Simultaneous multithreading technology (SMT) can effectively improve the overall throughput and fairness through improving the resources usage efficiency of processors. Traditional works have proposed some metrics for evaluation in real systems, each of which strikes a trade-off between fairness and throughput. How to choose an appropriate metric to meet the demand is still controversial. Therefore, we put forward suggestions on how to select the appropriate metrics through analyzing and comparing the characteristics of each metric. In addition, for the new application scenario of cloud computing, the data centers have high demand for the quality of service for killer applications, which bring new challenges to SMT in terms of performance guarantees. Therefore, we propose a new metric P-slowdown to evaluate the quality of performance guarantees. Based on experimental data, we show the feasibility of P-slowdown on performance evaluation. We also demonstrate the benefit of P-slowdown through two use cases, in which we not only improve the performance guarantee level of SMT processors through the cooperation of P-slowdown and resources allocation strategy, but also use P-slowdown to predict the occurrence of abnormal behavior against security attacks.

Simultaneous multithreading (SMT) technology improves CPU throughput, but also causes unpredictable performance fluctuations for co-running workloads. Although recent major SMT processors have adopted some techniques to promote hardware support for quality-of-service (QoS), achieving both precise performance guarantees and high throughput on SMT architectures is still a challenging open problem. In this paper, we demonstrate through some comprehensive investigations on a cycle-accurate simulator that not only almost all in-core resources suffer from severe contention as workloads vary but also there is a non-linear relationship between performance and available quotas of resources. We consider these observations as the fundamental reason leading to the challenging problem above. Thus, we introduce QoSMT, a novel hardware scheme that leverages a closed-loop controlling mechanism consisting of detection, prediction and adjustment to enforce precise performance guarantees for specific targets, e.g. achieving 85%, 90% or 95% of the performance of a workload running alone respectively. We implement a prototype on GEM5 simulator. Experimental results show that the average control error is only 1.4%, 0.5% and 3.6%.

This paper proposes two variants of improved Compression by Substring Enumeration (CSE) with a finite alphabet. In previous studies on CSE, an encoder utilizes inequalities which evaluate the number of occurrences of a substring or a minimal forbidden word (MFW) to be encoded. The inequalities are derived from a contingency table including the number of occurrences of a substring or an MFW. Moreover, codeword length of a substring and an MFW grows with the difference between the upper and lower bounds deduced from the inequalities, however the lower bound is not tight. Therefore, we derive a new tight lower bound based on the contingency table and consequently propose a new CSE algorithm using the new inequality. We also propose a new encoding order of substrings and MFWs based on a sorted contingency table such that both its row and column marginal total are sorted in descending order instead of a lexicographical order used in previous studies. We then propose a new CSE algorithm which is the first proposed CSE algorithm using the new encoding order. Experimental results show that compression ratios of all files of the Calgary corpus in the proposed algorithms are better than those of a previous study on CSE with a finite alphabet. Moreover, compression ratios under the second proposed CSE get better than or equal to that under a well-known compressor for 11 files amongst 14 files in the corpus.

The boomerang connectivity table (BCT) was introduced by C. Cid et al. Using the BCT, for SPN block cipher, the dependency between sub-ciphers in boomerang structure can be computed more precisely. However, the existing method to generate BCT is difficult to be applied to the ARX-based cipher, because of the huge domain size. In this paper, we show a method to compute the dependency between sub-ciphers in boomerang structure for modular addition. Using bit relation in modular addition, we compute the dependency sequentially in bitwise. And using this method, we find boomerang characteristics and amplified boomerang characteristics for the ARX-based ciphers LEA and SPECK. For LEA-128, we find a reduced 15-round boomerang characteristic and reduced 16-round amplified boomerang characteristic which is two rounds longer than previous boomerang characteristic. Also for SPECK64/128, we find a reduced 13-round amplified boomerang characteristic which is one round longer than previous rectangle characteristic.

In this article, we propose a method to identify the link layer home network topology, motivated by applications to cost reduction of support centers. If the topology of home networks can be identified automatically and efficiently, it is easier for operators of support centers to identify fault points. We use MAC address forwarding tables (AFTs) which can be collected from network devices. There are a couple of existing methods for identifying a network topology using AFTs, but they are insufficient for our purpose; they are not applicable to some specific network topologies that are typical in home networks. The advantage of our method is that it can handle such topologies. We also implemented these three methods and compared their running times. The result showed that, despite its wide applicability, our method is the fastest among the three.

We consider the leader election problem in the population protocol model, which Angluin et al. proposed in 2004. A self-stabilizing leader election is impossible for complete graphs, arbitrary graphs, trees, lines, degree-bounded graphs, and so on unless the protocol knows the exact number of nodes. In 2009, to circumvent the impossibility, we introduced the concept of loose stabilization, which relaxes the closure requirement of self-stabilization. A loosely stabilizing protocol guarantees that starting from any initial configuration, a system reaches a safe configuration, and after that, the system keeps its specification (e.g., the unique leader) not forever but for a sufficiently long time (e.g., an exponentially long time with respect to the number of nodes). Our previous works presented two loosely stabilizing leader election protocols for arbitrary graphs; one uses agent identifiers, and the other uses random numbers to elect a unique leader. In this paper, we present a loosely stabilizing protocol that solves leader election on arbitrary graphs without agent identifiers or random numbers. Given upper bounds N and Δ of the number of nodes n and the maximum degree of nodes δ, respectively, the proposed protocol reaches a safe configuration within O(mn2d log n+mNΔ2 log N) expected steps and keeps the unique leader for Ω(NeN) expected steps, where m is the number of edges and d is the diameter of the graph.

Interpretability has become an important issue in the machine learning field, along with the success of layered neural networks in various practical tasks. Since a trained layered neural network consists of a complex nonlinear relationship between large number of parameters, we failed to understand how they could achieve input-output mappings with a given data set. In this paper, we propose the non-negative task matrix decomposition method, which applies non-negative matrix factorization to a trained layered neural network. This enables us to decompose the inference mechanism of a trained layered neural network into multiple principal tasks of input-output mapping, and reveal the roles of hidden units in terms of their contribution to each principal task.

Conventional TLB (Translation Lookaside Buffer) coalescing schemes do not fully exploit the contiguity that a memory allocator provides. The conventional schemes accordingly have certain performance overheads due to page table walks. To address this issue, we propose an efficient scheme, called block contiguity translation (BCT), that accommodates the block size information in a page table considering the Buddy algorithm. By fully exploiting the block-level contiguity, we can reduce the page table walks as certain physical memory is allocated in the contiguous way. Additionally, we present unified per-level page sizes to simplify the design and better utilize the contiguity information. Considering the state-of-the-art schemes as references, the comparative analysis and the performance simulations are conducted. Experiments indicate that the proposed scheme can improve the memory system performance with moderate hardware overheads.

While the graph minor theorem by Robertson and Seymour assures that any minor-closed class of graphs can be characterized by a finite list of excluded minors, such a succinct characterization by excluded minors is not always possible in matroids which are combinatorial abstraction from graphs. The class of matroids representable over a given infinite field is known to have an infinite number of excluded minors. In this paper, we show that, for any algebraic element x over the rational field ℚ the degree of whose minimal polynomial is 2, there exist infinitely many ℚ[x]-representable excluded minors of rank 3 for ℚ-representability. This implies that the knowledge that a given matroid is F-representable where F is a larger field than ℚ does not decrease the difficulty of excluded minors' characterization of ℚ-representability.

The consistent alignment of point clouds obtained from multiple scanning positions is a crucial step for many 3D modeling systems. This is especially true for environment modeling. In order to observe the full scene, a common approach is to rotate the scanning device around a rotation axis using a turntable. The final alignment of each frame data can be computed from the position and orientation of the rotation axis. However, in practice, the precise mounting of scanning devices is impossible. It is hard to locate the vertical support of the turntable and rotation axis on a common line, particularly for lower cost consumer hardware. Therefore the calibration of the rotation axis of the turntable is an important step for the 3D reconstruction. In this paper we propose a novel calibration method for the rotation axis of the turntable. With the proposed rotation axis calibration method, multiple 3D profiles of the target scene can be aligned precisely. In the experiments, three different evaluation approaches are used to evaluate the calibration accuracy of the rotation axis. The experimental results show that the proposed rotation axis calibration method can achieve a high accuracy.

Flash storage suffers from severe performance degradation due to its inherent internal synchronization overhead. Especially, flushing an L2P (logical address to physical address) mapping table significantly contributes to the performance degradation. To relieve the problem, we propose an efficient L2P mapping table management scheme on the flash storage, which works along with a small-sized NVRAM. It flushes L2P mapping table from DRAM to NVRAM or flash memory selectively. In our experiments, the proposed scheme shows up to 9.37× better performance than conventional schemes.

Over the past several years, the research of micro-expression recognition (MER) has become an active topic in affective computing and computer vision because of its potential value in many application fields, e.g., lie detection. However, most previous works assumed an ideal scenario that both training and testing samples belong to the same micro-expression database, which is easily broken in practice. In this letter, we hence consider a more challenging scenario that the training and testing samples come from different micro-expression databases and investigated unsupervised cross-database MER in which the source database is labeled while the label information of target database is entirely unseen. To solve this interesting problem, we propose an effective method called target-adapted least-squares regression (TALSR). The basic idea of TALSR is to learn a regression coefficient matrix based on the source samples and their provided label information and also enable this learned regression coefficient matrix to suit the target micro-expression database. We are thus able to use the learned regression coefficient matrix to predict the micro-expression categories of the target micro-expression samples. Extensive experiments on CASME II and SMIC micro-expression databases are conducted to evaluate the proposed TALSR. The experimental results show that our TALSR has better performance than lots of recent well-performing domain adaptation methods in dealing with unsupervised cross-database MER tasks.

Spectrum sensing is a fundamental requirement for cognitive radio, and it is a challenging problem in impulsive noise modeled by symmetric alpha-stable (SαS) distributions. The Gaussian kernelized energy detector (GKED) performs better than the conventional detectors in SαS distributed noise. However, it fails to detect the DC signal and has high computational complexity. To solve these problems, this paper proposes a more efficient and robust detector based on a Gaussian function (GF). The analytical expressions of the detection and false alarm probabilities are derived and the best parameter for the statistic is calculated. Theoretical analysis and simulation results show that the proposed GF detector has much lower computational complexity than the GKED method, and it can successfully detect the DC signal. In addition, the GF detector performs better than the conventional counterparts including the GKED detector in SαS distributed noise with different characteristic exponents. Finally, we discuss the reason why the GF detector outperforms the conventional counterparts.

The electric properties of a field-effect transistor not only depend on gate surface sidewall but also on channel orientation when applying channel stain engineering. The change of the gate surface and channel orientation through the rotated FinFETs provides the capability to compare the orientation dependence of performance and reliability. This study characterized the <100> and <110> channels of FinFETs on the same wafer under tensile and compressive stresses by cutting the wafer into rectangular silicon pieces and evaluated their piezoresistance coefficients. The piezoresistance coefficients of the <100> and <110> silicon under tensile and compressive stresses were first evaluated based on the current setup. Tensile stresses enhance the mobilities of both <100> and <110> channels, whereas compressive stresses degrade them. Electrical characterization revealed that the threshold voltage variation and drive current degradation of the {100} surface were significantly higher than those of {110} for positive bias temperature instability and hot carrier injection with equal gate and drain voltage (VG=VD). By contrast, insignificant difference is noted for the subthreshold slope degradation. These findings imply that a higher ratio of bulk defect trapping is generated by gate voltage on the <100> surface than that on the <110> surface.

This paper presents a numerical study of the wireless channel characteristics of liver implants in a frequency range of 4.5-6.5GHz, considering different digital human phantoms by employing two inhomogeneous male and female models. Path loss data for in-body to on-body and in-body to off-body communication scenarios are provided. The influence of respiration-induced organ movement on signal attenuation is demonstrated. A narrower range of attenuation deviation is observed in the female model as compared to the male model. The path loss data in the female body is between 40-80dB which is around 5-10dB lower than the male model. Path loss data for the in-body to off-body scenario in both models suggest that in-body propagation is the main component of total path loss in the channel. The results demonstrate that channel characteristics are subject dependent, and thus indicate the need to take subject dependencies into consideration when investigating in-body communication channels.

DHT routing algorithms can provide efficient mechanisms for resource placement and lookup for distributed file sharing systems. However, we must still deal with irregular and frequent join/leave of nodes and the problem of load unbalancing between nodes in DHT-based file sharing systems. This paper presents an efficient file backup scheme based on dynamic DHT key space clustering in order to guarantee data availability and support load balancing. The main idea of our method is to dynamically divide the DHT network into a number of clusters, each of which locally stores and maintains data chunks of data files to guarantee the data availability of user data files even when node churn occurs. Further, high-capacity nodes in clusters are selected as backup nodes to achieve adequate load balancing. Simulation results demonstrate the superior effectiveness of the proposed scheme over other file replication schemes.

We propose to discover approximate primary functional dependency (aPFD) for web tables, which focus on the determination relationship between primary attributes and non-primary attributes and are more helpful for entity column detection and topic discovery on web tables. Based on association rules and information theory, we propose metrics Conf and InfoGain to evaluate PFDs. By quantifying PFDs' strength and designing pruning strategies to eliminate false positives, our method could select minimal non-trivial approximate PFD effectively and are scalable to large tables. The comprehensive experimental results on real web datasets show that our method significantly outperforms previous work in both effectiveness and efficiency.

This paper presents a formal approach for generating train timetables in a mesoscopic level that is more concrete than the macroscopic level, where each station is simply expressed in a black-box, and more abstract than the microscopic level, where the infrastructure in each station-area is expressed in detail. The accuracy of generated timetable and the computational effort for the generation is a trade-off. In this paper, we design a formal mesoscopic modeling language by analyzing real railways, for example Tazawako-line as the first step of this work. Then, we define the constraint formulae for generating train timetables with the help of SMT (Satisfiability Module Theories)-Solver, and explain our tool RW-Solver that is an implementation of the constraint formulae. Finally, we demonstrate how RW-Solver with the help of SMT-Solver can be used for generating timetables in a case study of Tazawako-line.

The spaces between words and paragraphs are popular places for embedding data in data hiding techniques for text documents. Due to the low redundancy in text documents, the payload is limited to be small. As each bit of data is independently inserted into specific spaces in conventional methods, a malicious party may be able to modify the data without causing serious visible distortions. In this paper, we regard a collection of space lengths as a one-dimensional feature vector and embed watermark into its frequency components. To keep the secrecy of the embedded information, a random permutation and dither modulation are introduced in the operation. Furthermore, robustness against additive noise is enhanced by controlling the payload. In the proposed method, through experiments, we evaluated the trade-off among payload, distortion, and robustness.

Maximal designed distances for nonbinary narrow-sense quantum Bose-Chaudhuri-Hocquenghem (BCH) codes of length $n=rac{q^4-1}{r}$ and new constructions for them are given, where q is an odd prime power. These constructions are capable of designing quantum BCH codes with new parameters. Furthermore, some codes obtained here have better parameters than those constructed by other known constructions.