Miracle-dz/UniFAR-FTUs huggingface dataset:
Clockwork cosmology

The higher order generalisation of the clockwork mechanism to gravitational interactions provides a means to generate an exponentially suppressed coupling to matter from a fundamental theory of multiple interacting gravitons, without introducing large hierarchies in the underlying potential and without the need for a dilaton, suggesting a possible application to the hierarchy problem. We work in the framework of ghost free multi-gravity with “nearest-neighbour” interactions, and present a formalism by which one is able to construct potentials such that the theory will always exhibit this clockwork effect. We also consider cosmological solutions to the general theory, where all metrics are of FRW form, with site-dependent scale factors/lapses. We demonstrate the existence of multiple deSitter vacua where all metrics share the same Hubble parameter, and we solve the modified Einstein equations numerically for an example clockwork model constructed using our formalism, finding that the evolution of the metric that matter couples to is essentially equivalent to that of general relativity at the modified Planck scale. It is important to stress that while we focus on the application to clockwork theories, our work is entirely general and facilitates finding cosmological solutions to any ghost free multi-gravity theory with “nearest-neighbour” interactions. Moreover, we clarify previous work on the continuum limit of the theory, which is generically a scalar-tensor braneworld, using the Randall-Sundrum model as a special case and showing how the discrete-clockwork cosmological results map to the continuum results in the appropriate limit.
Integrating Feature Direction Information with a Level Set Formulation for Image Segmentation

A feature-dependent variational level set formulation is proposed for image segmentation. Two second order directional derivatives act as the external constraint in the level set evolution, with the directional derivative across the image features direction playing a key role in contour extraction and another only slightly contributes. To overcome the local gradient limit, we integrate the information from the maximal (in magnitude) second-order directional derivative into a common variational framework. It naturally encourages the level set function to deform (up or down) in opposite directions on either side of the image edges, and thus automatically generates object contours. An additional benefit of this proposed model is that it does not require manual initial contours, and our method can capture weak objects in noisy or intensity-inhomogeneous images. Experiments on infrared and medical images demonstrate its advantages.
Deep Dynamic Cloud Lighting

Sky illumination is a core source of lighting in rendering, and a substantial amount of work has been developed to simulate lighting from clear skies. However, in reality, clouds substantially alter the appearance of the sky and subsequently change the scene's illumination. While there have been recent advances in developing sky models which include clouds, these all neglect cloud movement which is a crucial component of cloudy sky appearance. In any sort of video or interactive environment, it can be expected that clouds will move, sometimes quite substantially in a short period of time. Our work proposes a solution to this which enables whole-sky dynamic cloud synthesis for the first time. We achieve this by proposing a multi-timescale sky appearance model which learns to predict the sky illumination over various timescales, and can be used to add dynamism to previous static, cloudy sky lighting approaches.
Nearly Work-Efficient Parallel DFS in Undirected Graphs

We present the first parallel depth-first search algorithm for undirected graphs that has near-linear work and sublinear depth. Concretely, in any n-node m-edge undirected graph, our algorithm computes a DFS in Õ(√n) depth and using Õ (m + n) work. All prior work either required Ω(n) depth, and thus were essentially sequential, or needed a high poly(n) work and thus were far from being work-efficient.
Evaluation of a Canonical Image Representation for Sidescan Sonar

Acoustic sensors play an important role in autonomous underwater vehicles (AUVs). Sidescan sonar (SSS) detects a wide range and provides photo-realistic images in high resolution. However, SSS projects the 3D seafloor to 2D images, which are distorted by the AUV’s altitude, target’s range and sensor’s resolution. As a result, the same physical area can show significant visual differences in SSS images from different survey lines, causing difficulties in tasks such as pixel correspondence and template matching. In this paper, a canonical transformation method consisting of intensity correction and slant range correction is proposed to decrease the above distortion. The intensity correction includes beam pattern correction and incident angle correction using three different Lambertian laws (cos, cos2, cot), whereas the slant range correction removes the nadir zone and projects the position of SSS elements into equally horizontally spaced, view-point independent bins. The proposed method is evaluated on real data collected by a HUGIN AUV, with manually-annotated pixel correspondence as ground truth reference. Experimental results on patch pairs compare similarity measures and keypoint descriptor matching. The results show that the canonical transformation can improve the patch similarity, as well as SIFT descriptor matching accuracy in different images where the same physical area was ensonified.
The automorphism group of Kodaira surfaces

In this paper we give an explicit description of the automorphism group of a primary Kodaira surface $X$ in terms of suitable liftings to the universal cover $\mathbb{C}^2$. As it happens for complex tori, the automorphism group of $X$ is an extension of a finite cyclic group related to the action of the automorphisms on the tangent bundle of $X$ by a group of automorphisms which acts on $X$ as translations (both on the base and on the fibres of the elliptic fibraion of $X$). We can then characterize those automorphisms acting symplectically and those acting trivially on cohomology. Finally, we describe the fixed locus of an automorphism and we show that, if not empty, it is the union of a finite number of fibres of the elliptic fibration of $X$. As a byproduct, we provide a different proof of Borcea's description of the moduli space of Kodaira surfaces.
Circumstantial evidence and explanatory models for synapses in large-scale spike recordings

Whether, when, and how causal interactions between neurons can be meaningfully studied from observations of neural activity alone are vital questions in neural data analysis. Here we aim to better outline the concept of functional connectivity for the specific situation where systems neuroscientists aim to study synapses using spike train recordings. In some cases, cross-correlations between the spikes of two neurons are such that, although we may not be able to say that a relationship is causal without experimental manipulations, models based on synaptic connections provide precise explanations of the data. Additionally, there is often strong circumstantial evidence that pairs of neurons are monosynaptically connected. Here we illustrate how circumstantial evidence for or against synapses can be systematically assessed and show how models of synaptic effects can provide testable predictions for pair-wise spike statistics. We use case studies from large-scale multi-electrode spike recordings to illustrate key points and to demonstrate how modeling synaptic effects using large-scale spike recordings opens a wide range of data analytic questions.
Distributed Multi-writer Multi-reader Atomic Register with Optimistically Fast Read and Write

A distributed multi-writer multi-reader (MWMR) atomic register is an important primitive that enables a wide range of distributed algorithms. Hence, improving its performance can have large-scale consequences. Since the seminal work of ABD emulation in the message-passing networks, many researchers study fast implementations of atomic registers under various conditions. "Fast'' means that a read or a write can be completed with 1 round-trip time (RTT), by contacting a simple majority. In this work, we explore an atomic register with optimal resilience and ''optimistically fast'' read and write operations. That is, both operations can be fast if there is no concurrent write. This paper has three contributions: (i) We present Gus, the emulation of an MWMR atomic register with optimal resilience and optimistically fast reads and writes when there are up to 5 nodes; (ii) We show that when there are > 5 nodes, it is impossible to emulate an MWMR atomic register with both properties; and (iii) We implement Gus in the framework of EPaxos and Gryff, and show that Gus provides lower tail latency than state-of-the-art systems such as EPaxos, Gryff, Giza, and Tempo under various workloads in the context of geo-replicated object storage systems.
From Artifacts to Outcomes: Comparison of HMD VR, Desktop, and Slides Lectures for Food Microbiology Laboratory Instruction

Despite the value of VR (Virtual Reality) for educational purposes, the instructional power of VR in Biology Laboratory education remains under-explored. Laboratory lectures can be challenging due to students’ low motivation to learn abstract scientific concepts and low retention rate. Therefore, we designed a VR-based lecture on fermentation and compared its effectiveness with lectures using PowerPoint slides and a desktop application. Grounded in the theory of distributed cognition and motivational theories, our study examined how learning happens in each condition from students’ learning outcomes, behaviors, and perceptions. Our result indicates that VR facilitates students’ long-term retention to learn by cultivating their longer visual attention and fostering a higher sense of immersion, though students’ short-term retention remains the same across all conditions. This study extends current research on VR studies by identifying the characteristics of each teaching artifact and providing design implications for integrating VR technology into higher education.
Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans

We propose an unsupervised method for parsing large 3D scans of real-world scenes with easily-interpretable shapes. This work aims to provide a practical tool for analyzing 3D scenes in the context of aerial surveying and mapping, without the need for user annotations. Our approach is based on a probabilistic reconstruction model that decomposes an input 3D point cloud into a small set of learned prototypical 3D shapes. The resulting reconstruction is visually interpretable and can be used to perform unsupervised instance and low-shot semantic segmentation of complex scenes. We demonstrate the usefulness of our model on a novel dataset of seven large aerial LiDAR scans from diverse real-world scenarios. Our approach outperforms state-of-the-art unsupervised methods in terms of decomposition accuracy while remaining visually interpretable. Our code and dataset are available at https://romainloiseau.fr/learnable-earth-parser/
LLIC: Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression

Effective Receptive field (ERF) plays an important role in transform coding, which determines how much redundancy can be removed at most during transform and how many spatial priors can be utilized to synthesize textures during inverse transform. Existing methods rely on stacks of small kernels, whose ERF remains not large enough instead, or heavy non-local attention mechanisms, which limit the potential of high resolution image coding. To tackle this issue, we propose Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression (LLIC). Specifically, for the first time in learned image compression community, we introduce a few large kernel-based depth-wise convolutions to reduce more redundancy while maintaining modest complexity. Due to wide range of image diversity, we propose to enhance the adaptability of convolutions via generating weights in a self-conditioned manner. The large kernels cooperate with non-linear embedding and gate mechanisms for better expressiveness and lighter point-wise interactions. We also investigate improved training techniques to fully exploit the potential of large kernels. In addition, to enhance the interactions among channels, we propose the adaptive channel-wise bit allocation via generating channel importance factor in a self-conditioned manner. To demonstrate the effectiveness of proposed transform coding, we align the entropy model to compare with existing transform methods and obtain models LLIC-STF, LLIC-ELIC, LLIC-TCM. Extensive experiments demonstrate our proposed LLIC models have significant improvements over corresponding baselines and achieve state-of-the-art performances and better trade-off between performance and complexity.
TieFake: Title-Text Similarity and Emotion-Aware Fake News Detection

Fake news detection aims to detect fake news widely spreading on social media platforms, which can negatively influence the public and the government. Many approaches have been developed to exploit relevant information from news images, text, or videos. However, these methods may suffer from the following limitations: (1) ignore the inherent emotional information of the news, which could be beneficial since it contains the subjective intentions of the authors; (2) pay little attention to the relation (similarity) between the title and textual information in news articles, which often use irrelevant title to attract reader' attention. To this end, we propose a novel Title-Text similarity and emotion-aware Fake news detection (TieFake) method by jointly modeling the multi-modal context information and the author sentiment in a unified framework. Specifically, we respectively employ BERT and ResNeSt to learn the representations for text and images, and utilize publisher emotion extractor to capture the author's subjective emotion in the news content. We also propose a scale-dot product attention mechanism to capture the similarity between title features and textual features. Experiments are conducted on two publicly available multi-modal datasets, and the results demonstrate that our proposed method can significantly improve the performance of fake news detection. Our code is available at https://github.com/UESTC-GQJ/TieFake.
Temporal Betweenness Centrality on Shortest Paths

Betweenness centrality measure assesses the importance of nodes in a graph and has been used in a variety of contexts. Betweenness centrality has also been extended to temporal graphs. Temporal graphs have edges that bear labels according to the time of the interactions between the nodes. Betweenness centrality has been extended to the temporal graph settings, and the notion of paths has been extended to temporal paths. Recent results by Bu{\ss} et al. and Rymar et al. showed that the betweenness centrality of all nodes in a temporal graph can be computed in O(n^3 T^2) or O(n^2 m T^2 ), where T is the number of time units, m the number of temporal edges and n the number of nodes. In this paper, we improve the running time analysis of these previous approaches to compute the betweenness centrality of all nodes in a temporal graph. We give an algorithm that runs in O(n m T + n^2 T ).
Morpheus unleashed: Fast cross-platform SpMV on emerging architectures

Sparse matrices and linear algebra are at the heart of scientific simulations. Over the years, more than 70 sparse matrix storage formats have been developed, targeting a wide range of hardware architectures and matrix types, each of which exploit the particular strengths of an architecture, or the specific sparsity patterns of the matrices. In this work, we explore the suitability of storage formats such as COO, CSR and DIA for emerging architectures such as AArch64 CPUs and FPGAs. In addition, we detail hardware-specific optimisations to these targets and evaluate the potential of each contribution to be integrated into Morpheus, a modern library that provides an abstraction of sparse matrices (currently) across x86 CPUs and NVIDIA/AMD GPUs. Finally, we validate our work by comparing the performance of the Morpheus-enabled HPCG benchmark against vendor-optimised implementations.
UniCal: a Single-Branch Transformer-Based Model for Camera-to-LiDAR Calibration and Validation

We introduce a novel architecture, UniCal, for Camera-to-LiDAR (C2L) extrinsic calibration which leverages self-attention mechanisms through a Transformer-based backbone network to infer the 6-degree of freedom (DoF) relative transformation between the sensors. Unlike previous methods, UniCal performs an early fusion of the input camera and LiDAR data by aggregating camera image channels and LiDAR mappings into a multi-channel unified representation before extracting their features jointly with a single-branch architecture. This single-branch architecture makes UniCal lightweight, which is desirable in applications with restrained resources such as autonomous driving. Through experiments, we show that UniCal achieves state-of-the-art results compared to existing methods. We also show that through transfer learning, weights learned on the calibration task can be applied to a calibration validation task without re-training the backbone.
List Defective Colorings: Distributed Algorithms and Applications

The distributed coloring problem is at the core of the area of distributed graph algorithms and it is a problem that has seen tremendous progress over the last few years. Much of the remarkable recent progress on deterministic distributed coloring algorithms is based on two main tools: a) defective colorings in which every node of a given color can have a limited number of neighbors of the same color and b) list coloring, a natural generalization of the standard coloring problem that naturally appears when colorings are computed in different stages and one has to extend a previously computed partial coloring to a full coloring. In this paper, we introduce 'list defective colorings', which can be seen as a generalization of these two coloring variants. Essentially, in a list defective coloring instance, each node $v$ is given a list of colors $x_{v,1},\dots,x_{v,p}$ together with a list of defects $d_{v,1},\dots,d_{v,p}$ such that if $v$ is colored with color $x_{v, i}$, it is allowed to have at most $d_{v, i}$ neighbors with color $x_{v, i}$. We highlight the important role of list defective colorings by showing that faster list defective coloring algorithms would directly lead to faster deterministic $(\Delta+1)$-coloring algorithms in the LOCAL model. Further, we extend a recent distributed list coloring algorithm by Maus and Tonoyan [DISC '20]. Slightly simplified, we show that if for each node $v$ it holds that $\sum_{i=1}^p \big(d_{v,i}+1)^2>\mathrm{deg}_G^2(v)\cdot polylog\Delta$ then this list defective coloring instance can be solved in a communication-efficient way in only $O(\log\Delta)$ communication rounds. This leads to the first deterministic $(\Delta+1)$-coloring algorithm in the standard CONGEST model with a time complexity of $O(\sqrt{\Delta}\cdot polylog \Delta+\log^* n)$, matching the best time complexity in the LOCAL model up to a $polylog\Delta$ factor.
Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology

Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometer, which falls below the sensitivity limit of dMRI signals even when using the most advanced human MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii are needed. In this proof-of-concept investigation, we examine whether a surface-based axonal relaxation process could mediate a relationship between intra-axonal T2 and T1 times and inner axon radius, as measured using postmortem histology. A unique in vivo human diffusion-T1-T2 relaxation dataset was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a strong diffusion-weighting (i.e., b = 6,000 s/mm2) and multiple inversion and echo times. A second reduced diffusion-T2 dataset was collected at various echo times to evaluate the model further. The intra-axonal relaxation times were estimated by fitting a diffusion-relaxation model to the orientation-averaged spherical mean signals. Our analysis revealed that the proposed surface-based relaxation model effectively explains the relationship between the estimated relaxation times and the histological axon radius measured in various corpus callosum regions. Using these histological values, we developed a novel calibration approach to predict axon radius in other areas of the corpus callosum. Notably, the predicted radii and those determined from histological measurements were in close agreement.
Multilingual Query-by-Example Keyword Spotting with Metric Learning and Phoneme-to-Embedding Mapping

In this paper, we propose a multilingual query-by-example keyword spotting (KWS) system based on a residual neural network. The model is trained as a classifier on a multilingual keyword dataset extracted from Common Voice sentences and fine-tuned using circle loss. We demonstrate the generalization ability of the model to new languages and report a mean reduction in EER of 59.2% for previously seen and 47.9% for unseen languages compared to a competitive baseline. We show that the word embeddings learned by the KWS model can be accurately predicted from the phoneme sequences using a simple LSTM model. Our system achieves a promising accuracy for streaming keyword spotting and keyword search on Common Voice audio using just 5 examples per keyword. Experiments on the Hey-Snips dataset show a good performance with a false negative rate of 5.4% at only 0.1 false alarms per hour.
On the Effectiveness of Image Manipulation Detection in the Age of Social Media

Image manipulation detection algorithms designed to identify local anomalies often rely on the manipulated regions being ``sufficiently'' different from the rest of the non-tampered regions in the image. However, such anomalies might not be easily identifiable in high-quality manipulations, and their use is often based on the assumption that certain image phenomena are associated with the use of specific editing tools. This makes the task of manipulation detection hard in and of itself, with state-of-the-art detectors only being able to detect a limited number of manipulation types. More importantly, in cases where the anomaly assumption does not hold, the detection of false positives in otherwise non-manipulated images becomes a serious problem. To understand the current state of manipulation detection, we present an in-depth analysis of deep learning-based and learning-free methods, assessing their performance on different benchmark datasets containing tampered and non-tampered samples. We provide a comprehensive study of their suitability for detecting different manipulations as well as their robustness when presented with non-tampered data. Furthermore, we propose a novel deep learning-based pre-processing technique that accentuates the anomalies present in manipulated regions to make them more identifiable by a variety of manipulation detection methods. To this end, we introduce an anomaly enhancement loss that, when used with a residual architecture, improves the performance of different detection algorithms with a minimal introduction of false positives on the non-manipulated data. Lastly, we introduce an open-source manipulation detection toolkit comprising a number of standard detection algorithms.