Efficient and effective human action recognition in video through motion boundary description with a compact set of trajectories

Human action recognition (HAR) is at the core of human-computer interaction and video scene understanding. However, achieving effective HAR in an unconstrained environment is still a challenging task. To that end, trajectory-based video representations are currently widely used. Despite the promising levels of effectiveness achieved by these approaches, problems regarding computational complexity and the presence of redundant trajectories still need to be addressed in a satisfactory way. In this paper, we propose a method for trajectory rejection, reducing the number of redundant trajectories without degrading the effectiveness of HAR. Furthermore, to realize efficient optical flow estimation prior to trajectory extraction, we integrate a method for dynamic frame skipping. Experiments with four publicly available human action datasets show that the proposed approach outperforms state-of-the-art HAR approaches in terms of effectiveness, while simultaneously mitigating the computational complexity

What Charge-Off Rates Are Predictable by Macroeconomic Latent Factors?

Charge-offs signal important information about the riskiness of loan portfolios in the banking system, which can generate systemic risk towards deep recessions. We compiled the net charge-off rate (COR) data of the top 10 bank holding companies (BHCs) in the U.S., utilizing consolidated financial statements. We propose factor-augmented forecasting models for CORs by estimating latent common factors, including targeted factors, via an array of data dimensionality reduction methods for a large panel of macroeconomic predictors. Our models outperform the benchmark models especially well for business loan and real estate loan CORs, while enhancing predictive contents for consumer loan CORs is difficult especially at short horizons. Real activity factors improve the out-of-sample predictability over the benchmarks for business loan CORs even when financial sector factors are excluded

The Meaning of Fashion: Implicit and Explicit Self-esteem and Depression

This study investigates the relationship between the implicit self-esteem and the depression to fill the gap. In psychological field, the therapy is considered to be effective as both external and internal selves are healed. Hence, this study employed implicit self-reported method to examine the genuine therapeutic effect of fashion. This study is significant as it facilitated the implicit association test (IAT) in first place in fashion field. The purpose of the study is to develop the foundation of positive effect of fashion by revealing the relationship between the fashion and the substantial self

TextManiA: Enriching Visual Feature by Text-driven Manifold Augmentation

We propose TextManiA, a text-driven manifold augmentation method that semantically enriches visual feature spaces, regardless of class distribution. TextManiA augments visual data with intra-class semantic perturbation by exploiting easy-to-understand visually mimetic words, i.e., attributes. This work is built on an interesting hypothesis that general language models, e.g., BERT and GPT, encompass visual information to some extent, even without training on visual training data. Given the hypothesis, TextManiA transfers pre-trained text representation obtained from a well-established large language encoder to a target visual feature space being learned. Our extensive analysis hints that the language encoder indeed encompasses visual information at least useful to augment visual representation. Our experiments demonstrate that TextManiA is particularly powerful in scarce samples with class imbalance as well as even distribution. We also show compatibility with the label mix-based approaches in evenly distributed scarce data.Comment: Accepted at ICCV 2023. [Project Pages] https://textmania.github.io

Evaluation of conditional treatment effects of adjuvant treatments on patients with synovial sarcoma using Bayesian subgroup analysis

Background The impact of adjuvant chemotherapy or radiation therapy on the survival of patients with synovial sarcoma (SS), which is a rare soft-tissue sarcoma, remains controversial. Bayesian statistical approaches and propensity score matching can be employed to infer treatment effects using observational data. Thus, this study aimed to identify the individual treatment effects of adjuvant therapies on the overall survival of SS patients and recognize subgroups of patients who can benefit from specific treatments using Bayesian subgroup analyses. Methods We analyzed data from patients with SS obtained from the surveillance, epidemiology, and end results (SEER) public database. These data were collected between 1984 and 2014. The treatment effects of chemotherapy and radiation therapy on overall survival were evaluated using propensity score matching. Subgroups that could benefit from radiation therapy or chemotherapy were identified using Bayesian subgroup analyses. Results Based on a stratified Kaplan-Meier curve, chemotherapy exhibited a positive average causal effect on survival in patients with SS, whereas radiation therapy did not. The optimal subgroup for chemotherapy includes the following covariates: older than 20 years, male, large tumor (longest diameter > 5 cm), advanced stage (SEER 3), extremity location, and spindle cell type. The optimal subgroup for radiation therapy includes the following covariates: older than 20 years, male, large tumor (longest diameter > 5 cm), early stage (SEER 1), extremity location, and biphasic type. Conclusion In this study, we identified high-risk patients whose variables include age (age > 20 years), gender, tumor size, tumor location, and poor prognosis without adjuvant treatment. Radiation therapy should be considered in the early stages for high-risk patients with biphasic types. Conversely, chemotherapy should be considered for late-stage high-risk SS patients with spindle cell types

Direct observation of mammalian cell growth and size regulation

We introduce a microfluidic system for simultaneously measuring single cell mass and cell cycle progression over multiple generations. We use this system to obtain over 1,000 hours of growth data from mouse lymphoblast and pro-B-cell lymphoid cell lines. Cell lineage analysis revealed a decrease in the growth rate variability at the G1/S phase transition, which suggests the presence of a growth rate threshold for maintaining size homeostasis

The Meaning of Fashion: Implicit and Explicit Self-esteem and Depression

This study investigates the relationship between the implicit self-esteem and the depression to fill the gap. In psychological field, the therapy is considered to be effective as both external and internal selves are healed. Hence, this study employed implicit self-reported method to examine the genuine therapeutic effect of fashion. This study is significant as it facilitated the implicit association test (IAT) in first place in fashion field. The purpose of the study is to develop the foundation of positive effect of fashion by revealing the relationship between the fashion and the substantial self.</p

From the black box to the glass box: Using unsupervised and supervised learning processes to predict user engagement for the airline companies

Firms collect an enormous amount of user generated content (UGC), such as social media posts, to analyze consumers’ unfiltered opinions regarding brands and firms. A challenge in analyzing unstructured UGC is the lack of analytic frame. By adopting both unsupervised and supervised learning processes for using artificial intelligence (AI), we collected 680,410, tweets related to airline companies (United Airlines, Delta Airlines, Southwest Airlines, Alaska Airlines, and Hawaiian Airlines) and analyzed 4961 retweets to predict user engagement levels on Twitter. Rooted in the electronic word-of-mouth (eWOM) perspective, the results of this study indicated that consumer sentiment was positive for United Airlines, Delta Airlines, and Alaska Airlines, whereas it was negative for Southwest Airlines and Hawaiian Airlines. We also examined the effects of word count, gaps between the tweet generated date and the retweeted date, the number of the hashtag(s), and extracted topics on predicting the level of user engagement. Ultimately, this study provided a detailed guide to mangers on how to use an unstructured data analysis procedure incorporating both supervised and unsupervised learning processes

A Plasmonic Fiber Based Glucometer and Its Temperature Dependence

We present the plasmonic fiber based optical glucometer. A thin gold layer is coated on clad-free core of multimode optical fiber along 3 cm length to excite surface plasmons at 632.8 nm wavelength. Glucose oxidase is immobilized on the metal surface for glucose sensing. The effective surface refractive index increases by gluconic acid and hydrogen peroxide that are generated upon glucose injection, leading to plasmonic condition change with a consequence of optical power change at the fiber output. We obtain limit of detection of glucose concentration of 6.75 mg/dL, indicating higher sensitivity than the wavelength interrogating SPR glucometer that uses a spectrometer of 1nm spectral resolution. The coefficient of variation is 8.6% at a glucose concentration of 80 mg/dL at room temperature. We also examine the effects of ambient temperature variations from &minus;10 &deg;C to 40 &deg;C on the performance of the presented sensor and compared them with those on commercially available glucometers that are based on enzyme electrodes. We find that the presented fiber sensor produced standard deviation of 12.1 mg/dL at a glucose concentration of 80 mg/dL under such varying temperature, which is, even without additional temperature correction function, comparable to the commercialized ones

Enriching Visual Features via Text-driven Manifold Augmentation

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