Police in Washington state announced two suspects were arrested in connection with the murder of a missing elderly man who was allegedly poisoned with fentanyl by a pair who gained his trust through a dating app. The Mercer Island Police released a statement saying Philip J. Brewer, 32, and Christina Hardy, 47, are facing charges for the murder of Curtis Engeland, 74, by using an elaborate scheme to defraud and murder him. Police said that Brewer and Hardy are believed to have become acquainted with Engeland several months ago and subsequently financially defrauded him. Police also believe the suspects later violently confronted Engeland at his Mercer Island home in the late evening hours of February 23, and used Engeland's vehicle to leave Mercer Island that night. POLICE MADE'A DEAL WITH THE DEVIL' TO UNCOVER LOCATION OF MISSING BLOOD MOUNTAIN HIKER: KILLER WAS'HUNTING' Two suspects were arrested in connection to the homicide of missing Mercer Island resident Curtis Engeland, 74.

Amazon's Echo Buds are on sale for their lowest price yet. You can get the retailer's AirPods alternatives today for a mere 35 (that's 15 off). One of Engadget's picks for the best budget wireless earbuds, the Echo Buds have a clear, detailed and balanced sound profile, built-in Alexa support and five hours of battery life. The discount comes a few days before Amazon's Big Spring Sale is set to kick off on March 20. Amazon's Echo Buds are available today for a record-low price.

This poses challenges for applications interested in targeting specific demographics (e.g., an African American business or NGO; a voice-tutoring system for children that are not of White ethnicity, etc.). The ultimate goal of the project described in this paper is to provide to designers, developers, and enterprises the choice of having a professional voice which is clearly recognizable as African American, and therefore more able to address diversity and inclusiveness issues. Being more precise, our goal is to create an African American Text-to-Speech system, which we will refer simply as an African American voice or AA voice, able to produce synthetic audio segments from standard English texts, and which will be recognized by African American speakers and non-speakers as sounding like a native African American speaker. The AA voice should exhibit a level of technical quality similar to the Standard American English (SAE) synthetic voices currently available through professional platforms. The evaluation of the technical quality of the AA voice, however, is not addressed in this paper, which focuses primarily on whether the AA voice can be recognized as sounding like an African American speaker. Linguists [27, 28] have described a continuum of dialects under what is often termed African American Vernacular English (AAVE). At one end of the spectrum, one finds the largest deviation from SAE in terms of lexicon (including slang), syntax and morphology, and phonological/phonetic properties. At the other end, AAVE speakers begin to approach SAE in terms of lexicon and grammar but still retain marked speech characteristics (primarily in terms of intonation, phonation, and vowel placement [14, 28]) which grant the speech a distinctive identity which listeners use as cues in the perception of African American English [44].

Next Point-of-Interests (POIs) recommendation task aims to provide a dynamic ranking of POIs based on users' current check-in trajectories. The recommendation performance of this task is contingent upon a comprehensive understanding of users' personalized behavioral patterns through Location-based Social Networks (LBSNs) data. While prior studies have adeptly captured sequential patterns and transitional relationships within users' check-in trajectories, a noticeable gap persists in devising a mechanism for discerning specialized behavioral patterns during distinct time slots, such as noon, afternoon, or evening. In this paper, we introduce an innovative data structure termed the ``Mobility Tree'', tailored for hierarchically describing users' check-in records. The Mobility Tree encompasses multi-granularity time slot nodes to learn user preferences across varying temporal periods. Meanwhile, we propose the Mobility Tree Network (MTNet), a multitask framework for personalized preference learning based on Mobility Trees. We develop a four-step node interaction operation to propagate feature information from the leaf nodes to the root node. Additionally, we adopt a multitask training strategy to push the model towards learning a robust representation. The comprehensive experimental results demonstrate the superiority of MTNet over ten state-of-the-art next POI recommendation models across three real-world LBSN datasets, substantiating the efficacy of time slot preference learning facilitated by Mobility Tree.

This work introduces Divide-Factor-Combine (DFC), a parallel divide-andconquer framework for noisy matrix factorization.

Recommendation systems can provide accurate recommendations by analyzing user shopping history. A richer user history results in more accurate recommendations. However, in real applications, users prefer e-commerce platforms where the item they seek is at the lowest price. In other words, most users shop from multiple e-commerce platforms simultaneously; different parts of the user's shopping history are shared between different e-commerce platforms. Consequently, we assume in this study that any e-commerce platform has a complete record of the user's history but can only access some parts of it. If a recommendation system is able to predict the missing parts first and enrich the user's shopping history properly, it will be possible to recommend the next item more accurately. Our recommendation system leverages user shopping history to improve prediction accuracy. The proposed approach shows significant improvements in both NDCG@10 and HR@10.

Click-through rate (CTR) prediction is a core task in recommender systems. Existing methods (IDRec for short) rely on unique identities to represent distinct users and items that have prevailed for decades. On one hand, IDRec often faces significant performance degradation on cold-start problem; on the other hand, IDRec cannot use longer training data due to constraints imposed by iteration efficiency. Most prior studies alleviate the above problems by introducing pre-trained knowledge(e.g. pre-trained user model or multi-modal embeddings). However, the explosive growth of online latency can be attributed to the huge parameters in the pre-trained model. Therefore, most of them cannot employ the unified model of end-to-end training with IDRec in industrial recommender systems, thus limiting the potential of the pre-trained model. To this end, we propose a $\textbf{P}$re-trained $\textbf{P}$lug-in CTR $\textbf{M}$odel, namely PPM. PPM employs multi-modal features as input and utilizes large-scale data for pre-training. Then, PPM is plugged in IDRec model to enhance unified model's performance and iteration efficiency. Upon incorporating IDRec model, certain intermediate results within the network are cached, with only a subset of the parameters participating in training and serving. Hence, our approach can successfully deploy an end-to-end model without causing huge latency increases. Comprehensive offline experiments and online A/B testing at JD E-commerce demonstrate the efficiency and effectiveness of PPM.

This paper presents the design and development of an innovative interactive robotic system to enhance audience engagement using character-like personas. Built upon the foundations of persona-driven dialog agents, this work extends the agent application to the physical realm, employing robots to provide a more immersive and interactive experience. The proposed system, named the Masquerading Animated Social Kinematics (MASK), leverages an anthropomorphic robot which interacts with guests using non-verbal interactions, including facial expressions and gestures. A behavior generation system based upon a finite-state machine structure effectively conditions robotic behavior to convey distinct personas. The MASK framework integrates a perception engine, a behavior selection engine, and a comprehensive action library to enable real-time, dynamic interactions with minimal human intervention in behavior design. Throughout the user subject studies, we examined whether the users could recognize the intended character in film-character-based persona conditions. We conclude by discussing the role of personas in interactive agents and the factors to consider for creating an engaging user experience.

We introduce a new family of matrix norms, the "local max" norms, generalizing existing methods such as the max norm, the trace norm (nuclear norm), and the weighted or smoothed weighted trace norms, which have been extensively used in the literature as regularizers for matrix reconstruction problems. We show that this new family can be used to interpolate between the (weighted or unweighted) trace norm and the more conservative max norm. We test this interpolation on simulated data and on the large-scale Netflix and MovieLens ratings data, and find improved accuracy relative to the existing matrix norms. We also provide theoretical results showing learning guarantees for some of the new norms.

We present a new model based on Gaussian processes (GPs) for learning pairwise preferences expressed by multiple users. Inference is simplified by using a preference kernel for GPs which allows us to combine supervised GP learning of user preferences with unsupervised dimensionality reduction for multi-user systems. The model not only exploits collaborative information from the shared structure in user behavior, but may also incorporate user features if they are available. Approximate inference is implemented using a combination of expectation propagation and variational Bayes. Finally, we present an efficient active learning strategy for querying preferences. The proposed technique performs favorably on real-world data against state-of-the-art multi-user preference learning algorithms.