Miracle-dz/UniFAR-FTUs huggingface dataset:
Extremal p-Adic L-Functions

In this note, we propose a new construction of cyclotomic p-adic L-functions that are attached to classical modular cuspidal eigenforms. This allows for us to cover most known cases to date and provides a method which is amenable to generalizations to automorphic forms on arbitrary groups. In the classical setting of GL2 over Q, this allows for us to construct the p-adic L-function in the so far uncovered extremal case, which arises under the unlikely hypothesis that p-th Hecke polynomial has a double root. Although Tate’s conjecture implies that this case should never take place for GL2/Q, the obvious generalization does exist in nature for Hilbert cusp forms over totally real number fields of even degree, and this article proposes a method that should adapt to this setting. We further study the admissibility and the interpolation properties of these extremal p-adic L-functionsLpext(f,s), and relate Lpext(f,s) to the two-variable p-adic L-function interpolating cyclotomic p-adic L-functions along a Coleman family.
Quantum vortex reconnections mediated by trapped particles

Reconnections between quantum vortex filaments in presence of trapped particles are investigated using numerical simulations of the Gross--Pitaevskii equation. Particles are described with classical degrees of freedom and modeled as highly repulsive potentials which deplete the superfluid. First, the case of a vortex dipole with a single particle trapped inside one of the vortices is studied. It is shown that the reconnection takes place at the position of the particle as a consequence of the symmetry breaking induced by it. The separation rate between the reconnecting points is compatible with the known dynamics of quantum vortex reconnections and it is independent of the particle mass and size. After the reconnection, the particle is pushed away with a constant velocity and its trajectory is deflected because of the transverse momentum exchange with the vortex filaments. The momentum exchanges between the particle, the vortex, and a density pulse are characterized. Finally, the reconnection of two linked rings, each of them with several initially randomly distributed particles is studied. It is observed that generically, reconnections take place at the location of trapped particles. It is shown that reconnection dynamics is unaffected for light particles.
ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware Priors

The widely adopted sequential variant of Non Maximum Suppression (or Greedy-NMS) is a crucial module for object-detection pipelines. Unfortunately, for the region proposal stage of two/multi-stage detectors, NMS is turning out to be a latency bottleneck due to its sequential nature. In this article, we carefully profile Greedy-NMS iterations to find that a major chunk of computation is wasted in comparing proposals that are already far-away and have a small chance of suppressing each other. We address this issue by comparing only those proposals that are generated from nearby anchors. The translation-invariant property of the anchor lattice affords generation of a lookup table, which provides an efficient access to nearby proposals, during NMS. This leads to an Accelerated NMS algorithm which leverages Spatially Aware Priors, or ASAP-NMS, and improves the latency of the NMS step from 13.6ms to 1.2 ms on a CPU without sacrificing the accuracy of a state-of-the-art two-stage detector on COCO and VOC datasets. Importantly, ASAP-NMS is agnostic to image resolution and can be used as a simple drop-in module during inference. Using ASAP-NMS at run-time only, we obtain an mAP of 44.2\%@25Hz on the COCO dataset with a V100 GPU.
Semi-Supervised Learning Approach to Discover Enterprise User Insights from Feedback and Support

With the evolution of the cloud and customer centric culture, we inherently accumulate huge repositories of textual reviews, feedback, and support data.This has driven enterprises to seek and research engagement patterns, user network analysis, topic detections, etc.However, huge manual work is still necessary to mine data to be able to mine actionable outcomes. In this paper, we proposed and developed an innovative Semi-Supervised Learning approach by utilizing Deep Learning and Topic Modeling to have a better understanding of the user voice.This approach combines a BERT-based multiclassification algorithm through supervised learning combined with a novel Probabilistic and Semantic Hybrid Topic Inference (PSHTI) Model through unsupervised learning, aiming at automating the process of better identifying the main topics or areas as well as the sub-topics from the textual feedback and support.There are three major break-through: 1. As the advancement of deep learning technology, there have been tremendous innovations in the NLP field, yet the traditional topic modeling as one of the NLP applications lag behind the tide of deep learning. In the methodology and technical perspective, we adopt transfer learning to fine-tune a BERT-based multiclassification system to categorize the main topics and then utilize the novel PSHTI model to infer the sub-topics under the predicted main topics. 2. The traditional unsupervised learning-based topic models or clustering methods suffer from the difficulty of automatically generating a meaningful topic label, but our system enables mapping the top words to the self-help issues by utilizing domain knowledge about the product through web-crawling. 3. This work provides a prominent showcase by leveraging the state-of-the-art methodology in the real production to help shed light to discover user insights and drive business investment priorities.
A Comparison of Supervised Learning to Match Methods for Product Search

The vocabulary gap is a core challenge in information retrieval (IR). In e-commerce applications like product search, the vocabulary gap is reported to be a bigger challenge than in more traditional application areas in IR, such as news search or web search. As recent learning to match methods have made important advances in bridging the vocabulary gap for these traditional IR areas, we investigate their potential in the context of product search. In this paper we provide insights into using recent learning to match methods for product search. We compare both effectiveness and efficiency of these methods in a product search setting and analyze their performance on two product search datasets, with 50,000 queries each. One is an open dataset made available as part of a community benchmark activity at CIKM 2016. The other is a proprietary query log obtained from a European e-commerce platform. This comparison is conducted towards a better understanding of trade-offs in choosing a preferred model for this task. We find that (1) models that have been specifically designed for short text matching, like MV-LSTM and DRMMTKS, are consistently among the top three methods in all experiments; however, taking efficiency and accuracy into account at the same time, ARC-I is the preferred model for real world use cases; and (2) the performance from a state-of-the-art BERT-based model is mediocre, which we attribute to the fact that the text BERT is pre-trained on is very different from the text we have in product search. We also provide insights into factors that can influence model behavior for different types of query, such as the length of retrieved list, and query complexity, and discuss the implications of our findings for e-commerce practitioners, with respect to choosing a well performing method.
ANTIGENIC STRUCTURE OF CELL SURFACES

The representation of mouse alloantigens belonging to three systems, H-2, θ and TL, on the surface of cells from thymus, spleen, lymph nodes, and peritoneal cavity, was studied by electron microscopy with ferritin-labeled antibody. As expected from earlier serological data, TL was confined to thymocytes, θ was found on thymocytes and lymphocytes, and H-2 occurred to some extent on all cell types observed. On reticular cells, lymphocytes, plasma cells, and eosinophils, the majority of the cell surface was occupied by H-2; thymocytes had considerably less H-2, and erythrocytes and peritoneal macrophages least of all. In every instance the representation of antigen was discontinuous, the fraction of the cell surface covered being characteristic both of the antigen and of the type of cell. H-2 and θ provide a striking example of this; H-2 is present in far higher amounts on lymphocytes than on thymocytes, whereas the converse is true of θ. Within areas positive for H-2 or θ, protuberances of the surface membrane were often antigen-negative. A better definition of cell surface structure, gained from studies such as this, is necessary for further inquiry into how the cell surface is assembled, and into selective gene action in relation to cellular differentiation.
Design and Analysis of a Multi-Agent E-Learning System Using Prometheus Design Tool

Agent unified modeling languages (AUML) are agent-oriented approaches that supports the specification, design, visualization and documentation of an agent-based system. This paper presents the use of prometheus AUML approach for the modeling of a Pre-assessment System of five interactive agents. The Pre-assessment System, as previously reported, is a multi-agent-based e-learning system that is developed to support the assessment of prior learning skills in students so as to classify their skills and make recommendation for their learning. This paper discusses the detailed design approach of the system in a step-by-step manner; and domain knowledge abstraction and organization in the system. In addition, the analysis of the data collated and models of prediction for future pre-assessment results are also presented.
Hypersolvers: Toward Fast Continuous-Depth Models

The infinite-depth paradigm pioneered by Neural ODEs has launched a renaissance in the search for novel dynamical system-inspired deep learning primitives; however, their utilization in problems of non-trivial size has often proved impossible due to poor computational scalability. This work paves the way for scalable Neural ODEs with time-to-prediction comparable to traditional discrete networks. We introduce hypersolvers, neural networks designed to solve ODEs with low overhead and theoretical guarantees on accuracy. The synergistic combination of hypersolvers and Neural ODEs allows for cheap inference and unlocks a new frontier for practical application of continuous-depth models. Experimental evaluations on standard benchmarks, such as sampling for continuous normalizing flows, reveal consistent pareto efficiency over classical numerical methods.
Seasonal Habitat Use of Brook Trout and Juvenile Atlantic Salmon in a Tributary of Lake Ontario

Abstract The seasonal habitat use of Salvelinus fontinalis (Brook Trout) and sub-yearling Salmo salar (Atlantic Salmon) was examined in Hart Brook, a tributary of Lake Ontario. Fish habitat use and available habitat were examined during summer and autumn. Interspecific differences in habitat use occurred as well as intraspecific seasonal differences. Overyearling Brook Trout were more selective in their habitat preferences than subyearling Brook Trout or juvenile Atlantic Salmon. Depth and the amount of cover were significantly different among the three fish groups. Salmon occupied faster and shallower water than either age group of trout. Atlantic Salmon were also associated with larger-sized substrate materials than either trout age group, and salmon occurred in habitats with less cover than trout. Overyearling Brook Trout occupied deeper water with more cover than subyearling trout. All three salmonid groups occupied areas with more cover in autumn compared to summer. In autumn, subyearling Brook Trout used deeper areas than they had in the summer. In Hart Brook, the habitat of subyearling Atlantic Salmon can be generally characterized as riffles, the habitat of overyearling Brook Trout was deep pools with extensive cover (35%), and that of subyearling trout was any area with moderate flow and at least 20% cover. As efforts proceed to reintroduce Atlantic Salmon in Lake Ontario, further research is needed to ensure the conservation of Brook Trout populations.
Hierarchical Contrastive Motion Learning for Video Action Recognition

One central question for video action recognition is how to model motion. In this paper, we present hierarchical contrastive motion learning, a new self-supervised learning framework to extract effective motion representations from raw video frames. Our approach progressively learns a hierarchy of motion features that correspond to different abstraction levels in a network. This hierarchical design bridges the semantic gap between low-level motion cues and high-level recognition tasks, and promotes the fusion of appearance and motion information at multiple levels. At each level, an explicit motion self-supervision is provided via contrastive learning to enforce the motion features at the current level to predict the future ones at the previous level. Thus, the motion features at higher levels are trained to gradually capture semantic dynamics and evolve more discriminative for action recognition. Our motion learning module is lightweight and flexible to be embedded into various backbone networks. Extensive experiments on four benchmarks show that the proposed approach consistently achieves superior results.
Relativistic non-thermal particle acceleration in two-dimensional collisionless magnetic reconnection

Magnetic reconnection, especially in the relativistic regime, provides an efficient mechanism for accelerating relativistic particles and thus offers an attractive physical explanation for non-thermal high-energy emission from various astrophysical sources. I present a simple analytical model that elucidates key physical processes responsible for reconnection-driven relativistic non-thermal particle acceleration in the large-system, plasmoid-dominated regime in two dimensions. The model aims to explain the numerically observed dependencies of the power-law index $p$ and high-energy cutoff $\gamma _c$ of the resulting non-thermal particle energy spectrum $f(\gamma )$ on the ambient plasma magnetization $\sigma$, and (for $\gamma _c$) on the system size $L$. In this self-similar model, energetic particles are continuously accelerated by the out-of-plane reconnection electric field $E_{\rm rec}$ until they become magnetized by the reconnected magnetic field and eventually trapped in plasmoids large enough to confine them. The model also includes diffusive Fermi acceleration by particle bouncing off rapidly moving plasmoids. I argue that the balance between electric acceleration and magnetization controls the power-law index, while trapping in plasmoids governs the cutoff, thus tying the particle energy spectrum to the plasmoid distribution.
Physical properties and evolution of (sub-)millimetre-selected galaxies in the galaxy formation simulation shark

We thoroughly explore the properties of (sub)-millimetre (mm) selected galaxies (SMGs) in the shark semi-analytic model of galaxy formation. Compared to observations, the predicted number counts at wavelengths (λ) 0.6–2 mm and redshift distributions at 0.1–2 mm, agree well. At the bright end (≳1 mJy), shark galaxies are a mix of mergers and disc instabilities. These galaxies display a stacked far-ultraviolet (FUV)-to-far-infrared (FIR) spectrum that agrees well with observations. We predict that current optical/NIR surveys are deep enough to detect bright (>1 mJy) λ = 0.85–2 mm-selected galaxies at z ≲ 5, but too shallow to detect counterparts at higher redshift. A James Webb Space Telescope 10 000s survey should detect all counterparts for galaxies with S0.85mm ≳ 0.01 mJy. We predict SMG’s disks contribute significantly (negligibly) to the rest-frame UV (IR). We investigate the 0 ≤ z ≤ 6 evolution of the intrinsic properties of >1 mJy λ = 0.85–2 mm-selected galaxies finding their: (i) stellar masses are $\gt 10^{10.2}\rm \, M_{\odot }$, with the 2 mm ones tracing the most massive galaxies ($\gt 10^{11}\rm \, M_{\odot }$); (ii) specific star formation rates (SFR) are mildly (≈3–10 times) above the main sequence (MS); (iii) host halo masses are $\gtrsim 10^{12.3}\, \rm M_{\odot }$, with 2 mm galaxies tracing the most massive haloes (protoclusters); (iv) SMGs have lower dust masses ($\approx 10^{8}\, \rm M_{\odot }$), higher dust temperatures (≈40–45 K) and higher rest-frame V-band attenuation (>1.5) than MS galaxies; (v) sizes decrease with redshift, from 4 kpc at z = 1 to ≲1 kpc at z = 4; and (vi) the carbon monoxide line spectra of S0.85mm ≳ 1 mJy sources peak at 4 → 3. Finally, we study the contribution of SMGs to the molecular gas and cosmic SFR density at 0 ≤ z ≤ 10, finding that >1 mJy sources make a negligible contribution at z ≳ 3 and 5, respectively, suggesting current observations have unveiled the majority of the SF at 0 ≤ z ≤ 10.
Non-equilibrium band broadening, gap renormalization and band inversion in black phosphorus

Black phosphorous (BP) is a layered semiconductor with high carrier mobility, anisotropic optical response and wide bandgap tunability. In view of its application in optoelectronic devices, understanding transient photo-induced effects is crucial. Here, we investigate by time- and angle-resolved photoemission spectroscopy BP in its pristine state and in the presence of Stark splitting, chemically induced by Cs ad-sorption. We show that photo-injected carriers trigger bandgap renormalization, and a concurrent valence band flattening caused by Pauli blocking. In biased samples, photo-excitation leads to a long-lived (ns) surface photovoltage of few hundreds mV that counterbalances the Cs-induced surface band bending. This allows us to disentangle bulk from surface electronic states, and to clarify the mechanism underlying the band inversion observed in bulk samples.
Holographic complexity growth for a charged AdS-dilaton black holes with fixed and dynamical boundary respectively

The holographic complexity conjectures are considered in a Einstein-Maxwell-Dilaton gravity, by using the "Complexity-Volume" proposal. Specifically, we calculate the growth rate of complexity for an eternal charged AdS-dilaton black holes with fixed and dynamical boundaries respectively. The dynamical boundary is achieved by introducing a moving self-graviting brane on which the induced metric has an exact FLRW form. In case of fixed AdS boundary, there exists a bound for evolution of growth rate on late time, while this bound will become larger as the dilaton coupling constant $\alpha$ increases. In large $\alpha$ limit, we analytically prove that this bound is a finite value which is proportional to the black hole mass. In case of dynamical boundary, namely the brane-bulk system, the growth rate decreases monotonously on late time, after reaching a maximum value at a certain time. We find that the evolution of growth rate for brane-bulk system on late time is dominated by the velocity of the moving brane. We guess this result is model-independent.
Quantitative phase-contrast imaging: a bridge between qualitative phase-contrast and phase retrieval algorithms

In the last five decades, iterative phase retrieval methods draw large amount of interest across the research community as a non-interferometric approach to recover quantitative phase distributions from one (or more) intensity measurement. However, in cases where a unique solution does exist, these methods often require oversampling and high computational resources, which limits the use of this approach in important applications. On the other hand, phase contrast methods are based on a single camera exposure but provides only a qualitative description of the phase, thus are not useful for applications in which the quantitative phase description is needed. In this study we adopt a combined approach of the two above-mentioned methods to overcome their respective drawbacks. We show that a modified phase retrieval algorithm easily converges to the correct solution by initializing the algorithm with a phase-induced intensity measurement, namely with a phase contrast image of the examined object. Accurate quantitative phase measurements for both binary and continuously varying phase objects are demonstrated to support the suggested system as a single-shot quantitative phase contrast microscope.
Beyond Single Stage Encoder-Decoder Networks: Deep Decoders for Semantic Image Segmentation

Single encoder-decoder methodologies for semantic segmentation are reaching their peak in terms of segmentation quality and efficiency per number of layers. To address these limitations, we propose a new architecture based on a decoder which uses a set of shallow networks for capturing more information content. The new decoder has a new topology of skip connections, namely backward and stacked residual connections. In order to further improve the architecture we introduce a weight function which aims to re-balance classes to increase the attention of the networks to under-represented objects. We carried out an extensive set of experiments that yielded state-of-the-art results for the CamVid, Gatech and Freiburg Forest datasets. Moreover, to further prove the effectiveness of our decoder, we conducted a set of experiments studying the impact of our decoder to state-of-the-art segmentation techniques. Additionally, we present a set of experiments augmenting semantic segmentation with optical flow information, showing that motion clues can boost pure image based semantic segmentation approaches.
Constraining photohadronic scenarios for the unified origin of IceCube neutrinos and ultrahigh-energy cosmic rays

The diffuse neutrino flux measured in IceCube is comparable with the ultrahigh-energy cosmic ray (UHECR) flux, which has led to the concept of a unified origin of high-energy neutrino and UHECR backgrounds. We construct a generic unification model of sources to explain UHECR data at $\gtrsim 10^{19}$ eV, and high-energy neutrinos with energies that exceed $\sim100$ TeV in the framework of photo-meson production processes, and provide general constraints on the source properties. A source environment with moderately efficient in-situ production of $\gtrsim 100$ TeV neutrinos with an optical depth of $0.1 \lesssim \tau_{p\gamma}\lesssim 0.6$ must be realized to accelerate cosmic rays to ultrahigh energies. The measured fluxes of cosmic rays and neutrinos set a bound on the source luminosity and its rate density. Although the results are rather general and applicable to unknown source population, among the proposed source candidates, low-luminosity gamma-ray bursts (GRBs) and tidal disruption events (TDEs) could satisfy the requirements if the Lorentz bulk factor of plasma outflow and the equipartition parameters for cosmic rays and magnetic field are appropriately selected.