We exhibit examples of actions of countable discrete groups on both simple and non-simple nuclear stably finite C*-algebras that are tracially amenable but not amenable. We furthermore obtain that, under the additiona...
We exhibit examples of actions of countable discrete groups on both simple and non-simple nuclear stably finite C*-algebras that are tracially amenable but not amenable. We furthermore obtain that, under the additional assumption of strict comparison, amenability is equivalent to tracial amenability plus the equivariant analogue of Matui-Sato's property (SI). By virtue of this equivalence, our construction yields the first known examples of actions on classifiable C*-algebras that do not have equivariant a over show that such actions can be chosen to absorb the trivial action on the universal UHF algebra, thus proving that equivariant Z$\mathcal {Z}$-stability does not in general imply equivariant property (SI).
Sequential recommendation models are crucial for next-item prediction tasks in various online platforms, yet many focus on a single behavior, neglecting valuable implicit interactions. While multi-behavioral models ad...
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ISBN:
(纸本)9798400705052
Sequential recommendation models are crucial for next-item prediction tasks in various online platforms, yet many focus on a single behavior, neglecting valuable implicit interactions. While multi-behavioral models address this using graph-based approaches, they often fail to capture sequential patterns simultaneously. Our proposed Multi-Behavioral Sequential Recommendation framework (MBSRec) captures the multi-behavior dependencies between the heterogeneous historical interactions via multi-head self-attention. Furthermore, we utilize a weighted binary cross-entropy loss for precise behavior control. Experimental results on four datasets demonstrate MBSRec's significant outperformance of state-of-the-art approaches. The implementation code is available here (1).
Research has demonstrated that machinelearning algorithms (MLAs) are a powerful addition to the rock engineering toolbox, and yet they remain a largely untapped resource in engineering practice. The reluctance to ado...
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Event cameras offer the exciting possibility of tracking the camera's pose during high-speed motion and in adverse lighting conditions. Despite this promise, existing event-based monocular visual odometry (VO) app...
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ISBN:
(纸本)9798350362466;9798350362459
Event cameras offer the exciting possibility of tracking the camera's pose during high-speed motion and in adverse lighting conditions. Despite this promise, existing event-based monocular visual odometry (VO) approaches demonstrate limited performance on recent benchmarks. To address this limitation, some methods resort to additional sensors such as IMUs, stereo event cameras, or frame-based cameras. Nonetheless, these additional sensors limit the application of event cameras in real-world devices since they increase cost and complicate system requirements. Moreover, relying on a frame-based camera makes the system susceptible to motion blur and HDR. To remove the dependency on additional sensors and to push the limits of using only a single event camera, we present Deep Event VO (DEVO), the first monocular event-only system with strong performance on a large number of real-world benchmarks. DEVO sparsely tracks selected event patches over time. A key component of DEVO is a novel deep patch selection mechanism tailored to event data. We significantly decrease the state-of-the-art pose tracking error on seven real-world benchmarks by up to 97% compared to event-only methods and often surpass or are close to stereo or inertial methods.
Mitochondrial toxicityis a significant concern in the drug discoveryprocess, as compounds that disrupt the function of these organellescan lead to serious side effects, including liver injury and *** in vitro assays e...
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Mitochondrial toxicityis a significant concern in the drug discoveryprocess, as compounds that disrupt the function of these organellescan lead to serious side effects, including liver injury and *** in vitro assays exist to detect mitochondrial toxicity atvarying mechanistic levels: disruption of the respiratory chain, disruptionof the membrane potential, or general mitochondrial dysfunction. Inparallel, whole cell imaging assays like Cell Painting provide a phenotypicoverview of the cellular system upon treatment and enable the assessmentof mitochondrial health from cell profiling features. In this study,we aim to establish machinelearning models for the prediction ofmitochondrial toxicity, making the best use of the available *** this purpose, we first derived highly curated datasets of mitochondrialtoxicity, including subsets for different mechanisms of action. Dueto the limited amount of labeled data often associated with toxicologicalendpoints, we investigated the potential of using morphological featuresfrom a large Cell Painting screen to label additional compounds andenrich our dataset. Our results suggest that models incorporatingmorphological profiles perform better in predicting mitochondrialtoxicity than those trained on chemical structures alone (up to +0.08and +0.09 mean MCC in random and cluster cross-validation, respectively).Toxicity labels derived from Cell Painting images improved the predictionson an external test set up to +0.08 MCC. However, we also found thatfurther research is needed to improve the reliability of Cell Paintingimage labeling. Overall, our study provides insights into the importanceof considering different mechanisms of action when predicting a complexendpoint like mitochondrial disruption as well as into the challengesand opportunities of using Cell Painting data for toxicity prediction.
Searching is the process of information retrieval utilizing specific criteria or keywords. Integrating search functionalities on e-commerce platforms enables users to efficiently locate exactly what they are searching...
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ISBN:
(纸本)9798350372977;9798350372984
Searching is the process of information retrieval utilizing specific criteria or keywords. Integrating search functionalities on e-commerce platforms enables users to efficiently locate exactly what they are searching for through keyword matching. Beyond conventional keyword matching, semantic search involves aligning products with customer queries by capturing the essence of the queries, thereby retrieving semantically related products from the pertinent catalog. Semantic search enhances the e-commerce shopping experience by allowing platforms to tailor responses to user preferences through an in-depth understanding of search intents. Challenges such as morphological variations, spelling errors, and the interpretation of synonyms, antonyms, and hypernyms are addressed through deep learning models designed for semantic query-product matching. This study conducts a comparative analysis of various semantic search methodologies and assesses their efficacy, incorporating deep learning strategies for query auto-completion and spelling corrections. The evaluation employs sentence transformer models to determine the optimal approach for semantic searching, gauged by nDCG, MRR, and MAP metrics. LSTM, BART, and n-gram models are also examined for auto-completion capabilities. The research analyzes the Amazon Shopping Queries dataset and the Upstart Commerce catalog datasets.
作者：
Tale, AbhayBarhate, AdityaVerma, PrateekGourshettiwar, Palash
Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India
Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India
Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India
Breast cancer is still a global health concern, chiefly affecting women, as it is one of the major causes of cancer mortality. For the therapy to be effective, early diagnosis of cancer is critical to raising the surv...
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Individuals with Cerebral Palsy (CP) are impacted lifetime barriers in their everyday activities, especially in writing phrase, which results from innate neural motor in co-ordination. Numerous studies have focus...
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In this note, we describe an experiment on portfolio optimization using the Quadratic Unconstrained Binary Optimization (QUBO) formulation. The dataset we use is taken from a real-world problem for which a classical s...
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In this note, we describe an experiment on portfolio optimization using the Quadratic Unconstrained Binary Optimization (QUBO) formulation. The dataset we use is taken from a real-world problem for which a classical solution is currently deployed and used in production. In this work, carried out in a collaboration between the Raiffeisen Bank International (RBI) and Reply, we derive a QUBO formulation, which we solve using various methods: two D-Wave hybrid solvers, that combine the employment of a quantum annealer together with classical methods, and a purely classical algorithm. Particular focus is given to the implementation of the constraint that requires the resulting portfolio's variance to be below a specified threshold, whose representation in an Ising model is not straightforward. We find satisfactory results, consistent with the global optimum obtained by the exact classical strategy. However, since the tuning of QUBO parameters is crucial for the optimization, we investigate a hybrid method that allows for automatic tuning.
Stitch Fix, an online personal shopping and styling service, creates a personalized shopping experience to meet any purchase occasion across multiple platforms. For example, a client who wants more one-on-one support ...
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ISBN:
(纸本)9781450392785
Stitch Fix, an online personal shopping and styling service, creates a personalized shopping experience to meet any purchase occasion across multiple platforms. For example, a client who wants more one-on-one support in shopping for an outfit or look can request a stylist to curate a 'Fix', an assortment of 5 items;or they can browse their own personalized shop and make direct purchases in our 'Freestyle' experience. We know that personal style changes and evolves over time, so in order to provide the client with the most personalized and dynamic experience across platforms, it is important to recommend items based on our holistic and real-time understanding of their style across all of our platforms. This work introduces the Client Time Series Model (CTSM), a scalable and efficient recommender system based on Temporally-Masked Encoders (TME) that learns one client embedding across all platforms, yet is able to provide distinctive recommendations depending on the platform. An A/B test showed that our model outperformed the baseline model by 5.8% in terms of expected revenue.
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