Frames Per Second (FPS) significantly affects the gaming experience. Providing players with accurate FPS estimates prior to purchase benefits both players and game developers. However, we have a limited understanding of how to predict a game's technical performance on a specific device. In this paper, we first conduct a comprehensive analysis of a wide range of factors that may affect game FPS on a global-scale dataset to identify the determinants of FPS. This includes player-side and game-side characteristics, as well as country-level socio-economic statistics. Furthermore, recognizing that accurate FPS predictions require extensive user data, which raises privacy concerns, we propose a federated learning-based model to ensure user privacy. Each player and game is assigned a unique learnable knowledge kernel that gradually extracts latent features for improved accuracy. We also introduce a novel training and prediction scheme that allows these kernels to be dynamically plug-and-play, effectively addressing cold start issues. To train this model with minimal bias, we collected a large telemetry dataset from 224 countries and regions, 100,000 users, and 835 games. Our model achieved a mean Wasserstein distance of 0.469 between predicted and ground truth FPS distributions, outperforming all baseline methods.

Dynamic Vision Sensor (DVS)-based solutions have recently garnered significant interest across various computer vision tasks, offering notable benefits in terms of dynamic range, temporal resolution, and inference speed. However, as a relatively nascent vision sensor compared to Active Pixel Sensor (APS) devices such as RGB cameras, DVS suffers from a dearth of ample labeled datasets. Prior efforts to convert APS data into events often grapple with issues such as a considerable domain shift from real events, the absence of quantified validation, and layering problems within the time axis. In this paper, we present a novel method for video-to-events stream conversion from multiple perspectives, considering the specific characteristics of DVS. A series of carefully designed losses helps enhance the quality of generated event voxels significantly. We also propose a novel local dynamic-aware timestamp inference strategy to accurately recover event timestamps from event voxels in a continuous fashion and eliminate the temporal layering problem. Results from rigorous validation through quantified metrics at all stages of the pipeline establish our method unquestionably as the current state-of-the-art (SOTA). The code can be found at bit.ly/v2ce.

Regulations require video games to provide transparency regarding loot box odds to keep players informed, leading many games to disclose probabilities in various ways; yet, the extent of players' comprehension of loot box mechanics remains unclear. We performed a content analysis on 80 online posts to understand players' perceptions of loot box odds in two popular video games (Genshin Impact and Honkai: Star Rail). We then conducted semi-structured interviews with 24 players to explore the causes of these folk models across more games. Utilizing a bottom-up open coding approach, we created a taxonomy of folk models players have about loot boxes. We found that participants generally possessed inaccurate mental models of how loot boxes work, and they wanted game companies to enhance loot box transparency in three areas of probability disclosures: granularity, longitude, and scope.

Technology-mediated dance experiences, as a medium of entertainment, are a key element in both traditional and virtual reality-based gaming platforms. These platforms predominantly depend on unobtrusive and continuous human pose estimation as a means of capturing input. Current solutions primarily employ RGB or RGB-Depth cameras for dance gaming applications; however, the former is hindered by low-light conditions due to motion blur and reduced sensitivity, while the latter exhibits excessive power consumption, diminished frame rates, and restricted operational distance. Boasting ultra-low latency, energy efficiency, and a wide dynamic range, neuromorphic cameras present a viable solution to surmount these limitations. Here, we introduce YeLan, a neuromorphic camera-driven, three-dimensional, high-frequency human pose estimation (HPE) system capable of withstanding low-light environments and dynamic backgrounds. We have compiled the first-ever neuromorphic camera dance HPE dataset and devised a fully adaptable motion-to-event, physics-conscious simulator. YeLan surpasses baseline models under strenuous conditions and exhibits resilience against varying clothing types, background motion, viewing angles, occlusions, and lighting fluctuations.

Cancer genomes exhibit profound somatic copy number alterations (SCNAs). Studying tumor SCNAs using massively parallel sequencing provides unprecedented resolution and meanwhile gives rise to new challenges in data analysis, complicated by tumor aneuploidy and heterogeneity as well as normal cell contamination. While the majority of read depth based methods utilize total sequencing depth alone for SCNA inference, the allele specific signals are undervalued. We proposed a joint segmentation and inference approach using both signals to meet some of the challenges. Our method consists of four major steps: 1) extracting read depth supporting reference and alternative alleles at each SNP/Indel locus and comparing the total read depth and alternative allele proportion between tumor and matched normal sample; 2) performing joint segmentation on the two signal dimensions; 3) correcting the copy number baseline from which the SCNA state is determined; 4) calling SCNA state for each segment based on both signal dimensions. The method is applicable to whole exome/genome sequencing (WES/WGS) as well as SNP array data in a tumor-control study. We applied the method to a dataset containing no SCNAs to test the specificity, created by pairing sequencing replicates of a single HapMap sample as normal/tumor pairs, as well as a large-scale WGS dataset consisting of 88 liver tumors along with adjacent normal tissues. Compared with representative methods, our method demonstrated improved accuracy, scalability to large cancer studies, capability in handling both sequencing and SNP array data, and the potential to improve the estimation of tumor ploidy and purity.

BACKGROUND: Variations in DNA copy number carry information on the modalities of genome evolution and mis-regulation of DNA replication in cancer cells. Their study can help localize tumor suppressor genes, distinguish different populations of cancerous cells, and identify genomic variations responsible for disease phenotypes. A number of different high throughput technologies can be used to identify copy number variable sites, and the literature documents multiple effective algorithms. We focus here on the specific problem of detecting regions where variation in copy number is relatively common in the sample at hand. This problem encompasses the cases of copy number polymorphisms, related samples, technical replicates, and cancerous sub-populations from the same individual. RESULTS: We present a segmentation method named generalized fused lasso (GFL) to reconstruct copy number variant regions. GFL is based on penalized estimation and is capable of processing multiple signals jointly. Our approach is computationally very attractive and leads to sensitivity and specificity levels comparable to those of state-of-the-art specialized methodologies. We illustrate its applicability with simulated and real data sets. CONCLUSIONS: The flexibility of our framework makes it applicable to data obtained with a wide range of technology. Its versatility and speed make GFL particularly useful in the initial screening stages of large data sets.

In this paper, we introduce a new type of integrity constraint, which we call a statistical constraint, and discuss its applicability to enhancing database correctness. Statistical constraints manifest embedded relationships among current attribute values in the database and are characterized by their probabilistic nature. They can be used to detect potential errors not easily detected by the conventional constraints. Methods for extracting statistical constraints from a relation and enforcement of such constraints are described. Preliminary performance evaluation of enforcing statistical constraints on a real life database is also presented.

Article Statistical inference of unknown attribute values in databases Share on Authors: Wen-Chi Hou Department of Computer Science, Southern Illinois University at Carbondale, IL Department of Computer Science, Southern Illinois University at Carbondale, ILView Profile , Zhongyang Zhang Department of Computer Science, Southern Illinois University, at Carbondale, IL Department of Computer Science, Southern Illinois University, at Carbondale, ILView Profile , Nong Zhou Department of Computer Science, New Mexico Tech, Socorro, NM Department of Computer Science, New Mexico Tech, Socorro, NMView Profile Authors Info & Claims CIKM '93: Proceedings of the second international conference on Information and knowledge managementDecember 1993 Pages 21–30https://doi.org/10.1145/170088.170097Online:01 December 1993Publication History 5citation302DownloadsMetricsTotal Citations5Total Downloads302Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access