Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing Trajectory Gaps to Find Possible Rendezvous Region

Given trajectory data with gaps, we investigate methods to identify possible rendezvous regions. The problem has societal applications such as improving maritime safety and regulatory enforcement. The challenges come from two aspects. First, gaps in trajectory data make it difficult to identify regions where moving objects may have rendezvoused for nefarious reasons. Hence, traditional linear or shortest path interpolation methods may not be able to detect such activities, since objects in a rendezvous may have traveled away from their usual routes to meet. Second, user detecting a rendezvous regions involve a large number of gaps and associated trajectories, making the task computationally very expensive. In preliminary work, we proposed a more effective way of handling gaps and provided examples to illustrate potential rendezvous regions. In this article, we are providing detailed experiments with both synthetic and real-world data. Experiments on synthetic data show that the accuracy improved by 50 percent, which is substantial as compared to the baseline approach. In this article, we propose a refined algorithm Temporal Selection Search for finding a potential rendezvous region and finding an optimal temporal range to improve computational efficiency. We also incorporate two novel spatial filters: (i) a Static Ellipse Intersection Filter and (ii) a Dynamic Circle Intersection Spatial Filter. Both the baseline and proposed approaches account for every possible rendezvous pattern. We provide a theoretical evaluation of the algorithms correctness and completeness along with a time complexity analysis. Experimental results on synthetic and real-world maritime trajectory data show that the proposed approach substantially improves the area pruning effectiveness and computation time over the baseline technique. We also performed experiments based on accuracy and precision on synthetic dataset on both proposed and baseline techniques.
MiDTD: A Simple and Effective Distillation Framework for Distantly Supervised Relation Extraction

Relation extraction (RE), an important information extraction task, faced the great challenge brought by limited annotation data. To this end, distant supervision was proposed to automatically label RE data, and thus largely increased the number of annotated instances. Unfortunately, lots of noise relation annotations brought by automatic labeling become a new obstacle. Some recent studies have shown that the teacher-student framework of knowledge distillation can alleviate the interference of noise relation annotations via label softening. Nevertheless, we find that they still suffer from two problems: propagation of inaccurate dark knowledge and constraint of a unified distillation temperature. In this article, we propose a simple and effective Multi-instance Dynamic Temperature Distillation (MiDTD) framework, which is model-agnostic and mainly involves two modules: multi-instance target fusion (MiTF) and dynamic temperature regulation (DTR). MiTF combines the teacher’s predictions for multiple sentences with the same entity pair to amend the inaccurate dark knowledge in each student’s target. DTR allocates alterable distillation temperatures to different training instances to enable the softness of most student’s targets to be regulated to a moderate range. In experiments, we construct three concrete MiDTD instantiations with BERT, PCNN, and BiLSTM-based RE models, and the distilled students significantly outperform their teachers and the state-of-the-art (SOTA) methods.
Graffiti, Ban or Boon? A Bakhtinian Appreciation of Selected Latrinalia from Bulawayo in Zimbabwe

ABSTRACT This article explores the messages that are created and expressed in alternative and generally informal platforms in response to the cultural practice of tabooing certain themes and language usage in the presentation and sharing of feelings and ideas about such themes. Graffiti and the carnivalesque converge on the fact that their setup is informal in terms of language, themes and manipulated identity of participants. Their content is mainly on tabooed themes and the language that is used is not only crude but also generally “vulgar”, “dirty” or “offensive”. Graffiti has, therefore, been dismissed as mundane and serving the interests of people whose mental state is compromised. It is safe to argue that there are two schools of thought that view graffiti and the carnivalesque differently. These schools of thought can be separated into those in and with authority and those not in and without authority. Those in authority view graffiti and the carnivalesque as practices that go against official expectations and are therefore negative practices. Latrinalia from selected public toilets from the city of Bulawayo was photographed and subjected to Critical Discourse Analysis with attention being paid to carnivality, Bakhtinian dialogism and humour and its impact on the interaction process.
Smart Resource Allocations for Highly Adaptive Private Cloud Systems

Private cloud systems have the potential to offer the advantages of virtualization in terms of resource utilization to enterprises that can’t choose migrating their data and applications outside of their premises, for legal, privacy or compliancy reasons. However, in order to achieve their full potential, these private clouds need to be extended in order to support an integrated and adaptive behaviour in regards to the specific applications that are executed on the upper layers. Modern applications are more and more aware of the fact that the underlying platform is virtualized and so resources might be allocated and de-allocated in an adaptive fashion with respect to the current load and capacity. This paper presents a service oriented mechanism for adaptability of a typical private cloud system to work load fluctuations that is capable of intelligent resource allocation in both terms of amount and co-locations based on virtualization optimization. The real time monitoring information is gathered with a multi-agent system capable of multi-layer and multi-factor monitoring. The smart resource allocation is achieved with a distributed genetic algorithm that considers the workload characteristics in conjunction with physical optimum allocation and the current load. The pilot implementation is presented in the context of IBM Cloud Burst 2.1 private cloud implementation with a study on DayTrader J2EE benchmark application in load test scenarios. The results illustrate how the private cloud can show an adaptive behaviour related to the work load variations.
Stability of flow in a channel with repeated flow-facing wedge-shaped protrusions

Abstract Motivated by an interest in inciting instabilities and mixing for heat transfer enhancement in ducts, flow in a channel with repeated wedge-shaped protrusions is considered for various blockage ratios (wedge height to duct height), pitch (distance between wedges) and wedge angles. The stability of the two-dimensional base flow and its dependence on the geometric parameters is elucidated through a global linear stability analysis. A linearly unstable two-dimensional mode was found, contrasting similar confined flow set-ups. However, the primary instability is a three-dimensional mode manifesting as counter-rotating streamwise vortices over the wedge tip. Analysis of the kinetic energy budget indicates a lift-up mechanism leading to instability, with the dominant energy gain of the global three-dimensional mode due to shear in the base flow. Structural sensitivity and receptivity of the instability to momentum forcing identifies the core of the instability and locations important for flow control. An endogeneity approach is used to show that the local perturbation pressure gradient component dominates the distribution of the local contribution to the growth rate of the linear global eigenmode in most cases considered, despite its net contribution being identically zero. Weakly nonlinear Stuart–Landau analysis reveals that the primary bifurcation is supercritical across all tested geometric parameter combinations. This is consistent with the finding of low linear transient growth amplifications at subcritical Reynolds numbers, being orders of magnitude lower than in similar channel flow set-ups.
Evaluating privacy and security risks in smart home entertainment appliances, from a communication perspective

Konceptet "smarta hem" blir mer och mer en del av var vardag. Pa grund av den hastiga utvecklingen och med tanke pa att tradlos kommunikation har blivit normen, har sakerhet och integritet blivit mer av ett bekymmer. Syftet med detta arbete ar att undersoka vilken typ av information som kan utvinnas ur ett underhallning-baserat smart hem med inriktning pa en off -the-shelf spel-konsol, Playstation 4, ansluten till Internet. Detta scenario har undersokts med ett experiment som fokuserar pa avlyssning av natverkstrafik som intraffar vid anvandning av en sadan enhet i det dagliga livet. Resultatet av studien visar att kanslig data sasom bilder ar i sjalva verket mojligt att utvinna fran natverkskommunikationen.
A Granular Computing-Based Model for Group Decision-Making in Multi-Criteria and Heterogeneous Environments

Granular computing is a growing computing paradigm of information processing that covers any techniques, methodologies, and theories employing information granules in complex problem solving. Within the recent past, it has been applied to solve group decision-making processes and different granular computing-based models have been constructed, which focus on some particular aspects of these decision-making processes. This study presents a new granular computing-based model for group decision-making processes deﬁned in multi-criteria and heterogeneous environments that is able to improve with minimum adjustment both the consistency associated with individual decision-makers and the consensus related to the group. Unlike the existing granular computing-based approaches, this new one is able to take into account a higher number of features when dealing with this kind of decision-making processes.
Code Cities in Virtual and Augmented Reality

In this paper, we summarize existing research on visualizing software as code cities with a focus on virtual and augmented reality technology (VR/AR). The review of existing knowledge shows that several of the hopes in VR/AR have not been fulﬁlled yet. A possible reason for that may be that existing means of visualization and interaction cannot simply be transferred one-to-one from desktop to VR/AR environments as our own research suggests. Moreover, we view the true potential of VR/AR in supporting the communication among multiple developers. All studies we are aware of have had a single developer trying to solve a given task. VR/AR can bridge spatial gaps and more research should be targeted at that particular advantage for distributed teams.
Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace

Methods to reanalyze scRNA-seq data in a spatial perspective are vital but lacking. Here, the authors develop scSpace, an integrative method that uses ST data as spatial reference to reconstruct the pseudo-space of scRNA-seq data and identify spatially variable cell subpopulations, providing insights into spatial heterogeneity from scRNA-seq data. Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose s ingle- c ell s patial p osition a ssociated c o- e mbeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.
The kidney protects against sepsis by producing systemic uromodulin

Sepsis is a significant cause of mortality in hospitalized patients. Concomitant development of acute kidney injury (AKI) increases sepsis mortality through unclear mechanisms. While electrolyte disturbances and toxic metabolite buildup during AKI could be important, it is possible that the kidney produces a protective molecule lost during sepsis with AKI. We previously demonstrated that systemic Tamm-Horsfall Protein (THP, uromodulin), a kidney-derived protein with immunomodulatory properties, falls in AKI. Using a mouse sepsis model without severe kidney injury, we show that the kidney increases circulating THP by enhancing basolateral release of THP from medullary thick ascending limb cells. In sepsis patients, changes in circulating THP are positively associated with critical illness. THP is also found de novo in injured lungs. Genetic ablation of THP in mice leads to increased mortality and bacterial burden during sepsis. Consistent with the increased bacterial burden, the presence of THP in vitro and in vivo leads macrophages and monocytes to upregulate a transcriptional program promoting cell migration, phagocytosis and chemotaxis and treatment of macrophages with purified THP increases phagocytosis. Rescue of septic THP-/- mice with exogenous systemic THP improves survival. Together, these findings suggest that through releasing THP, the kidney modulates the immune response in sepsis by enhancing mononuclear phagocyte function and systemic THP has therapeutic potential in sepsis. Significance Statement Sepsis is a significant contributor to kidney injury as well as morbidity and mortality worldwide. Specific therapies to improve outcomes in sepsis with kidney injury have largely been limited to symptom management and infectious agent control, in part because it is unclear how kidney injury increases sepsis mortality. This paper describes the identification of Tamm-Horsfall protein, previously known to protect in ischemic models of AKI, as protective in preclinical models of sepsis. It demonstrates how the loss of THP leads to decreased mononuclear phagocyte function and diversity, increased pathogen burden and decreased survival. THP also increases in sepsis without severe kidney injury and concentrates in injured organs. Further study of THP in sepsis could lead to novel sepsis therapeutics.
Intracortical remodelling increases in highly loaded bone after exercise cessation

Resorption within cortices of long bones removes excess mass and damaged tissue, and increases during periods of reduced mechanical loading. Returning to high-intensity exercise may place bones at risk of failure due to increased porosity caused by bone resorption. We used microradiographs of bone slices from highly-loaded (metacarpal, tibia) and minimally-loaded (rib) bones from 12 racehorses, 6 that died during a period of high-intensity exercise and 6 that had a period of intense exercise followed by at least 35 days of rest prior to death, and measured intracortical canal cross-sectional area (Ca.Ar) and number (N.Ca) to infer remodelling activity across sites and exercise groups. Large canals that are the consequence of bone resorption (Ca.Ar > 0.04 mm2) were 1.4× to 18.7× greater in number and area in the third metacarpal bone from rested than exercised animals (p = 0.005– 0.008), but were similar in number and area in ribs from rested and exercised animals (p = 0.575–0.688). An intermediate relationship was present in the tibia, and when large canals and smaller canals that result from partial bony infilling (Ca.Ar > 0.002 mm2) were considered together. The mechanostat may override targeted remodelling during periods of high mechanical load by enhancing bone formation, reducing resorption and suppressing turnover. Both systems may work synergistically in rest periods to remove excess and damaged tissue.
New hypotheses of cell type diversity and novelty from comparative single cell and nuclei transcriptomics in echinoderms

Cell types are the fundamental building blocks of metazoan biodiversity and offer a powerful perspective for inferring evolutionary phenomena. With the development of single-cell transcriptomic techniques, new definitions of cell types are emerging. This allows a conceptual reassessment of traditional definitions of novel cell types and their evolution. Research in echinoderms, particularly sea star and sea urchin embryos have contributed significantly to understanding the evolution of novel cell types, in particular the primary mesenchyme cells (PMCs) and pigment cells that are found in sea urchin but not sea star embryos. This paper outlines the development of a gene expression atlas for the bat star, Patiria miniata, using single nuclear RNA sequencing (snRNA-seq) of embryonic stages. The atlas revealed 22 cell clusters covering all expected cell types from the endoderm, mesoderm and ectoderm germ layers. In particular, four distinct neural clusters, an immune cluster, and distinct right and left coelom clusters were revealed as distinct cell states. A comparison with Strongylocentrotus purpuratus embryo single cell transcriptomes was performed using 1:1 orthologs to anchor and then compare gene expression patterns. S. purpuratus primordial germ cell equivalents were not detected in P. minata, while the left coelom of P. miniata has no equivalent cell cluster in S. purpuratus. Pigment cells of S. purpuratus map to clusters containing immune mesenchyme and neural cells of P. miniata, while the PMCs of S. purpuratus are revealed as orthologous to the right coelom cluster of P. miniata. These results suggest a new interpretation of the evolution of these well-studied cell types and a reflection on the definition of novel cell types.
A reinforcement-based mechanism for discontinuous learning

Problem-solving and reasoning involve mental exploration and navigation in sparse relational spaces. A physical analogue is spatial navigation in structured environments such as a network of burrows. Recent experiments with mice navigating a labyrinth show a sharp discontinuity during learning, corresponding to a distinct moment of ‘sudden insight’ when mice figure out long, direct paths to the goal. This discontinuity is seemingly at odds with reinforcement learning (RL), which involves a gradual build-up of a value signal during learning. Here, we show that biologically-plausible RL rules combined with persistent exploration generically exhibit discontinuous learning. In tree-like structured environments, positive feedback from learning on behavior generates a ‘reinforcement wave’ with a steep profile. The discontinuity occurs when the wave reaches the starting point. By examining the nonlinear dynamics of reinforcement propagation, we establish a quantitative relationship between the learning rule, the agent’s exploration biases and learning speed. Predictions explain existing data and motivate specific experiments to isolate the phenomenon. Additionally, we characterize the exact learning dynamics of various RL rules for a complex sequential task.
Modelling the within-host spread of SARS-CoV-2 infection, and the subsequent immune response, using a hybrid, multiscale, individual-based model. Part I: Macrophages

Individual responses to SARS-CoV-2 infection vary significantly, ranging from mild courses of infection that do not require hospitalisation to the development of disease which not only requires hospitalisation but can be fatal. Whilst many immunological studies have revealed fundamental insights into SARS-CoV-2 infection and COVID-19, mathematical and computational modelling can offer an additional perspective and enhance understanding. The majority of mathematical models for the within-host spread of SARS-CoV-2 infection are ordinary differential equations, which neglect spatial variation. In this article, we present a hybrid, multiscale, individual-based model to study the within-host spread of SARS-CoV-2 infection. The model incorporates epithelial cells (each containing a dynamical model for viral entry and replication), macrophages and a subset of cytokines. We investigate the role of increasing initial viral deposition, increasing delay in type I interferon secretion from epithelial cells (as well as the magnitude of secretion), increasing macrophage virus internalisation rate and macrophage activation, on the spread of infection.
Messenger-RNA Modification Standards and Machine Learning Models Facilitate Absolute Site-Specific Pseudouridine Quantification

Enzyme-mediated chemical modifications to mRNA are important for fine-tuning gene expression, but they are challenging to quantify due to low copy number and limited tools for accurate detection. Existing studies have typically focused on the identification and impact of adenine modifications on mRNA (m6A and inosine) due to the availability of analytical methods. The pseudouridine (Ψ) mRNA modification is also highly abundant but difficult to detect and quantify because there is no available antibody, it is mass silent, and maintains canonical basepairing with adenine. Nanopores may be used to directly identify Ψ sites in RNAs using a systematically miscalled base, however, this approach is not quantitative and highly sequence dependent. In this work, we apply supervised machine learning models that are trained on sequence-specific, synthetic controls to endogenous transcriptome data and achieve the first quantitative Ψ occupancy measurement in human mRNAs. Our supervised machine learning models reveal that for every site studied, different signal parameters are required to maximize Ψ classification accuracy. We show that applying our model is critical for quantification, especially in low-abundance mRNAs. Our engine can be used to profile Ψ-occupancy across cell types and cell states, thus providing critical insights about physiological relevance of Ψ modification to mRNAs.
Opposing associations of Internet Use Disorder symptom domains with structural and functional organization of the striatum: A dimensional neuroimaging approach

Background Accumulating evidence suggests brain structural and functional alterations in Internet Use Disorder (IUD). However, conclusions are strongly limited due to the retrospective case-control design of the studies, small samples, and the focus on general rather than symptom-specific approaches. Methods We here employed a dimensional multi-methodical MRI-neuroimaging design in a final sample of n = 203 subjects to examine associations between levels of IUD and its symptom-dimensions (loss of control/time management, craving/social problems) with brain structure, resting state and task-based (pain empathy, affective go/no-go) brain function. Results Although the present sample covered the entire range of IUD, including normal, problematic as well as pathological levels, general IUD symptom load was not associated with brain structural or functional alterations. However, the symptom-dimensions exhibited opposing associations with the intrinsic and structural organization of the brain, such that loss of control/time management exhibited negative associations with intrinsic striatal networks and hippocampal volume, while craving/social problems exhibited a positive association with intrinsic striatal networks and caudate volume. Conclusions Our findings provided the first evidence for IUD symptom-domain specific associations with progressive alterations in the intrinsic structural and functional organization of the brain, particularly of striatal systems involved in reward, habitual and cognitive control processes.