Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Computer-generated graphics (CGs) are images generated by computer software. The~rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN) and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN)-based model, these images---CGs and NIs---are clipped into image patches. Furthermore, three high-pass filters (HPFs) are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The~experiments have demonstrated that (1) the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2) the proposed method with three HPFs achieves 100\% accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.Comment: This paper has been published by Sensors. doi:10.3390/s18041296; Sensors 2018, 18(4), 129

Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer

With the increasing research interest in dialogue response generation, there is an emerging branch formulating this task as selecting next sentences, where given the partial dialogue contexts, the goal is to determine the most probable next sentence. Following the recent success of the Transformer model, this paper proposes (1) a new variant of attention mechanism based on multi-head attention, called highway attention, and (2) a recurrent model based on transformer and the proposed highway attention, so-called Highway Recurrent Transformer. Experiments on the response selection task in the seventh Dialog System Technology Challenge (DSTC7) show the capability of the proposed model of modeling both utterance-level and dialogue-level information; the effectiveness of each module is further analyzed as well

The impact of high-risk medications on mortality risk among older adults with polypharmacy: evidence from the English Longitudinal Study of Ageing

BACKGROUND: Polypharmacy is common among older people and is associated with an increased mortality risk. However, little is known about whether the mortality risk is related to specific medications among older adults with polypharmacy. This study therefore aimed to investigate associations between high-risk medications and all-cause and cause-specific mortality among older adults with polypharmacy. METHODS: This study included 1356 older adults with polypharmacy (5+ long-term medications a day for conditions or symptoms) from Wave 6 (2012/2013) of the English Longitudinal Study of Ageing. First, using the agglomerative hierarchical clustering method, participants were grouped according to the use of 14 high-risk medication categories. Next, the relationship between the high-risk medication patterns and all-cause and cause-specific mortality (followed up to April 2018) was examined. All-cause mortality was assessed by Cox proportional hazards model and competing-risk regression was employed for cause-specific mortality. RESULTS: Five high-risk medication patterns-a renin-angiotensin-aldosterone system (RAAS) inhibitors cluster, a mental health drugs cluster, a central nervous system (CNS) drugs cluster, a RAAS inhibitors and antithrombotics cluster, and an antithrombotics cluster-were identified. The mental health drugs cluster showed increased risks of all-cause (HR = 1.55, 95%CI = 1.05, 2.28) and cardiovascular disease (CVD) (SHR = 2.11, 95%CI = 1.10, 4.05) mortality compared with the CNS drug cluster over 6 years, while others showed no differences in mortality. Among these patterns, the mental health drugs cluster showed the highest prevalence of antidepressants (64.1%), benzodiazepines (10.4%), antipsychotics (2.4%), antimanic agents (0.7%), opioids (33.2%), and muscle relaxants (21.5%). The findings suggested that older adults with polypharmacy who took mental health drugs (primarily antidepressants), opioids, and muscle relaxants were at higher risk of all-cause and CVD mortality, compared with those who did not take these types of medications. CONCLUSIONS: This study supports the inclusion of opioids in the current guidance on structured medication reviews, but it also suggests that older adults with polypharmacy who take psychotropic medications and muscle relaxants are prone to adverse outcomes and therefore may need more attention. The reinforcement of structured medication reviews would contribute to early intervention in medication use which may consequently reduce medication-related problems and bring clinical benefits to older adults with polypharmacy

1,4-Bis(thio­phen-2-yl)butane-1,4-dione

In the centrosymmetric title compound, C12H10O2S2, the alkyl chains adopt a fully extended all-trans conformation with respect to the C(thio­phene)—C bond. The non-H atoms of the mol­ecule are nearly planar, with a maximum deviation of 0.063 (2) Å from the mean plane of the constituent atoms. In the crystal, symmetry-related mol­ecules are linked via pairs of C—H⋯π contacts [H–centroid distances of the thio­phene units = 2.79 (9) and 2.82 (4) Å], in turn inter­digitating with each other along the bc plane, thus leading to an inter­woven two-dimensional network

Difference in Thermotolerance Between Green and Red Color Variants of the Japanese Sea Cucumber, Apostichopus japonicus Selenka: Hsp70 and Heat-Hardening Effect

We studied thermal tolerance limits, heat-hardening, and Hsp70 to elucidate the difference in thermotolerance between two color variants of the sea Cucumber Apostichopus japonicus. Green and Red variants Occupy different habitats and have different aestivation responses to high temperature in summer. In the absence of heat-hardening the variants showed no difference in the temperature at which 50% of the individuals died: Green 31.49 degrees C; Red, 31.39 degrees C. However. Green specimens acquired higher thermotolerance than Red after a prior Sublethal heat exposure. After 72 h of recovery from a heat-hardening treatment (30 degrees C for 2 h) the survival of Green variants was more than 50% and that of Red wits less than 5% when they were treated at 33 degrees C for 2 h. Levels of mRNA and protein for Hsp70 were significantly higher in Green than Red after the heat shock of 30 degrees C, and the stability of hsp70 mRNA of Green was significantly higher than that of Red. Our findings suggest that within the same species, different variants that have similar thermal limits in the absence of heat-hardening can acquire different thermotolerances after a prior sublethal heat shock. The difference in induced thermotolerance between Green and Red is closely related to the expression pattern of Hsp70, which was partly governed by the stability of hsp70 mRNA