Nonlocality-controlled interaction of spatial solitons in nematic liquid crystals

We demonstrate experimentally that the interactions between a pair of nonlocal spatial optical solitons in a nematic liquid crystal (NLC) can be controlled by the degree of nonlocality. For a given beam width, the degree of nonlocality can be modulated by varying the pretilt angle of NLC molecules via the change of the bias. When the pretilt angle is smaller than pi/4, the nonlocality is strong enough to guarantee the independence of the interactions on the phase difference of the solitons. As the pretilt angle increases, the degree of nonlocality decreases. When the degree is below its critical value, the two solitons behavior in the way like their local counterpart: the two in-phase solitons attract and the two out-of-phase solitons repulse.Comment: 3 pages, 4 figure

O(\alpha_s) QCD Corrections to Spin Correlations in e−e+→ttˉe^- e^+ \to t \bar t process at the NLC

Using a Generic spin basis, we present a general formalism of one-loop radiative corrections to the spin correlations in the top quark pair production at the Next Linear Collider, and calculate the O(\alpha_s) QCD corrections under the soft gluon approximation. We find that: (a) in Off-diagonal basis, the $O(\alpha_s)$ QCD corrections to $e_L^- e^+$ ($e_R^- e^+$) scattering process increase the differential cross sections of the dominant spin component $t_{\uparrow}\bar{t}_{\downarrow}$ ($t_{\downarrow}\bar{t}_{\uparrow}$) by $\sim 30$ and $\sim (0.1$ depending on the scattering angle for $\sqrt{s}=400 GeV$ and 1 TeV, respectively; (b) in {Off-diagonal basis} (Helicity basis), the dominant spin component makes up 99.8% ($\sim 53$) of the total cross section at both tree and one-loop level for $\sqrt{s}=400 GeV$, and the Off-diagonal basis therefore remains to be the optimal spin basis after the inclusion of $O(\alpha_s)$ QCD corrections.Comment: 12 pages, 4 figures, revised version (a few print mistakes are corrected, some numerical results are modified, and Fig.4 is added

A Survey on Backdoor Attack and Defense in Natural Language Processing

Deep learning is becoming increasingly popular in real-life applications, especially in natural language processing (NLP). Users often choose training outsourcing or adopt third-party data and models due to data and computation resources being limited. In such a situation, training data and models are exposed to the public. As a result, attackers can manipulate the training process to inject some triggers into the model, which is called backdoor attack. Backdoor attack is quite stealthy and difficult to be detected because it has little inferior influence on the model's performance for the clean samples. To get a precise grasp and understanding of this problem, in this paper, we conduct a comprehensive review of backdoor attacks and defenses in the field of NLP. Besides, we summarize benchmark datasets and point out the open issues to design credible systems to defend against backdoor attacks.Comment: 12 pages, QRS202

A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network

In this paper, we employ Probabilistic Neural Network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition algorithm. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.Comment: 6 pages, 3 figures, 2 table

Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds

Discovering latent topics from text corpora has been studied for decades. Many existing topic models adopt a fully unsupervised setting, and their discovered topics may not cater to users' particular interests due to their inability of leveraging user guidance. Although there exist seed-guided topic discovery approaches that leverage user-provided seeds to discover topic-representative terms, they are less concerned with two factors: (1) the existence of out-of-vocabulary seeds and (2) the power of pre-trained language models (PLMs). In this paper, we generalize the task of seed-guided topic discovery to allow out-of-vocabulary seeds. We propose a novel framework, named SeeTopic, wherein the general knowledge of PLMs and the local semantics learned from the input corpus can mutually benefit each other. Experiments on three real datasets from different domains demonstrate the effectiveness of SeeTopic in terms of topic coherence, accuracy, and diversity.Comment: 12 pages; Accepted to NAACL 202

Some field experience with subsynchronous vibration of centrifugal compressors

A lot of large chemical fertilizer plants producing 1000 ton NH3/day and 1700 ton urea/day were constructed in the 1970's in China. During operation, subsynchronous vibration takes place occasionally in some of the large turbine-compressor sets and has resulted in heavy economic losses. Two cases of subsynchronous vibration are described: Self-excited vibration of the low-pressure (LP) cylinder of one kind of N2-H2 multistage compressor; and Forced subsynchronous vibration of the high-pressure (HP) cylinder of the CO2 compressor

Reliability-oriented optimization of computation offloading for cooperative vehicle-infrastructure systems

Computation offloading is critical for mobile applications that are sensitive to computational power, while dynamic and random nature of vehicular networks makes it challenging to guarantee the reliability of vehicular computation offloading. In this letter, we propose a reliability-oriented stochastic optimization model based on dynamic programming for computation offloading in the presence of the deadline constraint on application execution. Specifically, a theoretical lower bound of the expected reliability of computation offloading is derived, and then an optimal data transmission scheduling mechanism is proposed to maximize the lower bound with consideration of randomness in vehicle-to-infrastructure (V2I) communications. Experimental results demonstrate that our mechanism can outperform the conventional scheme and benefits vehicular computation offloading in terms of reliability performance in stochastic situations