Miracle-dz/UniFAR-FTUs huggingface dataset:
Same Features, Different Day: Weakly Supervised Feature Learning for Seasonal Invariance

"Like night and day" is a commonly used expression to imply that two things are completely different. Unfortunately, this tends to be the case for current visual feature representations of the same scene across varying seasons or times of day. The aim of this paper is to provide a dense feature representation that can be used to perform localization, sparse matching or image retrieval, regardless of the current seasonal or temporal appearance. Recently, there have been several proposed methodologies for deep learning dense feature representations. These methods make use of ground truth pixel-wise correspondences between pairs of images and focus on the spatial properties of the features. As such, they don't address temporal or seasonal variation. Furthermore, obtaining the required pixel-wise correspondence data to train in cross-seasonal environments is highly complex in most scenarios. We propose Deja-Vu, a weakly supervised approach to learning season invariant features that does not require pixel-wise ground truth data. The proposed system only requires coarse labels indicating if two images correspond to the same location or not. From these labels, the network is trained to produce "similar" dense feature maps for corresponding locations despite environmental changes. Code will be made available at: https://github.com/jspenmar/DejaVu_Features
Actor-Transformers for Group Activity Recognition

This paper strives to recognize individual actions and group activities from videos. While existing solutions for this challenging problem explicitly model spatial and temporal relationships based on location of individual actors, we propose an actor-transformer model able to learn and selectively extract information relevant for group activity recognition. We feed the transformer with rich actor-specific static and dynamic representations expressed by features from a 2D pose network and 3D CNN, respectively. We empirically study different ways to combine these representations and show their complementary benefits. Experiments show what is important to transform and how it should be transformed. What is more, actor-transformers achieve state-of-the-art results on two publicly available benchmarks for group activity recognition, outperforming the previous best published results by a considerable margin
A Physics-Based Noise Formation Model for Extreme Low-Light Raw Denoising

Lacking rich and realistic data, learned single image denoising algorithms generalize poorly in real raw images that not resemble the data used for training. Although the problem can be alleviated by the heteroscedastic Gaussian noise model, the noise sources caused by digital camera electronics are still largely overlooked, despite their significant effect on raw measurement, especially under extremely low-light condition. To address this issue, we present a highly accurate noise formation model based on the characteristics of CMOS photosensors, thereby enabling us to synthesize realistic samples that better match the physics of image formation process. Given the proposed noise model, we additionally propose a method to calibrate the noise parameters for available modern digital cameras, which is simple and reproducible for any new device. We systematically study the generalizability of a neural network trained with existing schemes, by introducing a new low-light denoising dataset that covers many modern digital cameras from diverse brands. Extensive empirical results collectively show that by utilizing our proposed noise formation model, a network can reach the capability as if it had been trained with rich real data, which demonstrates the effectiveness of our noise formation model.
Resonating valence bond theory of anomalous spin dynamics of spin-
12
 triangular lattice Heisenberg antiferromagnet and its application to 
Ba3CoSb2O9

Although it is well accepted that the spin-$\frac{1}{2}$ triangular lattice Heisenberg antiferromagnet(TLHAF) has a long range ordered ground state, a thorough understanding of its spin dynamics is still missing. While the linear spin wave theory(LSWT) predicts three branches of magnon mode in the magnetic Brillouin zone(MBZ), the 1/S expansion at the next order is found to break down in a large portion of the MBZ centered around the M point, leaving the fate of the magnon modes there undecided. Recent neutron scattering measurement on Ba$_3$CoSb$_2$O$_9$, an ideal realization of the spin-$\frac{1}{2}$ TLHAF, provides a surprising answer to this issue. Two, rather than three branches of magnon mode are observed around the M point, whose dispersion are strongly renormalized with respect to the LSWT prediction and exhibit pronounced roton-like minimum at the M point. This is accompanied by a strong spin fluctuation continuum at higher energy, inside which two strong and broad spectral peaks of unknown origin are observed. In this work, we propose a simple picture for these spectral anomalies by invoking the resonating valence bond(RVB) physics in the description of the ground state of the system. We find that the roton-like minimum in the magnon dispersion can be explained by the coupling between the collective spin fluctuation and the continuum of Dirac spinon excitation moving in a $\pi$-flux background. We also propose that the two broad peaks in the continuum can be understood respectively as the Landau damped third magnon mode and the Landau damped longitudinal mode. Such a picture can be verified by studying the polarization character of the various spectral features.
GeoFusion: Geometric Consistency Informed Scene Estimation in Dense Clutter

We propose GeoFusion, a SLAM-based scene estimation method for building an object-level semantic map in dense clutter. In dense clutter, objects are often in close contact and severe occlusions, which brings more false detections and noisy pose estimates from existing perception methods. To solve these problems, our key insight is to consider geometric consistency at the object level within a general SLAM framework. The geometric consistency is defined in two parts: geometric consistency score and geometric relation. The geometric consistency score describes the compatibility between object geometry model and observation point cloud. Meanwhile, it provides a reliable measure to filter out false positives in data association. The geometric relation represents the relationship (e.g., contact) between geometric features (e.g., planes) among objects. The geometric relation makes the graph optimization for poses more robust and accurate. GeoFusion can robustly and efficiently infer the object labels, 6D object poses, and spatial relations from continuous noisy semantic measurements. We quantitatively evaluate our method using observations from a Fetch mobile manipulation robot. Our results demonstrate greater robustness against false estimates than frame-by-frame pose estimation from the state-of-the-art convolutional neural network.
Detecting symmetries with neural networks

Identifying symmetries in data sets is generally difficult, but knowledge about them is crucial for efficient data handling. Here we present a method how neural networks can be used to identify symmetries. We make extensive use of the structure in the embedding layer of the neural network which allows us to identify whether a symmetry is present and to identify orbits of the symmetry in the input. To determine which continuous or discrete symmetry group is present we analyse the invariant orbits in the input. We present examples based on rotation groups SO(n) and the unitary group SU(2). Further we find that this method is useful for the classification of complete intersection Calabi-Yau manifolds where it is crucial to identify discrete symmetries on the input space. For this example we present a novel data representation in terms of graphs.
OneShot Global Localization: Instant LiDAR-Visual Pose Estimation

Globally localizing in a given map is a crucial ability for robots to perform a wide range of autonomous navigation tasks. This paper presents OneShot – a global localization algorithm that uses only a single 3D LiDAR scan at a time, while outperforming approaches based on integrating a sequence of point clouds. Our approach, which does not require the robot to move, relies on learning-based descriptors of point cloud segments and computes the full 6 degree-of-freedom pose in a map. The segments are extracted from the current LiDAR scan and are matched against a database using the computed descriptors. Candidate matches are then verified with a geometric consistency test. We additionally present a strategy to further improve the performance of the segment descriptors by augmenting them with visual information provided by a camera. For this purpose, a custom-tailored neural network architecture is proposed. We demonstrate that our LiDAR-only approach outperforms a state-of-the-art baseline on a sequence of the KITTI dataset and also evaluate its performance on the challenging NCLT dataset. Finally, we show that fusing in visual information boosts segment retrieval rates by up to 26% compared to LiDAR-only description.
On Coordination of Smart Grid and Cooperative Cloud Providers

Cooperative cloud providers in the form of cloud federations can potentially reduce their energy costs by exploiting electricity price fluctuations across different locations. In this environment, on the one hand, the electricity price has a significant influence on the federations formed, and, thus, on the profit earned by the cloud providers, and on the other hand, the cloud cooperation has an inevitable impact on the performance of the smart grid. In this regard, the interaction between independent cloud providers and the smart grid is modeled as a two-stage Stackelberg game interleaved with a coalitional game in this article. In this game, in the first stage the smart grid, as a leader chooses a proper electricity pricing mechanism to maximize its own profit. In the second stage, cloud providers cooperatively manage their workload to minimize their electricity costs. Given the dynamic of cloud providers in the federation formation process, an optimization model based on a constrained Markov decision process (CMDP) has been used by the smart grid to achieve the optimal policy. Numerical results show that the proposed solution yields around 28% and 29% profit improvement on average for the smart grid, and the cloud providers, respectively, compared to the noncooperative scheme.
Screening and Identification of Potential Biomarkers for Hepatocellular Carcinoma: An Analysis of TCGA Database and Clinical Validation

Introduction Hepatocellular carcinoma (HCC) is the fifth most common cancer in the world. Up to now, many genes associated with HCC have not yet been identified. In this study, we screened the HCC-related genes through the integrated analysis of the TCGA database, of which the potential biomarkers were also further validated by clinical specimens. The discovery of potential biomarkers for HCC provides more opportunities for diagnostic indicators or gene-targeted therapies. Methods Cancer-related genes in The Cancer Genome Atlas (TCGA) HCC database were screened by a random forest (RF) classifier based on the RF algorithm. Proteins encoded by the candidate genes and other associated proteins obtained via protein–protein interaction (PPI) analysis were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The newly identified genes were further validated in the HCC cell lines and clinical tissue specimens by Western blotting, immunofluorescence, and immunohistochemistry (IHC). Survival analysis verified the clinical value of genes. Results Ten genes with the best feature importance in the RF classifier were screened as candidate genes. By comprehensive analysis of PPI, GO and KEGG, these genes were confirmed to be closely related to HCC tumors. Representative NOX4 and FLVCR1 were selected for further validation by biochemical analysis which showed upregulation in both cancer cell lines and clinical tumor tissues. High expression of NOX4 or FLVCR1 in cancer cells predicts low survival. Conclusion Herein, we report that NOX4 and FLVCR1 are promising biomarkers for HCC that may be used as diagnostic indicators or therapeutic targets.
Tumor endothelial cell up-regulation of IDO1 is an immunosuppressive feed-back mechanism that reduces the response to CD40-stimulating immunotherapy

ABSTRACT CD40-stimulating immunotherapy can elicit potent anti-tumor responses by activating dendritic cells and enhancing T-cell priming. Tumor vessels orchestrate T-cell recruitment during immune response, but the effect of CD40-stimulating immunotherapy on tumor endothelial cells has not been evaluated. Here, we have investigated how tumor endothelial cells transcriptionally respond to CD40-stimulating immunotherapy by isolating tumor endothelial cells from agonistic CD40 mAb- or isotype-treated mice bearing B16-F10 melanoma, and performing RNA-sequencing. Gene set enrichment analysis revealed that agonistic CD40 mAb therapy increased interferon (IFN)-related responses in tumor endothelial cells, including up-regulation of the immunosuppressive enzyme Indoleamine 2, 3-Dioxygenase 1 (IDO1). IDO1 was predominantly expressed in endothelial cells within the tumor microenvironment, and its expression in tumor endothelium was positively correlated to T-cell infiltration and to increased intratumoral expression of IFNγ. In vitro, endothelial cells up-regulated IDO1 in response to T-cell-derived IFNγ, but not in response to CD40-stimulation. Combining agonistic CD40 mAb therapy with the IDO1 inhibitor epacadostat delayed tumor growth in B16-F10 melanoma, associated with increased activation of tumor-infiltrating T-cells. Hereby, we show that the tumor endothelial cells up-regulate IDO1 upon CD40-stimulating immunotherapy in response to increased IFNγ-secretion by T-cells, revealing a novel immunosuppressive feedback mechanism whereby tumor vessels limit T-cell activation.
Factors That Influence the Relationship Between Social Support and Health-Related Quality of Life of Older People Living in the Community

As the proportion of older people in Greece grows and the need for promoting healthy aging is apparent, it becomes increasingly important to investigate the relationship between social support and health-related quality of life (HRQoL) of older people in the community. A cross-sectional study was conducted in 451 older people living in the community in greater Athens, Greece, with an anonymous questionnaire, including demographics, and the EQ5D including EuroQol–visual analogue scale (EQ-VAS) and the Multidimensional Scale of Perceived Social Support. Multivariate linear regression analysis was applied to identify independent factors related to perceived social support and HRQoL. The majority of older people were women, married or widowed, living with their family or alone. Perceived social support was medium and higher from significant others and family. A statistically significant positive relationship was found between social support from significant others/friends and HRQoL, as well as between social support from friends/family/significant others and EQ-VAS. In conclusion, HRQoL of older people is influenced by the received social support.
Potential of the economic valuation of soil-based ecosystem services to inform sustainable soil management and policy

The concept of ecosystem services, especially in combination with economic valuation, can illuminate trade-offs involved in soil management, policy and governance, and thus support decision making. In this paper, we investigate and highlight the potential and limitations of the economic valuation of soil-based ecosystem services to inform sustainable soil management and policy. We formulate a definition of soil-based ecosystem services as basis for conducting a review of existing soil valuation studies with a focus on the inclusion of ecosystem services and the choice of valuation methods. We find that, so far, the economic valuation of soil-based ecosystem services has covered only a small number of such services and most studies have employed cost-based methods rather than state-of-the-art preference-based valuation methods, even though the latter would better acknowledge the public good character of soil related services. Therefore, the relevance of existing valuation studies for political processes is low. Broadening the spectrum of analyzed ecosystem services as well as using preference-based methods would likely increase the informational quality and policy relevance of valuation results. We point out options for improvement based on recent advances in economic valuation theory and practice. We conclude by investigating the specific roles economic valuation results can play in different phases of the policy-making process, and the specific requirements for its usefulness in this context.
Dual SGLT1/SGLT2 Inhibitor Phlorizin Ameliorates Non-Alcoholic Fatty Liver Disease and Hepatic Glucose Production in Type 2 Diabetic Mice

Purpose NAFLD is a hepatic component of type 2 diabetes mellitus (T2D), in which impaired hepatic glucose production plays an important role. Inhibitors of sodium glucose transporter 2 (SGLT2) reduce glycemia and exert beneficial effects on diabetic complications. Recently, dual SGLT1/2 inhibition has been proposed to be more effective in reducing glycemia. We hypothesized that improving hepatic glucose metabolism induced by SGLT1/2 inhibition could be accompanied by beneficial effects on NAFLD progression. Methods Glycemic homeostasis, hepatic glucose production and NAFLD features were investigated in obese T2D mice, treated with SGLT1/2 inhibitor phlorizin for 1 week. Results T2D increased glycemia; insulinemia; hepatic expression of phosphoenolpyruvate carboxykinase (PEPCK), glucose-6-phosphatase (G6Pase) and glucose transporter 2 (Slc2a2 gene); hepatocyte nuclear factors 1A/4A/3B-binding activity in Slc2a2; endogenous glucose production; liver weight, plasma transaminase concentration as well as hepatic inflammation markers, and induced histological signals of non-alcoholic steatohepatitis (NASH, according to NASH-CRN Pathology Committee System). Phlorizin treatment restored all these parameters (mean NASH score reduced from 5.25 to 2.75 P<0.001); however, plasma transaminase concentration was partially reverted and some hepatic inflammation markers remained unaltered. Conclusion NAFLD accompanies altered hepatic glucose metabolism in T2D mice and that greatly ameliorated through short-term treatment with the dual SGLT1/2 inhibitor. This suggests that altered hepatic glucose metabolism participates in T2D-related NAFLD and highlights the pharmacological inhibition of SGLTs as a useful approach not only for controlling glycemia but also for mitigating development and/or progression of NAFLD.
Breath figure–derived porous semiconducting films for organic electronics

We show here that breath figure–derived semiconducting films are suitable and universal for porous organic electronics. Porous semiconductor film morphologies facilitate fluid diffusion and mass transport into the charge-carrying layers of diverse electronic devices. Here, we report the nature-inspired fabrication of several porous organic semiconductor-insulator blend films [semiconductor: P3HT (p-type polymer), C8BTBT (p-type small-molecule), and N2200 (n-type polymer); insulator: PS] by a breath figure patterning method and their broad and general applicability in organic thin-film transistors (OTFTs), gas sensors, organic electrochemical transistors (OECTs), and chemically doped conducting films. Detailed morphological analysis of these films demonstrates formation of textured layers with uniform nanopores reaching the bottom substrate with an unchanged solid-state packing structure. Device data gathered with both porous and dense control semiconductor films demonstrate that the former films are efficient TFT semiconductors but with added advantage of enhanced sensitivity to gases (e.g., 48.2%/ppm for NO2 using P3HT/PS), faster switching speeds (4.7 s for P3HT/PS OECTs), and more efficient molecular doping (conductivity, 0.13 S/m for N2200/PS).
Genetic Diversity of Non-O157 Shiga Toxin-Producing Escherichia coli Recovered From Patients in Michigan and Connecticut

Shiga toxin-producing Escherichia coli (STEC) are important foodborne pathogens and non-O157 serotypes have been gradually increasing in frequency. The non-O157 STEC population is diverse and is often characterized using serotyping and/or multilocus sequence typing (MLST). Although spacers within clustered regularly interspaced repeat (CRISPR) regions were shown to comprise horizontally acquired DNA elements, this region does not actively acquire spacers in STEC. Hence, it is useful for further characterizing non-O157 STEC and examining relationships between strains. Our study goal was to evaluate the genetic relatedness of 41 clinical non-O157 isolates identified in Michigan between 2001 and 2005 while comparing to 114 isolates from Connecticut during an overlapping time period. Whole genome sequencing (WGS) was performed, and sequences were extracted for serotyping, MLST and CRISPR analysis. Phylogenetic analysis of MLST and CRISPR data was performed using the Neighbor joining and unweighted pair group method with arithmetic mean (UPGMA) algorithms, respectively. In all, 29 serogroups were identified; eight were unique to Michigan and 13 to Connecticut. “Big-six” serogroup frequencies were similar by state (Michigan: 73.2%, Connecticut: 81.6%), though STEC O121 was not found in Michigan. The distribution of sequence types (STs) and CRISPR profiles was also similar across states. Interestingly, big-six serogroups such as O103 and O26, grouped into different STs located on distinct branches of the phylogeny, further confirming that serotyping alone is not adequate for evaluating strain relatedness. Comparatively, the CRISPR analysis identified 361 unique spacers that grouped into 80 different CRISPR profiles. CRISPR spacers 231 and 317 were isolated from 79.2% (n = 118) and 59.1% (n = 88) of strains, respectively, regardless of serogroup and ST. Spacer profiles clustered according to the MLST analysis, though some discrepancies were noted. Indeed, use of both MLST and CRISPR typing enhanced the discriminatory power when compared to the use of each tool separately. These data highlight the genetic diversity of clinical STEC from different locations and show that CRISPR profiling can be used alongside MLST to discriminate related strains. Use of targeted sequencing approaches are particularly helpful for sites without WGS capabilities and can help define which strains require additional characterization using more discriminatory methods.