Temporal Locality Research Articles

Spatial and temporal localization of cell wall associated pili in Enterococcus faecalis.

Enterococcus faecalis virulence requires cell wall-associated proteins, including the sortase-assembled endocarditis and biofilm associated pilus (Ebp), important for biofilm formation in vitro and in vivo. The current paradigm for sortase-assembled pilus biogenesis in Gram-positive bacteria is that sortases attach substrates to lipid II peptidoglycan (PG) precursors, prior to their incorporation into the growing cell wall. Contrary to prevailing dogma, by following the distribution of Ebp and PG throughout the E. faecalis cell cycle, we found that cell surface Ebp do not co-localize with newly synthesized PG. Instead, surface-exposed Ebp are localized to the older cell hemisphere and excluded from sites of new PG synthesis at the septum. Moreover, Ebp deposition on the younger hemisphere of the E. faecalis diplococcus appear as foci adjacent to the nascent septum. We propose a new model whereby sortase substrate deposition can occur on older PG rather than at sites of new cell wall synthesis. Consistent with this model, we demonstrate that sequestering lipid II to block PG synthesis via ramoplanin, does not impact new Ebp deposition at the cell surface. These data support an alternative paradigm for sortase substrate deposition in E. faecalis, in which Ebp are anchored directly onto uncrosslinked cell wall, independent of new PG synthesis.

ИННОВАЦИОННЫЕ ИНСТИТУЦИОНАЛЬНЫЕ ПРАКТИКИ КАК ФАКТОР КОНСОЛИДАЦИИ РЕГИОНАЛЬНОГО ПРОСТРАНСТВА

Objective of the study is to consider the existing innovative institutional practices that contribute to the development of regions in the context of consolidation processes. The methodological basis of the research. Based on the spatial approach, the region is considered from the point of view of a space with different resource potential, which can be directed, with the right interaction of social communities, to a successful trajectory of the region’s development. Using the resource approach, it is possible to show the process of consolidation of the regional space, which will allow filling the regional space with new meanings, as well as bringing the region to a new, higher level of development. Innovative, institutional approaches and the theory of social practices make it possible to analyze the currently existing set of actions of social institutions aimed at consolidating the regional space. Research results. Within the framework of the article, the regional space is considered, presented as a space whose structure includes elements that are interconnected with each other and form a semantic space that determines how various processes can proceed within the framework of spatial and temporal localization. One of the important processes in the region is the consolidation of the regional space, which contributes to the innovative development of the region. It is innovative regional practices that act as a factor in this consolidation.  Prospects of the study. It seems most relevant and significant at present to turn to the reflection not only of the innovative institutional practices realized and implemented in the region, aimed at rallying the regional community around the acute social problems facing it, but also those management mechanisms that will help the most successful implementation of innovative institutional practices. Examples of such practices are: the work of volunteer movements, the Immortal Regiment movement, the creation of scientific and educational centers, the development of civic initiatives.

Human-Centric Spatio-Temporal Video Grounding With Visual Transformers

In this work, we introduce a novel task – Human-centric Spatio-Temporal Video Grounding (HC-STVG). Unlike the existing referring expression tasks in images or videos, by focusing on humans, HC-STVG aims to localize a spatio-temporal tube of the target person from an untrimmed video based on a given textural description. This task is useful, especially for healthcare and security related applications, where the surveillance videos can be extremely long but only a specific person during a specific period is concerned. HC-STVG is a video grounding task that requires both spatial (where) and temporal (when) localization. Unfortunately, the existing grounding methods cannot handle this task well. We tackle this task by proposing an effective baseline method named Spatio-Temporal Grounding with Visual Transformers (STGVT), which utilizes Visual Transformers to extract cross-modal representations for video-sentence matching and temporal localization. To facilitate this task, we also contribute an HC-STVG datasetThe new dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tzhhhh123/HC-STVG</uri> . consisting of 5,660 video-sentence pairs on complex multi-person scenes. Specifically, each video lasts for 20 seconds, pairing with a natural query sentence with an average of 17.25 words. Extensive experiments are conducted on this dataset, demonstrating that the newly-proposed method outperforms the existing baseline methods.

Background-Click Supervision for Temporal Action Localization.

Weakly supervised temporal action localization aims at learning the instance-level action pattern from the video-level labels, where a significant challenge is action-context confusion. To overcome this challenge, one recent work builds an action-click supervision framework. It requires similar annotation costs but can steadily improve the localization performance when compared to the conventional weakly supervised methods. In this paper, by revealing that the performance bottleneck of the existing approaches mainly comes from the background errors, we find that a stronger action localizer can be trained with labels on the background video frames rather than those on the action frames. To this end, we convert the action-click supervision to the background-click supervision and develop a novel method, called BackTAL. Specifically, BackTAL implements two-fold modeling on the background video frames, i.e., the position modeling and the feature modeling. In position modeling, we not only conduct supervised learning on the annotated video frames but also design a score separation module to enlarge the score differences between the potential action frames and backgrounds. In feature modeling, we propose an affinity module to measure frame-specific similarities among neighboring frames and dynamically attend to informative neighbors when calculating temporal convolution. Extensive experiments on three benchmarks are conducted, which demonstrate the high performance of the established BackTAL and the rationality of the proposed background-click supervision.

ПОСЛЕОПЕРАЦИОННАЯ ПЕРФУЗИОННАЯ МАГНИТНО-РЕЗОНАНСНАЯ ТОМОГРАФИЯ КАК ИНСТРУМЕНТ ПРОГНОЗА ПРИ ГЛИОБЛАСТОМЕ ГОЛОВНОГО МОЗГА

Glioblastoma is the most frequently detected primary brain tumor (pGB), the prognosis of which significantly depends on the magnitude of residual GB (rGB), for which magnetic resonance imaging (MRI) is used in the postoperative period. Purpose: To analyze the data of ASL perfusion MRI (ASL-pMRI) performed prior to adjuvant radiation therapy (aRT), 6 to 8 weeks after resection of pGB, in terms of their prognostic significance for survival in this group of patients. Material and methods: The study included 54 patients (median age ‒ 58 years; gender: 29 men, 25 women). The Karnovsky index in 81.5 % of patients was ≥80 %. To visualize and calculate the dimensions of the rGB, ASL-pMRI was used according to the type of pseudo-continuous three-dimensional marking of arterial spins. The focus/foci of hyperperfusion (CBFmean &gt; 64 ml/100g/min) in the area of the wall of the postoperative cyst were considered as rGB. Results: Survival in the total group of 54 patients with pGB was 18 months (95 % CI:14.23) . The use of ASL-pMRI made it possible to visualize rGB in 37 (68.5 %) patients. The probability of visualization of rGB was significantly higher (p=0.02) in the case of temporal localization of the tumor. Age (HR:1.04; 95 % CI: 1.01‒1.07; p=0.007), the maximum diameter of the rGB (HR:1.04; 95 % CI: (1.01‒1.07); p=0.03) and localization of pGB in the temporal lobe (HR:2.00; 95 % CI: 1.05‒3.80; p=0.034) had a significant negative impact on survival. The use of the multifactorial Cox model showed that only the age ≥60 years (HR:2.78; 95 % CI:1.26‒6.15; p=0.012) and the maximum diameter of rGB ≥25 mm (HR:3.35; 95 % CI:1.36‒8.22; p=0.008) retained their significant negative impact on the survival of patients with pGB. Conclusions: the use of ASL – pMRI 6 to 8 weeks after resection of pGB indicates that the results obtained can become an effective tool for predicting survival in this group of patients.

Взаимные связи в сфере временных категорий (на материале немецкого языка)

Statement of the problem. Due to the lack of coverage of issues describing inter-categorial relations in the sphere of interaction between taxis and other related semantic categories involved in the verbalization of the idea of time, this problem is relevant today. The purpose of this paper is to describe inter-categorial relations in the field of integrative interaction of inter-connected functional and semantic categories: taxis, temporality, aspectuality, and temporal localization. Methodology (materials and methods). The research is carried out within the framework of a functional and semantic approach to the study and description of semantic categories. The main methods used are: the method of directed sampling, hypothetical-deductive, inductive, descriptive methods, classification method, as well as a method of generalization and interpretation of linguistic material. The research material includes statements with taxis prepositions obtained by the method of directed sampling from the Leipzig National Corpus, the Russian National Corpus and the German Electronic Dictionary. Research results. In the course of the study, it was found out that due to the inter-categorial crossing of functional and semantic categories of taxis, temporality, aspectuality and temporal localization in German statements with prepositional deverbatives, various variants of primary-taxis categorial situations of simultaneity, precedence and consecution are actualized.

Quality Control of CyGNSS Reflectivity for Robust Spatiotemporal Detection of Tropical Wetlands

The aim of this study was to develop a robust methodology for evaluating the spatiotemporal dynamics of the inundation status in tropical wetlands with the currently available Global Navigation Satellite System-Reflectometry (GNSS-R) data by proposing a new quality control technique called the “precision index”. The methodology was applied over the Mekong Delta, one of the most important rice-production systems comprising aquaculture areas and natural wetlands (e.g., mangrove forests, peatlands). Cyclone Global Navigation Satellite System (CyGNSS) constellation data (August 2018–December 2021) were used to evaluate the spatiotemporal dynamics of the reflectivity Γ over the delta. First, the reflectivity Γ, shape and size of each specular footprint and the precision index were calibrated at each specular point and reprojected to a 0.0045° resolution (approximately equivalent to 500 m) grid at a daily temporal resolution (Lv. 2 product); then, the results were obtained considering bias-causing factors (e.g., the velocity/effective scattering area/incidence angle). The Lv. 2 product was temporally integrated every 15 days with a Kalman smoother (+/− 14 days temporal localization with Gaussian kernel: 1σ = 5 days). By applying the smoother, the regional-annual dynamics over the delta could be clearly visualized. The behaviors of the GNSS-R reflectivity and the Advanced Land Observing Satellite-2 Phased-Array type L-band Synthetic Aperture Radar-2 quadruple polarimetric scatter signals were compared and found to be nonlinearly correlated due to the influence of the incidence angle and the effective scattering area.

Self-attention relational modeling and background suppression for weakly supervised temporal action localization

Weakly supervised temporal action localization aims to locate the start and end boundaries of action instances and recognize the corresponding categories. Classical methods include random erasure, attention mechanism, and cross-temporal graph relationship modeling. Despite their great progress, there are still two challenges: localization integrity and background interference. Therefore, we propose a framework with self-attention relationship modeling and background suppression to address these issues. First, the input features of background frames are suppressed by the filtering module, which prevents interference from background noise. Second, a self-attention mechanism is designed to model the relationship between different segments in the video, which refines action features to encourage smoother temporal classification scores for completeness localization. Finally, under the guidance of classified loss Lact, the refined segment features and foreground weights are further combined in an attention-weighted pool to achieve video-level prediction. The algorithm is experimentally verified on THUMOS14 and ActivityNet1.2 datasets and compared with other relevant literature, which proves its feasibility and effectiveness.

Anchor-free temporal action localization via Progressive Boundary-aware Boosting

Enormous untrimmed videos from the real world are difficult to analyze and manage. Temporal action localization algorithms can help us to locate and recognize human activity clips in untrimmed videos. Recently, anchor-free temporal action localization methods have gained increasing attention due to small computational costs and no complex hyperparameters of pre-set anchors. Although the performance has been significantly improved, most existing anchor-free temporal action localization methods still suffer from inaccurate action boundary predictions. In this paper, we want to alleviate the above problem through boundary refinement and temporal context aggregation. To this end, a novel Progressive Boundary-aware Boosting Network (PBBNet) is proposed for anchor-free temporal action localization. The PBBNet consists of three main modules: Temporal Context-aware Module (TCM), Instance-wise Boundary-aware Module (IBM), and Frame-wise Progressive Boundary-aware Module (FPBM). The TCM aggregates the temporal context information and provides features for the IBM and the FPBM. The IBM generates multi-scale video features to predict action results coarsely. Compared with IBM, the FPBM focuses on instance features corresponding to action predictions and uses more supervision information for boundary regression. Given action results from IBM, the FPBM uses a progressive boosting strategy to refine the boundary predictions multiple times with supervision from weak to strong. Extensive experiments on three benchmark datasets THUMOS14, ActivityNet-v1.3 and HACS show our PPBNet outperforms all existing anchor-free methods. Further, our PPBNet achieves state-of-the-art performance (72.5% mAP at tIoU = 0.5) on THUMOS14 dataset.

Memory access optimization for particle operations in computational fluid dynamics-discrete element method simulations

Computational Fluid Dynamics – Discrete Element Method is used to model gas-solid systems in several applications in energy, pharmaceutical and petrochemical industries. Computational performance bottlenecks often limit the problem sizes that can be simulated at industrial scale. The data structures used to store several millions of particles in such large-scale simulations have a large memory footprint that does not fit into the processor cache hierarchies on current high-performance-computing platforms, leading to reduced computational performance. This paper specifically addresses this aspect of memory access bottlenecks in industrial scale simulations. The use of space-filling curves to improve memory access patterns is described and their impact on computational performance is quantified in both shared and distributed memory parallelization paradigms. The Morton space filling curve applied to uniform grids and k-dimensional tree partitions are used to reorder the particle data-structure thus improving spatial and temporal locality in memory. The performance impact of these techniques when applied to two benchmark problems, namely the homogeneous-cooling-system and a fluidized-bed, are presented. These optimization techniques lead to approximately two-fold performance improvement in particle focused operations such as neighbor-list creation and data-exchange, with ∼ 1.5 times overall improvement in a fluidization simulation with 1.27 million particles.

Enabling Weakly Supervised Temporal Action Localization From On-Device Learning of the Video Stream

Detecting actions in videos have been widely applied in on-device applications, such as cars, robots, etc. Practical on-device videos are always untrimmed with both action and background. It is desirable for a model to both recognize the class of action and localize the temporal position where the action happens. Such a task is called temporal action location (TAL), which is always trained on the cloud where multiple untrimmed videos are collected and labeled. It is desirable for a TAL model to continuously and locally learn from new data, which can directly improve the action detection precision while protecting customers’ privacy. However, directly training a TAL model on the device is nontrivial. To train a TAL model which can precisely recognize and localize each action, tremendous video samples with temporal annotations are required. However, annotating videos frame by frame is exorbitantly time consuming and expensive. Although weakly supervised temporal action localization (W-TAL) has been proposed to learn from untrimmed videos with only video-level labels, such an approach is also not suitable for on-device learning scenarios. In practical on-device learning applications, data are collected in streaming. For example, the camera on the device keeps collecting video frames for hours or days, and the actions of nearly all classes are included in a single long video stream. Dividing such a long video stream into multiple video segments requires lots of human effort, which hinders the exploration of applying the TAL tasks to realistic on-device learning applications. To enable W-TAL models to learn from a long, untrimmed streaming video, we propose an efficient video learning approach that can directly adapt to new environments. We first propose a self-adaptive video dividing approach with a contrast score-based segment merging approach to convert the video stream into multiple segments. Then, we explore different sampling strategies on the TAL tasks to request as few labels as possible. To the best of our knowledge, we are the first attempt to directly learn from the on-device, long video stream. Experimental results on the THUMOS’14 dataset show that the performance of our approach is comparable to the current W-TAL state-of-the-art (SOTA) work without any laborious manual video splitting.

When Did It Happen? Duration-informed Temporal Localization of Narrated Actions in Vlogs

We consider the task of temporal human action localization in lifestyle vlogs. We introduce a novel dataset consisting of manual annotations of temporal localization for 13,000 narrated actions in 1,200 video clips. We present an extensive analysis of this data, which allows us to better understand how the language and visual modalities interact throughout the videos. We propose a simple yet effective method to localize the narrated actions based on their expected duration. Through several experiments and analyses, we show that our method brings complementary information with respect to previous methods, and leads to improvements over previous work for the task of temporal action localization.

The essence of online data processing

Data processing systems are a fundamental component of the modern computing stack. These systems are routinely deployed online: they continuously receive the requests of data processing operations, and continuously return the results to end users or client applications. Online data processing systems have unique features beyond conventional data processing, and the optimizations designed for them are complex, especially when data themselves are structured and dynamic. This paper describes DON Calculus, the first rigorous foundation for online data processing. It captures the essential behavior of both the backend data processing engine and the frontend application, with the focus on two design dimensions essential yet unique to online data processing systems: incremental operation processing (IOP) and temporal locality optimization (TLO). A novel design insight is that the operations continuously applied to the data can be defined as an operation stream flowing through the data structure, and this abstraction unifies diverse designs of IOP and TLO in one calculus. DON Calculus is endowed with a mechanized metatheory centering around a key observable equivalence property: despite the significant non-deterministic executions introduced by IOP and TLO, the observable result of DON Calculus data processing is identical to that of conventional data processing without IOP and TLO. Broadly, DON Calculus is a novel instance in the active pursuit of providing rigorous guarantees to the software system stack. The specification and mechanization of DON Calculus provide a sound base for the designers of future data processing systems to build upon, helping them embrace rigorous semantic engineering without the need of developing from scratch.

Midbody Proteins Display Distinct Dynamics during Cytokinesis.

The midbody is an organelle that forms between the two daughter cells during cytokinesis. It co-ordinates the abscission of the nascent daughter cells and is composed of a multitude of proteins that are meticulously arranged into distinct temporal and spatial localization patterns. However, very little is known about the mechanisms that regulate the localization and function of midbody proteins. Here, we analyzed the temporal and spatial profiles of key midbody proteins during mitotic exit under normal conditions and after treatment with drugs that affect phosphorylation and proteasome-mediated degradation to decipher the impacts of post-translational modifications on midbody protein dynamics. Our results highlighted that midbody proteins show distinct spatio-temporal dynamics during mitotic exit and cytokinesis that depend on both ubiquitin-mediated proteasome degradation and phosphorylation/de-phosphorylation. They also identified two discrete classes of midbody proteins: 'transient' midbody proteins-including Anillin, Aurora B and PRC1-which rapidly accumulate at the midbody after anaphase onset and then slowly disappear, and 'stable' midbody proteins-including CIT-K, KIF14 and KIF23-which instead persist at the midbody throughout cytokinesis and also post abscission. These two classes of midbody proteins display distinct interaction networks with ubiquitylation factors, which could potentially explain their different dynamics and stability during cytokinesis.

Актуализация таксисных ситуаций, локализованных/нелокализованных во времени (на материале немецих высказываний с предложными девербатами)

This article dwells on the actualization of various categorial taxis situations of simultaneity and non-simultaneity of actions localized and non-localized in time in modern German. The material included German utterances with prepositional deverbatives obtained by continuous sampling from the Digital Dictionary of the German Language (Digitales Wörterbuch der deutschen Sprache) and Leipzig Corpora Collection (Wortschatz Leipzig). The total number of examined fragments exceeded 7000. The following methods were used: descriptive, inductive, hypothetical-deductive, classification method, generalization and interpretation of linguistic material, as well as corpus and contextual analysis. The functional-semantic categories of taxis and temporary localization/non-localization considered here are closely interconnected with each other. The author defines their intercategorial interaction as intercategorial crossing, which determines the actualization of different variants of categorial taxis situations of simultaneity and non-simultaneity. In utterances with prepositional deverbatives, two types of categorial taxis situations are possible, relevant in terms of intercategorial crossing of the categories of taxis and temporal localization/ non-localization: 1) specific (localized in time); 2) non-specific (non-localized). The research found that in German utterances with prepositional deverbatives, interconnected categorial temporal-taxis, iterative-taxis and phase-taxis situations of simultaneity and non-simultaneity of localized/non-localized actions (processes, events) can be actualized. The plurality of specific subjects or objects of verbal actions, as well as various temporal, aspectual, iterative, taxis, local/temporal-local explicators (monocomponent, bicomponent and multicomponent), assume a prototypical character. Temporal explicators participate in the expression of categorial taxis situations of simultaneity and non-simultaneity of actions localized in time with “exact” or “approximate” indication of the time of their implementation or course. Local/ temporal-local explicators represent categorial taxis situations of simultaneity and non-simultaneity of actions (processes, events) localized in time, indicating their exact location in space or time. Iterative explicators (attributes, adverbials) determine the categorial iterative-taxis situations of simultaneity and non-simultaneity of actions (processes, events) that are not localized in time.

Research on Android-odometry based on the estimation of GNSS temporal ambiguity variation with Xiaomi MI8

Following extant studies on temporal relative localization (TRL) methods, this study uses a doppler velocity constraint-based temporal ambiguity variation (TAV) detection method and a two-step TAV resolution method to obtain the high-precisiontemporal relative position variations over time based on raw GNSS observations of Android smartphones, commonly plagued by frequent half-cycle slips and lock-loss. Results show that the proportion of the detectable satellites over 0.65 reaches the 95.57% level and the fix rate of global TAV resolution reaches 82.93%. We then propose the Android-odometry method which is a real-time high-precision TRL method with time-differenced carrier-phase observations of Android smartphones. The experimental results show that the method achieves centimeter-level temporal relative position accuracy during the 13-min dynamic test with Xiaomi MI8, showing a dramatic improvement of over 90% compared to the traditional RTK method.

Graph Convolutional Module for Temporal Action Localization in Videos.

Temporal action localization, which requires a machine to recognize the location as well as the category of action instances in videos, has long been researched in computer vision. The main challenge of temporal action localization lies in that videos are usually long and untrimmed with diverse action contents involved. Existing state-of-the-art action localization methods divide each video into multiple action units (i.e., proposals in two-stage methods and segments in one-stage methods) and then perform action recognition/regression on each of them individually, without explicitly exploiting their relations during learning. In this paper, we claim that the relations between action units play an important role in action localization, and a more powerful action detector should not only capture the local content of each action unit but also allow a wider field of view on the context related to it. To this end, we propose a general graph convolutional module (GCM) that can be easily plugged into existing action localization methods, including two-stage and one-stage paradigms. To be specific, we first construct a graph, where each action unit is represented as a node and their relations between two action units as an edge. Here, we use two types of relations, one for capturing the temporal connections between different action units, and the other one for characterizing their semantic relationship. Particularly for the temporal connections in two-stage methods, we further explore two different kinds of edges, one connecting the overlapping action units and the other one connecting surrounding but disjointed units. Upon the graph we built, we then apply graph convolutional networks (GCNs) to model the relations among different action units, which is able to learn more informative representations to enhance action localization. Experimental results show that our GCM consistently improves the performance of existing action localization methods, including two-stage methods (e.g., CBR [15] and R-C3D [47]) and one-stage methods (e.g., D-SSAD [22]), verifying the generality and effectiveness of our GCM. Moreover, with the aid of GCM, our approach significantly outperforms the state-of-the-art on THUMOS14 (50.9 percent versus 42.8 percent). Augmentation experiments on ActivityNet also verify the efficacy of modeling the relationships between action units. The source code and the pre-trained models are available at https://github.com/Alvin-Zeng/GCM.

Clinical and pathogenetic prediction of the dynamics of the course of psychoses in epilepsy

Transient epileptic psychoses in 30-40% of cases turn into psychotic states with a long, paroxysmal or chronic course. The objective of this article is to conduct a systematic analysis of modern literature sources to clarify the factors, clinical and pathogenetic patterns of transformation of transient (short-term) epileptic psychosis into psychotic states with prolonged and chronic course, as well as to identify pathogenetically oriented principles of treatment of such patients. The prognostic value of clinical and neurophysiological factors regarding increasing of the psychotic process duration has been noticed, clinical patterns of transformation of short-term psychosis into long-term (changes in the dynamics and structure of psychosis, features of the relationship with organic personality disorder and dementia in epilepsy, as well as epileptic seizures) have been shown. A number of important neurophysiological pathogenetic mechanisms of increasing the psychosis duration (growing cerebral hypofrontality, temporal localization and regular spread of the epileptic process) have been identified. Pathogenetically oriented principles of treatment tactics of patients with epileptic psychoses have been described and analyzed taking into account the revealed regularities of increasing of their duration.

Expression of Concern: Protein phosphatase 4 interacts with the Survival of Motor Neurons complex and enhances the temporal localisation of snRNPs.

Expression of Concern: Protein phosphatase 4 interacts with the Survival of Motor Neurons complex and enhances the temporal localisation of snRNPs.

Action-Aware Network with Upper and Lower Limit Loss for Weakly-Supervised Temporal Action Localization

Action-Aware Network with Upper and Lower Limit Loss for Weakly-Supervised Temporal Action Localization