Miracle-dz/UniFAR-FTUs huggingface dataset:
HISR: Hybrid Implicit Surface Representation for Photorealistic 3D Human Reconstruction

Neural reconstruction and rendering strategies have demonstrated state-of-the-art performances due, in part, to their ability to preserve high level shape details. Existing approaches, however, either represent objects as implicit surface functions or neural volumes and still struggle to recover shapes with heterogeneous materials, in particular human skin, hair or clothes. To this aim, we present a new hybrid implicit surface representation to model human shapes. This representation is composed of two surface layers that represent opaque and translucent regions on the clothed human body. We segment different regions automatically using visual cues and learn to reconstruct two signed distance functions (SDFs). We perform surface-based rendering on opaque regions (e.g., body, face, clothes) to preserve high-fidelity surface normals and volume rendering on translucent regions (e.g., hair). Experiments demonstrate that our approach obtains state-of-the-art results on 3D human reconstructions, and also shows competitive performances on other objects.
Dual-Functional Artificial Noise (DFAN) Aided Robust Covert Communications in Integrated Sensing and Communications

This paper investigates covert communications in an integrated sensing and communications system, where a dual-functional base station (called Alice) covertly transmits signals to a covert user (called Bob) while sensing multiple targets, with one of them acting as a potential watcher (called Willie) and maliciously eavesdropping on legitimate communications. To shelter the covert communications, Alice transmits additional dual-functional artificial noise (DFAN) with a varying power not only to create uncertainty at Willie's signal reception to confuse Willie but also to sense the targets simultaneously. Based on this framework, the weighted sum of the sensing beampattern means square error (MSE) and cross correlation is minimized by jointly optimizing the covert communication and DFAN signals subject to the minimum covert rate requirement. The robust design considers both cases of imperfect Willie's CSI (WCSI) and statistical WCSI. Under the worst-case assumption that Willie can adaptively adjust the detection threshold to achieve the best detection performance, the minimum detection error probability (DEP) at Willie is analytically derived in the closed-form expression. The formulated covertness constrained optimization problems are tackled by a feasibility-checking based difference-of-convex relaxation (DC) algorithm utilizing the S-procedure, Bernstein-type inequality, and the DC method. Simulation results validate the feasibility of the proposed scheme and demonstrate the covertness performance gains achieved by our proposed design over various benchmarks.
Automating Knowledge Acquisition for Content-Centric Cognitive Agents Using LLMs

The paper describes a system that uses large language model (LLM) technology to support automatic learning of new entries in an intelligent agent’s semantic lexicon. The process is bootstrapped by an existing non-toy lexicon and a natural language generator that converts formal, ontologically-grounded representations of meaning into natural language sentences. The learning method involves a sequence of LLM requests and includes an automatic quality control step. To date, this learning method has been applied to learning multiword expressions whose meanings are equivalent to those of transitive verbs in the agent’s lexicon. The experiment demonstrates the benefits of a hybrid learning architecture that integrates knowledge-based methods and resources with both traditional data analytics and LLMs.
Generalized Dualities for Heterotic and Type I Strings

We define generalized dualities for heterotic and type I strings based on consistent truncations to half-maximal gauged supergravities in more than three dimensions. The latter are constructed from a generalized Scherk-Schwarz ansatz in heterotic double field theory that satisfies the strong constraint. Necessary and sufficient conditions on the resulting embedding tensor are discussed, showing that only certain gaugings, called geometric, can arise from this procedure. For all of them, we explicitly construct the internal geometry and gauge potentials. In general, this construction is not unique and permits different uplifts which are used to define generalized T-duality. Two examples are worked out underlying the utility of our approach to explore new dualities and uplifts of half-maximal gauged supergravities.
A Generalization of the Convolution Theorem and its Connections to Non-Stationarity and the Graph Frequency Domain

In this paper, we present a novel convolution theorem which encompasses the well known convolution theorem in (graph) signal processing as well as the one related to time-varying filters. Specifically, we show how a node-wise convolution for signals supported on a graph can be expressed as another node-wise convolution in a frequency domain graph, different from the original graph. This is achieved through a parameterization of the filter coefficients following a basis expansion model. After showing how the presented theorem is consistent with the already existing body of literature, we discuss its implications in terms of non-stationarity. Finally, we propose a data-driven algorithm based on subspace fitting to learn the frequency domain graph, which is then corroborated by experimental results on synthetic and real data.
High-order Finite-Volume Central Targeted ENO Family Scheme for Compressible Flows in Unstructured Meshes

Due to its innovative weighting method, the high-order target ENO (TENO) system has shown tremendous potential for complicated flow prediction. For unstructured meshes, we have developed non-oscillatory central target ENO family schemes in this study. In comparison with standard WENO schemes, the compact directional stencils significantly enhance the possibility that one of them will be in smooth area. The scheme is inherently limited in size in order to simplify the directional stencils in implementation. Following an effective scale separation method, we employ the ENO-like stencil selection technique, which selects candidate reconstructions from large central stencils in smooth areas to enforce the ENO property and from small directional stencils at discontinuities to restore high-order accuracy. With the validation of a number of test cases, a detailed comparison of CWENO, TENO, CTENO and CTENOZ schemes was carried out to assess their performance based on accuracy, robustness, parallel scalability and run time. Our findings demonstrate high-order precision, lower numerical dissipation, and superior sharp shock-capturing performance of proposed CTENO and CTENOZ schemes.
Selective-Memory Meta-Learning with Environment Representations for Sound Event Localization and Detection

Environment shifts and conflicts present significant challenges for learning-based sound event localization and detection (SELD) methods. SELD systems, when trained in particular acoustic settings, often show restricted generalization capabilities for diverse acoustic environments. Furthermore, it is notably costly to obtain annotated samples for spatial sound events. Deploying a SELD system in a new environment requires extensive time for re-training and fine-tuning. To overcome these challenges, we propose environment-adaptive Meta-SELD, designed for efficient adaptation to new environments using minimal data. Our method specifically utilizes computationally synthesized spatial data and employs Model-Agnostic Meta-Learning (MAML) on a pre-trained, environment-independent model. The method then utilizes fast adaptation to unseen real-world environments using limited samples from the respective environments. Inspired by the Learning-to-Forget approach, we introduce the concept of selective memory as a strategy for resolving conflicts across environments. This approach involves selectively memorizing target-environment-relevant information and adapting to the new environments through the selective attenuation of model parameters. In addition, we introduce environment representations to characterize different acoustic settings, enhancing the adaptability of our attenuation approach to various environments. We evaluate our proposed method on the development set of the Sony-TAU Realistic Spatial Soundscapes 2023 (STARSS23) dataset and computationally synthesized scenes. Experimental results demonstrate the superior performance of the proposed method compared to conventional supervised learning methods, particularly in localization.
Computing superspecial hyperelliptic curves of genus 4 with automorphism group properly containing the Klein 4-group

In algebraic geometry, enumerating or finding superspecial curves in positive characteristic $p$ is important both in theory and in computation. In this paper, we propose feasible algorithms to enumerate or find superspecial hyperelliptic curves of genus $4$ with automorphism group properly containing the Klein $4$-group. Executing the algorithms on Magma, we succeeded in enumerating such superspecial curves for every $p$ with $19 \leq p<500$, and in finding a single one for every $p$ with $19 \leq p<7000$.
Replica Tree-based Federated Learning using Limited Data

Learning from limited data has been extensively studied in machine learning, considering that deep neural networks achieve optimal performance when trained using a large amount of samples. Although various strategies have been proposed for centralized training, the topic of federated learning with small datasets remains largely unexplored. Moreover, in realistic scenarios, such as settings where medical institutions are involved, the number of participating clients is also constrained. In this work, we propose a novel federated learning framework, named RepTreeFL. At the core of the solution is the concept of a replica, where we replicate each participating client by copying its model architecture and perturbing its local data distribution. Our approach enables learning from limited data and a small number of clients by aggregating a larger number of models with diverse data distributions. Furthermore, we leverage the hierarchical structure of the client network (both original and virtual), alongside the model diversity across replicas, and introduce a diversity-based tree aggregation, where replicas are combined in a tree-like manner and the aggregation weights are dynamically updated based on the model discrepancy. We evaluated our method on two tasks and two types of data, graph generation and image classification (binary and multi-class), with both homogeneous and heterogeneous model architectures. Experimental results demonstrate the effectiveness and outperformance of RepTreeFL in settings where both data and clients are limited. Our code is available at https://github.com/basiralab/RepTreeFL.
Understanding News Creation Intents: Frame, Dataset, and Method

As the disruptive changes in the media economy and the proliferation of alternative news media outlets, news intent has progressively deviated from ethical standards that serve the public interest. News intent refers to the purpose or intention behind the creation of a news article. While the significance of research on news intent has been widely acknowledged, the absence of a systematic news intent understanding framework hinders further exploration of news intent and its downstream applications. To bridge this gap, we propose News INTent (NINT) frame, the first component-aware formalism for understanding the news creation intent based on research in philosophy, psychology, and cognitive science. Within this frame, we define the news intent identification task and provide a benchmark dataset with fine-grained labels along with an efficient benchmark method. Experiments demonstrate that NINT is beneficial in both the intent identification task and downstream tasks that demand a profound understanding of news. This work marks a foundational step towards a more systematic exploration of news creation intents.
Nighttime Person Re-Identification via Collaborative Enhancement Network with Multi-domain Learning

Prevalent nighttime ReID methods typically combine relighting networks and ReID networks in a sequential manner, which not only restricts the ReID performance by the quality of relighting images, but also neglects the effective collaborative modeling between image relighting and person ReID tasks. To handle these problems, we propose a novel Collaborative Enhancement Network called CENet, which performs the multilevel feature interactions in a parallel framework, for nighttime person ReID. In particular, CENet is a parallel Transformer network, in which the designed parallel structure can avoid the impact of the quality of relighting images on ReID performance. To perform effective collaborative modeling between image relighting and person ReID tasks, we integrate the multilevel feature interactions in CENet. Specifically, we share the Transformer encoder to build the low-level feature interaction, and then perform the feature distillation to transfer the high-level features from image relighting to ReID. In addition, the sizes of existing real-world nighttime person ReID datasets are small, and large-scale synthetic ones exhibit substantial domain gaps with real-world data. To leverage both small-scale real-world and large-scale synthetic training data, we develop a multi-domain learning algorithm, which alternately utilizes both kinds of data to reduce the inter-domain difference in the training of CENet. Extensive experiments on two real nighttime datasets, \textit{Night600} and \textit{RGBNT201$_{rgb}$}, and a synthetic nighttime ReID dataset are conducted to validate the effectiveness of CENet. We will release the code and synthetic dataset.
INFAMOUS-NeRF: ImproviNg FAce MOdeling Using Semantically-Aligned Hypernetworks with Neural Radiance Fields

We propose INFAMOUS-NeRF, an implicit morphable face model that introduces hypernetworks to NeRF to improve the representation power in the presence of many training subjects. At the same time, INFAMOUS-NeRF resolves the classic hypernetwork tradeoff of representation power and editability by learning semantically-aligned latent spaces despite the subject-specific models, all without requiring a large pretrained model. INFAMOUS-NeRF further introduces a novel constraint to improve NeRF rendering along the face boundary. Our constraint can leverage photometric surface rendering and multi-view supervision to guide surface color prediction and improve rendering near the surface. Finally, we introduce a novel, loss-guided adaptive sampling method for more effective NeRF training by reducing the sampling redundancy. We show quantitatively and qualitatively that our method achieves higher representation power than prior face modeling methods in both controlled and in-the-wild settings. Code and models will be released upon publication.
Field redefinitions and evolutions in relativistic Navier-Stokes

In recent years the equations of relativistic first-order viscous hydrodynamics, that is, the relativistic version of Navier-Stokes, have been shown to be well posed and causal under appropriate field redefinitions, also known as hydrodynamic frames. We perform real-time evolutions of these equations for a conformal fluid and explore, quantitatively, the consequences of using different causal frames for different sets of initial data. By defining specific criteria, we make precise and provide evidence for the statement that the arbitrarily chosen frame does not affect the physics up to first order, as long as the system is in the effective field theory regime. Motivated by the physics of the quark-gluon plasma created in heavy-ion collisions we also explore systems which are marginally in the effective field theory regime, finding that even under these circumstances the first order physics is robust under field redefinitions.
Spectroastrometric Survey of Protoplanetary Disks with Inner Dust Cavities

We present high-resolution spectra and spectroastrometric (SA) measurements of fundamental rovibrational CO emission from nine nearby (≲300 pc) protoplanetary disks where large inner dust cavities have been observed. The emission-line profiles and SA signals are fit with a slab disk model that allows the eccentricity of the disk and intensity of the emission to vary as power laws. Six of the sources are well fit with our model, and three of these sources show asymmetric line profiles that can be fit by adopting a nonzero eccentricity. The three other sources have components in either their line profile or SA signal that are not captured by our disk model. Two of these sources (V892 Tau and CQ Tau) have multi-epoch observations that reveal significant variability. CQ Tau and AB Aur have CO line profiles with centrally peaked components that are similar to line profiles which have been interpreted as evidence of molecular gas arising from a wide-angle disk wind. Alternatively, emission from a circumplanetary disk could also account for this component. The interpretations of these results can be clarified in the future with additional epochs that will test the variability timescale of these SA signals. We discuss the utility of using high-resolution spectroscopy for probing the dynamics of gas in the disk and the scenarios that can give rise to profiles that are not fit with a simple disk model.
The Long-term Spin-down Trend of Ultraluminous X-Ray Pulsar M82 X-2

The discovery in 2014 of the pulsation from the ultraluminous X-ray source (ULX) M82 X-2 has changed our view of ULXs. Because of the relatively short baseline over which pulsations have been detected so far, M82 X-2's spin state had been assumed to be in an equilibrium state. Using Chandra and XMM-Newton archive data, we are able to investigate the pulsation of M82 X-2 back to 2005 and 2001. The newly determined spin frequencies clearly show a long-term spin-down trend. If this trend is caused by magnetic threading, we infer a dipolar magnetic field of ∼1.2 × 1013 G and that a mild beaming factor (∼4) is needed to match the braking torque with the mass accretion torque. On the other hand, there are NuSTAR observations showing instantaneous spin-down behaviors, which might favor a variable prograde/retrograde flow scenario for M82 X-2.
Interacting supernovae as high-energy multimessenger transients

Multiwavelength observations have revealed that dense, confined circumstellar material (CCSM) commonly exists in the vicinity of supernova (SN) progenitors, suggesting enhanced mass losses years to centuries before their core collapse. Interacting SNe, which are powered or aided by interaction with the CCSM, are considered to be promising high-energy multimessenger transient sources. We present detailed results of broadband electromagnetic emission, following the time-dependent model proposed in the previous work on high-energy SN neutrinos [Murase, Phys. Rev. D 97, 081301(R) (2018)]. We investigate electromagnetic cascades in the presence of Coulomb losses, including inverse-Compton and synchrotron components that significantly contribute to MeV and high-frequency radio bands, respectively. We also discuss the application to SN 2023ixf.
Lower bounds for bulk deviations for the simple random walk on $\mathbb{Z}^d$, $d\geq 3$

This article investigates the behavior of the continuous-time simple random walk on $\mathbb{Z}^d$, $d \geq 3$. We derive an asymptotic lower bound on the principal exponential rate of decay for the probability that the average value over a large box of some non-decreasing local function of the field of occupation times of the walk exceeds a given positive value. This bound matches at leading order the corresponding upper bound derived by Sznitman in arXiv:1906.05809, and is given in terms of a certain constrained minimum of the Dirichlet energy of functions on $\mathbb{R}^d$ decaying at infinity. Our proof utilizes a version of tilted random walks, a model originally constructed by Li in arXiv:1412.3959 to derive lower bounds on the probability of the event that the trace of a simple random walk disconnects a macroscopic set from an enclosing box.
Time Scales in Many-Body Fast Neutrino Flavor Conversion

Time scales associated with many-body fast neutrino flavor conversions in core-collapse supernova are explored in the context of an effective two-flavor model with axial symmetry. We present a preliminary study of time scales obtained from a linear stability analysis and from the distributions of Loschmidt echo crossing times (intimately connected to dynamical phase transitions in non-equilibrium systems) determined by time evolution with the exact many-body Hamiltonian. Starting from a tensor-product initial state describing systems of $N$ neutrinos, with $N/2$ electron-type and $N/2$ heavy-type, with uniform angular distributions, the Loschmidt echo crossing times, $t_{\mathcal{L}_{\times}}$, are found to exhibit two distinct time scales that are exponentially separated. The second peak structure at longer times, effectively absent for $N=4$, develops with increasing $N$. When re-scaled in terms of $\log t_{\mathcal{L}_{\times}}$, the distributions are found to become increasingly well described by the sum of two stable distributions. The distribution of Loschmidt echo crossing times differs somewhat from the results of the (numerical) linear stability analysis, which exhibits a peak at finite frequency and a second peak consistent with zero frequency. The exact analysis suggests that the zero-frequency instability manifests itself as a modest flavor-conversion time scale.
DarkShot: Lighting Dark Images with Low-Compute and High-Quality

Nighttime photography encounters escalating challenges in extremely low-light conditions, primarily attributable to the ultra-low signal-to-noise ratio. For real-world deployment, a practical solution must not only produce visually appealing results but also require minimal computation. However, most existing methods are either focused on improving restoration performance or employ lightweight models at the cost of quality. This paper proposes a lightweight network that outperforms existing state-of-the-art (SOTA) methods in low-light enhancement tasks while minimizing computation. The proposed network incorporates Siamese Self-Attention Block (SSAB) and Skip-Channel Attention (SCA) modules, which enhance the model's capacity to aggregate global information and are well-suited for high-resolution images. Additionally, based on our analysis of the low-light image restoration process, we propose a Two-Stage Framework that achieves superior results. Our model can restore a UHD 4K resolution image with minimal computation while keeping SOTA restoration quality.
Reconfigurable Intelligent Surface Deployment for Wideband Millimeter Wave Systems

The performance of wireless communication systems is fundamentally constrained by random and uncontrollable wireless channels. Recently, reconfigurable intelligent surfaces (RIS) has emerged as a promising solution to enhance wireless network performance by smartly reconfiguring the radio propagation environment. While significant research has been conducted on RIS-assisted wireless systems, this paper focuses specifically on the deployment of RIS in a wideband millimeter wave (mmWave) multiple-input-multiple-output (MIMO) system to achieve maximum sum-rate. First, we derive the average user rate as well as the lower bound rate when the covariance of the channel follows the Wishart distribution. Based on the lower bound of users' rate, we propose a heuristic method that transforms the problem of optimizing the RIS's orientation into maximizing the number of users served by the RIS. Simulation results show that the proposed RIS deployment strategy can effectively improve the sum-rate. Furthermore, the performance of the proposed RIS deployment algorithm is only approximately 7.6\% lower on average than that of the exhaustive search algorithm.