MojiTalk: Generating Emotional Responses at Scale

Generating emotional language is a key step towards building empathetic natural language processing agents. However, a major challenge for this line of research is the lack of large-scale labeled training data, and previous studies are limited to only small sets of human annotated sentiment labels. Additionally, explicitly controlling the emotion and sentiment of generated text is also difficult. In this paper, we take a more radical approach: we exploit the idea of leveraging Twitter data that are naturally labeled with emojis. More specifically, we collect a large corpus of Twitter conversations that include emojis in the response, and assume the emojis convey the underlying emotions of the sentence. We then introduce a reinforced conditional variational encoder approach to train a deep generative model on these conversations, which allows us to use emojis to control the emotion of the generated text. Experimentally, we show in our quantitative and qualitative analyses that the proposed models can successfully generate high-quality abstractive conversation responses in accordance with designated emotions

Causal link between gut microbiota and four types of pancreatitis: a genetic association and bidirectional Mendelian randomization study

BackgroundA number of recent observational studies have indicated a correlation between the constitution of gut microbiota and the incidence of pancreatitis. Notwithstanding, observational studies are unreliable for inferring causality because of their susceptibility to confounding, bias, and reverse causality, the causal relationship between specific gut microbiota and pancreatitis is still unclear. Therefore, our study aimed to investigate the causal relationship between gut microbiota and four types of pancreatitis.MethodsAn investigative undertaking encompassing a genome-wide association study (GWAS) comprising 18,340 participants was undertaken with the aim of discerning genetic instrumental variables that exhibit associations with gut microbiota, The aggregated statistical data pertaining to acute pancreatitis (AP), alcohol-induced AP (AAP), chronic pancreatitis (CP), and alcohol-induced CP (ACP) were acquired from the FinnGen Consortium. The two-sample bidirectional Mendelian randomization (MR) approach was utilized. Utilizing the Inverse-Variance Weighted (IVW) technique as the cornerstone of our primary analysis. The Bonferroni analysis was used to correct for multiple testing, In addition, a number of sensitivity analysis methodologies, comprising the MR-Egger intercept test, the Cochran’s Q test, MR polymorphism residual and outlier (MR-PRESSO) test, and the leave-one-out test, were performed to evaluate the robustness of our findings.ResultsA total of 28 intestinal microflora were ascertained to exhibit significant associations with diverse outcomes of pancreatitis. Among them, Class Melainabacteria (OR = 1.801, 95% CI: 1.288–2.519, p = 0.008) has a strong causality with ACP after the Bonferroni-corrected test, in order to assess potential reverse causation effects, we used four types of pancreatitis as the exposure variable and scrutinized its impact on gut microbiota as the outcome variable, this analysis revealed associations between pancreatitis and 30 distinct types of gut microflora. The implementation of Cochran’s Q test revealed a lack of substantial heterogeneity among the various single nucleotide polymorphisms (SNP).ConclusionOur first systematic Mendelian randomization analysis provides evidence that multiple gut microbiota taxa may be causally associated with four types of pancreatitis disease. This discovery may contribute significant biomarkers conducive to the preliminary, non-invasive identification of Pancreatitis. Additionally, it could present viable targets for potential therapeutic interventions in the disease’s treatment

Hot Carrier Injection Effects in the Ultrashallow Body SONOS Gate Power MOSFET

In this paper, threshold voltage (VTH) stability of the ultrashallow body silicon-oxide-nitride-oxide-silicon gate power MOSFET (SG-MOSFET) under hot carrier injection conditions is characterized and discussed. Experimental results indicate that hot electron injection will increase the VTH from 1 to 2 V in the lifetime of the device. On the other hand, hot hole injection has no significant influence on the VTH stability of the device. The different effects caused by hot electron injection and hot hole injection are explained by using numerical analysis and experimental characterization, and results suggest that the VTH stability of the ultrashallow body SG-MOSFET will be improved if the short channel effect of the structure can be suppressed

Planar SONOS Gate Power MOSFET with an Ultra-Shallow Body Region

In this paper, a planar silicon-oxide-nitride-oxide-silicon (SONOS) gate power MOSFET (SG-MOSFET) with a 0.3 mu m ultra-shallow heavily doped p-body region is presented. The ultra-shallow body provides a much reduced parasitic JFET resistance, resulting in a low specific on-resistance of 18 m Omega.mm(2) for a planar device. At the same time, no punch-through problem is caused by the ultra-shallow body, and the avalanche breakdown voltage of the device is 29.5 V. The product of the on-resistance and gate charge of the ultra-shallow body SG-MOSFET is 43 m Omega.nC at V-GS = 4.5 V. The non-optimized performance obtained for this structure is comparable to that of trench power MOSFETs fabricated using more advanced technologies

A Novel SONOS Gate Power MOSFET With Excellent UIS Capability

A novel silicon-oxide-nitride-oxide-silicon gate power MOSFET is proposed and experimentally demonstrated. In the novel device, the doping concentration of the p-body is increased by an order of magnitude compared to that of the conventional power MOSFET. However, the positive shift of the threshold voltage due to the heavily doped p-body is fully compensated by the positive fixed charges preprogrammed in the silicon nitride of the oxide-nitride-oxide gate dielectric. As a result, a normal threshold voltage can be obtained, and the avalanche energy absorption of the novel device at unclamped inductive switching is 5.2 times that of the conventional power MOSFET

Micro-Doppler Feature Extraction of Inverse Synthetic Aperture Imaging Laser Radar Using Singular-Spectrum Analysis

Different from microwave radar, laser radar could be more sensitive to the micro-Doppler (m-D) effect due to its wave length. This limits the application of conventional methods, such as time–frequency based approach, since the processing needs a receiver with much higher sampling frequency than microwave radar. In this paper, a micro-Doppler feature extraction algorithm is proposed for the inverse synthetic aperture imaging laser radar (ISAIL). Singular-spectrum analysis (SSA) is employed for separation and reconstruction of the micro-Doppler and rigid body signal. Clear ISAIL image is obtained by minimum entropy criteria after echo signal decomposition. After theoretical derivation, the computation efficiency and ability of the proposed method is proved by the results of simulation and real data of An-26

C-RISE: A Post-Hoc Interpretation Method of Black-Box Models for SAR ATR

The integration of deep learning methods, especially Convolutional Neural Networks (CNN), and Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has been widely deployed in the field of radar signal processing. Nevertheless, these methods are frequently regarded as black-box models due to the limited visual interpretation of their internal feature representation and parameter organization. In this paper, we propose an innovative approach named C-RISE, which builds upon the RISE algorithm to provide a post-hoc interpretation technique for black-box models used in SAR Images Target Recognition. C-RISE generates saliency maps that effectively visualize the significance of each pixel. Our algorithm outperforms RISE by clustering masks that capture similar fusion features into distinct groups, enabling more appropriate weight distribution and increased focus on the target area. Furthermore, we employ Gaussian blur to process the masked area, preserving the original image structure with optimal consistency and integrity. C-RISE has been extensively evaluated through experiments, and the results demonstrate superior performance over other interpretation methods based on perturbation when applied to neural networks for SAR image target recognition. Furthermore, our approach is highly robust and transferable compared to other interpretable algorithms, including white-box methods

Subthreshold Operation of Photodiode-Gated Transistors Enabling High-Gain Optical Sensing and Imaging Applications

In optical sensors and imagers, high gain that leads to high sensitivity and high signal to noise ratio (SNR) is often desirable. One popular approach is avalanche photomultiplication initiated by impact ionization in an avalanche photodiode or similar devices and the other approach is active pixel sensor (APS) with in-pixel amplifier. However, the former requires high electric field which induces high shot noise and the latter needs a multiple-transistor pixel circuit which compromises the fill factor and consequently, reduces the SNR. This work proposes and summarizes our recent efforts taken to achieve high gain optical sensors through subthreshold operation of photodiode-gated transistors