Review of Research on Privacy Decision Making from a Time Perspective

Managing privacy is a process in which people continuously negotiate the boundaries of their personal space. Time is embedded in and influences this continuous negotiation. Digital technologies increasingly incorporate temporal elements, such as allowing users to define the expiration date of social network postings. Yet, researchers have not systematically examined the effects of temporal elements in privacy decision making. In this paper, we review how existing information privacy research has related to time in terms of three dimensions: duration, timing, and past, present, and future modalities. Our findings suggest that 1) duration has a negative influence on information disclosure; 2) timing, in the form of personal and external events, influences how people make privacy decisions; and 3) sensemaking that involves prior experience and planning for the future affect privacy decisions. We discuss how privacy decision making frameworks need to be adjusted to account for a time perspective

3D Printing PTFE with Direct Ink Writing

Polytetrafluoroethylene (PTFE) is a unique fluoropolymer comprising of only fluorine and carbon atoms with various desirable properties such as non-stick, chemical inertness, thermal stability and electrical insulation. Molding and sintering techniques following by pressurized preforming are commonly used to cast PTFE for desirable shapes and forms with considerable amount of waste under high cost. However, rapid prototyping and customizable tooling of PTFE is yet developed. Herein, we reported a novel and facile way for PTFE 3D printing by Direct Ink Writing (DIW). PTFE dispersion based composite, with varying amount of Gellan gum additives, was developed as 3D printable ink to generate millimeter features following by multi-steps thermal process. In order to fabricate molding PTFE properties similar structures, the design of experiments (DOE) method based on Taguchi’s orthogonal arrays were applied. The printed structures were prepared by varying three controlled factors including the Gellan gum weight percentage, the maximum temperature, and the cooling rate with three selected levels. An optimal parameter setting is obtained through a desirability function analysis of variance (ANOVA) that balances the desired Young’s modulus and yield strength targets. The Young’s modulus and yield strength are found to be controllable by varying the amount of Gellan gum. Based on its mechanical, hydrophobic and chemical inert properties, tubular structures with various designs were fabricated to demonstrate its potential in medical implants

Understanding the Influence of Temporal Focus on Users’ Self-Disclosure on Social Networking Sites

Self disclosure decision making on social networking sites (SNSs) can be considered an intertemporal choice between gaining benefits at the present and experiencing privacy harm in the future. Prior research shows that people tend to overemphasize the immediate benefits while discounting the delayed risks, but it remains unclear how and why different SNS users may subjectively discount the long term risks against the short-term benefits. This paper considers heterogeneity in users’ self disclosure decisions by focusing on the effects of temporal focus (i.e., the degree to which people think about the past, present, and future) on users’ self disclosure willingness. Using online experiments, this study tests the effectiveness of different interventions that manipulate people’s temporal focus in influencing SNS self disclosure willingness. The findings of this study provide practical implications for the design of SNS platforms and development of data policies

Patterns of nucleotides that flank substitutions in human orthologous genes

<p>Abstract</p> <p>Background</p> <p>Sequence context is an important aspect of base mutagenesis, and three-base periodicity is an intrinsic property of coding sequences. However, how three-base periodicity is influenced in the vicinity of substitutions is still unclear. The effect of context on mutagenesis should be revealed in the usage of nucleotides that flank substitutions. Relative entropy (also known as Kullback-Leibler divergence) is useful for finding unusual patterns in biological sequences.</p> <p>Results</p> <p>Using relative entropy, we visualized the periodic patterns in the context of substitutions in human orthologous genes. Neighbouring patterns differed both among substitution categories and within a category that occurred at three codon positions. Transition tended to occur in periodic sequences relative to transversion. Periodic signals were stronger in a set of flanking sequences of substitutions that occurred at the third-codon positions than in those that occurred at the first- or second-codon positions. To determine how the three-base periodicity was affected near the substitution sites, we fitted a sine model to the values of the relative entropy. A sine of period equal to 3 is a good approximation for the three-base periodicity at sites not in close vicinity to some substitutions. These periods were interrupted near the substitution site and then reappeared away from substitutions. A comparative analysis between the native and codon-shuffled datasets suggested that the codon usage frequency was not the sole origin of the three-base periodicity, implying that the native order of codons also played an important role in this periodicity. Synonymous codon shuffling revealed that synonymous codon usage bias was one of the factors responsible for the observed three-base periodicity.</p> <p>Conclusions</p> <p>Our results offer an efficient way to illustrate unusual periodic patterns in the context of substitutions and provide further insight into the origin of three-base periodicity. This periodicity is a result of the native codon order in the reading frame. The length of the period equal to 3 is caused by the usage bias of nucleotides in synonymous codons. The periodic features in nucleotides surrounding substitutions aid in further understanding genetic variation and nucleotide mutagenesis.</p

Impacts of mutation effects and population size on mutation rate in asexual populations: a simulation study

<p>Abstract</p> <p>Background</p> <p>In any natural population, mutation is the primary source of genetic variation required for evolutionary novelty and adaptation. Nevertheless, most mutations, especially those with phenotypic effects, are harmful and are consequently removed by natural selection. For this reason, under natural selection, an organism will evolve to a lower mutation rate. Overall, the action of natural selection on mutation rate is related to population size and mutation effects. Although theoretical work has intensively investigated the relationship between natural selection and mutation rate, most of these studies have focused on individual competition within a population, rather than on competition among populations. The aim of the present study was to use computer simulations to investigate how natural selection adjusts mutation rate among asexually reproducing subpopulations with different mutation rates.</p> <p>Results</p> <p>The competition results for the different subpopulations showed that a population could evolve to an "optimum" mutation rate during long-term evolution, and that this rate was modulated by both population size and mutation effects. A larger population could evolve to a higher optimum mutation rate than could a smaller population. The optimum mutation rate depended on both the fraction and the effects of beneficial mutations, rather than on the effects of deleterious ones. The optimum mutation rate increased with either the fraction or the effects of beneficial mutations. When strongly favored mutations appeared, the optimum mutation rate was elevated to a much higher level. The competition time among the subpopulations also substantially shortened.</p> <p>Conclusions</p> <p>Competition at the population level revealed that the evolution of the mutation rate in asexual populations was determined by both population size and mutation effects. The most striking finding was that beneficial mutations, rather than deleterious mutations, were the leading force that modulated the optimum mutation rate. The initial configuration of the population appeared to have no effect on these conclusions, confirming the robustness of the simulation method developed in the present study. These findings might further explain the lower mutation rates observed in most asexual organisms, as well as the higher mutation rates in some viruses.</p

Estimating Causal Effects using a Multi-task Deep Ensemble

A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images. To fill this gap, we propose Causal Multi-task Deep Ensemble (CMDE), a novel framework that learns both shared and group-specific information from the study population. We provide proofs demonstrating equivalency of CDME to a multi-task Gaussian process (GP) with a coregionalization kernel a priori. Compared to multi-task GP, CMDE efficiently handles high-dimensional and multi-modal covariates and provides pointwise uncertainty estimates of causal effects. We evaluate our method across various types of datasets and tasks and find that CMDE outperforms state-of-the-art methods on a majority of these tasks.Comment: 18 pages, 7 figures, 3 tables, published at the 40th International Conference on Machine Learning (ICML 2023

UniHPE: Towards Unified Human Pose Estimation via Contrastive Learning

In recent times, there has been a growing interest in developing effective perception techniques for combining information from multiple modalities. This involves aligning features obtained from diverse sources to enable more efficient training with larger datasets and constraints, as well as leveraging the wealth of information contained in each modality. 2D and 3D Human Pose Estimation (HPE) are two critical perceptual tasks in computer vision, which have numerous downstream applications, such as Action Recognition, Human-Computer Interaction, Object tracking, etc. Yet, there are limited instances where the correlation between Image and 2D/3D human pose has been clearly researched using a contrastive paradigm. In this paper, we propose UniHPE, a unified Human Pose Estimation pipeline, which aligns features from all three modalities, i.e., 2D human pose estimation, lifting-based and image-based 3D human pose estimation, in the same pipeline. To align more than two modalities at the same time, we propose a novel singular value based contrastive learning loss, which better aligns different modalities and further boosts the performance. In our evaluation, UniHPE achieves remarkable performance metrics: MPJPE $50.5$mm on the Human3.6M dataset and PAMPJPE $51.6$mm on the 3DPW dataset. Our proposed method holds immense potential to advance the field of computer vision and contribute to various applications

Progress and summary of reinforcement learning on energy management of MPS-EV

The high emission and low energy efficiency caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) introduce different clean energy systems to improve powertrain efficiency. The energy management strategy (EMS) is a critical technology for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement learning (RL) has become an effective methodology for the development of EMS. RL has received continuous attention and research, but there is still a lack of systematic analysis of the design elements of RL-based EMS. To this end, this paper presents an in-depth analysis of the current research on RL-based EMS (RL-EMS) and summarizes the design elements of RL-based EMS. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. The contribution of advanced algorithms to the training effect is shown, the perception and control schemes in the literature are analyzed in detail, different reward function settings are classified, and innovative training methods with their roles are elaborated. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Finally, this paper suggests potential development directions for implementing advanced artificial intelligence (AI) solutions in EMS