A search for pair production of the supersymmetric partners of the Higgs boson (higgsinos H˜) in gauge-mediated scenarios is reported. Each higgsino is assumed to decay to a Higgs boson and a gravitino. Two complement...
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A search for pair production of the supersymmetric partners of the Higgs boson (higgsinos H˜) in gauge-mediated scenarios is reported. Each higgsino is assumed to decay to a Higgs boson and a gravitino. Two complementary analyses, targeting high- and low-mass signals, are performed to maximize sensitivity. The two analyses utilize LHC pp collision data at a center-of-mass energy s=13 TeV, the former with an integrated luminosity of 36.1 fb−1 and the latter with 24.3 fb−1, collected with the ATLAS detector in 2015 and 2016. The search is performed in events containing missing transverse momentum and several energetic jets, at least three of which must be identified as b-quark jets. No significant excess is found above the predicted background. Limits on the cross section are set as a function of the mass of the H˜ in simplified models assuming production via mass-degenerate higgsinos decaying to a Higgs boson and a gravitino. Higgsinos with masses between 130 and 230 GeV and between 290 and 880 GeV are excluded at the 95% confidence level. Interpretations of the limits in terms of the branching ratio of the higgsino to a Z boson or a Higgs boson are also presented, and a 45% branching ratio to a Higgs boson is excluded for mH˜≈400 GeV.
ATLAS measurements of the production of muons from heavy-flavor decays in sNN=2.76 TeV Pb+Pb collisions and s=2.76 TeV pp collisions at the LHC are presented. Integrated luminosities of 0.14 nb−1 and 570 nb−1 are used...
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ATLAS measurements of the production of muons from heavy-flavor decays in sNN=2.76 TeV Pb+Pb collisions and s=2.76 TeV pp collisions at the LHC are presented. Integrated luminosities of 0.14 nb−1 and 570 nb−1 are used for the Pb+Pb and pp measurements, respectively, which are performed over the muon transverse momentum range 4
A measurement of the rapidity and transverse momentum dependence of dijet azimuthal decorrelations is presented, using the quantity RΔϕ. The quantity RΔϕ specifies the fraction of the inclusive dijet events in which...
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A measurement of the rapidity and transverse momentum dependence of dijet azimuthal decorrelations is presented, using the quantity RΔϕ. The quantity RΔϕ specifies the fraction of the inclusive dijet events in which the azimuthal opening angle of the two jets with the highest transverse momenta is less than a given value of the parameter Δϕmax. The quantity RΔϕ is measured in proton-proton collisions at s=8 TeV as a function of the dijet rapidity interval, the event total scalar transverse momentum, and Δϕmax. The measurement uses an event sample corresponding to an integrated luminosity of 20.2 fb−1 collected with the ATLAS detector at the CERN Large Hadron Collider. Predictions of a perturbative QCD calculation at next-to-leading order in the strong coupling with corrections for nonperturbative effects are compared to the data. The theoretical predictions describe the data in the whole kinematic region. The data are used to determine the strong coupling αS and to study its running for momentum transfers from 260 GeV to above 1.6 TeV. Analysis that combines data at all momentum transfers results in αS(mZ)=0.1127−0.0027+0.0063.
High-quality algorithms for dense optical flow computation are computationally intensive. To compute them with high speed and low power is vital to make optical flow computation applicable in real-world applications. ...
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ISBN:
(纸本)9781479962464
High-quality algorithms for dense optical flow computation are computationally intensive. To compute them with high speed and low power is vital to make optical flow computation applicable in real-world applications. In contrast to only the Horn-Schunck model being studied on FPGA-based systems today, one of the best linear variational methods for dense optical flow computation, Combine-Brightness-Gradient, is implemented on FPGA-accelerated heterogeneous platforms in this paper. C instead of HDLs is employed and optimizing techniques based on the algorithmic parallelism and hardware architecture are introduced. Experimental results show that 30-110x improvement of the computing efficiency over CPUs was achieved. The FPGA-accelerated version is able to process 640 × 480 image at 12 fps with 0.38 J per frame, while it is 0.8 fps and around 40 J on CPUs. Through demonstrating high performance and low power of dense optical flow algorithm on FPGA-based heterogeneous platforms implemented in C, this paper shows that the off-the-shelf commodity FPGAs coupled with High-Level-Synthesis (HLS) tools could provide an available option when computational efficiency together with development speed are required.
The sentiment mining is a fast growing topic of both academic research and commercial applications, especially with the widespread of short-text applications on the Web. A fundamental problem that confronts sentiment ...
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ISBN:
(纸本)9781479967162
The sentiment mining is a fast growing topic of both academic research and commercial applications, especially with the widespread of short-text applications on the Web. A fundamental problem that confronts sentiment mining is the automatics and correctness of mined sentiment. This paper proposes an DLDA (Double Latent Dirichlet Allocation) model to analyze sentiment for short-texts based on topic model. Central to DLDA is to add sentiment to topic model and consider sentiment as equal to topic, but independent of topic. DLDA is actually two methods DLDA I and its improvement DLDA II. Compared to the single topic-word LDA, the double LDA I, i.e., DLDA I designs another sentiment-word LDA. Both LDAs are independent of each other, but they combine to influence the selected words in short-texts. DLDA II is an improvement of DLDA I. It employs entropy formula to assign weights of words in the Gibbs sampling based on the ideas that words with stronger sentiment orientation should be assigned with higher weights. Experiments show that compared with other traditional topic methods, both DLDA I and II can achieve higher accuracy with less manual needs.
Emotion is a fundamental object of human existence and determined by a complex set of factors. With the rapid development of online social networks (OSNs), more and more people would like to express their emotion in O...
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Emotion is a fundamental object of human existence and determined by a complex set of factors. With the rapid development of online social networks (OSNs), more and more people would like to express their emotion in OSNs, which provides wonderful opportunities to gain insight into how and why individual emotion is evolved in social network. In this paper, we focus on emotion dynamics in OSNs, and try to recognize the evolving process of collective emotions. As a basis of this research, we first construct a corpus and build an emotion classifier based on Bayes theory, and some effective strategies (entropy and salience) are introduced to improve the performance of our classifier, with which we can classify any Chinese tweet into a particular emotion with an accuracy as high as 82%. By analyzing the collective emotions in our sample networks in detail, we get some interesting findings, including a phenomenon of emotion synchronization between friends in OSNs, which offers good evidence for that human emotion can be spread from one person to another. Furthermore, we find that the number of friends has strong correlation with individual emotion. Based on those useful findings, we present a dynamic evolution model of collective emotions, in which both self-evolving process and mutual-evolving process are considered. To this end, extensive simulations on both real and artificial networks have been done to estimate the parameters of our emotion dynamic model, and we find that mutual-evolution plays a more important role than self-evolution in the distribution of collective emotions. As an application of our emotion dynamic model, we design an efficient strategy to control the collective emotions of the whole network by selecting seed users according to k-core rather than degree.
With the continuous advent of malware, in order to improve the utilization ratio of resources and deal with the threat of malware effectively, in this paper, we provide a malware threats decision model for multilevel ...
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With the continuous advent of malware, in order to improve the utilization ratio of resources and deal with the threat of malware effectively, in this paper, we provide a malware threats decision model for multilevel emergency response and early warning system. Different from traditional analyzing and decision-making, we establish a dynamic multi-source data acquisition framework of malicious behavior, which is used to collect the host behavior of malware, then make a quantitative analysis of the collected information, establish the vector of evaluation, and establish the Threat Decision Model based on Dynamic Multi-source data Acquisition DAMD, which used to assess the threat of malware. This paper solves the problem of lack of malware threats decision of multilevel emergency response and early warning system, at the same time;it provides valuable reference and basis for further research of malware threats analysis.
Most processors employ hardware data prefetching to hide memory access latencies. However the prefetching requests from different threads on a multi-core processor can cause severe interference with prefetching and/or...
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ISBN:
(纸本)9781479964932
Most processors employ hardware data prefetching to hide memory access latencies. However the prefetching requests from different threads on a multi-core processor can cause severe interference with prefetching and/or demand requests of others. The data prefetching can lead to significant performance degradation due to shared resource contention on shared memory multi-core systems. This paper proposes a thread-aware data prefetching mechanism based on low-overhead run-time information to tune prefetching modes and aggressiveness, mitigating the resource contention in the memory system. Our solution has two new components: 1) a filtering mechanism that informs the hardware about which prefetching requests can cause shared data invalidation and should be discarded, and 2) a self-tuning prefetcher that uses run-time feedback to adjust each thread's data prefetching mode and arguments. On a set of parallel benchmarks, our thread-aware data prefetching mechanisms improve the overall performance of 64-core system by 11% and reduce the energy-delay product by 13% over a multi-mode prefetch baseline system with a two level cache organization and a conventional MESI-based directory coherence protocol. We compare our approach to the feedback directed prefetching (FDP) technique and find that it provides better performance on multi-core systems, while reducing the energy delay product.
TTM cryptosystems proposed by *** are very fast due to the properties of tame automorphisms and small finite fields. The success of the first implementation of this system relies on the construction of Q 8 -module. Un...
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TTM cryptosystems proposed by *** are very fast due to the properties of tame automorphisms and small finite fields. The success of the first implementation of this system relies on the construction of Q 8 -module. Unfortunately, Jintai Ding and Timonthy Hodges have defeated it by decomposing function Q 8 into terms S, T 1 , T 2 . Later Chunyen Chou, D. J. Guan and Jiunming Chen gave a systematic way to construct Q 2 k -module. In this paper, we describe an implementation of TTM cryptosystems based on this Q 2 k -module, then with method similar to Ding-Hodges, we break this implementation. For any given ciphertext, we can derive the corresponding plaintext within O((n+r) 6 ) F 2 m -operations, where n+r is the number of ciphertext variables.
Symbolic Execution is a key and useful technology in current refinement software test, but there still exists some problems such as space explosion. In order to mitigate this problem and improve the ability for detect...
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ISBN:
(纸本)9781479954599
Symbolic Execution is a key and useful technology in current refinement software test, but there still exists some problems such as space explosion. In order to mitigate this problem and improve the ability for detecting vulnerabilities, this paper presents the improving guide-based vulnerability detection with hybrid symbolic execution, which aims to test suspicious objects. This method conducts path traversal with a hybrid symbolic execution model, which alternates between dynamic and static symbolic execution, and verify whether it is vulnerability through summarizing the characteristics of vulnerabilities and generating a constraint expression. Experimental result shows that this method can successfully detect errors in 56 seconds, which exceeds any other modern mainstream symbolic execution tools including CUTE, KLEE, S2E and Cloud9. Compared with CUTE, this method alleviates the problem of space explosion. Besides, this papaer successfully verifies the vulnerabilities of OpenSSL and some other commonly used software.
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