parts is 2 and 3 and evaluate the performance in terms of the capacity and the bit error rate. In addition, we also discuss the probability of the signal separation when the number of the separate parts is equal or greater than 4. Theoretical results and simulation results demonstrate the performance of the proposed scheme.

In this paper, we propose a Pyramid Predictive Attention Network (PPAN) for medical image segmentation. In the medical field, the size of dataset generally restricts the performance of deep CNN and deploying the trained network with gross parameters into the terminal device with limited memory is an expectation. Our team aims to the future home medical diagnosis and search for lightweight medical image segmentation network. Therefore, we designed PPAN mainly made of Xception blocks which are modified from DeepLab v3+ and consist of separable depthwise convolutions to speed up the computation and reduce the parameters. Meanwhile, by utilizing pyramid predictions from each dimension stage will guide the network more accessible to optimize the training process towards the final segmentation target without degrading the performance. IoU metric is used for the evaluation on the test dataset. We compared our designed network performance with the current state of the art segmentation networks on our RGB tongue dataset which was captured by the developed TIAS system for tongue diagnosis. Our designed network reduced 80 percentage parameters compared to the most widely used U-Net in medical image segmentation and achieved similar or better performance. Any terminal with limited storage which is needed a segment of RGB image can refer to our designed PPAN.

The Gabidulin-based locally repairable code (LRC) construction by Silberstein et al. is an important example of distance optimal (r,δ)-LRCs. Its distance optimality has been further shown to cover the case of multiple (r,δ)-locality, where the (r,δ)-locality constraints are different among different symbols. However, the optimality only holds under the ordered (r,δ) condition, where the parameters of the multiple (r,δ)-locality satisfy a specific ordering condition. In this letter, we show that Gabidulin-based LRCs are still distance optimal even without the ordered (r,δ) condition.

Miller [9] proposed a linear-time algorithm for computing small separators for 2-connected planar graphs. We explain his algorithm and present a way to modify it to a space efficient version. Our algorithm can be regarded as a log-space reduction from the separator construction to the breadth first search tree construction.

Heterogeneous defect prediction (HDP) is to detect the largest number of defective software modules in one project by using historical data collected from other projects with different metrics. However, these data can not be directly used because of different metrics set among projects. Meanwhile, software data have more non-defective instances than defective instances which may cause a significant bias towards defective instances. To completely solve these two restrictions, we propose unsupervised deep domain adaptation approach to build a HDP model. Specifically, we firstly map the data of source and target projects into a unified metric representation (UMR). Then, we design a simple neural network (SNN) model to deal with the heterogeneous and class-imbalanced problems in software defect prediction (SDP). In particular, our model introduces the Maximum Mean Discrepancy (MMD) as the distance between the source and target data to reduce the distribution mismatch, and use the cross-entropy loss function as the classification loss. Extensive experiments on 18 public projects from four datasets indicate that the proposed approach can build an effective prediction model for heterogeneous defect prediction (HDP) and outperforms the related competing approaches.

In this letter, we propose a new blind source separation method, independent low-rank matrix analysis based on generalized Kullback-Leibler divergence. This method assumes a time-frequency-varying complex Poisson distribution as the source generative model, which yields convex optimization in the spectrogram estimation. The experimental evaluation confirms the proposed method's efficacy.

The separating redundancy is an important property in the analysis of the error-and-erasure decoding of a linear block code. In this work, we investigate the separating redundancy of the duals of first-order generalized Reed-Muller (GRM) codes, a class of nonbinary linear block codes that have nice algebraic properties. The dual of a first-order GRM code can be specified by two positive integers m and q and denoted by R(m,q), where q is the power of a prime number and q≠2. We determine the first separating redundancy value of R(m,q) for any m and q. We also determine the second separating redundancy values of R(m,q) for any q and m=1 and 2. For m≥3, we set up a binary integer linear programming problem, the optimum of which gives a lower bound on the second separating redundancy of R(m,q).

In this paper, we investigate how to obtain binary locally repairable codes (LRCs) with good locality and availability from binary Simplex codes. We first propose a Combination code having the generator matrix with all the columns of positive weights less than or equal to a given value. Such a code can be also obtained by puncturing all the columns of weights larger than a given value from a binary Simplex Code. We call by block-puncturing such puncturing method. Furthermore, we suggest a heuristic puncturing method, called subblock-puncturing, that punctures a few more columns of the largest weight from the Combination code. We determine the minimum distance, locality, availability, joint information locality, joint information availability of Combination codes in closed-form. We also demonstrate the optimality of the proposed codes with certain choices of parameters in terms of some well-known bounds.

Locally repairable codes, which can repair erased symbols from other symbols, have attracted a good deal of attention in recent years because its local repair property is effective on distributed storage systems. (ru, δu)u∈[s]-locally repairable codes with multiple localities, which are an extension of ordinary locally repairable codes, can repair δu-1 erased symbols simultaneously from a set consisting of at most ru symbols. An upper bound on the minimum distance of these codes and a construction method of optimal codes, attaining this bound with equality, were given by Chen, Hao, and Xia. In this paper, we discuss the parameter restrictions of the existing construction, and we propose explicit constructions of optimal codes with multiple localities with relaxed restrictions based on the encoding polynomial introduced by Tamo and Barg. The proposed construction can design a code whose minimum distance is unrealizable by the existing construction.

In this paper, we examined the transfer method of fluororesin as the novel formation method of polymer wall in order to realize the lattice-shaped polymer walls without patterned light irradiation using photomask. We clarified that the transfer method was effective for formation of polymer wall structure on flexible substrate.

In this paper, we improve upon the accuracy of existing tracklet generation methods by repairing tracklets based on their quality evaluation and detection propagation. Starting from object detections, we generate tracklets using three existing methods. Then we perform co-tracklet quality evaluation to score each tracklet and filtered out good tracklet based on their scores. A detection propagation method is designed to transfer the detections in the good tracklets to the bad ones so as to repair bad tracklets. The tracklet quality evaluation in our method is implemented by intra-tracklet detection consistency and inter-tracklet detection completeness. Two propagation methods; global propagation and local propagation are defined to achieve more accurate tracklet propagation. We demonstrate the effectiveness of the proposed method on the MOT 15 dataset