Miracle-dz/UniFAR-FTUs huggingface dataset:
Mr. Right: Multimodal Retrieval on Representation of ImaGe witH Text

Multimodal learning is a recent challenge that extends unimodal learning by generalizing its domain to diverse modalities, such as texts, images, or speech. This extension requires models to process and relate information from multiple modalities. In Information Retrieval, traditional retrieval tasks focus on the similarity between unimodal documents and queries, while image-text retrieval hypothesizes that most texts contain the scene context from images. This separation has ignored that real-world queries may involve text content, image captions, or both. To address this, we introduce Multimodal Retrieval on Representation of ImaGe witH Text (Mr. Right), a novel and comprehensive dataset for multimodal retrieval. We utilize the Wikipedia dataset with rich text-image examples and generate three types of text-based queries with different modality information: text-related, image-related, and mixed. To validate the effectiveness of our dataset, we provide a multimodal training paradigm and evaluate previous text retrieval and image retrieval frameworks. The results show that proposed multimodal retrieval can improve retrieval performance, but creating a well-unified document representation with texts and images is still a challenge. We hope Mr. Right allows us to broaden current retrieval systems better and contributes to accelerating the advancement of multimodal learning in the Information Retrieval.
Knowledge-aware Neural Networks with Personalized Feature Referencing for Cold-start Recommendation

—Incorporating knowledge graphs (KGs) as side information in recommendation has recently attracted considerable attention. Despite the success in general recommendation scenarios, prior methods may fall short of performance satisfaction for the cold-start problem in which users are associated with very limited interactive information. Since the conventional methods rely on exploring the interaction topology, they may however fail to capture sufﬁcient information in cold-start scenarios. To mitigate the problem, we propose a novel Knowledge-aware Neural Networks with Personalized Feature Referencing Mechanism, namely KPER. Different from most prior methods which simply enrich the targets’ semantics from KGs, e.g., product attributes, KPER utilizes the KGs as a “semantic bridge” to extract feature references for cold-start users or items. Speciﬁcally, given cold-start targets, KPER ﬁrst probes semantically relevant but not necessarily structurally close users or items as adaptive seeds for referencing features. Then a Gated Information Aggregation module is introduced to learn the combinatorial latent features for cold-start users and items. Our extensive experiments over four real-world datasets show that, KPER consistently outperforms all competing methods in cold-start scenarios, whilst maintaining superiority in general scenarios without compromising overall performance, e.g., by achieving 0.81% ∼ 16.08% and 1.01% ∼ 14.49% performance improvement across all datasets in Top-10 recommendation.
Bounds from multi-messenger astronomy on the Super Heavy Dark Matter

The purely gravitational evidence supporting the need for dark matter (DM) particles is compelling and based on Galactic to cosmological scale observations. Thus far, the promising WIMP scenarios particles have eluded detection, motivating alternative models for DM. We consider the scenarios involving the super-heavy dark matter (SHDM) that potentially can be emitted by primordial black holes (PBH) and can decay or annihilate into ultra-high energy (UHE) neutrinos and photons. The observation of a population of photons with energies E ≥ 100 EeV would imply the existence of completely new physical phenomena, or shed some light on DM models. Only the ultra-high energy cosmic ray observatories have the capabilities to detect such UHE decay products via the measurements of UHE photon induced extensive air showers. Using the upper bound on the ﬂux of UHE cosmic rays beyond 10 11 . 3 GeV implying J ( > 10 11 . 3 GeV ) 3 . 6 × 10 − 5 km 2 sr − 1 y − , at the 90% C.L. reported by the Pierre Auger Observatory, we obtain global limits on the lifetime of the DM particles with masses 10 15 ≤ M X ≤ 10 17 GeV. The constraints derived here are new and cover a region of the parameter space not yet explored. We compare our results with the projected constraints from future POEMMA and JEM-EUSO experiments, in order to quantify the improvement that will be obtained by these missions. Moreover, assuming that an epoch of early PBHs domination introduces a unique spectral break, f ∗ , in the gravitational wave (GW) spectrum, which frequency is related to the SHDM mass, we map potential probes and limits of the DM particles masses on the f ∗ − M X parameter space.
Lagrangian characterization of sub-Alfvénic turbulence energetics

The energetics of strongly magnetized turbulence has so far resisted all attempts to under-stand them. Numerical simulations of compressible turbulence reveal that kinetic energy can be orders of magnitude larger than ﬂuctuating magnetic energy. We solve this lack-of-balance puzzle by calculating the energetics of compressible and sub-Alfvénic turbulence based on the dynamics of coherent cylindrical ﬂuid parcels. Using a Lagrangian formulation, we prove analytically that the bulk of the magnetic energy transferred to kinetic is the energy stored in the coupling between the initial and ﬂuctuating magnetic ﬁeld. The analytical relations are in striking agreement with numerical data, up to second order terms. This work presents a Lagrangian description of the energy transfer between kinetic and magnetic ﬂuctuations of compressible and sub-Alfvénic ﬂuids. From the ﬂux-freezing theorem, we showed that 𝛿𝐵 (cid:107) and 𝛿𝐵 ⊥ are generalized coordinates of the local perturbed Lagrangian. We derived analytically the relations which connect kinetic and magnetic energy of sub-Alfvénic and compressible ﬂuids, as a function of M A . We conclude that when M A ≤ 1, the total
PCB-RandNet: Rethinking Random Sampling for LIDAR Semantic Segmentation in Autonomous Driving Scene

Fast and efficient semantic segmentation of large-scale LiDAR point clouds is a fundamental problem in autonomous driving. To achieve this goal, the existing point-based methods mainly choose to adopt Random Sampling strategy to process large-scale point clouds. However, our quantative and qualitative studies have found that Random Sampling may be less suitable for the autonomous driving scenario, since the LiDAR points follow an uneven or even long-tailed distribution across the space, which prevents the model from capturing sufficient information from points in different distance ranges and reduces the model's learning capability. To alleviate this problem, we propose a new Polar Cylinder Balanced Random Sampling method that enables the downsampled point clouds to maintain a more balanced distribution and improve the segmentation performance under different spatial distributions. In addition, a sampling consistency loss is introduced to further improve the segmentation performance and reduce the model's variance under different sampling methods. Extensive experiments confirm that our approach produces excellent performance on both SemanticKITTI and SemanticPOSS benchmarks, achieving a 2.8% and 4.0% improvement, respectively. The source code is available at https://github.com/huixiancheng/PCB-RandNet.
From thin plates to Ahmed bodies: linear and weakly nonlinear stability of rectangular prisms

Abstract We study the stability of laminar wakes past three-dimensional rectangular prisms. The width-to-height ratio is set to $W/H=1.2$, while the length-to-height ratio $1/6< L/H<3$ covers a wide range of geometries from thin plates to elongated Ahmed bodies. First, global linear stability analysis yields a series of pitchfork and Hopf bifurcations: (i) at lower Reynolds numbers $Re$, two stationary modes, $A$ and $B$, become unstable, breaking the top/bottom and left/right planar symmetries, respectively; (ii) at larger $Re$, two oscillatory modes become unstable and, again, each mode breaks one of the two symmetries. The critical $Re$ values of these four modes increase with $L/H$, reproducing qualitatively the trend of stationary and oscillatory bifurcations in axisymmetric wakes (e.g. thin disk, sphere and bullet-shaped bodies). Next, a weakly nonlinear analysis based on the two stationary modes $A$ and $B$ yields coupled amplitude equations. For Ahmed bodies, as $Re$ increases, state $(A,0)$ appears first, followed by state $(0,B)$. While there is a range of bistability of those two states, only $(0,B)$ remains stable at larger $Re$, similar to the static wake deflection (across the larger base dimension) observed in the turbulent regime. The bifurcation sequence, including bistability and hysteresis, is validated with fully nonlinear direct numerical simulations, and is shown to be robust to variations in $W$ and $L$ in the range of common Ahmed bodies.
Gravitational waves and primordial black holes from chirality imbalanced QCD first-order phase transition with $\mathcal {P}$ and $\mathcal {CP}$ violation

The chirality imbalance in QCD is spontaneously induced by a repulsive axial-vector interaction from the instanton anti-instanton pairing at high temperature above the chiral phase transition, and vanishes at low temperature. The chiral chemical potential $\mu_5$ is in the same magnitude as estimated from the sphaleron transition. Phase transition of the chirality imbalance is always a first-order one in the early universe with $\mathcal {P}$ and $\mathcal {CP}$ violation. The spectra of gravitational waves and the formation of the primordial black holes from this first-order phase transition is investigated in this work, and the effect of a strong magnetic field is also analyzed. The gravitational waves produced by chirality imbalance can be detected by LISA, Taiji and DECIGO, with the peak energy density locating in the range of $10^{-11}$ to $10^{-9}$ and the peak frequencies lying in the range of $10^{-5}$ Hz to $10^{-2}$ Hz. The spectrum with larger axial vector coupling strength and stronger magnetic field has higher peak energy density and lower peak frequency. According to this trend, the gravitational waves spectra might also be able to be detected by SKA, IPTA and EPTA. The Phase transition inverse duration $\beta/H_*$ calculated from the bounce solution is in the order of $10^4$, which is much higher than typical value $10-100$ from electroweak phase transitions. Based on the mechanism of postponement of the false vacuum decay, it is found that the formation of the primordial black hole is not favored because the phase transition completes in an extreme short time due to the large value of $\beta/H_*$ and thus the false vacuum energy density decays sharply.
A survey of Heffter arrays

Heffter arrays were introduced by Archdeacon in 2015 as an interesting link between combinatorial designs and topological graph theory. Since the initial paper on this topic, there has been a good deal of interest in Heffter arrays as well as in related topics such as the sequencing of subsets of a group, biembeddings of cycle systems on a surface, and orthogonal cycle systems. This survey presents an overview of the current state of the art of this topic. We begin with an introduction to Heffter arrays for the reader who is unfamiliar with the subject, then we give a unified and comprehensive presentation of the major results, showing some proof methods also. This survey also includes sections on the connections of Heffter arrays to several other combinatorial objects, such as problems on partial sums and sequenceability, biembedding graphs on surfaces, difference families and orthogonal graph decompositions. These sections are followed by a section discussing the variants and generalizations of Heffter arrays which have been proposed. The survey itself is complemented by a list of unsolved problems as well as an updated and complete bibliography.
CSSAM: U-net Network for Application and Segmentation of Welding Engineering Drawings

Heavy equipment manufacturing splits specific contours in drawings and cuts sheet metal to scale for welding. Currently, most of the segmentation and extraction of weld map contours is achieved manually. Its efficiency is greatly reduced. Therefore, we propose a U-net-based contour segmentation and extraction method for welding engineering drawings. The contours of the parts required for engineering drawings can be automatically divided and blanked, which significantly improves manufacturing efficiency. U-net includes an encoder-decoder, which implements end-to-end mapping through semantic differences and spatial location feature information between the encoder and decoder. While U-net excels at segmenting medical images, our extensive experiments on the Welding Structural Diagram dataset show that the classic U-Net architecture falls short in segmenting welding engineering drawings. Therefore, we design a novel Channel Spatial Sequence Attention Module (CSSAM) and improve on the classic U-net. At the same time, vertical max pooling and average horizontal pooling are proposed. Pass the pooling operation through two equal convolutions into the CSSAM module. The output and the features before pooling are fused by semantic clustering, which replaces the traditional jump structure and effectively narrows the semantic gap between the encoder and the decoder, thereby improving the segmentation performance of welding engineering drawings. We use vgg16 as the backbone network. Compared with the classic U-net, our network has good performance in engineering drawing dataset segmentation.
Keyword Extraction from Short Texts with~a~Text-To-Text Transfer Transformer

The paper explores the relevance of the Text-To-Text Transfer Transformer language model (T5) for Polish (plT5) to the task of intrinsic and extrinsic keyword extraction from short text passages. The evaluation is carried out on the new Polish Open Science Metadata Corpus (POSMAC), which is released with this paper: a collection of 216,214 abstracts of scientific publications compiled in the CURLICAT project. We compare the results obtained by four different methods, i.e. plT5kw, extremeText, TermoPL, KeyBERT and conclude that the plT5kw model yields particularly promising results for both frequent and sparsely represented keywords. Furthermore, a plT5kw keyword generation model trained on the POSMAC also seems to produce highly useful results in cross-domain text labelling scenarios. We discuss the performance of the model on news stories and phone-based dialog transcripts which represent text genres and domains extrinsic to the dataset of scientific abstracts. Finally, we also attempt to characterize the challenges of evaluating a text-to-text model on both intrinsic and extrinsic keyword extraction.
Time and Energy Efficient Contention Resolution in Asynchronous Shared Channels

A number of stations, independently activated over time, is able to communicate by transmitting and listening to a shared channel in discrete time slots, and a message is successfully delivered to all stations if and only if its source station is the only transmitter at a time. Despite a vast amount of work in the last decades, many fundamental questions remain open in the realistic situation where stations do not start synchronously but are awaken in arbitrary times. In this work we present a broad picture of results for the fundamental problem of Contention resolution, in which each of the contending stations needs to broadcast successfully its message. We show that adaptive algorithms or algorithms with the knowledge of the contention size $k$ achieve a linear $O(k)$ message latency even if the channel feedback is restricted to simple acknowledgements in case of successful transmissions and in the absence of synchronization. This asymptotically optimal performance cannot be extended to other settings: we prove that there is no non-adaptive algorithm without the knowledge of contention size $k$ admitting latency $o(k\log k/(\log\log k)^2)$. This means, in particular, that coding (even random) with acknowledgements is not very efficient on a shared channel without synchronization or an estimate of the contention size. We also present a non-adaptive algorithm with no knowledge of contention size that almost matches the lower bound on latency. Finally, despite the absence of a collision detection mechanism, we show that our algorithms are also efficient in terms of energy, understood as the total number of transmissions performed by the stations during the execution.
Room-temperature dynamic correlation between methylammonium molecules in lead-iodine based perovskites: An ab initio molecular dynamics perspective

The high efficiency of lead organo-metal-halide perovskite solar cells has raised many questions about the role of the methylammonium (MA) molecules in the Pb-I framework. Experiments indicate that the MA molecules are able to ``freely'' spin around at room temperature even though they carry an intrinsic dipole moment. We have performed large supercell (2592 atoms) finite-temperature ab initio molecular dynamics calculations to study the correlation between the molecules in the framework. An underlying long-range antiferroelectric ordering of the molecular dipoles is observed. The dynamical correlation between neighboring molecules shows a maximum around room temperature in the mid-temperature phase. In this phase, the rotations are slow enough to (partially) couple to neighbors via the Pb-I cage. This results in a collective motion of neighboring molecules in which the cage acts as the mediator. At lower and higher temperatures, the motions are less correlated.
Algorithm Unfolding for Block-sparse and MMV Problems with Reduced Training Overhead

In this study, we consider algorithm unfolding for the multiple measurement vector (MMV) problem in the case where only few training samples are available. Algorithm unfolding has been shown to empirically speed-up in a data-driven way the convergence of various classical iterative algorithms, but for supervised learning, it is important to achieve this with minimal training data. For this, we consider learned block iterative shrinkage thresholding algorithm (LBISTA) under different training strategies. To approach almost data-free optimization at minimal training overhead, the number of trainable parameters for algorithm unfolding has to be substantially reduced. We therefore explicitly propose a reduced-size network architecture based on the Kronecker structure imposed by the MMV observation model and present the corresponding theory in this context. To ensure proper generalization, we then extend the analytic weight approach by Liu and Chen to LBISTA and the MMV setting. Rigorous theoretical guarantees and convergence results are stated for this case. We show that the network weights can be computed by solving an explicit equation at the reduced MMV dimensions which also admits a closed-form solution. Toward more practical problems, we then considered convolutional observation models and show that the proposed architecture and the analytical weight computation can be further simplified and thus open new directions for convolutional neural networks. Finally, we evaluate the unfolded algorithms in numerical experiments and discuss connections to other sparse recovering algorithms.
LATE-TIME COSMOLOGY WITH 21 cm INTENSITY MAPPING EXPERIMENTS

We present a framework for forecasting cosmological constraints from future neutral hydrogen intensity mapping experiments at low to intermediate redshifts. In the process, we establish a simple way of comparing such surveys with optical galaxy redshift surveys. We explore a wide range of experimental configurations and assess how well a number of cosmological observables (the expansion rate, growth rate, and angular diameter distance) and parameters (the densities of dark energy and dark matter, spatial curvature, the dark energy equation of state, etc.) will be measured by an extensive roster of upcoming experiments. A number of potential contaminants and systematic effects are also studied in detail. The overall picture is encouraging—if autocorrelation calibration can be controlled to a sufficient level, Phase I of the Square Kilometre Array should be able to constrain the dark energy equation of state about as well as a DETF Stage IV galaxy redshift survey like Euclid, in roughly the same time frame.
Interactions between delta-like sources and potentials

The modified scalar boson propagator due to the presence of a hyperplane semi-transparent mirror is computed. From this, the classical interaction between static charges and the mirror is investigated employing delta-like potentials and sources. Although the calculations for hyperplane mirrors are performed in arbitrary dimensions, and in a completely general way, it is shown that the results give rise to the usual image method as a particular case. The interaction between a point-like mirror and a point-like source is also considered in $3+1$ dimensions, where a central $1/R^{2}$ attractive potential is also obtained as a special case.
Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets

The statistical supervised learning framework assumes an input-output set with a joint probability distribution that is reliably represented by the training dataset. The learner is then required to output a prediction rule learned from the training dataset's input-output pairs. In this work, we provide meaningful insights into the asymptotic equipartition property (AEP) \citep{Shannon:1948} in the context of machine learning, and illuminate some of its potential ramifications for few-shot learning. We provide theoretical guarantees for reliable learning under the information-theoretic AEP, and for the generalization error with respect to the sample size. We then focus on a highly efficient recurrent neural net (RNN) framework and propose a reduced-entropy algorithm for few-shot learning. We also propose a mathematical intuition for the RNN as an approximation of a sparse coding solver. We verify the applicability, robustness, and computational efficiency of the proposed approach with image deblurring and optical coherence tomography (OCT) speckle suppression. Our experimental results demonstrate significant potential for improving learning models' sample efficiency, generalization, and time complexity, that can therefore be leveraged for practical real-time applications.
An inequality characterizing convex domains

A property of smooth convex domains Ω ⊂ R n is that if two points on the boundary x,y ∈ ∂ Ω are close to each other, then their normal vectors n ( x ) ,n ( y ) point roughly in the same direction and this direction is almost orthogonal to x − y (for ‘nearby’ x and y ). We prove there exists a constant c n > 0 such that if Ω ⊂ R n is a bounded domain with C 1 − boundary ∂ Ω, then equality if and only domain convex.