Augmented 2D-TAN: A Two-stage Approach for Human-centric Spatio-Temporal Video Grounding

We propose an effective two-stage approach to tackle the problem of language-based Human-centric Spatio-Temporal Video Grounding (HC-STVG) task. In the first stage, we propose an Augmented 2D Temporal Adjacent Network (Augmented 2D-TAN) to temporally ground the target moment corresponding to the given description. Primarily, we improve the original 2D-TAN from two aspects: First, a temporal context-aware Bi-LSTM Aggregation Module is developed to aggregate clip-level representations, replacing the original max-pooling. Second, we propose to employ Random Concatenation Augmentation (RCA) mechanism during the training phase. In the second stage, we use pretrained MDETR model to generate per-frame bounding boxes via language query, and design a set of hand-crafted rules to select the best matching bounding box outputted by MDETR for each frame within the grounded moment.Comment: Best Paper Award at the 3rd Person in Context (PIC) Challenge CVPR Workshop 202

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Dopamine Release Dynamics Change during Adolescence and after Voluntary Alcohol Intake

Adolescence is associated with high impulsivity and risk taking, making adolescent individuals more inclined to use drugs. Early drug use is correlated to increased risk for substance use disorders later in life but the neurobiological basis is unclear. The brain undergoes extensive development during adolescence and disturbances at this time are hypothesized to contribute to increased vulnerability. The transition from controlled to compulsive drug use and addiction involve long-lasting changes in neural networks including a shift from the nucleus accumbens, mediating acute reinforcing effects, to recruitment of the dorsal striatum and habit formation. This study aimed to test the hypothesis of increased dopamine release after a pharmacological challenge in adolescent rats. Potassium-evoked dopamine release and uptake was investigated using chronoamperometric dopamine recordings in combination with a challenge by amphetamine in early and late adolescent rats and in adult rats. In addition, the consequences of voluntary alcohol intake during adolescence on these effects were investigated. The data show a gradual increase of evoked dopamine release with age, supporting previous studies suggesting that the pool of releasable dopamine increases with age. In contrast, a gradual decrease in evoked release with age was seen in response to amphetamine, supporting a proportionally larger storage pool of dopamine in younger animals. Dopamine measures after voluntary alcohol intake resulted in lower release amplitudes in response to potassium-chloride, indicating that alcohol affects the releasable pool of dopamine and this may have implications for vulnerability to addiction and other psychiatric diagnoses involving dopamine in the dorsal striatum

Low temperature annealing for vanadium dioxide in photonic integrated circuits

We show at the annealing temperature of vanadium dioxide (VO2) fabricated using atomic layer deposition can be suppressed to 300 °C, significantly improving the compatibility of VO2 with photonic integrated circuits

Metasurface-integrated microring resonators for off-chip vortex beam generation

Vortex beams that carry orbital angular momentum (OAM) have garnered significant attention, as they bring the degree of freedom of OAM to modern optical communication, beyond the traditional degrees of freedom such as amplitude, phase and polarization. Meanwhile, metasurfaces composed of ultra-thin layers of subwavelength structures have also been utilized for light manipulation. Nevertheless, the combination of these two concepts has not been explored in the form of microring resonator-based light emitter. In this work, we demonstrate a Si-based, passive, conjugate symmetry-breaking emitter in numerical simulation. This broken conjugate symmetry enables the emitter to generate OAMs with different topological charges, when it is driven at two opposite input directions

Vanadium dioxide-enabled tunable metasurfaces

We numerically demonstrate output tuning in vanadium dioxide (VO2) metasurfaces at 1550 nm, which is enabled by the phase transition of VO2. The designs could be utilized in applications such as imaging and LiDAR sensing

Optical modulation in a Si microring resonator inspired by biological classical conditioning

In this oral presentation, we propose and numerically demonstrate photonic classical conditioning in a Si microring resonator, to emulate Pavlov’s dog experiment using the insulator-metal transition in a VO2 thin film patch integrated with the resonator

Effect of Vibration Procedure on Particle Distribution of Cement Paste

Vibration procedures significantly affect the performances of cement-based materials. However, studies on the distribution of certain particles within cement-based materials are limited due to the complexity and difficulty of identifying each specific particle. This paper presents a new method for simulating and quantifying the movements of particles within cement paste through the use of “tagged materials”. By separating the tagged particles from the cement paste after vibration, the distribution of the particles in the cement paste can be calculated statistically. The effect of the vibration time and frequency, fresh behavior, and powder characteristics of cement paste on particle motions are investigated. The results demonstrate that when the vibration exceeds 1800 s, it induces a significant uneven dispersion of microparticles. This effect is more pronounced at low viscosities (200 Hz). Larger and denser particles exhibit greater dispersion. This method provides a valuable tool for investigating the theory of particle motion in cement paste, which is crucial for understanding the influence of vibration on the properties of cement-based materials