Miracle-dz/UniFAR-FTUs huggingface dataset:
Tell-the-difference: Fine-grained Visual Descriptor via a Discriminating Referee

In this paper, we investigate a novel problem of telling the difference between image pairs in natural language. Compared to previous approaches for single image captioning, it is challenging to fetch linguistic representation from two independent visual information. To this end, we have proposed an effective encoder-decoder caption framework based on Hyper Convolution Net. In addition, a series of novel feature fusing techniques for pairwise visual information fusing are introduced and a discriminating referee is proposed to evaluate the pipeline. Because of the lack of appropriate datasets to support this task, we have collected and annotated a large new dataset with Amazon Mechanical Turk (AMT) for generating captions in a pairwise manner (with 14764 images and 26710 image pairs in total). The dataset is the first one on the relative difference caption task that provides descriptions in free language. We evaluate the effectiveness of our model on two datasets in the field and it outperforms the state-of-the-art approach by a large margin.
Morrey’s $\varepsilon $-conformality lemma in metric spaces

We provide a simpler proof and slight strengthening of Morrey's famous lemma on $\varepsilon$-conformal mappings. Our result more generally applies to Sobolev maps with values in a complete metric space and we obtain applications to the existence of area minimizing surfaces of higher genus in metric spaces. Unlike Morrey's proof, which relies on the measurable Riemann mapping theorem, we only need the existence of smooth isothermal coordinates established by Korn and Lichtenstein.
Learning Generalisable Omni-Scale Representations for Person Re-Identification

An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation. In this paper, we develop novel CNN architectures to address both challenges. First, we present a re-ID CNN termed omni-scale network (OSNet) to learn features that not only capture different spatial scales but also encapsulate a synergistic combination of multiple scales, namely omni-scale features. The basic building block consists of multiple convolutional streams, each detecting features at a certain scale. For omni-scale feature learning, a unified aggregation gate is introduced to dynamically fuse multi-scale features with channel-wise weights. OSNet is lightweight as its building blocks comprise factorised convolutions. Second, to improve generalisable feature learning, we introduce instance normalisation (IN) layers into OSNet to cope with cross-dataset discrepancies. Further, to determine the optimal placements of these IN layers in the architecture, we formulate an efficient differentiable architecture search algorithm. Extensive experiments show that, in the conventional same-dataset setting, OSNet achieves state-of-the-art performance, despite being much smaller than existing re-ID models. In the more challenging yet practical cross-dataset setting, OSNet beats most recent unsupervised domain adaptation methods without using any target data. Our code and models are released at https://github.com/KaiyangZhou/deep-person-reid.
The low-frequency properties of FR 0 radio galaxies

Using the alternative data release of the TIFR GMRT Sky Survey (TGSS), we studied the low-frequency properties of FR 0 radio galaxies, the large population of compact radio sources associated with red massive early-type galaxies revealed by surveys at 1.4 GHz. We considered TGSS observations from FR0CAT, a sample formed by 104 FR 0s at z < 0.05: all but one of them are covered by the TGSS, and 43 of them are detected above a 5σ limit of 17.5 mJy. No extended emission has been detected around the FR 0s, corresponding to a luminosity limit of ≲4 × 1023 W Hz−1 over an area of 100 kpc × 100 kpc. All but eight FR 0s have a flat or inverted spectral shape (α < 0.5) between 150 MHz and 1.4 GHz: this spectral behavior confirms the general paucity of optically thin extended emission within the TGSS beam, as is expected for their compact 1.4 GHz morphology. Data at 5 GHz were used to build their radio spectra, which are also generally flat at higher frequencies. By focusing on a sub-sample of FR 0s with flux density > 50 mJy at 1.4 GHz, we found that ∼75% of them have a convex spectrum, but with a smaller curvature than the more powerful gigahertz peaked-spectrum sources (GPS). The typical FR 0s radio spectrum is better described by a gradual steepening toward high frequencies, rather than to a transition from an optically-thick to an optically-thin regime, possibly observed in only ∼15% of the sample.
Sovereign Risk Indices and Bayesian Theory Averaging

In economic applications, model averaging has found principal use in examining the validity of various theories related to observed heterogeneity in outcomes such as growth, development, and trade. Though often easy to articulate, these theories are imperfectly captured quantitatively. A number of different proxies are often collected for a given theory and the uneven nature of this collection requires care when employing model averaging. Furthermore, if valid, these theories ought to be relevant outside of any single narrowly focused outcome equation. We propose a methodology which treats theories as represented by latent indices, these latent processes controlled by model averaging on the proxy level. To achieve generalizability of the theory index our framework assumes a collection of outcome equations. We accommodate a flexible set of generalized additive models, enabling non-Gaussian outcomes to be included. Furthermore, selection of relevant theories also occurs on the outcome level, allowing for theories to be differentially valid. Our focus is on creating a set of theory-based indices directed at understanding a country’s potential risk of macroeconomic collapse. These Sovereign Risk Indices are calibrated across a set of different “collapse” criteria, including default on sovereign debt, heightened potential for high unemployment or inflation and dramatic swings in foreign exchange values. The goal of this exercise is to render a portable set of country/year theory indices which can find more general use in the research community.
Gravity-mode period spacings and near-core rotation rates of 611 γ Doradus stars with Kepler

We report our survey of γ Dor stars from the 4-yr Kepler mission. These stars pulsate mainly in g modes and r modes, showing period-spacing patterns in the amplitude spectra. The period-spacing patterns are sensitive to the chemical composition gradients and the near-core rotation, hence they are essential for understanding the stellar interior. We identified period-spacing patterns in 611 γ Dor stars. Almost every star pulsates in dipole g modes, while about 30% of stars also show clear patterns for quadrupole g modes and 16% of stars present r mode patterns. We measure periods, period spacings, and the gradient of the period spacings. These three observables guide the mode identifications and can be used to estimate the near-core rotation rate. We find many stars are hotter and show longer period-spacing patterns than theory. Using the Traditional Approximation of Rotation (TAR), we inferred the asymptotic spacings, the near-core rotation rates, and the radial orders of the g and r modes. Most stars have a near-core rotation rate around 1 d−1and an asymptotic spacing around 4000 s. We also find that many stars rotate more slowly than predicted by theory for unclear reasons. 11 stars show rotational splittings with fast rotation rates. We compared the observed slope–rotation relation with the theory and find a large spread. We detected rotational modulations in 58 stars and used them to derive the core-to-surface rotation ratios. The interiors rotate faster than the cores in most stars, but by no more than 5%.
Minimizing immersions of a hyperbolic surface in a hyperbolic 3-manifold

abstract:Let $(S,h)$ be a closed hyperbolic surface and $M$ be a quasi-Fuchsian $3$-manifold. We consider incompressible maps from $S$ to $M$ that are critical points of an energy functional $F$ which is homogeneous of degree $1$. These ``minimizing'' maps are solutions of a non-linear elliptic equation, and reminiscent of harmonic maps---but when the target is Fuchsian, minimizing maps are minimal Lagrangian diffeomorphisms to the totally geodesic surface in $M$. We prove the uniqueness of smooth minimizing maps from $(S,h)$ to $M$ in a given homotopy class. When $(S,h)$ is fixed, smooth minimizing maps from $(S,h)$ are described by a simple holomorphic data on $S$: a complex self-adjoint Codazzi tensor of determinant $1$. The space of admissible data is smooth and naturally equipped with a complex structure, for which the monodromy map taking a data to the monodromy representation of the image is holomorphic. Minimizing maps are in this way reminiscent of shear-bend coordinates, with the complexification of $F$ analoguous to the complex length.
Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks

In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response. Remote sensing provides this data at an unprecedented scale, but extracting operationalizable information from satellite images is slow and labor-intensive. In this work, we use machine learning to automate the detection of building damage in satellite imagery. We compare the performance of four different convolutional neural network models in detecting damaged buildings in the 2010 Haiti earthquake. We also quantify how well the models will generalize to future disasters by training and testing models on different disaster events.
Probability Logic

This chapter presents probability logic as a rationality framework for human reasoning under uncertainty. Selected formal-normative aspects of probability logic are discussed in the light of experimental evidence. Specifically, probability logic is characterized as a generalization of bivalent truth-functional propositional logic (short "logic"), as being connexive, and as being nonmonotonic. The chapter discusses selected argument forms and associated uncertainty propagation rules. Throughout the chapter, the descriptive validity of probability logic is compared to logic, which was used as the gold standard of reference for assessing the rationality of human reasoning in the 20th century.
Deployment and impact of support staff in schools : report on findings from the second national questionnaire survey of schools, support staff and teachers (Strand 1, Wave 2, 2006)

Background This Research Brief reports on the deployment and characteristics of all categories of support staff in primary, secondary and special schools in England and Wales. It presents results from a second large scale, nationally representative postal survey of schools, support staff and teachers, conducted in 2006. The first wave was conducted in 2004. It is the largest study of support staff yet undertaken and was commissioned by the DfES 1
Abstract 17973: Biomass Fuel Use is Associated With Elevated Blood Pressure and Hypertension Status in Puno, Peru

Background: Household air pollution from biomass fuel use affects 3 billion people worldwide. There are few studies that examine the relationship between biomass fuel use and blood pressure. We sought to determine if daily biomass fuel use was associated with higher blood pressure and increased hypertension in Peru. Methods: We analyzed baseline information from an age- and sex-matched, population-based study in Puno, Peru. Daily biomass fuel use was self-reported. Hypertension was defined as a systolic blood pressure (SBP) ≥140 mmHg; diastolic blood pressure (DBP) ≥90 mmHg; or self-report of diagnosis and anti-hypertensive medications. We used linear and logistic multivariable regressions, adjusting for age, sex, daily cigarette use and body mass index, to examine the relationship between daily biomass fuel use with blood pressure and hypertension, respectively. Results: Data from 1004 individuals (mean age 55.3 years, 51.7% female) were included. There was evidence of an association between daily biomass fuel use and hypertension (adjusted OR = 2.1, 95%CI 1.2 to 3.5). Subjects who reported daily use of biomass fuels had 6.0 mmHg (95% CI 4.1 to 8.0) higher SBP and 4.8 mmHg (95%CI 3.5 to 6.0) higher DBP compared to those who did not use biomass fuels daily. In a secondary analysis, we used rural residence (vs. urban) as a proxy of biomass daily use and the relationship with hypertension and systolic blood pressure was similar: a higher odds of hypertension (adjusted OR = 2.1, 95%CI 1.3 to 3.6), and higher SBP (6.7mmHg, 95% CI 4.8 to 8.6) and DBP (5.5 mmHg, 95% CI 4.3 to 6.8) in rural vs. urban participants. Conclusion: Biomass fuel use is associated with increased odds of hypertension and higher blood pressure in Puno, Peru. Reducing exposure to air pollution from biomass fuel use represents a potential opportunity for cardiovascular prevention in rural communities worldwide. Longitudinal studies to evaluate the impact of reducing household air pollution are needed.
G-equivariance of formal models of flag varieties

Let G be a split connected reductive group scheme over the ring of integers of a finite extension L of Q_p and \lambda an algebraic character of a split maximal torus T. Let us also consider X^{rig} the rigid analytic flag variety of G. In the first part of this paper, we introduce a family of \lambda-twisted differential operators on a formal model Y of X^{rig}. We compute their global sections and we prove coherence together with several cohomological properties. In the second part, we define the category of coadmissible G(L)-equivariant arithmetic D(\lambda)-modules over the family of formal models of the rigid flag variety X^{rig}. We show that if /lambda is such that \lambda+\rho is dominant and regular (\rho being the Weyl character), then the preceding category is anti-equivalent to the category of admissible locally analytic-representations, with central character \lambda. In particular, we generalize the results in [25] for algebraic characters.
Warm brane inflation with an exponential potential: A consistent realization away from the swampland

It has very recently been realized that coupling branes to higher dimensional quantum gravity theories and considering the consistency of what lives on the branes, one is able to understand whether such theories can belong either to the swampland or to the landscape. In this regard, in the present work, we study a warm inflation model embedded in the Randall-Sundrum braneworld scenario. It is explicitly shown that this model belongs to the landscape by supporting a strong dissipative regime with an inflaton steep exponential potential. The presence of extra dimension effects from the braneworld allow achieving this strong dissipative regime, which is shown to be both theoretically and observationally consistent. In fact, such strong dissipation effects, which decrease towards the end of inflation, together with the extra dimension effect, allow the present realization to simultaneously satisfy all previous restrictions imposed on such a model and to evade the recently proposed swampland conjectures. The present implementation of this model, in terms of an exponential potential for the scalar field, makes it also a possible candidate for describing the late-time Universe in the context of a dissipative quintessential inflation model and we discuss this possibility in the Conclusions.
End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds

Recent work on 3D object detection advocates point cloud voxelization in birds-eye view, where objects preserve their physical dimensions and are naturally separable. When represented in this view, however, point clouds are sparse and have highly variable point density, which may cause detectors difficulties in detecting distant or small objects (pedestrians, traffic signs, etc.). On the other hand, perspective view provides dense observations, which could allow more favorable feature encoding for such cases. In this paper, we aim to synergize the birds-eye view and the perspective view and propose a novel end-to-end multi-view fusion (MVF) algorithm, which can effectively learn to utilize the complementary information from both. Specifically, we introduce dynamic voxelization, which has four merits compared to existing voxelization methods, i) removing the need of pre-allocating a tensor with fixed size; ii) overcoming the information loss due to stochastic point/voxel dropout; iii) yielding deterministic voxel embeddings and more stable detection outcomes; iv) establishing the bi-directional relationship between points and voxels, which potentially lays a natural foundation for cross-view feature fusion. By employing dynamic voxelization, the proposed feature fusion architecture enables each point to learn to fuse context information from different views. MVF operates on points and can be naturally extended to other approaches using LiDAR point clouds. We evaluate our MVF model extensively on the newly released Waymo Open Dataset and on the KITTI dataset and demonstrate that it significantly improves detection accuracy over the comparable single-view PointPillars baseline.
Semantic Guided and Response Times Bounded Top-k Similarity Search over Knowledge Graphs

Recently, graph query is widely adopted for querying knowledge graphs. Given a query graph GQ, the graph query finds subgraphs in a knowledge graph G that exactly or approximately match GQ. We face two challenges on graph query: (1) the structural gap between GQ and the predefined schema in G causes mismatch with query graph, (2) users cannot view the answers until the graph query terminates, leading to a longer system response time (SRT). In this paper, we propose a semantic-guided and response-time-bounded graph query to return the top-k answers effectively and efficiently. We leverage a knowledge graph embedding model to build the semantic graph SGQ, and we define the path semantic similarity (pss) over SGQ as the metric to evaluate the answer’s quality. Then, we propose an A* semantic search on SGQ to find the top-k answers with the greatest pss via a heuristic pss estimation. Furthermore, we make an approximate optimization on A* semantic search to allow users to trade off the effectiveness for SRT within a user- specific time bound. Extensive experiments over real datasets confirm the effectiveness and efficiency of our solution.
Refresh Triggered Computation

To employ a Convolutional Neural Network (CNN) in an energy-constrained embedded system, it is critical for the CNN implementation to be highly energy efficient. Many recent studies propose CNN accelerator architectures with custom computation units that try to improve the energy efficiency and performance of CNNs by minimizing data transfers from DRAM-based main memory. However, in these architectures, DRAM is still responsible for half of the overall energy consumption of the system, on average. A key factor of the high energy consumption of DRAM is the refresh overhead, which is estimated to consume 40% of the total DRAM energy. In this article, we propose a new mechanism, Refresh Triggered Computation (RTC), that exploits the memory access patterns of CNN applications to reduce the number of refresh operations. RTC uses two major techniques to mitigate the refresh overhead. First, Refresh Triggered Transfer (RTT) is based on our new observation that a CNN application accesses a large portion of the DRAM in a predictable and recurring manner. Thus, the read/write accesses of the application inherently refresh the DRAM, and therefore a significant fraction of refresh operations can be skipped. Second, Partial Array Auto-Refresh (PAAR) eliminates the refresh operations to DRAM regions that do not store any data. We propose three RTC designs (min-RTC, mid-RTC, and full-RTC), each of which requires a different level of aggressiveness in terms of customization to the DRAM subsystem. All of our designs have small overhead. Even the most aggressive RTC design (i.e., full-RTC) imposes an area overhead of only 0.18% in a 16 Gb DRAM chip and can have less overhead for denser chips. Our experimental evaluation on six well-known CNNs shows that RTC reduces average DRAM energy consumption by 24.4% and 61.3% for the least aggressive and the most aggressive RTC implementations, respectively. Besides CNNs, we also evaluate our RTC mechanism on three workloads from other domains. We show that RTC saves 31.9% and 16.9% DRAM energy for Face Recognition and Bayesian Confidence Propagation Neural Network (BCPNN), respectively. We believe RTC can be applied to other applications whose memory access patterns remain predictable for a sufficiently long time.
Statistical localization: From strong fragmentation to strong edge modes

Certain disorder-free Hamiltonians can be non-ergodic due to a \emph{strong fragmentation} of the Hilbert space into disconnected sectors. Here, we characterize such systems by introducing the notion of `statistically localized integrals of motion' (SLIOM), whose eigenvalues label the connected components of the Hilbert space. SLIOMs are not spatially localized in the operator sense, but appear localized to sub-extensive regions when their expectation value is taken in typical states with a finite density of particles. We illustrate this general concept on several Hamiltonians, both with and without dipole conservation. Furthermore, we demonstrate that there exist perturbations which destroy these integrals of motion in the bulk of the system, while keeping them on the boundary. This results in statistically localized \emph{strong zero modes}, leading to infinitely long-lived edge magnetizations along with a thermalizing bulk, constituting the first example of such strong edge modes in a non-integrable model. We also show that in a particular example, these edge modes lead to the appearance of topological string order in a certain subset of highly excited eigenstates. Some of our suggested models can be realized in Rydberg quantum simulators.
Comparison of clinical and pedobarographic measures in clubfeet treated with posteromedial soft-tissue release

Background Many studies have evaluated the results of different therapy regimes for congenital clubfeet. Most analyzed clinical, radiographic or pedobarographic measurements alone. Few studies analyzed the correlation between foot pressure distribution and clinical outcome. Methods We evaluated the foot pressure measurements of 33 clubfeet treated with posteromedial soft-tissue release. The average age at examination was 64 months. Correlations between pedobarographic and clinical results were assessed and the McKay score was calculated. Results Significant pedobarographic differences between clubfeet and healthy feet were observed in several regions of the foot, but a correlation between pedobarographic results and clinical outcome was observed in only two regions. Longer contact time of the midfoot was observed in clubfeet, with a negative correlation of 0.53 to clinical outcome. Lower peak pressure was observed at the area under the first metatarsal bone in clubfeet, with a positive correlation of 0.33. Conclusion Foot pressure measurement is a useful tool to analyze outcomes after clubfoot treatment, but not all measured results are of clinical importance. Our study showed that especially overcorrection of cavus and recurrent supinatus are limiting factors for a good clinical outcome.