Practical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling

This paper addresses the prediction stability, prediction accuracy and control capability of the current probabilistic model-based reinforcement learning (MBRL) built on neural networks. A novel approach dropout-based probabilistic ensembles with trajectory sampling (DPETS) is proposed where the system uncertainty is stably predicted by combining the Monte-Carlo dropout and trajectory sampling in one framework. Its loss function is designed to correct the fitting error of neural networks for more accurate prediction of probabilistic models. The state propagation in its policy is extended to filter the aleatoric uncertainty for superior control capability. Evaluated by several Mujoco benchmark control tasks under additional disturbances and one practical robot arm manipulation task, DPETS outperforms related MBRL approaches in both average return and convergence velocity while achieving superior performance than well-known model-free baselines with significant sample efficiency. The open source code of DPETS is available at https://github.com/mrjun123/DPETS

A Forward-Secure Certificate-based Signature Scheme

Cryptographic computations are often carried out on insecure devices for which the threat of key exposure raises a serious concern. In an effort to address the key exposure problem, the notion of forward security was first presented by Günther in 1990. In a forward-secure scheme, secret keys are updated at regular periods of time; exposure of the secret key corresponding to a given time period does not enable an adversary to ‘break’ the scheme for any prior time period. In this paper, we first introduce forward security into certificate-based cryptography and define the security model of forward-secure certificate-based signatures (CBSs). Then we propose a forward-secure CBS scheme, which is shown to be secure against adaptive chosen message attacks under the computational Diffie–Hellman assumption in the random oracle model. Our result can be viewed as the first step toward solving the key exposure problem in CBSs and thus improving the security of the whole system

High operating temperature mid-infrared InGaAs/GaAs submonolayer quantum dot quantum cascade detectors on silicon

Monolithic integration of infrared photodetectors on a silicon platform is a promising solution for the development of scalable and affordable photodetectors and infrared focal plane arrays. We report on integration of submonolayer quantum dot quantum cascade detectors (SML QD QCDs) on Si substrates via direct growth. Threading dislocation density has been reduced to the level of ~10 7 cm -2 with the high-quality GaAs-on-Si virtual substrate. We also conducted a morphology analysis for the SML QD QCDs through a transmission electron microscope strain contrast image and to the best of our knowledge, high quality InGaAs/GaAs SML QDs were clearly observed on silicon for the first time. Photoluminescence decay time of the as-grown SML QD QCDs on Si was measured to be around 300 ps, which is comparable to the reference QCDs on lattice-matched GaAs substrates. With the high-quality III-V epitaxial layers and SML QDs, the quantum cascade detectors on Si achieved a normal incident photoresponse temperature up to 160 K under zero bias

Extensive analysis of D7S486 in primary gastric cancer supports TESTIN as a candidate tumor suppressor gene

<p>Abstract</p> <p>Background</p> <p>High frequency of loss of heterozygosity (LOH) was found at D7S486 in primary gastric cancer (GC). And we found a high frequency of LOH region on 7q31 in primary GC from China, and identified D7S486 to be the most frequent LOH locus. This study was aimed to determine what genes were affected by the LOH and served as tumor suppressor genes (TSGs) in this region. Here, a high-throughput single nucleotide polymorphisms (SNPs) microarray fabricated in-house was used to analyze the LOH status around D7S486 on 7q31 in 75 patients with primary GC. Western blot, immunohistochemistry, and RT-PCR were used to assess the protein and mRNA expression of TESTIN (TES) in 50 and 140 primary GC samples, respectively. MTS assay was used to investigate the effect of TES overexpression on the proliferation of GC cell lines. Mutation and methylation analysis were performed to explore possible mechanisms of TES inactivation in GC.</p> <p>Results</p> <p>LOH analysis discovered five candidate genes (<it>ST7</it>, <it>FOXP2</it>, <it>MDFIC</it>, <it>TES </it>and <it>CAV1</it>) whose frequencies of LOH were higher than 30%. However, only <it>TES </it>showed the potential to be a TSG associated with GC. Among 140 pairs of GC samples, decreased <it>TES </it>mRNA level was found in 96 (68.6%) tumor tissues when compared with matched non-tumor tissues (<it>p </it>< 0.001). Also, reduced TES protein level was detected in 36 (72.0%) of all 50 tumor tissues by Western blot (<it>p </it>= 0.001). In addition, immunohistochemical staining result was in agreement with that of RT-PCR and Western blot. Down regulation of TES was shown to be correlated with tumor differentiation (<it>p </it>= 0.035) and prognosis (<it>p </it>= 0.035, log-rank test). Its overexpression inhibited the growth of three GC cell lines. Hypermethylation of <it>TES </it>promoter was a frequent event in primary GC and GC cell lines. However, no specific gene mutation was observed in the coding region of the <it>TES </it>gene.</p> <p>Conclusions</p> <p>Collectively, all results support the role of <it>TES </it>as a TSG in gastric carcinogenesis and that <it>TES </it>is inactivated primarily by LOH and CpG island methylation.</p

Collaborative collection effort strategies based on “Internet + recycling” business model

"Internet + recycling", a new and emerging collecting mode, is booming in conjunction with widespread Internet use in China. For the recycling of waste electrical and electronic equipment (WEEE), this paper studies collaborative collection effort strategies in a collection system consisting of a third-party and an e-tailer based on the "Internet + recycling" business model. Considering the collaboration occurring during collecting and selling and mutual influences of partners on the recycling of old products, the paper applies collection effort cost sharing mechanisms to promote recycling. Four models, namely, the centralized model (C-Model), unit transfer price model (P-Model), unilateral cost sharing model (U-Model) and bilateral cost sharing model (B-Model), are established, and optimal decisions and members' profits in various collaborative models are derived and compared. The results show that there exists an interval of profit sharing proportions in which each of the two cost sharing models is a Pareto improvement of the P-Model, and the total collection volume and profit of the collecting system increase in the B-Model relative to those in the U-Model under the same proportion of profit sharing. However, the B-Model is not necessarily a Pareto improvement of the U-Model. The results also show that profit improvements of both parties can be achieved without the third-party sharing the e-tailer's collection effort cost in the B-Model when the collaborative marginal profit is large enough. The paper further explores the impact of the collaborative marginal profit and third-party's market influence on the total collection volume and the efficiency of the collecting system. This study provides insight into the promotion of WEEE recycling and into the selection of collaborative strategies for Internet recycling enterprises. The work will prove beneficial to the development of the WEEE "Internet + recycling" industry

Research on Speech Emotion Recognition Method Based A-CapsNet

Speech emotion recognition is a crucial work direction in speech recognition. To increase the performance of speech emotion detection, researchers have worked relentlessly to improve data augmentation, feature extraction, and pattern formation. To address the concerns of limited speech data resources and model training overfitting, A-CapsNet, a neural network model based on data augmentation methodologies, is proposed in this research. In order to solve the issue of data scarcity and achieve the goal of data augmentation, the noise from the Noisex-92 database is first combined with four different data division methods (emotion-independent random-division, emotion-dependent random-division, emotion-independent cross-validation and emotion-dependent cross-validation methods, abbreviated as EIRD, EDRD, EICV and EDCV, respectively). The database EMODB is then used to analyze and compare the performance of the model proposed in this paper under different signal-to-noise ratios, and the results show that the proposed model and data augmentation are effective

Performance Modelling of V2V based Collective Perceptions in Connected and Autonomous Vehicles

With the introduction of Connected and Autonomous Vehicles (CAVs), it is possible to extend the limited horizon of vehicles on the road by collective perceptions, where vehicles periodically share their sensory information with others using Vehide-2-Vehicle (V2V) communications. This technique relies on a certain number of participants to have a measurable advantage. Nevertheless, the spread of CAVs will take a considerable period of time, it is critical to understand the benefits and limits of V2V based collective perceptions in different market stages. In this work, we characterise the effective Field of View (eFoV) of a vehicle as the perception range using local sensors only, and the collective Field of View (cFoV) as the region learn from the network. Applying analytic and simulation studies in highway scenarios, we show that the eFoV drops quickly with the increase in traffic density due to blockage effects of surrounding vehicles, and it is insufficient to overcome this problem by increasing the sensing range of local sensors. On the other hand, vehicles can gain around 16 folds more information about the road environment by leveraging collective perceptions with only 10\% CAV penetration rate. When the penetration rate reaches to around 30\%, collective perceptions can provide 95\% coverage over the road environments. Our analyses also show that apart from the benefits, employing collective perceptions could result in heavy broadcast redundancy, hence wasting the already scarce network resources. This observation suggests that the sharing of sensory information should be controlled appropriately to avoid overloading the communication networks

Do the purpose and directional income impact of earnings management affect the ethical judgement and behavioural intentions of accounting students and auditors?

This paper investigates whether earnings management purpose and its directional income impact affect accounting student's and auditors' ethical judgement and behavioural intentions