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ComPEQ-MR: Compressed point cloud dataset with eye tracking and quality assessment in mixed reality
Get PDFPoint clouds (PCs) have attracted researchers and developers due to their ability to provide immersive experiences with six degrees of freedom (6DoF). However, there are still several open issues in understanding the Quality of Experience (QoE) and visual attention of end users while experiencing 6DoF volumetric videos. First, encoding and decoding point clouds require a significant amount of both time and computational resources. Second, QoE prediction models for dynamic point clouds in 6DoF have not yet been developed due to the lack of visual quality databases. Third, visual attention in 6DoF is hardly explored, which impedes research into more sophisticated approaches for adaptive streaming of dynamic point clouds. In this work, we provide an open-source Compressed Point cloud dataset with Eye-tracking and Quality assessment in Mixed Reality (ComPEQ--MR). The dataset comprises four compressed dynamic point clouds processed by Moving Picture Experts Group (MPEG) reference tools (i.e., VPCC and GPCC), each with 12 distortion levels. We also conducted subjective tests to assess the quality of the compressed point clouds with different levels of distortion. The rating scores are attached to ComPEQ--MR so that they can be used to develop QoE prediction models in the context of MR environments. Additionally, eye-tracking data for visual saliency is included in this dataset, which is necessary to predict where people look when watching 3D videos in MR experiences. We collected opinion scores and eye-tracking data from 41 participants, resulting in 2132 responses and 164 visual attention maps in total. The dataset is available at https://ftp.itec.aau.at/datasets/ComPEQ-MR/
Investigation of positive streamers in CO2: Experiments and 3D particle-in-cell simulations
Get PDFWe investigate the propagation of positive streamers in CO2 through 3D particle-in-cell simulations, which are qualitatively compared against experimental results at 50 mbar. The experiments show that CO2 streamers are much more stochastic than air streamers at the same applied voltage, indicating that few electrons are available in front of the streamer head. In the simulations, we include a photoionization model for CO2. The computational results show that even a small amount of photoionization can sustain positive streamer propagation, but this requires a background electric field close to the critical field. When we compare streamers in CO2 and in air at the same applied voltage, the electric field at the streamer head and the electron density in the streamer channel are higher in CO2. We discuss the uncertainties in CO2 photoionization and provide an estimate for the quenching pressure, which is based on the radiative lifetime of emitting states and the collision frequency of the gas. Furthermore, a criterion for self-sustained streamer growth in CO2 is presented and compared against simulation results
Proactive DP: A multiple target optimization framework for DP-SGD
Get PDFWe introduce a multiple target optimization framework for DP-SGD referred to as pro-active DP. In contrast to traditional DP accountants, which are used to track the expenditure of privacy budgets, the pro-active DP scheme allows one to a-priori select parameters of DP-SGD based on a fixed privacy budget (in terms of ϵ and δ) in such a way to optimize the anticipated utility (test accuracy) the most. To achieve this objective, we first propose significant improvements to the moment account method, presenting a closed-form (ϵ, δ)-DP guarantee that connects all parameters in the DP-SGD setup. We show that DP-SGD is (equation presented) with T at least ≈ 2k2/ϵ and (equation presented), where T is the total number of rounds, and K = kN is the total number of gradient computations where k measures K in number of epochs of size N of the local data set. We prove that our expression is close to tight in that if T is more than a constant factor ≈ 4 smaller than the lower bound ≈ 2k2/ϵ, then the (ϵ, δ)-DP guarantee is violated. The above DP guarantee can be enhanced in that DP-SGD is (equation presented) with T at least ≈ 2k2/ϵ together with two additional, less intuitive, conditions that allow larger ϵ ≥ 0.5. Our DP theory allows us to create a utility graph and DP calculator. These tools link privacy and utility objectives and search for optimal experiment setups, efficiently taking into account both accuracy and privacy objectives, as well as implementation goals. We furnish a comprehensive implementation flow of our proactive DP, with rigorous experiments to showcase the proof-of-concept
Proactive DP: A multiple target optimization framework for DP-SGD
Get PDFWe introduce a multiple target optimization framework for DP-SGD referred to as pro-active DP. In contrast to traditional DP accountants, which are used to track the expenditure of privacy budgets, the pro-active DP scheme allows one to a-priori select parameters of DP-SGD based on a fixed privacy budget (in terms of ϵ and δ) in such a way to optimize the anticipated utility (test accuracy) the most. To achieve this objective, we first propose significant improvements to the moment account method, presenting a closed-form (ϵ, δ)-DP guarantee that connects all parameters in the DP-SGD setup. We show that DP-SGD is (equation presented) with T at least ≈ 2k2/ϵ and (equation presented), where T is the total number of rounds, and K = kN is the total number of gradient computations where k measures K in number of epochs of size N of the local data set. We prove that our expression is close to tight in that if T is more than a constant factor ≈ 4 smaller than the lower bound ≈ 2k2/ϵ, then the (ϵ, δ)-DP guarantee is violated. The above DP guarantee can be enhanced in that DP-SGD is (equation presented) with T at least ≈ 2k2/ϵ together with two additional, less intuitive, conditions that allow larger ϵ ≥ 0.5. Our DP theory allows us to create a utility graph and DP calculator. These tools link privacy and utility objectives and search for optimal experiment setups, efficiently taking into account both accuracy and privacy objectives, as well as implementation goals. We furnish a comprehensive implementation flow of our proactive DP, with rigorous experiments to showcase the proof-of-concept
Beyond Neyman-Pearson: E-values enable hypothesis testing with a data-driven alpha
Get PDFA standard practice in statistical hypothesis testing is to mention the p-value alongside the accept/reject decision. We show the advantages of mentioning an e-value instead. With p-values, it is not clear how to use an extreme observation (e.g. p ≪α) for getting better frequentist decisions. With e-values it is straightforward, since they provide Type-I risk control in a generalized Neyman-Pearson setting with the decision task (a general loss function) determined post-hoc, after observation of the data -- thereby providing a handle on `roving α's'. When Type-II risks are taken into consideration, the only admissible decision rules in the post-hoc setting turn out to be e-value-based. Similarly, if the loss incurred when specifying a faulty confidence interval is not fixed in advance, standard confidence intervals and distributions may fail whereas e-confidence sets and e-posteriors still provide valid risk guarantees. Sufficiently powerful e-values have by now been developed for a range of classical testing problems. We discuss the main challenges for wider development and deployment
The LDBC social network benchmark interactive workload v2: A transactional graph query benchmark with deep delete operations
Get PDFThe LDBC Social Network Benchmark’s Interactive workload captures an OLTP scenario operating on a correlated social network graph. It consists of complex graph queries executed concurrently with a stream of updates operation. Since its initial release in 2015, the Interactive workload has become the de facto industry standard for benchmarking transactional graph data management systems. As graph systems have matured and the community’s understanding of graph processing features has evolved, we initiated the renewal of this benchmark. This paper describes the draft Interactive v2 workload with several new features: delete operations, a cheapest path-finding query, support for larger data sets, and a novel temporal parameter curation algorithm that ensures stable runtimes for path queries
Autonomous workflow for multimodal fine-grained training assistants towards mixed reality
Get PDFAutonomous artificial intelligence (AI) agents have emerged as promising protocols for automatically understanding the language-based environment, particularly with the exponential development of large language models (LLMs). However, a fine-grained, comprehensive understanding of multimodal environments remains under-explored. This work designs an autonomous workflow tailored for integrating AI agents seamlessly into mixed reality (MR) applications for fine-grained training. We present a demonstration of a multimodal fine-grained training assistant for LEGO brick assembly in a pilot MR environment. Specifically, we design a cerebral language agent that integrates LLMs with memory, planning, and interaction with MR tools and a vision-language agent, enabling agents to decide their actions based on past experiences. Furthermore, we introduce LEGO-MRTA, a multimodal fine-grained assembly dialogue dataset synthesized automatically in the workflow served by a commercial LLM. This dataset comprises multimodal instruction manuals, conversations, MR responses, and vision question answering. Last, we present several prevailing open-resource LLMs as benchmarks, assessing their performance with and without fine-tuning on the proposed dataset. We anticipate that the broader impact of this workflow will advance the development of smarter assistants for seamless user interaction in MR environments, fostering research in both AI and HCI communities
Reverse information projections and optimal e-statistics
Get PDFInformation projections have found important applications in probability theory, statistics, and related areas. In the field of hypothesis testing in particular, the reverse information projection (RIPr) has recently been shown to lead to growth-rate optimal (GRO) e-statistics for testing simple alternatives against composite null hypotheses. However, the RIPr as well as the GRO criterion are undefined whenever the infimum information divergence between the null and alternative is infinite. We show that in such scenarios, under some assumptions, there still exists a measure in the null that is closest to the alternative in a specific sense. Whenever the information divergence is finite, this measure coincides with the usual RIPr. It therefore gives a natural extension of the RIPr to certain cases where the latter was previously not defined. This extended notion of the RIPr is shown to lead to optimal e-statistics in a sense that is a novel, but natural, extension of the GRO criterion. We also give conditions under which the (extension of the) RIPr is a strict sub-probability measure, as well as conditions under which an approximation of the RIPr leads to approximate e-statistics. For this case we provide tight relations between the corresponding approximation rates
AGAR - Attention graph-RNN for adaptative motion prediction of point clouds of eformable objects
Get PDFThis article focuses on motion prediction for point cloud sequences in the challenging case of deformable 3D objects, such as human body motion. First, we investigate the challenges caused by deformable shapes and complex motions present in this type of representation, with the ultimate goal of understanding the technical limitations of state-of-the-art models. From this understanding, we propose an improved architecture for point cloud prediction of deformable 3D objects. Specifically, to handle deformable shapes, we propose a graph-based approach that learns and exploits the spatial structure of point clouds to extract more representative features. Then, we propose a module able to combine the learned features in a adaptative manner according to the point cloud movements. The proposed adaptative module controls the composition of local and global motions for each point, enabling the network to model complex motions in deformable 3D objects more effectively. We tested the proposed method on the following datasets: MNIST moving digits, the Mixamo human bodies motions [15], JPEG [5] and CWIPC-SXR [32] real-world dynamic bodies. Simulation results demonstrate that our method outperforms the current baseline methods given its improved ability to model complex movements as well as preserve point cloud shape. Furthermore, we demonstrate the generalizability of the proposed framework for dynamic feature learning by testing the framework for action recognition on the MSRAction3D dataset [19] and achieving results on par with state-of-the-art methods
Axisymmetric fluid streamer model in the AMReX library
Get PDFWe have implemented an axisymmetric fluid model of streamers in the AMReX open-source library. Our implementation is based on the first-order fluid model with a local field approximation. Photoionization is implemented by employing the Zhelenznyak’s model using the Helmholtz approach. We have verified our code in standard conditions by comparing our results for positive streamers in air with the existing benchmarks from the literature. To verify the performance of our code in strongly attaching gases, we compare the properties of negative streamers with those obtained from the Afivo-streamer open-source code. Calculations have been performed in mixtures of carbon dioxide and perfluoro-nitrile with a background number density of electrons and positive ions of 1013 m−3. We found an excellent agreement between the two sets of results, which indicates the numerical integrity of our code