A Strong Baseline for Fashion Retrieval with Person Re-Identification Models

Fashion retrieval is the challenging task of finding an exact match for fashion items contained within an image. Difficulties arise from the fine-grained nature of clothing items, very large intra-class and inter-class variance. Additionally, query and source images for the task usually come from different domains - street photos and catalogue photos respectively. Due to these differences, a significant gap in quality, lighting, contrast, background clutter and item presentation exists between domains. As a result, fashion retrieval is an active field of research both in academia and the industry. Inspired by recent advancements in Person Re-Identification research, we adapt leading ReID models to be used in fashion retrieval tasks. We introduce a simple baseline model for fashion retrieval, significantly outperforming previous state-of-the-art results despite a much simpler architecture. We conduct in-depth experiments on Street2Shop and DeepFashion datasets and validate our results. Finally, we propose a cross-domain (cross-dataset) evaluation method to test the robustness of fashion retrieval models.Comment: 33 pages, 14 figure

Multi-modal Embedding Fusion-based Recommender

Recommendation systems have lately been popularized globally, with primary use cases in online interaction systems, with significant focus on e-commerce platforms. We have developed a machine learning-based recommendation platform, which can be easily applied to almost any items and/or actions domain. Contrary to existing recommendation systems, our platform supports multiple types of interaction data with multiple modalities of metadata natively. This is achieved through multi-modal fusion of various data representations. We deployed the platform into multiple e-commerce stores of different kinds, e.g. food and beverages, shoes, fashion items, telecom operators. Here, we present our system, its flexibility and performance. We also show benchmark results on open datasets, that significantly outperform state-of-the-art prior work.Comment: 7 pages, 8 figure

Table_2_A deep learning approach for detecting liver cirrhosis from volatolomic analysis of exhaled breath.docx

Liver disease such as cirrhosis is known to cause changes in the composition of volatile organic compounds (VOC) present in patient breath samples. Previous studies have demonstrated the diagnosis of liver cirrhosis from these breath samples, but studies are limited to a handful of discrete, well-characterized compounds. We utilized VOC profiles from breath samples from 46 individuals, 35 with cirrhosis and 11 healthy controls. A deep-neural network was optimized to discriminate between healthy controls and individuals with cirrhosis. A 1D convolutional neural network (CNN) was accurate in predicting which patients had cirrhosis with an AUC of 0.90 (95% CI: 0.75, 0.99). Shapley Additive Explanations characterized the presence of discrete, observable peaks which were implicated in prediction, and the top peaks (based on the average SHAP profiles on the test dataset) were noted. CNNs demonstrate the ability to predict the presence of cirrhosis based on a full volatolomics profile of patient breath samples. SHAP values indicate the presence of discrete, detectable peaks in the VOC signal.</p

Table_1_A deep learning approach for detecting liver cirrhosis from volatolomic analysis of exhaled breath.docx

Liver disease such as cirrhosis is known to cause changes in the composition of volatile organic compounds (VOC) present in patient breath samples. Previous studies have demonstrated the diagnosis of liver cirrhosis from these breath samples, but studies are limited to a handful of discrete, well-characterized compounds. We utilized VOC profiles from breath samples from 46 individuals, 35 with cirrhosis and 11 healthy controls. A deep-neural network was optimized to discriminate between healthy controls and individuals with cirrhosis. A 1D convolutional neural network (CNN) was accurate in predicting which patients had cirrhosis with an AUC of 0.90 (95% CI: 0.75, 0.99). Shapley Additive Explanations characterized the presence of discrete, observable peaks which were implicated in prediction, and the top peaks (based on the average SHAP profiles on the test dataset) were noted. CNNs demonstrate the ability to predict the presence of cirrhosis based on a full volatolomics profile of patient breath samples. SHAP values indicate the presence of discrete, detectable peaks in the VOC signal.</p

Image_1_A deep learning approach for detecting liver cirrhosis from volatolomic analysis of exhaled breath.jpg

Liver disease such as cirrhosis is known to cause changes in the composition of volatile organic compounds (VOC) present in patient breath samples. Previous studies have demonstrated the diagnosis of liver cirrhosis from these breath samples, but studies are limited to a handful of discrete, well-characterized compounds. We utilized VOC profiles from breath samples from 46 individuals, 35 with cirrhosis and 11 healthy controls. A deep-neural network was optimized to discriminate between healthy controls and individuals with cirrhosis. A 1D convolutional neural network (CNN) was accurate in predicting which patients had cirrhosis with an AUC of 0.90 (95% CI: 0.75, 0.99). Shapley Additive Explanations characterized the presence of discrete, observable peaks which were implicated in prediction, and the top peaks (based on the average SHAP profiles on the test dataset) were noted. CNNs demonstrate the ability to predict the presence of cirrhosis based on a full volatolomics profile of patient breath samples. SHAP values indicate the presence of discrete, detectable peaks in the VOC signal.</p

Table_3_A deep learning approach for detecting liver cirrhosis from volatolomic analysis of exhaled breath.docx

Liver disease such as cirrhosis is known to cause changes in the composition of volatile organic compounds (VOC) present in patient breath samples. Previous studies have demonstrated the diagnosis of liver cirrhosis from these breath samples, but studies are limited to a handful of discrete, well-characterized compounds. We utilized VOC profiles from breath samples from 46 individuals, 35 with cirrhosis and 11 healthy controls. A deep-neural network was optimized to discriminate between healthy controls and individuals with cirrhosis. A 1D convolutional neural network (CNN) was accurate in predicting which patients had cirrhosis with an AUC of 0.90 (95% CI: 0.75, 0.99). Shapley Additive Explanations characterized the presence of discrete, observable peaks which were implicated in prediction, and the top peaks (based on the average SHAP profiles on the test dataset) were noted. CNNs demonstrate the ability to predict the presence of cirrhosis based on a full volatolomics profile of patient breath samples. SHAP values indicate the presence of discrete, detectable peaks in the VOC signal.</p

Synthesis and spin-crossover characteristics of polynuclear 4-(2´-hydroxy-ethyl)-1,2,4-triazole Fe(II) molecular materials

A new series of Fe(II) spin-crossover materials of formula [Fe(hyetrz)3](Anion)2•xH2O, where hyetrz = 4-(2´-hydroxy-ethyl)-1,2,4-triazole and Anion = Cl–, NO3–, Br–, I–, BF4–, ClO4–, PF6–, have been prepared and the spin transition characteristics studied. The structure of these compounds consists of linear chains in which the Fe(II) ions are linked by triple N1,N2-1,2,4-triazole bridges. Most of the hydrated compounds show non-classical spin-crossover behaviour associated with the removal of lattice water molecules, which initially stabilize the low-spin state. However for two of them, the perchlorate and the iodide compounds, the transition temperature is shifted to higher temperatures by dehydration. For the corresponding dehydrated compounds, the transition temperature T1/2 increases with decreasing anion radii. [Fe(hyetrz)3]I2 represents one of the few Fe(II) spin-crossover materials showing a spin transition in the close vicinity of room temperature (291 K) accompanied by a thermal hysteresis (12 K).

A PDE10A inhibitor CPL500036 is a novel agent modulating striatal function devoid of most neuroleptic side-effects

Background: Phosphodiesterase 10A (PDE10A) is expressed almost exclusively in the striatum and its inhibition is suggested to offer potential treatment in disorders associated with basal ganglia. We evaluated the selectivity, cytotoxicity, genotoxicity, pharmacokinetics and potential adverse effects of a novel PDE10A inhibitor, CPL500036, in vivo. Methods: The potency of CPL500036 was demonstrated by microfluidic technology, and selectivity was investigated in a radioligand binding assay against 44 targets. Cardiotoxicity in vitro was evaluated in human ether-a-go-go related gene (hERG)-potassium channel-overexpressing cells by the patch-clamp method and by assessing key parameters in 3D cardiac spheroids. Cytotoxicity was determined in H1299, HepG2 and SH-SY5Y cell lines. The Ames test was used for genotoxicity analyses. During in vivo studies, CPL500036 was administered by oral gavage. CPL500036 exposure were determined by liquid chromatography–tandem mass spectrometry and plasma protein binding was assessed. The bar test was employed to assess catalepsy. Prolactin and glucose levels in rat blood were measured by ELISAs and glucometers, respectively. Cardiovascular safety in vivo was investigated in dogs using a telemetry method. Results: CPL500036 inhibited PDE10A at an IC(50) of 1 nM, and interacted only with the muscarinic M2 receptor as a negative allosteric modulator with an IC(50) of 9.2 µM. Despite inhibiting hERG tail current at an IC(25) of 3.2 μM, cardiovascular adverse effects were not observed in human cardiac 3D spheroids or in vivo. Cytotoxicity in vitro was observed only at > 60 μM and genotoxicity was not recorded during the Ames test. CPL500036 presented good bioavailability and penetration into the brain. CPL500036 elicited catalepsy at 0.6 mg/kg, but hyperprolactinemia or hyperglycemic effects were not observed in doses up to 3 mg/kg. Conclusion: CPL500036 is a potent, selective and orally bioavailable PDE10A inhibitor with a good safety profile distinct from marketed antipsychotics. CPL500036 may be a compelling drug candidate