From Speech to Data: Unraveling Google's Use of Voice Data for User Profiling

Smart home voice assistants enable users to conveniently interact with IoT devices and perform Internet searches; however, they also collect the voice input that can carry sensitive personal information about users. Previous papers investigated how information inferred from the contents of users' voice commands are shared or leaked for tracking and advertising purposes. In this paper, we systematically evaluate how voice itself is used for user profiling in the Google ecosystem. To do so, we simulate various user personas by engaging with specific categories of websites. We then use \textit{neutral voice commands}, which we define as voice commands that neither reveal personal interests nor require Google smart speakers to use the search APIs, to interact with these speakers. We also explore the effects of the non-neutral voice commands for user profiling. Notably, we employ voices that typically would not match the predefined personas. We then iteratively improve our experiments based on observations of profile changes to better simulate real-world user interactions with smart speakers. We find that Google uses these voice recordings for user profiling, and in some cases, up to 5 out of the 8 categories reported by Google for customizing advertisements are altered following the collection of the voice commands.Comment: 11 pages, 1 figure, 7 table

E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation

The recent advancements in Large Language Models (LLMs) have sparked interest in harnessing their potential within recommender systems. Since LLMs are designed for natural language tasks, existing recommendation approaches have predominantly transformed recommendation tasks into open-domain natural language generation tasks. However, this approach necessitates items to possess rich semantic information, often generates out-of-range results, and suffers from notably low efficiency and limited extensibility. Furthermore, practical ID-based recommendation strategies, reliant on a huge number of unique identities (IDs) to represent users and items, have gained prominence in real-world recommender systems due to their effectiveness and efficiency. Nevertheless, the incapacity of LLMs to model IDs presents a formidable challenge when seeking to leverage LLMs for personalized recommendations. In this paper, we introduce an Elegant Effective Efficient Extensible solution for large language models for Sequential Recommendation (E4SRec), which seamlessly integrates LLMs with traditional recommender systems that exclusively utilize IDs to represent items. Specifically, E4SRec takes ID sequences as inputs, ensuring that the generated outputs fall within the candidate lists. Furthermore, E4SRec possesses the capability to generate the entire ranking list in a single forward process, and demands only a minimal set of pluggable parameters, which are trained for each dataset while keeping the entire LLM frozen. We substantiate the effectiveness, efficiency, and extensibility of our proposed E4SRec through comprehensive experiments conducted on four widely-used real-world datasets. The implementation code is accessible at https://github.com/HestiaSky/E4SRec/

Deceptive-NeRF: Enhancing NeRF Reconstruction using Pseudo-Observations from Diffusion Models

This paper introduces Deceptive-NeRF, a new method for enhancing the quality of reconstructed NeRF models using synthetically generated pseudo-observations, capable of handling sparse input and removing floater artifacts. Our proposed method involves three key steps: 1) reconstruct a coarse NeRF model from sparse inputs; 2) generate pseudo-observations based on the coarse model; 3) refine the NeRF model using pseudo-observations to produce a high-quality reconstruction. To generate photo-realistic pseudo-observations that faithfully preserve the identity of the reconstructed scene while remaining consistent with the sparse inputs, we develop a rectification latent diffusion model that generates images conditional on a coarse RGB image and depth map, which are derived from the coarse NeRF and latent text embedding from input images. Extensive experiments show that our method is effective and can generate perceptually high-quality NeRF even with very sparse inputs

Energy metabolism and maternal-fetal tolerance working in decidualization

One pivotal aspect of early pregnancy is decidualization. The decidualization process includes two components: the differentiation of endometrial stromal cells to decidual stromal cells (DSCs), as well as the recruitment and education of decidual immune cells (DICs). At the maternal-fetal interface, stromal cells undergo morphological and phenotypic changes and interact with trophoblasts and DICs to provide an appropriate decidual bed and tolerogenic immune environment to maintain the survival of the semi-allogeneic fetus without causing immunological rejection. Despite classic endocrine mechanism by 17 β-estradiol and progesterone, metabolic regulations do take part in this process according to recent studies. And based on our previous research in maternal-fetal crosstalk, in this review, we elaborate mechanisms of decidualization, with a special focus on DSC profiles from aspects of metabolism and maternal-fetal tolerance to provide some new insights into endometrial decidualization in early pregnancy

Revisiting Event-based Video Frame Interpolation

Dynamic vision sensors or event cameras provide rich complementary information for video frame interpolation. Existing state-of-the-art methods follow the paradigm of combining both synthesis-based and warping networks. However, few of those methods fully respect the intrinsic characteristics of events streams. Given that event cameras only encode intensity changes and polarity rather than color intensities, estimating optical flow from events is arguably more difficult than from RGB information. We therefore propose to incorporate RGB information in an event-guided optical flow refinement strategy. Moreover, in light of the quasi-continuous nature of the time signals provided by event cameras, we propose a divide-and-conquer strategy in which event-based intermediate frame synthesis happens incrementally in multiple simplified stages rather than in a single, long stage. Extensive experiments on both synthetic and real-world datasets show that these modifications lead to more reliable and realistic intermediate frame results than previous video frame interpolation methods. Our findings underline that a careful consideration of event characteristics such as high temporal density and elevated noise benefits interpolation accuracy.Comment: Accepted by IROS2023 Project Site: https://jiabenchen.github.io/revisit_even

Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle Pursuit

Multi-vehicle pursuit (MVP) such as autonomous police vehicles pursuing suspects is important but very challenging due to its mission and safety-critical nature. While multi-agent reinforcement learning (MARL) algorithms have been proposed for MVP in structured grid-pattern roads, the existing algorithms use random training samples in centralized learning, which leads to homogeneous agents showing low collaboration performance. For the more challenging problem of pursuing multiple evaders, these algorithms typically select a fixed target evader for pursuers without considering dynamic traffic situation, which significantly reduces pursuing success rate. To address the above problems, this paper proposes a Progression Cognition Reinforcement Learning with Prioritized Experience for MVP (PEPCRL-MVP) in urban multi-intersection dynamic traffic scenes. PEPCRL-MVP uses a prioritization network to assess the transitions in the global experience replay buffer according to each MARL agent’s parameters. With the personalized and prioritized experience set selected via the prioritization network, diversity is introduced to the MARL learning process, which can improve collaboration and task-related performance. Furthermore, PEPCRL-MVP employs an attention module to extract critical features from dynamic urban traffic environments. These features are used to develop a progression cognition method to adaptively group pursuing vehicles. Each group efficiently targets one evading vehicle. Extensive experiments conducted with a simulator over unstructured roads of an urban area show that PEPCRL-MVP is superior to other state-of-the-art methods. Specifically, PEPCRL-MVP improves pursuing efficiency by 3.95 % over Twin Delayed Deep Deterministic policy gradient-Decentralized Multi-Agent Pursuit and its success rate is 34.78 % higher than that of Multi-Agent Deep Deterministic Policy Gradient. Codes are open-sourced

Large-Scale in Vitro Transcription, RNA Purification and Chemical Probing Analysis

Background/Aims: RNA elements such as catalytic RNA, riboswitch, microRNA, and long non coding RNA (lncRNA) play central roles in many cellular processes. Studying diverse RNA functions require large quantities of RNA for precise structure analysis. Current RNA structure and function studies can benefit from improved RNA quantity and quality, simpler separation procedure and enhanced accuracy of structural analysis. Methods: Here we present an optimized protocol for analyzing the structure of any RNA, including in vitro transcription, size-exclusion chromatography (SEC) based denaturing purification and improved secondary structure analysis by chemical probing. Results: We observed that higher Mg2+, nucleoside triphosphate (NTP) concentrations and longer reaction duration can improve the RNA yield from in vitro transcription, specifically for longer and more complicated constructs. Our improved SEC-based denaturing RNA purification effectively halved the experiment duration and labor without introducing any contaminant. Finally, this study increased the accuracy and signal-to-noise ratio (SNR) of selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) chemical probing for analyzing RNA structure. Conclusion: Part or all of our modified method can improve almost any RNA-related study from protein-RNA interaction analysis to crystallography