Miracle-dz/UniFAR-FTUs huggingface dataset:
Test-time Similarity Modification for Person Re-identification toward Temporal Distribution Shift

Person re-identification (re-id), which aims to retrieve images of the same person in a given image from a database, is one of the most practical image recognition applications. In the real world, however, the environments that the images are taken from change over time. This causes a distribution shift between training and testing and degrades the performance of re-id. To maintain re-id performance, models should continue adapting to the test environment's temporal changes. Test-time adaptation (TTA), which aims to adapt models to the test environment with only unlabeled test data, is a promising way to handle this problem because TTA can adapt models instantly in the test environment. However, the previous TTA methods are designed for classification and cannot be directly applied to re-id. This is because the set of people's identities in the dataset differs between training and testing in re-id, whereas the set of classes is fixed in the current TTA methods designed for classification. To improve re-id performance in changing test environments, we propose TEst-time similarity Modification for Person re-identification (TEMP), a novel TTA method for re-id. TEMP is the first fully TTA method for re-id, which does not require any modification to pre-training. Inspired by TTA methods that refine the prediction uncertainty in classification, we aim to refine the uncertainty in re-id. However, the uncertainty cannot be computed in the same way as classification in re-id since it is an open-set task, which does not share person labels between training and testing. Hence, we propose re-id entropy, an alternative uncertainty measure for re-id computed based on the similarity between the feature vectors. Experiments show that the re-id entropy can measure the uncertainty on re-id and TEMP improves the performance of re-id in online settings where the distribution changes over time.
OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation

The booming of 3D recognition in the 2020s began with the introduction of point cloud transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models, especially in 3D semantic segmentation. However, sparse CNNs are still valuable networks, due to their efficiency treasure, and ease of application. In this work, we reexamine the design distinctions and test the limits of what a sparse CNN can achieve. We discover that the key credit to the performance difference is adaptivity. Specifically, we propose two key components, i.e., adaptive receptive fields (spatially) and adaptive relation, to bridge the gap. This exploration led to the creation of Omni-Adaptive 3D CNNs (OA-CNNs), a family of networks that integrates a lightweight module to greatly enhance the adaptivity of sparse CNNs at minimal computational cost. Without any self-attention modules, OA-CNNs favorably surpass point transformers in terms of accuracy in both indoor and outdoor scenes, with much less latency and memory cost. Notably, it achieves 76.1%, 78.9%, and 70.6% mIoU on ScanNet v2, nuScenes, and SemanticKITTI validation benchmarks respectively, while maintaining at most 5x better speed than transformer counterparts. This revelation highlights the potential of pure sparse CNNs to outperform transformer-related networks.
ReFeree: Radar-based efficient global descriptor using a Feature and Free space for Place Recognition

Radar is highlighted for robust sensing capabilities in adverse weather conditions (e.g. dense fog, heavy rain, or snowfall). In addition, Radar can cover wide areas and penetrate small particles. Despite these advantages, Radar-based place recognition remains in the early stages compared to other sensors due to its unique characteristics such as low resolution, and significant noise. In this paper, we propose a Radarbased place recognition utilizing a descriptor called ReFeree using a feature and free space. Unlike traditional methods, we overwhelmingly summarize the Radar image. Despite being lightweight, it contains semi-metric information and is also outstanding from the perspective of place recognition performance. For concrete validation, we test a single session from the MulRan dataset and a multi-session from the Oxford Radar RobotCar and the Boreas dataset.
On the lineshapes of temperature-dependent transport measurements of superconductors under pressure

Recent reports of superconductivity in the vicinity of room temperature have been the subject of discussion by the community. Specifically, features in the resistance-temperature (R-T) relations have raised questions. We show that many of these features can arise from previously unaccounted-for dynamic effects associated with the AC transport techniques often used in high-pressure experiments. These dynamic AC effects can can cause the apparent resistance (Rapparent) to diverge from the DC resistance (RDC), sharpen measured superconducting transitions, and produce other features in the measured R-T response. We also show that utilizing the full output of phase-sensitive transport measurements provides a valuable probe of superconducting samples in difficult to measure systems
The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty

The Hearsay-II system, developed during the DARPA-sponsored five-year speech-understanding research program, represents both a specific solution to the speech-understanding problem and a general framework for coordinating independent processes to achieve cooperative problem-solving behavior. As a computational problem, speech understanding reflects a large number of intrinsically interesting issues. Spoken sounds are achieved by a long chain of successive transformations, from intentions, through semantic and syntactic structuring, to the eventually resulting audible acoustic waves. As a consequence, interpreting speech means effectively inverting these transformations to recover the speaker's intention from the sound. At each step in the interpretive process, ambiguity and uncertainty arise. The Hearsay-II problem-solving framework reconstructs an intention from hypothetical interpretations formulated at various levels of abstraction. In addition, it allocates limited processing resources first to the most promising incremental actions. The final configuration of the Hearsay-II system comprises problem-solving components to generate and evaluate speech hypotheses, and a focus-of-control mechanism to identify potential actions of greatest value. Many of these specific procedures reveal novel approaches to speech problems. Most important, the system successfully integrates and coordinates all of these independent activities to resolve uncertainty and control combinatorics. Several adaptations of the Hearsay-II framework have already been undertaken in other problem domains, and it is anticipated that this trend will continue; many future systems necessarily will integrate diverse sources of knowledge to solve complex problems cooperatively. Discussed in this paper are the characteristics of the speech problem in particular, the special kinds of problem-solving uncertainty in that domain, the structure of the Hearsay-II system developed to cope with that uncertainty, and the relationship between Hearsay-II's structure and those of other speech-understanding systems. The paper is intended for the general computer science audience and presupposes no speech or artificial intelligence background.
Consistency in Non-Transactional Distributed Storage Systems

Over the years, different meanings have been associated with the word consistency in the distributed systems community. While in the ’80s “consistency” typically meant strong consistency, later defined also as linearizability, in recent years, with the advent of highly available and scalable systems, the notion of “consistency” has been at the same time both weakened and blurred. In this article, we aim to fill the void in the literature by providing a structured and comprehensive overview of different consistency notions that appeared in distributed systems, and in particular storage systems research, in the last four decades. We overview more than 50 different consistency notions, ranging from linearizability to eventual and weak consistency, defining precisely many of these, in particular where the previous definitions were ambiguous. We further provide a partial order among different consistency predicates, ordering them by their semantic “strength,” which we believe will be useful in future research. Finally, we map the consistency semantics to different practical systems and research prototypes. The scope of this article is restricted to non-transactional semantics, that is, those that apply to single storage object operations. As such, our article complements the existing surveys done in the context of transactional, database consistency semantics.
Glacier and glacial lake classification for change detection studies using satellite data: a case study from Baspa basin, western Himalaya

Abstract Using high-resolution Google EarthTM images in conjunction with Landsat images, the glaciers and lakes in the Baspa basin are classified to explore the recent changes. A total number of 109 glaciers (187 ± 3.7 km2) are mapped and subsequently classified as compound valley glaciers, simple valley glaciers, cirques, niches, glacieretes and ice aprons. The compound and simple valley glaciers contribute 67.1 ± 1.3% and 19.8 ± 0.3% to the total glacier cover of the basin. Similarly, a total number of 129 glacial lakes (0.360 ± 0.007 km2) are identified. From 1976 to 2011, the compound valley glaciers have lost a small area of 10.3 ± 0.03% at a rate of 0.41 ± 0.002 km2 a-1, whereas the niche glaciers have lost higher area of 40.1 ± 0.001% at a rate of 0.04 ± 0.0001 km2 a-1. Change detection of two benchmark glacial lakes revealed a progressive expansion during recent decades. The Baspa Bamak proglacial lake has expanded from 0.020 ± 0.0004 km2 (2000) to 0.069 ± 0.001 km2 (2011). Due to the complete loss of source ice, another glacial lake has expanded from 0.09 ± 0.001 km2 (1994) to 0.10 ± 0.002 km2 (2011). During the study period, the mean annual temperature that is Tavg, Tmin and Tmax have increased significantly at the 95% confidence level by 1.5 oC (0.070 °C a-1), 1.8 oC (0.076 °C a-1) and 1.6 oC (0.0071 °C a-1) from 1985 to 2008. However, the precipitation has decreased significantly from 1976 and 1985 to 2008.
Unified Static and Dynamic Network: Efficient Temporal Filtering for Video Grounding

Inspired by the activity-silent and persistent activity mechanisms in human visual perception biology, we design a Unified Static and Dynamic Network (UniSDNet), to learn the semantic association between the video and text/audio queries in a cross-modal environment for efficient video grounding. For static modeling, we devise a novel residual structure (ResMLP) to boost the global comprehensive interaction between the video segments and queries, achieving more effective semantic enhancement/supplement. For dynamic modeling, we effectively exploit three characteristics of the persistent activity mechanism in our network design for a better video context comprehension. Specifically, we construct a diffusely connected video clip graph on the basis of 2D sparse temporal masking to reflect the"short-term effect"relationship. We innovatively consider the temporal distance and relevance as the joint"auxiliary evidence clues"and design a multi-kernel Temporal Gaussian Filter to expand the context clue into high-dimensional space, simulating the"complex visual perception", and then conduct element level filtering convolution operations on neighbour clip nodes in message passing stage for finally generating and ranking the candidate proposals. Our UniSDNet is applicable to both Natural Language Video Grounding (NLVG) and Spoken Language Video Grounding (SLVG) tasks. Our UniSDNet achieves SOTA performance on three widely used datasets for NLVG, as well as three datasets for SLVG, e.g., reporting new records at 38.88% R@1,IoU@0.7 on ActivityNet Captions and 40.26% R@1,IoU@0.5 on TACoS. To facilitate this field, we collect two new datasets (Charades-STA Speech and TACoS Speech) for SLVG task. Meanwhile, the inference speed of our UniSDNet is 1.56$\times$ faster than the strong multi-query benchmark. Code is available at: https://github.com/xian-sh/UniSDNet.
Soft Learning Probabilistic Circuits

Probabilistic Circuits (PCs) are prominent tractable probabilistic models, allowing for a range of exact inferences. This paper focuses on the main algorithm for training PCs, LearnSPN, a gold standard due to its efficiency, performance, and ease of use, in particular for tabular data. We show that LearnSPN is a greedy likelihood maximizer under mild assumptions. While inferences in PCs may use the entire circuit structure for processing queries, LearnSPN applies a hard method for learning them, propagating at each sum node a data point through one and only one of the children/edges as in a hard clustering process. We propose a new learning procedure named SoftLearn, that induces a PC using a soft clustering process. We investigate the effect of this learning-inference compatibility in PCs. Our experiments show that SoftLearn outperforms LearnSPN in many situations, yielding better likelihoods and arguably better samples. We also analyze comparable tractable models to highlight the differences between soft/hard learning and model querying.
Learning with structured sparsity

This paper investigates a new learning formulation called structured sparsity, which is a natural extension of the standard sparsity concept in statistical learning and compressive sensing. By allowing arbitrary structures on the feature set, this concept generalizes the group sparsity idea. A general theory is developed for learning with structured sparsity, based on the notion of coding complexity associated with the structure. Moreover, a structured greedy algorithm is proposed to efficiently solve the structured sparsity problem. Experiments demonstrate the advantage of structured sparsity over standard sparsity.
Measuring Diversity in Co-creative Image Generation

Quality and diversity have been proposed as reasonable heuristics for assessing content generated by co-creative systems, but to date there has been little agreement around what constitutes the latter or how to measure it. Proposed approaches for assessing generative models in terms of diversity have limitations in that they compare the model's outputs to a ground truth that in the era of large pre-trained generative models might not be available, or entail an impractical number of computations. We propose an alternative based on entropy of neural network encodings for comparing diversity between sets of images that does not require ground-truth knowledge and is easy to compute. We also compare two pre-trained networks and show how the choice relates to the notion of diversity that we want to evaluate. We conclude with a discussion of the potential applications of these measures for ideation in interactive systems, model evaluation, and more broadly within computational creativity.
A Delta Oriented Approach to the Evolution and Reconciliation of Enterprise Software Products Lines

Over the last years, software product line engineering has been applied and adopted by different companies. Existing software product line approaches promote the development of a centralized infrastructure of core assets that addresses the common features and provides variation points to the integration of the variable features of the SPL. In the context of distributed development of enterprise information systems, there are several scenarios where the adoption of these centralized approaches is not enough to accommodate the several requests for the integration of new features and maintenance of existing ones. In such scenarios, the SPL engineering team needs to fork the SPL core assets in order to address the customer needs and due to the marked pressure. In this paper, we propose a delta-oriented approach that promotes the reconciliation of software product lines that are independently evolved. Our approach allows: (i) the automated detection of feature conflicts of the SPLs independently evolved; and (ii) the resolution and merge of such feature
On K\"{o}the's normality question for locally finite-dimensional central division algebras

This paper considers K\"{o}the's question of whether every associative locally finite-dimensional (abbr., LFD) central division algebra $R$ over a field $K$ is a normally locally finite (abbr., NLF) algebra over $K$, that is, whether every nonempty finite subset $Y$ of $R$ is contained in a finite-dimensional central $K$-subalgebra $\mathcal{R} _{Y}$ of $R$. It shows that the answer to the posed question is negative if $K$ is a purely transcendental extension of infinite transcendence degree over an algebraically closed field $k$. On the other hand, central division LFD-algebras over $K$ turn out to be NLF in the following special cases: (i) $K$ is a finitely-generated extension of a finite or a pseudo-algebraically closed field; (ii) $K$ is a higher-dimensional local field with a finite last residue field.
Amplatzer vascular plugs in congenital cardiovascular malformations

Background: Amplatzer vascular plugs (AVPs) are devices ideally suited to close medium-to-large vascular communications. There is limited published literature regarding the utility of AVPs in congenital cardiovascular malformations (CCVMs). Aims: To describe the use of AVPs in different CCVMs and to evaluate their safety and efficacy. Materials and Methods: All patients who required an AVP for the closure of CCVM were included in this retrospective review of our catheterization laboratory data. The efficacy and safety of AVPs are reported. Results: A total of 39 AVPs were implanted in 31 patients. Thirteen (33%) were AVP type I and 23 (59%) were AVP type II. AVP type III were implanted in two patients and type IV in one patient. The major indications for their use included closure of pulmonary arteriovenous malformation (AVM) (n = 7), aortopulmonary collaterals (n = 7), closure of a patent Blalock-Taussig shunt (n = 5), systemic AVM (n = 5), coronary AVM (n = 4), patent ductus arteriosus (PDA) (n = 3), pulmonary artery aneurysms (n = 3), and venovenous collaterals (n = 2). Deployment of the AVP was done predominantly via the 5 – 7F Judkin's right coronary guide catheter. Overall 92% of the AVPs could be successfully deployed and resulted in occlusion of the target vessel in all cases, within 10 minutes. No procedure-related or access site complication occurred. Conclusions: AVPs are versatile, easy to use, and effective devices to occlude the vascular communications in a variety of settings. AVP II is especially useful in the closure of tubular structures with a high flow.
Palaeozoic record of Thaumatoporella PIA , 1927 ( incertae sedis ) ?

From Palaeozoic (mainly Devonian) shallow-water carbonates, spherical to irregular shaped microfossils with thin, apparently homogeneous or perforate micritic walls are widely reported. They are classically referred either to unilocular parathuramminid foraminifera, algae incertae sedis or calcispheres (e.g., Bisphaera, Cribrosphaeroides, Uslo­ nia, Vermiporella myna, Irregularina). Due to their morphology and microstructural features, they are interpreted here as possibly belonging to Thaumatoporella PIA, a widespread Mesozoic-Early Cenozoic taxon of incertae sedis showing a remarkably high morphological variability. In analogy to Mesozoic thaumatoporellaceans, Bisphaera ma­ levkensis BIRINA is interpreted as the cyst (i.e.= resting) stage of forms ascribed to different genera, i.e., Cribro­ sphaeroides, Uslonia and Vermiporella (here: Vermiporella myna WRAY). Note that in the Mesozoic many taxa were also synonymized with Thaumatoporella: Polygonella ELLIOTT, Lithoporella elliotti (EMBERGER), Messopota­ mella DRAGASTAN et al., Vermiporella crisiae DRAGASTAN et al., Micritosphaera SCOTT. This new interpretation, based on material from the Devonian of W-Germany and the Czech Republic, leads to taxonomic reassessment as Thaumatoporella? malevkensis (BIRINA) nov. comb. As a consequence of our interpretation, the rather long Mesozoic to Early Cenozoic (Anisian? to Lutetian) record of thaumatoporellaceans is supposed to be significantly larger than formerly assumed. The possible gap of no records of thaumatoporellaceans between the Middle Carboniferous and the Lower Triassic cannot substantially be argued at the moment (Lazarus effect of one taxon? ; two homeomorphic but taxonomically different taxa).
Genetic Testing

This Molina Clinical Policy (MCP) is intended to facilitate the Utilization Management process. It expresses Molina's determination as to whether certain services or supplies are medically necessary, experimental, investigational, or cosmetic for purposes of determining appropriateness of payment. The conclusion that a particular service or supply is medically necessary does not constitute a representation or warranty that this service or supply is covered (i.e., will be paid for by Molina) for a particular member. The member's benefit plan determines coverage. Each benefit plan defines which services are covered, which are excluded, and which are subject to dollar caps or other limits. Members and their providers will need to consult the member's benefit plan to determine if there are any exclusion(s) or other benefit limitations applicable to this service or supply. If there is a discrepancy between this policy and a member's plan of benefits, the benefits plan will govern. In addition, coverage may be mandated by applicable legal requirements of a State, the Federal government or CMS for Medicare and Medicaid members. CMS's Coverage Database can be found on the CMS website. The coverage directive(s) and criteria from an existing National Coverage Determination (NCD) or Local Coverage Determination (LCD) will supersede the contents of this Molina Clinical Policy (MCP) document and provide the directive for all Medicare members.
AnyV2V: A Plug-and-Play Framework For Any Video-to-Video Editing Tasks

Video-to-video editing involves editing a source video along with additional control (such as text prompts, subjects, or styles) to generate a new video that aligns with the source video and the provided control. Traditional methods have been constrained to certain editing types, limiting their ability to meet the wide range of user demands. In this paper, we introduce AnyV2V, a novel training-free framework designed to simplify video editing into two primary steps: (1) employing an off-the-shelf image editing model (e.g. InstructPix2Pix, InstantID, etc) to modify the first frame, (2) utilizing an existing image-to-video generation model (e.g. I2VGen-XL) for DDIM inversion and feature injection. In the first stage, AnyV2V can plug in any existing image editing tools to support an extensive array of video editing tasks. Beyond the traditional prompt-based editing methods, AnyV2V also can support novel video editing tasks, including reference-based style transfer, subject-driven editing, and identity manipulation, which were unattainable by previous methods. In the second stage, AnyV2V can plug in any existing image-to-video models to perform DDIM inversion and intermediate feature injection to maintain the appearance and motion consistency with the source video. On the prompt-based editing, we show that AnyV2V can outperform the previous best approach by 35\% on prompt alignment, and 25\% on human preference. On the three novel tasks, we show that AnyV2V also achieves a high success rate. We believe AnyV2V will continue to thrive due to its ability to seamlessly integrate the fast-evolving image editing methods. Such compatibility can help AnyV2V to increase its versatility to cater to diverse user demands.