Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization

Existing methods for CWS usually rely on a large number of labeled sentences to train word segmentation models, which are expensive and time-consuming to annotate. Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful information for CWS. In this paper, we propose a neural approach for Chinese word segmentation which can exploit both lexicon and unlabeled data. Our approach is based on a variant of posterior regularization algorithm, and the unlabeled data and lexicon are incorporated into model training as indirect supervision by regularizing the prediction space of CWS models. Extensive experiments on multiple benchmark datasets in both in-domain and cross-domain scenarios validate the effectiveness of our approach.Comment: 7 pages, 11 figures, accepted by the 2019 World Wide Web Conference (WWW '19

Twenty-six circulating antigens and two novel diagnostic candidate molecules identified in the serum of canines with experimental acute toxoplasmosis

List of CAg proteins identified by LC-MS/MS after IP enrichment and purification with ESA antibodies. (XLSX 27 kb

Measurement-based quantum control of mechanical motion

Controlling a quantum system based on the observation of its dynamics is inevitably complicated by the backaction of the measurement process. Efficient measurements, however, maximize the amount of information gained per disturbance incurred. Real-time feedback then enables both canceling the measurement's backaction and controlling the evolution of the quantum state. While such measurement-based quantum control has been demonstrated in the clean settings of cavity and circuit quantum electrodynamics, its application to motional degrees of freedom has remained elusive. Here we show measurement-based quantum control of the motion of a millimetre-sized membrane resonator. An optomechanical transducer resolves the zero-point motion of the soft-clamped resonator in a fraction of its millisecond coherence time, with an overall measurement efficiency close to unity. We use this position record to feedback-cool a resonator mode to its quantum ground state (residual thermal occupation n = 0.29 +- 0.03), 9 dB below the quantum backaction limit of sideband cooling, and six orders of magnitude below the equilibrium occupation of its thermal environment. This realizes a long-standing goal in the field, and adds position and momentum to the degrees of freedom amenable to measurement-based quantum control, with potential applications in quantum information processing and gravitational wave detectors.Comment: New version with corrected detection efficiency as determined with a NIST-calibrated photodiode, added references and revised structure. Main conclusions are identical. 41 pages, 18 figure

Exploiting the Security Aspects of Compressive Sampling

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A temporal Convolutional Network for EMG compressed sensing reconstruction

Electromyography (EMG) plays a vital role in detecting medical abnormalities and analyzing the biomechanics of human or animal movements. However, long-term EMG signal monitoring will increase the bandwidth requirements and transmission system burden. Compressed sensing (CS) is attractive for resource-limited EMG signal monitoring. However, traditional CS reconstruction algorithms require prior knowledge of the signal, and the reconstruction process is inefficient. To solve this problem, this paper proposed a reconstruction algorithm based on deep learning, which combines the Temporal Convolutional Network (TCN) and the fully connected layer to learn the mapping relationship between the compressed measurement value and the original signal, and it has been verified in the Ninapro database. The results show that, for the same subject, compared with the traditional reconstruction algorithms orthogonal matching pursuit (OMP), basis pursuit (BP), and Modified Compressive Sampling Matching Pursuit (MCo), the reconstruction quality and efficiency of the proposed method is significantly improved under various compression ratios (CR)

On the security of optical ciphers under the architecture of compressed sensing combining with double random phase encoding

This work investigates the security of optical ciphers integrating compressed sensing (CS) with double random phase encoding. Theoretical analysis demonstrates that the combined system, regardless of the implementation order of the two procedures, can be normalized as a single CS projection process, whose equivalent measurement matrix can be recovered by plaintext attack. The proved restricted isometry property of the equivalent measurement matrices further renders the adversary great convenience to recover the plaintext with only a single-step ℓ1 optimization. Computer simulations are also carried out for verification

Cryptanalysis of optical ciphers integrating double random phase encoding with permutation

This paper presents the cryptanalysis of optical ciphers combining double random phase encoding with permutation techniques, and shows its vulnerability against plaintext attack regardless of the implementation order of the two procedures. The equivalent secret keys of both the combination fashions can be retrieved, instead of the recovery of random phase masks and permutation matrix. Numerical simulations are also given for validation

Exploring causal relationships between inflammatory cytokines and allergic rhinitis, chronic rhinosinusitis, and nasal polyps: a Mendelian randomization study

ObjectivesPrevious research has suggested connections between specific inflammatory cytokines and nasal conditions, including Allergic Rhinitis (AR), Chronic Rhinosinusitis (CRS), and Nasal Polyps (NP). However, a lack of robust research establishing the causal underpinnings of them. This Mendelian Randomization (MR) study aims to evaluate the causal relationships between 41 inflammatory cytokines and the incidence of AR, CRS and NP.MethodsThis study employed a two-sample MR design, harnessing genetic variations derived from publicly accessible genome-wide association studies (GWAS) datasets. AR data was sourced from a GWAS with 25,486 cases and 87,097 controls (identifier: ukb-b-7178). CRS data originated from a GWAS encompassing 1,179 cases and 360,015 controls (identifier: ukb-d-J32). NP data was extracted from a GWAS involving 1,637 cases and 335,562 controls (identifier: ukb-a-541). The data for 41 inflammatory cytokines were obtained from an independent GWAS encompassing 8,293 participants. Inverse variance weighted (IVW), MR Egger regression and Weighted median were used to evaluate the causalities of exposures and outcomes. A range of sensitivity analyses were implemented to assess the robustness of the results.ResultsThe results revealed significant associations between elevated circulating levels of MIP-1α (odds ratio, OR: 1.01798, 95% confidence interval, CI: 1.00217–1.03404, p = 0.02570) and TNF-α (OR: 1.01478, 95% CI: 1.00225–1.02746, p = 0.02067) with an augmented risk of AR in the IVW approach. Heightened levels of circulating IL-2 exhibited a positive correlation with an increased susceptibility to NP in the IVW approach (OR: 1.00129, 95% CI: 1.00017–1.00242, p = 0.02434), whereas elevated levels of circulating PDGF-BB demonstrated a decreased risk of NP (OR: 0.99920, 95% CI: 0.99841–0.99999, p = 0.047610). The MR analysis between levels of 41 inflammatory cytokines and the incidence of CRS yielded no positive outcomes.ConclusionThis investigation proposes a potential causal association between elevated levels of MIP-1α and TNF-α with an elevated risk of AR, as well as an increased risk of NP linked to elevated IL-2 levels. Furthermore, there appears to be a potential association between increased levels of circulating PDGF-BB and a reduced risk of NP