Miracle-dz/UniFAR-FTUs huggingface dataset:
Weakly-supervised Video Anomaly Detection with Contrastive Learning of Long and Short-range Temporal Features

In this paper, we address the problem of weakly-supervised video anomaly detection, in which given video-level labels for training, we aim to identify in test videos, the snippets containing abnormal events. Although current methods based on multiple instance learning (MIL) show effective detection performance, they ignore important video temporal dependencies. Also, the number of abnormal snip-pets can vary per anomaly video, which complicates the training process of MIL-based methods because they tend to focus on the most abnormal snippet – this can cause it to mistakenly select a normal snippet instead of an abnormal snippet, and also to fail to select all abnormal snip-pets available. We propose a novel method, named Multi-scale Temporal Network trained with top-K Contrastive Multiple Instance Learning (MTN-KMIL), to address the issues above. The main contributions of MTN-KMIL are: 1) a novel synthesis of a pyramid of dilated convolutions and a self-attention mechanism, with the former capturing the multi-scale short-range temporal dependencies be-tween snippets and the latter capturing long-range temporal dependencies; and 2) a novel contrastive MIL learning method that enforces large margins between the top-K normal and abnormal video snippets at the feature representation level and anomaly score level, resulting in accurate anomaly discrimination. Extensive experiments show that our method outperforms several state-of-the-art meth-ods by a large margin on three benchmark data sets (Shang-haiTech, UCF-Crime and XD-Violence). Code is available at https://github.com/tianyu0207/MTN-KMIL
Cross-lingual Visual Pre-training for Multimodal Machine Translation

Pre-trained language models have been shown to improve performance in many natural language tasks substantially. Although the early focus of such models was single language pre-training, recent advances have resulted in cross-lingual and visual pre-training methods. In this paper, we combine these two approaches to learn visually-grounded cross-lingual representations. Specifically, we extend the translation language modelling (Lample and Conneau, 2019) with masked region classification and perform pre-training with three-way parallel vision & language corpora. We show that when fine-tuned for multimodal machine translation, these models obtain state-of-the-art performance. We also provide qualitative insights into the usefulness of the learned grounded representations.
The Ion Transition Range of Solar Wind Turbulence in the Inner Heliosphere: Parker Solar Probe Observations

The scaling of the turbulent spectra provides a key measurement that allows us to discriminate between different theoretical predictions of turbulence. In the solar wind, this has driven a large number of studies dedicated to this issue using in situ data from various orbiting spacecraft. While a semblance of consensus exists regarding the scaling in the magnetohydrodynamic (MHD) and dispersive ranges, the precise scaling in the transition range and the actual physical mechanisms that control it remain open questions. Using the high-resolution data in the inner heliosphere from the Parker Solar Probe mission, we find that the sub-ion scales (i.e., at the frequency f ∼ [2, 9] Hz) follow a power-law spectrum f α with a spectral index α varying between −3 and −5.7. Our results also show that there is a trend toward an anticorrelation between the spectral slopes and the power amplitudes at the MHD scales, in agreement with previous studies: the higher the power amplitude the steeper the spectrum at sub-ion scales. A similar trend toward an anticorrelation between steep spectra and increasing normalized cross helicity is found, in agreement with previous theoretical predictions about the imbalanced solar wind. We discuss the ubiquitous nature of the ion transition range in solar wind turbulence in the inner heliosphere.
Regions of Attraction Estimation using Level SetMethod for Complex Network System

Many complex engineering systems network together functional elements and balance demand loads (e.g.information on data networks, electric power on grids). This allows load spikes to be shifted and avoid a local overload. In mobile wireless networks, base stations(BSs) receive data demand and shift high loads to neighbouring BSs to avoid the outage. The stability of cascade load balancing is important because unstable networks can cause high inefficiency. The research challenge is to prove the stability conditions for any arbitrarily large, complex, and dynamic network topology, and for any balancing dynamic function. Our previous work has proven the conditions for stability for stationary networks near equilibrium for any load balancing dynamic and topology. Most current analyses in dynamic complex networks linearize the system around the fixed equilibrium solutions. This approach is insufficient for dynamic networks with changing equilibrium and estimating the Region of Attraction(ROA) is needed. The novelty of this paper is that we compress this high-dimensional system and use Level Set Methods (LSM) to estimate the ROA. Our results show how we can control the ROA via network topology (local degree control) as a way to configure the mobility of transceivers to ensure the preservation of stable load balancing.
D-Net: Siamese based Network with Mutual Attention for Volume Alignment

Alignment of contrast and non contrast-enhanced imaging is essential for quantification of changes in several biomedical applications. In particular, the extraction of cartilage shape from contrast-enhanced Computed Tomography (CT) of tibiae requires accurate alignment of the bone, currently performed manually. Existing deep learning-based methods for alignment require a common template or are limited in rotation range. Therefore, we present a novel network, D-net, to estimate arbitrary rotation and translation between 3D CT scans that additionally does not require a prior template. D-net is an extension to the branched Siamese encoder-decoder structure connected by new mutual non-local links, which efficiently capture long-range connections of similar features between two branches. The 3D supervised network is trained and validated using preclinical CT scans of mouse tibiae with and without contrast enhancement in cartilage. The presented results show a significant improvement in the estimation of CT alignment, outperforming the current comparable methods.
OpenGF: An Ultra-Large-Scale Ground Filtering Dataset Built Upon Open ALS Point Clouds Around the World

Ground filtering has remained a widely studied but incompletely resolved bottleneck for decades in the automatic generation of high-precision digital elevation model, due to the dramatic changes of topography and the complex structures of objects. The recent breakthrough of supervised deep learning algorithms in 3D scene understanding brings new solutions for better solving such problems. However, there are few large-scale and scene-rich public datasets dedicated to ground extraction, which considerably limits the development of effective deep-learning-based ground filtering methods. To this end, we present OpenGF, first Ultra-Large-Scale Ground Filtering dataset covering over 47 km2 of 9 different typical terrain scenes built upon open ALS point clouds of 4 different countries around the world. OpenGF contains more than half a billion finely labeled ground and non-ground points, thousands of times the number of labeled points than the de facto standard ISPRS filtertest dataset. We extensively evaluate the performance of state-of-the-art rule-based algorithms and 3D semantic segmentation networks on our dataset and provide a comprehensive analysis. The results have confirmed the capability of OpenGF to train deep learning models effectively. This dataset is released at https://github.com/Nathan-UW/OpenGF to promote more advancing research for ground filtering and large-scale 3D geographic environment understanding.
Learning Structral coherence Via Generative Adversarial Network for Single Image Super-Resolution

Among the major remaining challenges for single image super resolution (SISR) is the capacity to recover coherent images with global shapes and local details conforming to human vision system. Recent generative adversarial network (GAN) based SISR methods have yielded overall realistic SR images, however, there are always unpleasant textures accompanied with structural distortions in local regions. To target these issues, we introduce the gradient branch into the generator to preserve structural information by restoring high-resolution gradient maps in SR process. In addition, we utilize a U-net based discriminator to consider both the whole image and the detailed per-pixel authenticity, which could encourage the generator to maintain overall coherence of the reconstructed images. Moreover, we have studied objective functions and LPIPS perceptual loss is added to generate more realistic and natural details. Experimental results show that our proposed method outperforms state-of-the-art perceptual-driven SR methods in perception index (PI), and obtains more geometrically consistent and visually pleasing textures in natural image restoration.
Writers Gonna Wait: The Effectiveness of Notifications to Initiate Aversive Action in Writing Procrastination

This paper evaluates the use of notifications to reduce aversive-task-procrastination by helping initiate action. Specifically, we focus on aversion to graded writing tasks. We evaluate software designs commonly used by behavior change applications, such as goal setting and action support systems. We conduct a two-phase control trial experiment with 21 college students tasked to write two 3000-word writing assignments (14 students fully completed the experiment). Participants use a customized text editor designed to continuously collect writing behavior. The results from the study reveal that notifications have minimal effect in encouraging users to get started. They can also increase negative effects on participants. Other techniques, such as eliminating distraction and showing simple writing statistics, yield higher satisfaction among participants as they complete the writing task. Furthermore, the incorporation of text mining decreases aversion to the task and helps participants overcome writer's block. Finally, we discuss lessons learned from our evaluation that help quantify the difficulty of behavior change for writing procrastination, with emphasis on goals for the HCI community.
Kinetic Analysis of the Reaction between Electrogenerated Superoxide and Carbon Dioxide in the Room Temperature Ionic Liquids 1-Ethyl-3-methylimidazolium Bis(trifluoromethylsulfonyl)imide and Hexyltriethylammonium Bis(trifluoromethylsulfonyl)imide

The reduction of oxygen in the presence of carbon dioxide has been investigated by cyclic voltammetry at a gold microdisk electrode in the two room-temperature ionic liquids 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([EMIM][N(Tf)2]) and hexyltriethylammonium bis(trifluoromethylsulfonyl)imide ([N6222][N(Tf)2]). With increasing levels of CO2, cyclic voltammetry shows an increase in the reductive wave and diminishing of the oxidative wave, indicating that the generated superoxide readily reacts with carbon dioxide. The kinetics of this reaction are investigated in both ionic liquids. The reaction was found to proceed via a DISP1 type mechanism in [EMIM][N(Tf)2], with an overall second-order rate constant of 1.4 ± 0.4 × 103 M-1 s-1. An ECE or DISP1 mechanism was determined to be the most likely pathway for the reaction in [N6222][N(Tf)2], with an overall second-order rate constant of 1.72 ± 0.45 × 103 M-1 s-1.
Conjugator length in Thompson's groups

We prove that Thompson's group F$F$ has quadratic conjugator length function. That is, for any two conjugate elements of F$F$ of length n$n$ or less, there exists an element of F$F$ of length O(n2)$O(n^2)$ that conjugates one to the other. Moreover, there exist conjugate pairs of elements of F$F$ of length at most n$n$ such that the shortest conjugator between them has length Ω(n2)$\Omega (n^2)$ . This latter statement holds for T$T$ and V$V$ as well.
An optimal gradient method for smooth (possibly strongly) convex minimization

We present an optimal gradient method for smooth (possibly strongly) convex optimization. The method is optimal in the sense that its worst-case bound exactly matches the lower bound on the oracle complexity for the class of problems, meaning that no black-box ﬁrst-order method can have a better worst-case guarantee without further assumptions on the class of problems at hand. The method is in some sense a generalization of the Optimized Gradient Method of Kim and Fessler [2016], and asymptotically corresponds to the Triple Momentum Method [Van Scoy et al., 2017], in the presence of strong convexity. Furthermore, the method is numerically stable to arbitrarily large condition numbers and admits a conceptually very simple proof, which involves a Lyapunov argument and a sum of two inequalities. Finally, we provide a numerical recipe for obtaining the algorithmic parameters of the method, using semideﬁnite programming, and illustrate that it can be used for developing other methods as well.
Thermal corrections in Bardeen type regular black holes

We carry quantum (thermal) corrections to the thermodynamics of regular black holes in detail. Firstly, we discuss the non-extended phase space thermodynamics of regular black holes. We obtain expressions for various thermodynamic quantities like temperature, entropy, internal energy, Helmholtz free energy, pressure, enthalpy, and Gibbs free energy. Then we introduce quantum corrections to the thermodynamics of Bardeen type (toy model) of regular black holes. The qualitative and quantitative analysis of these corrections is carried by plotting all the corrected thermodynamic quantities. The comparative analysis is then done where the effects of these corrections can be clearly seen.
Ripe rot of south eastern Australian wine grapes is caused by two species of Colletotrichum: C. acutatum and C. gloeosporioides with differences in infection and fungicide sensitivity

Background and Aims: Ripe rot of grapes is widespread in subtropical vineyards of south-eastern Australia. We aimed to re-evaluate the causative agent(s), berry susceptibility, fungicide sensitivity and differences in histopathology. Methods and Results: Both Colletotrichum acutatum and C. gloeosporioides were shown to be responsible for ripe rot in three vineyards surveyed in the Hastings Valley, NSW in 2007 and 2009. Observation on detached berries of Vitis vinifera (cv. Cabernet Sauvignon and Chardonnay) revealed that C. acutatum had a faster infection rate than C. gloeosporioides. C. acutatum also formed appressoria and penetrated grape tissue faster than C. gloeosporioides, which produced longer hyphae on the berry surface before penetration. Both C. acutatum and C. gloeosporioides produced acervuli 48 h after inoculation. In contrast, spore germination rates and growth rates on potato dextrose agar were greater for C. gloeosporioides. The two species differed in their sensitivity to the fungicides benomyl, captan and triadimenol. C. acutatum was significantly more sensitive to captan and triadimenol, whereas C. gloeosporioides was more sensitive to benomyl. Conclusions: Both C. acutatum and C. gloeosporioides cause ripe rot of wine grapes in Australia. Subtle differences in the infection process may explain the relative prevalence of the two species. Significance of the Study: This study confirms the involvement of C. acutatum and C. gloeosporioides in ripe rot of grapes in Australia. Knowledge of growth characteristics and fungicide sensitivity of ripe rot pathogens should aid disease management strategies.
Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews

In this paper, we propose the Brand-Topic Model (BTM) which aims to detect brand-associated polarity-bearing topics from product reviews. Different from existing models for sentiment-topic extraction which assume topics are grouped under discrete sentiment categories such as ‘positive’, ‘negative’ and ‘neural’, BTM is able to automatically infer real-valued brand-associated sentiment scores and generate fine-grained sentiment-topics in which we can observe continuous changes of words under a certain topic (e.g., ‘shaver’ or ‘cream’) while its associated sentiment gradually varies from negative to positive. BTM is built on the Poisson factorisation model with the incorporation of adversarial learning. It has been evaluated on a dataset constructed from Amazon reviews. Experimental results show that BTM outperforms a number of competitive baselines in brand ranking, achieving a better balance of topic coherence and unique-ness, and extracting better-separated polarity-bearing topics.
Star formation quenching stages of active and non-active galaxies

The mechanisms that bring galaxies to strongly reduce their star formation activity (star-formation quenching) remain poorly understood. To better study galaxy evolution, we propose a classification based on maps of ionised hydrogen distribution traced by the kiloparsec-resolved, equivalent width of Hα maps, and the nuclear activity of the galaxies using information from the Baldwin-Philips-Terlevich diagnostic diagrams. Using these tools, we group a sample of 238 galaxies from the Calar Alto Legacy Integral Field spectroscopy Area survey in six quenching stages (QSs): (i) objects dominated by recent star formation; (ii) systems that present a quiescent-nuclear-ring structure in their centre; (iii) galaxies that are centrally quiescent; (iv) galaxies with no clear pattern in their ionisation gas distribution (mixed); (v) systems that posses only a few star-forming regions (nearly retired), or (vi) galaxies that are completely quiescent (fully retired). Regarding their nuclear activity, we further divide the galaxies into two groups: active systems that host a weak or strong active galactic nucleus (AGN) at their centre, and non-active objects. Galaxies grouped into quenching-stage classes occupy specific locations on the star-formation-rate versus stellar mass diagram. The ‘blue cloud’ is populated by the star-forming and the quiescent-nuclear-ring galaxies, the ‘green valley’ is populated by centrally quiescent and mixed systems, and the ‘red sequence’ by the nearly- and fully retired objects. Generally, galaxies that host a weak or strong AGN show properties comparable to those of their non-active counterparts at the same QSs, except for the AGN-hosting star-forming systems. The degree of star-formation quenching increases along the present emission-line pattern sequence from star-forming to fully retired. The proposed emission-line classes reinforce the ‘inside-out’ quenching scenario, which foresees that the suppression of star formation begins from the central regions of the galaxies.
COL1A1 Is a Potential Prognostic Biomarker and Correlated with Immune Infiltration in Mesothelioma

Objective Mesothelioma (MESO) is a rare tumor derived from mesothelium cells. The aim of this study was to explore key candidate genes and potential molecular mechanisms for mesothelioma through bioinformatics analysis. Methods The MESO expression profiles came from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The differences in the infiltration levels of immune cells between MESO and normal tissues were assessed using CIBERSORT. Differentially expressed genes (DEGs) were identified by comprehensive analysis of multiple datasets. A protein-protein interaction (PPI) network was constructed, and a hub gene COL1A1 was selected for MESO. The expression and mutation of COL1A1 in MESO were analyzed in the cBioPortal database. The correlation between COL1A1 expression and immune cell infiltration was evaluated using the TIMER database. Gene Set Enrichment Analysis (GSEA) of COL1A1 was then performed. Finally, Kaplan-Meier survival analysis was presented to predict the survival times between high and low COL1A1 expression groups for MESO patients. Results There were distinct differences in the infiltration levels of immune cells between MESO and normal tissues. A total of 118 DEGs were identified by comprehensively analyzing three expression profile datasets. COL1A1, a hub gene, was identified to be highly expressed in MESO compared to normal tissues. COL1A1 genetic mutation occurred in 9% of MESO samples, and amplification was the most common type of mutation. COL1A1 expression was significantly correlated to the infiltration levels of CD4+ T cells, macrophages, and neutrophils. GSEA results indicated that COL1A1 could be involved in key biological processes and pathways like extracellular matrix and PI3K-Akt pathway. Patients with high COL1A1 expression usually experienced shorten overall survival time than those with its low expression. Conclusion Our findings revealed that COL1A1 could become a potential prognostic biomarker for MESO, which was significantly related to immune cell infiltration.
A Unique Case of Incomplete Bifid Ureter and Associated Arterial Variations

Introduction. Urogenital and vascular anomalies, including a left duplex kidney and a left aberrant renal artery that gave rise to the left ovarian artery, were observed in a 77-year-old female cadaver during a routine dissection. Description. A left aberrant renal artery, which gave rise to the left ovarian artery, was observed originating from the aorta 4 cm below the left renal artery. Two independent contributions to a bifid ureter were found originating from the hilum of the left kidney. These two contributions descended 12.4 cm and 10.6 cm, respectively, posterior to the left aberrant renal artery and lateral to the left ovarian artery before uniting anterior to the psoas major muscle to descend 12.7 cm to the bladder. Significance. While the duplex kidney is a relatively common congenital anomaly that can be asymptomatic, it can also potentially be associated with compression of renal vasculature or the ureter. Ureteral compression can then result in several pathologies including reflux, urinary tract infection (UTI), ureteropelvic junction obstruction, or hydronephrosis. Compression of renal and ovarian vasculature can result in altered blood flow to the kidney and ovary, potentially causing fibrosis, atrophy, or organ failure. Current imaging techniques alone are insufficient for correct diagnostics of such complications, and they must be supplemented with a thorough understanding of the respective anatomical variations.