Miracle-dz/UniFAR-FTUs huggingface dataset:
Accretion-driven turbulence in filaments II: effects of self-gravity

We extend our previous work on simulations with the code RAMSES on accretion driven turbulence by including self-gravity and study the effects of core formation and collapse. We show that radial accretion onto filaments drives turbulent motions which are not isotropic but radially dominated. In contrast to filaments without gravity, the velocity dispersion of self-gravitating filaments does not settle in an equilibrium. Despite showing similar amounts of driven turbulence, they continually dissipate their velocity dispersion until the onset of core formation. This difference is connected to the evolution of the radius as it determines the dissipation rate. In the non-gravitational case filament growth is not limited and its radius grows linearly with time. In contrast, there is a maximum extent in the self-gravitational case resulting in an increased dissipation rate. Furthermore, accretion driven turbulence shows a radial profile which is anti-correlated with density. This leads to a constant turbulent pressure throughout the filament. As the additional turbulent pressure does not have a radial gradient it does not contribute to the stability of filaments and does not increase the critical line-mass. However, this radial turbulence does affect the radius of a filament, adding to the extent and setting its maximum value. Moreover, the radius evolution also affects the growth timescale of cores which compared to the timescale of collapse of an accreting filament limits core formation to high line-masses.
Synergistic CPU-FPGA Acceleration of Sparse Linear Algebra

This paper describes REAP, a software-hardware approach that enables high performance sparse linear algebra computations on a cooperative CPU-FPGA platform. REAP carefully separates the task of organizing the matrix elements from the computation phase. It uses the CPU to provide a first-pass re-organization of the matrix elements, allowing the FPGA to focus on the computation. We introduce a new intermediate representation that allows the CPU to communicate the sparse data and the scheduling decisions to the FPGA. The computation is optimized on the FPGA for effective resource utilization with pipelining. REAP improves the performance of Sparse General Matrix Multiplication (SpGEMM) and Sparse Cholesky Factorization by 3.2X and 1.85X compared to widely used sparse libraries for them on the CPU, respectively.
A 2Cat-inspired model structure for double categories

We construct a model structure on the category $\mathrm{DblCat}$ of double categories and double functors. Unlike previous model structures for double categories, it recovers the homotopy theory of 2-categories through the horizontal embedding functor $\mathbb{H}\colon 2\mathrm{Cat}\to\mathrm{DblCat}$, which is both left and right Quillen, and homotopically fully faithful. Furthermore, we show that Lack's model structure on $2\mathrm{Cat}$ is both left- and right-induced along $\mathbb{H}$ from our model structure on $\mathrm{DblCat}$. In addition, we obtain a $2\mathrm{Cat}$-enrichment of our model structure on $\mathrm{DblCat}$, by using a variant of the Gray tensor product. Under certain conditions, we prove a version of the Whitehead theorem, characterizing our weak equivalences as the double functors which admit an inverse pseudo double functor up to horizontal pseudo natural equivalence. This retrieves the Whitehead theorem for 2-categories as a special case. Analogous statements hold for the category $\mathrm{wkDblCat}_s$ of weak double categories and strict double functors, whose homotopy theory recovers that of bicategories through the horizontal embedding. Moreover, we show that the full embedding $\mathrm{DblCat}\to\mathrm{wkDblCat}_s$ is a Quillen equivalence.
6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented.
A Relative Theory of Interleavings

The interleaving distance, although originally developed for persistent homology, has been generalized to measure the distance between functors modeled on many posets or even small categories. Existing theories require that such a poset have a superlinear family of translations or a similar structure. However, many posets of interest to topological data analysis, such as zig-zag posets and the face relation poset of a cell-complex, do not admit interesting translations, and consequently don't admit a nice theory of interleavings. In this paper we show how one can side-step this limitation by providing a general theory where one maps to a poset that does admit interesting translations, such as the lattice of down sets, and then defines interleavings relative to this map. Part of our theory includes a rigorous notion of discretization or "pixelization" of poset modules, which in turn we use for interleaving inference. We provide an approximation condition that in the setting of lattices gives rise to two possible pixelizations, both of which are guaranteed to be close in the interleaving distance. Finally, we conclude by considering interleaving inference for cosheaves over a metric space and give an explicit description of interleavings over a grid structure on Euclidean space.
Skeleton Ideals of Certain Graphs, Standard Monomials and Spherical Parking Functions

Let $G$ be a graph on the vertex set $V = \{ 0, 1,\ldots,n\}$ with root $0$. Postnikov and Shapiro were the first to consider a monomial ideal $\mathcal{M}_G$, called the $G$-parking function ideal, in the polynomial ring $ R = {\mathbb{K}}[x_1,\ldots,x_n]$ over a field $\mathbb{K}$ and explained its connection to the chip-firing game on graphs. The standard monomials of the Artinian quotient $\frac{R}{\mathcal{M}_G}$ correspond bijectively to $G$-parking functions. Dochtermann introduced and studied skeleton ideals of the graph $G$, which are subideals of the $G$-parking function ideal with an additional parameter $k ~(0\le k \le n-1)$. A $k$-skeleton ideal $\mathcal{M}_G^{(k)}$ of the graph $G$ is generated by monomials corresponding to non-empty subsets of the set of non-root vertices $[n]$ of size at most $k+1$. Dochtermann obtained many interesting homological and combinatorial properties of these skeleton ideals. In this paper, we study the $k$-skeleton ideals of graphs and for certain classes of graphs provide explicit formulas and combinatorial interpretation of standard monomials and the Betti numbers.
A Detailed View of the Circumstellar Environment and Disk of the Forming O-star AFGL 4176

We present a detailed analysis of the disk and circumstellar environment of the forming O-type star AFGL 4176 mm1, placing results from the Atacama Large Millimeter/submillimeter Array (ALMA) into context with multiwavelength data. With ALMA, we detect seventeen 1.2 mm continuum sources within 5″ (21,000 au) of AFGL 4176 mm1. We find that mm1 has a spectral index of 3.4 ± 0.2 across the ALMA band, with >87% of its 1.2 mm continuum emission from dust. The source mm2, projected 4200 au from mm1, may be a companion or a blueshifted knot in a jet. We also explore the morphological differences between the molecular lines detected with ALMA, finding 203 lines from 25 molecules, which we categorize into several morphological types. Our results show that AFGL 4176 mm1 provides an example of a forming O-star with a large and chemically complex disk, which is mainly traced by nitrogen-bearing molecules. Lines that show strong emission on the blueshifted side of the disk are predominantly oxygen-bearing, which we suggest are tracing a disk accretion shock. The molecules C34S, H2CS, and CH3CCN trace a slow wide-angle wind or dense structures in the outflow cavity walls. With the Australia Telescope Compact Array (ATCA), we detect a compact continuum source (<2000 × 760 au) at 1.2 cm, associated with mm1, of which >96% is from ionized gas. The ATCA NH3(1, 1) and (2, 2) emission traces a large-scale (r ∼ 0.5 pc) rotating toroid with the disk source mm1 in the blueshifted part of this structure offset to the northwest.
Realtime Collision Avoidance for Mobile Robots in Dense Crowds using Implicit Multi-sensor Fusion and Deep Reinforcement Learning

We present a novel learning-based collision avoidance algorithm, CrowdSteer, for mobile robots operating in dense and crowded environments. Our approach is end-to-end and uses multiple perception sensors such as a 2-D lidar along with a depth camera to sense surrounding dynamic agents and compute collision-free velocities. Our training approach is based on the sim-to-real paradigm and uses high fidelity 3-D simulations of pedestrians and the environment to train a policy using Proximal Policy Optimization (PPO). We show that our learned navigation model is directly transferable to previously unseen virtual and dense real-world environments. We have integrated our algorithm with differential drive robots and evaluated its performance in narrow scenarios such as dense crowds, narrow corridors, T-junctions, L-junctions, etc. In practice, our approach can perform real-time collision avoidance and generate smooth trajectories in such complex scenarios. We also compare the performance with prior methods based on metrics such as trajectory length, mean time to goal, success rate, and smoothness and observe considerable improvement.
Error Propagation Mitigation in Sliding Window Decoding of Braided Convolutional Codes

We investigate error propagation in sliding window decoding of braided convolutional codes (BCCs). Previous studies of BCCs have focused on iterative decoding thresholds, minimum distance properties, and their bit error rate (BER) performance at small to moderate frame length. Here, we consider a sliding window decoder in the context of large frame length or one that continuously outputs blocks in a streaming fashion. In this case, decoder error propagation, due to the feedback inherent in BCCs, can be a serious problem. To mitigate the effects of error propagation, we propose several schemes: a window extension algorithm where the decoder window size can be extended adaptively, a resynchronization mechanism where we reset the encoder to the initial state, and a retransmission strategy where erroneously decoded blocks are retransmitted. In addition, we introduce a soft BER stopping rule to reduce computational complexity, and the tradeoff between performance and complexity is examined. Simulation results show that, using the proposed window extension algorithm, resynchronization mechanism, and retransmission strategy, the BER performance of BCCs can be improved by up to four orders of magnitude in the signal-to-noise ratio operating range of interest, and the soft BER stopping rule can be employed to reduce computational complexity.
Retinal Vessel Segmentation by Probing Adaptive to Lighting Variations

We introduce a novel method to extract the vessels in eye fundus images which is adaptive to lighting variations. In the Logarithmic Image Processing framework, a 3-segment probe detects the vessels by probing the topographic surface of an image from below. A map of contrasts between the probe and the image allows to detect the vessels by a threshold. In a lowly contrasted image, results show that our method better extract the vessels than another state-of the-art method. In a highly contrasted image database (DRIVE) with a reference, ours has an accuracy of 0.9454 which is similar or better than three state-of-the-art methods and below three others. The three best methods have a higher accuracy than a manual segmentation by another expert. Importantly, our method automatically adapts to the lighting conditions of the image acquisition.
Versatile black-box optimization

Choosing automatically the right algorithm using problem descriptors is a classical component of combinatorial optimization. It is also a good tool for making evolutionary algorithms fast, robust and versatile. We present Shiwa, an algorithm good at both discrete and continuous, noisy and noise-free, sequential and parallel, black-box optimization. Our algorithm is experimentally compared to competitors on YABBOB, a BBOB comparable testbed, and on some variants of it, and then validated on several real world testbeds.
Less is More: Sample Selection and Label Conditioning Improve Skin Lesion Segmentation

Segmenting skin lesions images is relevant both for itself and for assisting in lesion classification, but suffers from the challenge in obtaining annotated data. In this work, we show that segmentation may improve with less data, by selecting the training samples with best inter-annotator agreement, and conditioning the ground-truth masks to remove excessive detail. We perform an exhaustive experimental design considering several sources of variation, including three different test sets, two different deep-learning architectures, and several replications, for a total of 540 experimental runs. We found that sample selection and detail removal may have impacts corresponding, respectively, to 12% and 16% of the one obtained by picking a better deep-learning model.
Skeleton Focused Human Activity Recognition in RGB Video

The data-driven approach that learns an optimal representation of vision features like skeleton frames or RGB videos is currently a dominant paradigm for activity recognition. While great improvements have been achieved from existing single modal approaches with increasingly larger datasets, the fusion of various data modalities at the feature level has seldom been attempted. In this paper, we propose a multimodal feature fusion model that utilizes both skeleton and RGB modalities to infer human activity. The objective is to improve the activity recognition accuracy by effectively utilizing the mutual complemental information among different data modalities. For the skeleton modality, we propose to use a graph convolutional subnetwork to learn the skeleton representation. Whereas for the RGB modality, we will use the spatial-temporal region of interest from RGB videos and take the attention features from the skeleton modality to guide the learning process. The model could be either individually or uniformly trained by the back-propagation algorithm in an end-to-end manner. The experimental results for the NTU-RGB+D and Northwestern-UCLA Multiview datasets achieved state-of-the-art performance, which indicates that the proposed skeleton-driven attention mechanism for the RGB modality increases the mutual communication between different data modalities and brings more discriminative features for inferring human activities.
Angular magnetic-field dependence of vortex matching in pinning lattices fabricated by focused or masked helium ion beam irradiation of superconducting YBa2Cu3O7−δ thin films

The angular dependence of magnetic-field commensurability effects in thin films of the cuprate high-critical-temperature superconductor YBa$_{2}$Cu$_{3}$O$_{7-\delta}$ (YBCO) with an artificial pinning landscape is investigated. Columns of point defects are fabricated by two different methods of ion irradiation -- scanning the focused 30 keV ion beam in a helium ion microscope or employing the wide-field 75 keV He$^+$ beam of an ion implanter through a stencil mask. Simulations of the ion-target interactions and the resulting collision cascades reveal that with both methods square arrays of defect columns with sub-$\mu$m spacings can be created. They consist of dense point-defect clusters, which act as pinning centers for Abrikosov vortices. This is verified by the measurement of commensurable peaks of the critical current and related minima of the flux-flow resistance vs magnetic field at the matching fields. In oblique magnetic fields the matching features are exclusively governed by the component of the magnetic field parallel to the axes of the columnar defects, which confirms that the magnetic flux is penetrated along the defect columns. We demonstrate that the latter dominate the pinning landscape despite of the strong intrinsic pinning in thin YBCO films.
Training Curricula for Open Domain Answer Re-Ranking

In precision-oriented tasks like answer ranking, it is more important to rank many relevant answers highly than to retrieve all relevant answers. It follows that a good ranking strategy would be to learn how to identify the easiest correct answers first (i.e., assign a high ranking score to answers that have characteristics that usually indicate relevance, and a low ranking score to those with characteristics that do not), before incorporating more complex logic to handle difficult cases (e.g., semantic matching or reasoning). In this work, we apply this idea to the training of neural answer rankers using curriculum learning. We propose several heuristics to estimate the difficulty of a given training sample. We show that the proposed heuristics can be used to build a training curriculum that down-weights difficult samples early in the training process. As the training process progresses, our approach gradually shifts to weighting all samples equally, regardless of difficulty. We present a comprehensive evaluation of our proposed idea on three answer ranking datasets. Results show that our approach leads to superior performance of two leading neural ranking architectures, namely BERT and ConvKNRM, using both pointwise and pairwise losses. When applied to a BERT-based ranker, our method yields up to a 4% improvement in MRR and a 9% improvement in P@1 (compared to the model trained without a curriculum). This results in models that can achieve comparable performance to more expensive state-of-the-art techniques.
Image Morphing With Perceptual Constraints and STN Alignment

In image morphing, a sequence of plausible frames are synthesized and composited together to form a smooth transformation between given instances. Intermediates must remain faithful to the input, stand on their own as members of the set and maintain a well‐paced visual transition from one to the next. In this paper, we propose a conditional generative adversarial network (GAN) morphing framework operating on a pair of input images. The network is trained to synthesize frames corresponding to temporal samples along the transformation, and learns a proper shape prior that enhances the plausibility of intermediate frames. While individual frame plausibility is boosted by the adversarial setup, a special training protocol producing sequences of frames, combined with a perceptual similarity loss, promote smooth transformation over time. Explicit stating of correspondences is replaced with a grid‐based freeform deformation spatial transformer that predicts the geometric warp between the inputs, instituting the smooth geometric effect by bringing the shapes into an initial alignment. We provide comparisons to classic as well as latent space morphing techniques, and demonstrate that, given a set of images for self‐supervision, our network learns to generate visually pleasing morphing effects featuring believable in‐betweens, with robustness to changes in shape and texture, requiring no correspondence annotation.
End-to-End Slot Alignment and Recognition for Cross-Lingual NLU

Natural language understanding in the context of goal oriented dialog systems typically includes intent classification and slot labeling tasks. An effective method to expand an NLU system to new languages is using machine translation (MT) with annotation projection to the target language. Previous work focused on using word alignment tools or complex heuristics for slot annotation projection. In this work, we propose a novel end-to-end model that learns to align and predict slots. Existing multilingual NLU data sets only support up to three languages which limits the study on cross-lingual transfer. To this end, we construct a multilingual NLU corpus, MultiATIS++, by extending the Multilingual ATIS corpus to nine languages across various language families. We use the corpus to explore various cross-lingual transfer methods focusing on the zero-shot setting and leveraging MT for language expansion. Results show that our soft-alignment method significantly improves slot F1 over strong baselines on most languages. In addition, our experiments show the strength of using multilingual BERT for both cross-lingual training and zero-shot transfer.
Cross-modal Speaker Verification and Recognition: A Multilingual Perspective

Recent years have seen a surge in finding association between faces and voices within a cross-modal biometric application along with speaker recognition. Inspired from this, we introduce a challenging task in establishing association between faces and voices across multiple languages spoken by the same set of persons. The aim of this paper is to answer two closely related questions: "Is face-voice association language independent? " and "Can a speaker be recognized irrespective of the spoken language? ". These two questions are important to understand effectiveness and to boost development of multilingual biometric systems. To answer these, we collected a Multilingual Audio-Visual dataset, containing human speech clips of 154 identities with 3 language annotations extracted from various videos uploaded online. Extensive experiments on the two splits of the proposed dataset have been performed to investigate and answer these novel research questions that clearly point out the relevance of the multilingual problem.
Commutativity, Comeasurability, and Contextuality in the Kochen-Specker Arguments

I will argue that Kochen-Specker arguments do not provide an algebraic proof for quantum contextuality since, for the argument to be effective, (1) operators must be uniquely associated with measurements and (2) commuting operators must represent simultaneous measurements. However, in all Kochen-Specker arguments discussed in the literature either 1 or 2 is not met. Arguments meeting 1 contain commuting operators that do not represent simultaneous measurements and hence fail to physically justify the functional composition principle. Arguments meeting 2 associate some operators with more than one measurement and hence need to invoke an extra assumption different from noncontextuality.