Miracle-dz/UniFAR-FTUs huggingface dataset:
Skeletal Video Anomaly Detection Using Deep Learning: Survey, Challenges, and Future Directions

The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or community-based setting. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in the background and making it difficult to focus on the actions of humans in the foreground. Structural information in the form of skeletons describing the human motion in the videos is privacy-protecting and can overcome some of the problems posed by appearance-based features. In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. We present a novel taxonomy of algorithms based on the various learning approaches. We conclude that skeleton-based approaches for anomaly detection can be a plausible privacy-protecting alternative for video anomaly detection. Lastly, we identify major open research questions and provide guidelines to address them.
A principled distributional approach to trajectory similarity measurement

Existing measures and representations for trajectories have two longstanding fundamental shortcomings, i.e., they are computationally expensive and they can not guarantee the `uniqueness' property of a distance function: dist(X,Y) = 0 if and only if X=Y, where $X$ and $Y$ are two trajectories. This paper proposes a simple yet powerful way to represent trajectories and measure the similarity between two trajectories using a distributional kernel to address these shortcomings. It is a principled approach based on kernel mean embedding which has a strong theoretical underpinning. It has three distinctive features in comparison with existing approaches. (1) A distributional kernel is used for the very first time for trajectory representation and similarity measurement. (2) It does not rely on point-to-point distances which are used in most existing distances for trajectories. (3) It requires no learning, unlike existing learning and deep learning approaches. We show the generality of this new approach in three applications: (a) trajectory anomaly detection, (b) anomalous sub-trajectory detection, and (c) trajectory pattern mining. We identify that the distributional kernel has (i) a unique data-dependent property and the above uniqueness property which are the key factors that lead to its superior task-specific performance; and (ii) runtime orders of magnitude faster than existing distance measures.
Universal Galois Coverings of Self-Injective Algebras by Repetitive Algebrasand Hochschild Cohomology☆

In this paper all algebras are basic, connected, finite-dimensional algebras over an algebraically closed field k. For an algebra A we denote by mod A the category of finitely generated right A-modules and by D modA → modAop the standard duality Homk · k . We are interested in studying the representation theory of self-injective algebras. An algebra A is called self-injective if A D A in modA, that is, if the projective A-modules are injective. Examples of self-injective algebras are provided by algebras of finite groups. Frequently, self-injective algebras have triangular Galois coverings, and then we may reduce the study of such algebras and their representations to that for the corresponding algebras of finite global dimension. This is the case for all representation–finite self-injective algebras [5, 15], certain classes of tame representation–infinite self-injective algebras [2, 3, 8, 10, 20], and certain classes of wild representation–infinite self-injective algebras [9]. There is a close connection between self-injective algebras and repetitive algebras [15]. We recall that if B is an algebra of finite global dimension and B̂ is the repetitive algebra of B, then the stable module category mod B̂ of mod B̂ is equivalent, as a triangulated category, to the derived category D modB of bounded complexes over modB (see [13]).
Finite energy well-posedness for nonlinear Schr{\"o}dinger equations with non-vanishing conditions at infinity

The Cauchy-Problem for 2D and 3D nonlinear Schr¨odinger equations with non-vanishing conditions at inﬁnity is investigated. Local well- posedness in the energy space for energy-subcritical nonlinearities merely satisfying Kato-type assumptions is proven. Our result provides the analogue of the well-established local H 1 -theory for solutions vanishing at inﬁnity, no further regularity assumptions on the nonlinearity are given. Global well- posedness is shown for defocusing nonlinearities provided that the nonlinear potential is non-negative. In addition, we also introduce global well-posedness in 3D for a class of nonlinearities for which the Hamiltonian energy fails to be sign-deﬁnite such as e.g. competing focusing-defocusing nonlinearities.
Muse: Text-To-Image Generation via Masked Generative Transformers

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github. io
Semidefinite programming on population clustering: a global analysis

In this paper, we consider the problem of partitioning a small data sample of size $n$ drawn from a mixture of $2$ sub-gaussian distributions. Our work is motivated by the application of clustering individuals according to their population of origin using markers, when the divergence between the two populations is small. We are interested in the case that individual features are of low average quality $\gamma$, and we want to use as few of them as possible to correctly partition the sample. We consider semidefinite relaxation of an integer quadratic program which is formulated essentially as finding the maximum cut on a graph where edge weights in the cut represent dissimilarity scores between two nodes based on their features. A small simulation result in Blum, Coja-Oghlan, Frieze and Zhou (2007, 2009) shows that even when the sample size $n$ is small, by increasing $p$ so that $np= \Omega(1/\gamma^2)$, one can classify a mixture of two product populations using the spectral method therein with success rate reaching an ``oracle'' curve. There the ``oracle'' was computed assuming that distributions were known, where success rate means the ratio between correctly classified individuals and the sample size $n$. In this work, we show the theoretical underpinning of this observed concentration of measure phenomenon in high dimensions, simultaneously for the semidefinite optimization goal and the spectral method, where the input is based on the gram matrix computed from centered data. We allow a full range of tradeoffs between the sample size and the number of features such that the product of these two is lower bounded by $1/{\gamma^2}$ so long as the number of features $p$ is lower bounded by $1/\gamma$.
Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment

Domain adaptive detection aims to improve the generalization of detectors on target domain. To reduce discrepancy in feature distributions between two domains, recent approaches achieve domain adaption through feature alignment in different granularities via adversarial learning. However, they neglect the relationship between multiple granularities and different features in alignment, degrading detection. Addressing this, we introduce a unified multi-granularity alignment (MGA)-based detection framework for domain-invariant feature learning. The key is to encode the dependencies across different granularities including pixel-, instance-, and category-levels simultaneously to align two domains. Specifically, based on pixel-level features, we first develop an omni-scale gated fusion (OSGF) module to aggregate discriminative representations of instances with scale-aware convolutions, leading to robust multi-scale detection. Besides, we introduce multi-granularity discriminators to identify where, either source or target domains, different granularities of samples come from. Note that, MGA not only leverages instance discriminability in different categories but also exploits category consistency between two domains for detection. Furthermore, we present an adaptive exponential moving average (AEMA) strategy that explores model assessments for model update to improve pseudo labels and alleviate local misalignment problem, boosting detection robustness. Extensive experiments on multiple domain adaption scenarios validate the superiority of MGA over other approaches on FCOS and Faster R-CNN detectors. Code will be released at https://github.com/tiankongzhang/MGA.
A Survey of Diversification Techniques in Search and Recommendation

Diversifying search results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers. There has been a growing attention on diversity-aware research during recent years, accompanied by a proliferation of literature on methods to promote diversity in search and recommendation. However, the diversity-aware studies in retrieval systems lack a systematic organization and are rather fragmented. In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems. We begin the survey with a brief discussion of why diversity is important in retrieval systems, followed by a summary of the various diversity concerns in search and recommendation, highlighting their relationship and differences. For the survey’s main body, we present a unified taxonomy of diversification metrics and approaches in retrieval systems, from both the search and recommendation perspectives. In the later part of the survey, we discuss the openness research questions of diversity-aware research in search and recommendation in an effort to inspire future innovations and encourage the implementation of diversity in real-world systems.
Dynamo action in the Taylor-Green vortex near threshold

Dynamo action is demonstrated numerically in the forced Taylor–Green (TG) vortex made up of a confined swirling flow composed of a shear layer between two counter-rotating eddies, corresponding to a standard experimental setup in the study of turbulence. The critical magnetic Reynolds number above which the dynamo sets in depends crucially on the fundamental symmetries of the TG vortex. These symmetries can be broken by introducing a scale separation in the flow, or by letting develop a small non-symmetric perturbation which can be either kinetic and magnetic, or only magnetic. The nature of the boundary conditions for the magnetic field (either conducting or insulating) is essential in selecting the fastest growing mode; implications of these results to a planned laboratory experiment are briefly discussed.
Phase transitions

In this present paper, we consider the p-adic Ising Vannimenus model on the Cayley tree of order two. A new measure-theoretical approach (in the padic sense) to investigate such a model is proposed. The main result of this paper is to establish the existence of the phase transition for the model. By the phase transition we mean the existence of at least two non-trivial p-adic quasi Gibbs measures, such that one is bounded and the second one is unbounded (note that in the p-adic probability, unlike a real setting, the probability measures could even be unbounded). To prove the main result, we investigate a nonlinear recurrence equation via the methods of p-adic analysis. Note that the methods used in the paper are not valid in a real setting.
AN EXPLICIT TRANSIENT ALGORITHM FOR PREDICTING INCOMPRESSIBLE VISCOUS FLOWS IN ARBITRARY GEOMETRY

A numerical method for predicting viscous flows in complex geometries is presented. Integral mass and momentum conservation equations were deployed and these were discretized into algebraic form through numerical quadrature. The physical domain was divided into a number of non-orthogonal control volumes which were isoparametrically mapped on to standard rectangular cells. Numerical integration for unsteady momentum equations was performed over such non-orthogonal cells. The explicitly advanced velocity components obtained from unsteady momentum equations may not necessarily satisfy the mass conservation condition in each cell. Compliance of the mass conservation equation and the consequent evolution of correct pressure distribution was obtained in each cell. The algorithm was applied on a few test problems, namely, lid-driven square and oblique cavities, developing flow in a rectangular channel and flow over square and circular cylinders placed in rectangular channels. The results exhibited good accuracy and justified the applicability of the algorithm.
Casimir elements of ε Lie algebras

The classical framework for investigating the Casimir elements of a Lie algebra is generalized to the case of an e Lie algebra L. We construct the standard L‐module isomorphism of the e‐symmetric algebra of L onto its enveloping algebra, and we introduce the Harish–Chandra homomorphism. In case the generators of L can be written in a canonical two‐index form, we construct the associated standard sequence of Casimir elements and derive a formula for their eigenvalues in an arbitrary highest weight module.
The Need for a Black Feminist Climate Justice: A Case of Haunting Ecology and Eco-Deconstruction

This essay makes the case for an approach to climate justice grounded in Black feminist thought. I outline one such approach by adopting the analytic and method of Black feminist hauntology. One of my aims is to depart from the erasure of Black womxn in climate discourse and strategy, as marked either by vulnerability, abjection, and the inevitability of dispossession and death, or by astounding “resilience” in the face of repeated devastation, violence, and abandonment (Baker 2019; Sharpe 2013). 1 Although there is much to be done in terms of registering differential impacts or racist violence against Black womxn, I want to center (and make the case for centering) Black feminist thought as a crucial way to reimagine climate justice. In this aspiration, I am guided by Hortense Spillers’ self-described labor. In Spillers’ words:
Chromosome Translocation t(10;19)(q26;q13) in a CIC-sarcoma

Abstract Background/Aim: CIC-sarcomas are characterized by rearrangements of the capicua transcriptional repressor (CIC) gene on chromosome subband 19q13.2, generating chimeras in which CIC is the 5′-end partner. Most reported CIC-sarcomas have been detected using PCR amplifications together with Sanger sequencing, high throughput sequencing, and fluorescence in situ hybridization (FISH). Only a few CIC-rearranged tumors have been characterized cytogenetically. Here, we describe the cytogenetic and molecular genetic features of a CIC-sarcoma carrying a t(10;19)(q26;q13), a chromosomal rearrangement not previously detected in such neoplasms. Materials and Methods: A round cell sarcoma removed from the right thigh of a 57-year-old man was investigated by G-banding cytogenetics, FISH, PCR and Sanger sequencing. Results: The tumor cells had three cytogenetically related clones with the translocations t(9;18)(q22;q21) and t(10;19)(q26;q13) common to all of them. FISH with a BAC probe containing the CIC gene hybridized to the normal chromosome 19, to der(10)t(10;19), and to der(19)t(10;19). PCR using tumor cDNA as template together with Sanger sequencing detected two CIC::DUX4 fusion transcripts which both had a stop TAG codon immediately after the fusion point. Both transcripts are predicted to encode truncated CIC polypeptides lacking the carboxy terminal part of the native protein. This missing part is crucial for CIC’s DNA binding capacity and interaction with other proteins. Conclusion: In addition to demonstrating that CIC rearrangement in sarcomas can occur via the microscopically visible translocation t(10;19)(q26;q13), the findings in the present case provide evidence that the missing part in CIC-truncated proteins has important functions whose loss may be important in tumorigenesis.
Biomarkers of Inflammation and Progression During Immunotherapy in Patients With Metastatic Renal Cell Carcinoma

Abstract Background/Aim: Biomarkers that would identify patients unlikely to respond to immunotherapy with immune checkpoint inhibitors (ICIs) remain an unmet medical need. Patients and Methods: In the present study, we have retrospectively evaluated the association between biomarkers of immune activation and outcome in metastatic renal cell carcinoma (mRCC) patients treated with ICIs. The laboratory and clinical data of 79 consecutive patients with histologically confirmed mRCC treated with ICI-based immunotherapy have been analyzed. Results: Patients who progressed or died at 4 months had higher prognostic score, higher serum C-reactive protein (CRP) and neopterin, and urinary neopterin, and lower serum albumin and hemoglobin concentration. Conclusion: Biomarkers of activation of immune response, in particular serum neopterin/creatinine ratio, are associated with outcome in mRCC patients treated with ICI immunotherapy.
Vitamin D3 Supplementation Promotes Regulatory T-Cells to Maintain Immune Homeostasis After Surgery for Early Stages of Colorectal Cancer

Abstract Background/Aim: Vitamin D3 (VD3) affects the regulation of the immune system, including the differentiation and function of regulatory T-cells (Tregs). Tregs play an important role in maintaining immune homeostasis in patients with colorectal cancer (CRC). The effects of VD3 on Treg-associated immune function were investigated in Thai patients in the early stages of CRC. Materials and Methods: Twenty-eight patients were randomized to one of two groups: Untreated or treatment with VD3 for 3 months. Whole blood samples were collected at baseline, and at 1 and 3 months. Peripheral blood mononuclear cells were isolated and the populations of forkhead box P3-positive Treg cells was analyzed by flow cytometry. The levels of Treg-associated cytokines, interleukin 10 (IL-10) and transforming growth factor beta 1 (TGF-β1), were measured by enzyme-linked immunosorbent assays. Results: Serum VD3 levels of the VD3-treated group were significantly increased at 1 (p=0.017) and 3 months (p<0.001) compared to the untreated control group. The mean percentage of Tregs was maintained between 1 and 3 months in the VD3-treated group. At 3 months, the untreated group had significantly lower Treg levels than the VD3-treated group (p=0.043). Serum IL-10 levels of the VD3-treated group were statistically increased at 1 month compared to the control group (p=0.032). No significant difference in serum TGF-β1 levels was observed between the two groups. However, the TGF-β1 level in the VD3-treated group at 1 month was lower than that of the control. Conclusion: Our findings suggest that VD3 supplementation can maintain immune responses in the early stages of CRC, helping to control Treg function. Therefore, VD3 should be supplemented to maintain immune homeostasis, especially in patients with vitamin D deficiency.
Turbulent flow and scalar transport through and over aligned and staggered wind farms

Wind farm–atmosphere interaction is complicated by the effect of turbine array configuration on momentum, scalar and kinetic energy fluxes. Wind turbine arrays are often arranged in rectilinear grids and, depending on the prevailing wind direction, may be perfectly aligned or perfectly staggered. The two extreme configurations are end members with a spectrum of infinite possible layouts. A wind farm of finite length may be modeled as an added roughness or as a canopy in large-scale weather and climate models. However, it is not clear which analogy is physically more appropriate. Also, surface scalar flux, including heat, moisture and trace gas (e.g. CO2), are affected by wind farms, and need to be properly parameterized in large-scale models. Experiments involving model wind farms, in aligned and staggered configurations, were conducted in a thermally controlled boundary-layer wind tunnel. Measurements of the turbulent flow were made using a custom x-wire/cold-wire probe. Particular focus was placed on st.
Some New Results on Indirect Effects and Their Standard Errors in Covariance Structure Models

I am grateful to Gerhard Arminger, Bruno A. Baldessari, William T. Bielby, George W. Bohrnstedt, Clifford C. Clogg, Hermann Flaschka, Edward H. Freeman, Kenneth J. Singleton, and Nancy B. Tuma for helpful advice. I am also grateful to Clement A. Stone for writing a computer program that provides the standard errors discussed in this chapter. This program can be obtained at nominal cost and is described in Stone (1985).
Change of Direction Deficit: A Promising Method to Measure a Change of Direction Ability in Adolescent Basketball Players

Abstract The aims of this study were to determine the relationship between vertical jumping performance, linear speed, change of direction speed (CODs) time and the COD deficit (CODD) in adolescent basketball players and to analyze the CODD between faster and slower players based on linear speed performance. Thirty-eight male basketball players (age: 15.47 ± 0.51 years; body height: 185.19 ± 5.67 cm; body mass: 71.87 ± 7.29 kg) completed countermovement jumps (CMJ) with and without an arm swing, squat jumps (SJs), linear sprints at 20 m with split times at 5 and 10 m, the pro–agility test and the zig-zag tests. Furthermore, the CODD was calculated as the difference between the 20 m linear speed result and CODs time in both COD tests. Pearson and Spearman analyses were used to determine the correlations between power-speed-related variables and the CODD. Moreover, independent t-tests and Cohen’s d effect size (ES) were used to analyze the differences between the faster and slower players in the CODD. Moderate to strong significant negative correlations were observed between the CODD in the pro-agility test and linear speed at 5 m, 10 m, and 20 m (r = –0.55 to –0.46), while moderate negative significant correlation was found between the zig-zag CODD and SJs (r = –0.37) as well as small positive significant correlation between the CODD in the zig–zag test and the eccentric utilization ratio (EUR) (r = 0.23). Additionally, faster basketball players displayed significantly higher CODD values performing the pro-agility test. In summary, these findings underline the complexity of COD performance and the importance of remodeling traditional training programs in basketball players.