Miracle-dz/UniFAR-FTUs huggingface dataset:
Transfer and Alignment Network for Generalized Category Discovery

Generalized Category Discovery (GCD) is a crucial real-world task that aims to recognize both known and novel categories from an unlabeled dataset by leveraging another labeled dataset with only known categories. Despite the improved performance on known categories, current methods perform poorly on novel categories. We attribute the poor performance to two reasons: biased knowledge transfer between labeled and unlabeled data and noisy representation learning on the unlabeled data. The former leads to unreliable estimation of learning targets for novel categories and the latter hinders models from learning discriminative features. To mitigate these two issues, we propose a Transfer and Alignment Network (TAN), which incorporates two knowledge transfer mechanisms to calibrate the biased knowledge and two feature alignment mechanisms to learn discriminative features. Specifically, we model different categories with prototypes and transfer the prototypes in labeled data to correct model bias towards known categories. On the one hand, we pull instances with known categories in unlabeled data closer to these prototypes to form more compact clusters and avoid boundary overlap between known and novel categories. On the other hand, we use these prototypes to calibrate noisy prototypes estimated from unlabeled data based on category similarities, which allows for more accurate estimation of prototypes for novel categories that can be used as reliable learning targets later. After knowledge transfer, we further propose two feature alignment mechanisms to acquire both instance- and category-level knowledge from unlabeled data by aligning instance features with both augmented features and the calibrated prototypes, which can boost model performance on both known and novel categories with less noise. Experiments on three benchmark datasets show that our model outperforms SOTA methods, especially on novel categories. Theoretical analysis is provided for an in-depth understanding of our model in general. Our code and data are available at https://github.com/Lackel/TAN.
Systems of nonlocal balance laws For Dense multilane vehicular traffic

We discuss a class of coupled system of nonlocal balance laws modeling multilane traffic, with the nonlocality present in both convective and source terms. The uniqueness and existence of the entropy solution is proven via doubling of the variables arguments and convergent finite volume approximations, respectively. The numerical approximations are proven to converge to the unique entropy solution of the system at the rate $\sqrt{\Delta t}$. The applicability of the proven theory to a general class of systems of nonlocal balance laws coupled strongly through the convective part and weakly through the source part, is also indicated. Numerical simulations illustrating the theory and the behavior of the entropy solution as the support of the kernel goes to zero(nonlocal to local limit), are shown.
Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting

Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS). Recent studies offer evidence that the use of graph neural networks to capture latent correlations between time series holds a potential for enhanced forecasting. However, most existing methods rely on pre-defined or self-learning graphs, which are either static or unintentionally dynamic, and thus cannot model the time-varying correlations that exhibit trends and periodicities caused by the regularity of the underlying processes in CPS. To tackle such limitation, we propose Time-aware Graph Structure Learning (TagSL), which extracts time-aware correlations among time series by measuring the interaction of node and time representations in high-dimensional spaces. Notably, we introduce time discrepancy learning that utilizes contrastive learning with distance-based regularization terms to constrain learned spatial correlations to a trend sequence. Additionally, we propose a periodic discriminant function to enable the capture of periodic changes from the state of nodes. Next, we present a Graph Convolution-based Gated Recurrent Unit (GCGRU) that jointly captures spatial and temporal dependencies while learning time-aware and node-specific patterns. Finally, we introduce a unified framework named Time-aware Graph Convolutional Recurrent Network (TGCRN), combining TagSL, and GCGRU in an encoder-decoder architecture for multi-step spatio-temporal forecasting. We report on experiments with TGCRN and popular existing approaches on five real-world datasets, thus providing evidence that TGCRN is capable of advancing the state-of-the-art. We also cover a detailed ablation study and visualization analysis, offering detailed insight into the effectiveness of time-aware structure learning.
In-Lab Implementation of DSRC PHY Layer

Connected and autonomous vehicles are already right around the corner of our everyday life. One of the key technologies actualizing the connected vehicles is vehicle-to-everything communications (V2X), which has been enhanced along the lines of two technologies--i.e., dedicated short-range communications (DSRC) and cellular V2X (C-V2X). While the United States (U.S.) federal government is on the move of transitioning to C-V2X, a vast majority of stakeholders are still heavily relying on DSRC. This makes a compelling case where DSRC still necessitates thorough studies not only theoretically but experimentally as well. To this line, we present a testbed that is dedicated to evaluating the performance of a DSRC system. Our study encompasses a few different test scenarios for precise confirmation of the performance. We claim that this experimental setup will play a vital role in future studies to assess the DSRC technology applied to various use cases.
Seismic bearing capacity with variable shear transfer

The seismic degradation of bearing capacity for drained soils is shown to depend primarily on two factors related to earthquake acceleration: (a) the lateral inertial forces in the structure transmitted as shear at the foundation-soil interface and (b) the lateral body forces in the soil itself. Both induce shear stresses using up the reserve strength of the soil to carry the footing load. During those short periods when this reserve strength provided by the static design factor of safety is exhausted, the footing settles and moves laterally. Solutions for this seismic limit state defining the critical acceleration at which it occurs are determined for any value of shear transfer first by the exact method of characteristics and then by a simple Coulomb-type approximate mechanism. Expressions for seismic bearing capacity factors that are directly related to their static counterparts are nearly identical by either method. Thus a straightforward sliding block procedure based on the Coulomb mechanism with examples is presented for computing accumulating settlements due to the periodic loss of bearing capacity. Conversely, this approach leads to a modified static design procedure for shallow footings to limit seismic settlements in a prescribed earthquake intensity zone.
Optimal Beamforming Structure and Efficient Optimization Algorithms for Generalized Multi-Group Multicast Beamforming Optimization

In this work, we focus on solving non-smooth non-convex maximization problems in multi-group multicast transmission. Leveraging Karush-Kuhn-Tucker (KKT) optimality conditions and successive incumbent transcending (SIT) duality, we thoroughly analyze the optimal beamforming structure for a set of optimization problems characterized by a general utility-based objective function. By exploiting the identified optimal structure, we further unveil inherent low-dimensional beamforming structures within the problems, which are asymptotically optimal in various regimes of transmit signal-to-noise ratios (SNRs) or the number of transmit antennas. Building upon the discovered optimal and low-dimensional beamforming structures, we then propose highly efficient and toolbox-free optimization algorithms to solve a specific multi-group multicast optimization problem based on the weighted sum rate (WSR) utility function. The proposed algorithms first use the cyclic maximization (CM) framework to decompose the problem into multiple subproblems, each has an optimal or low-dimensional closed-form beamforming solution structure. Then, we propose the projected adaptive gradient descent (PAGD) algorithm to compute the optimal Lagrangian dual variables for each subproblem. Numerical results show that the proposed algorithms maintain comparable or improved WSR performance compared to baseline algorithms, while dramatically reducing the computational complexity. Notably, the proposed ultra-low-complexity algorithms based on low-dimensional beamforming structures achieve near optimal WSR performance with extremely low computational complexity. This complexity remains independent of the number of transmit antennas, making them promising and practical for extremely large multiple-input multiple-output (XL-MIMO) applications in 6G.
On Chernoff Lower-Bound of Outage Threshold for Non-Central χ2-Distributed Beamforming Gain in URLLC Systems

The cumulative distribution function (CDF) of a non-central $\chi^2$-distributed random variable (RV) is often used when measuring the outage probability of communication systems. For ultra-reliable low-latency communication (URLLC), it is important but mathematically challenging to determine the outage threshold for an extremely small outage target. This motivates us to investigate lower bounds of the outage threshold, and it is found that the one derived from the Chernoff inequality (named Cher-LB) is the most effective lower bound. This finding is associated with three rigorously established properties of the Cher-LB with respect to the mean, variance, reliability requirement, and degrees of freedom of the non-central $\chi^2$-distributed RV. The Cher-LB is then employed to predict the beamforming gain in URLLC for both conventional multi-antenna systems (i.e., MIMO) under first-order Markov time-varying channel and reconfigurable intellgent surface (RIS) systems. It is exhibited that, with the proposed Cher-LB, the pessimistic prediction of the beamforming gain is made sufficiently accurate for guaranteed reliability as well as the transmit-energy efficiency.
Influence of Dynamical Floquet Spectrum on the Plasmon Excitations and Exchange Energy of tilted monolayer 1T$^\prime$MoS$_2$

It is now well established that a high-frequency electromagnetic dressing field within the off-resonance regime significantly modifies the electronic transport and optical properties on Dirac materials. Here, using light with circular polarization, we investigate its effect on the energy spectrum of tilted monolayer 1T$^\prime$MoS$_2$ which acquires two energy gaps associated with up- and down- pseudospin. We can adjust its electronic properties over a wider range by varying these two band gaps in contrast with graphene. With the use of the Lindhard approach for the frequency-dependent polarizability propagator, we have developed a rigorous theoretical formalism for employing the Floquet energy spectrum for investigating the many-body effects on the plasmon excitations, their lifetimes due to Landau damping and the exchange energy of tilted monolayer 1T$^\prime$MoS$_2$ under normal incidence of electromagnetic radiation at arbitrary temperature. The dressed states at very low temperature corresponding to circular polarization suppress the response of the system to the external probe. This gives rise to the weak but long lived plasmon excitations at small wavenumber $q$ when compared to the plasmon spectrum in this regime in the absence of irradiation. However, $\sqrt{qT}$-dependent plasmons are restored at high temperatures. Our calculations have shown that the tilting, anisotropy, direct and indirect band gaps lead to a reduced exchange energy, which has some potential applications such as, tunability of exciton polariton and plasmon excitations.
A novel two-dimensional all-carbon Dirac node-line semimetal

Carbon allotropes have vast potential in various applications, including superconductivity, energy storage, catalysis, and photoelectric semiconductor devices. Recently, there has been significant research interest in exploring new carbon materials that exhibit unique electronic structures. Here, we propose a novel two-dimensional (2D) carbon allotrope called TCH-SSH-2D, which possesses a Dirac node-line (DNL) semimetallic state. The structure of TCH-SSH-2D is derived from the TCH-type Archimedean polyhedral carbon cluster units, combined with the SSH lattice model, possessing a space group of tetragonal P4/mmm. Using first-principles calculations, we demonstrate that the system is dynamically, thermodynamically, and mechanically stable. It exhibits an energetically favorable structure with no imaginary frequency in the phonon dispersion curves and elastic constants satisfying the Born-Huang stability criterion. Our findings not only contribute to a deeper understanding of the carbon allotrope family but also provide an opportunity to explore unique Dirac states in two-dimensional pure carbon systems.
Accent-VITS: accent transfer for end-to-end TTS

Accent transfer aims to transfer an accent from a source speaker to synthetic speech in the target speaker's voice. The main challenge is how to effectively disentangle speaker timbre and accent which are entangled in speech. This paper presents a VITS-based end-to-end accent transfer model named Accent-VITS.Based on the main structure of VITS, Accent-VITS makes substantial improvements to enable effective and stable accent transfer.We leverage a hierarchical CVAE structure to model accent pronunciation information and acoustic features, respectively, using bottleneck features and mel spectrums as constraints.Moreover, the text-to-wave mapping in VITS is decomposed into text-to-accent and accent-to-wave mappings in Accent-VITS. In this way, the disentanglement of accent and speaker timbre becomes be more stable and effective.Experiments on multi-accent and Mandarin datasets show that Accent-VITS achieves higher speaker similarity, accent similarity and speech naturalness as compared with a strong baseline.
Beyond the Surface: Advanced Wash Trading Detection in Decentralized NFT Markets

Wash trading in decentralized markets remains a significant concern magnified by the pseudonymous and public nature of blockchains. In this paper we introduce an innovative methodology designed to detect wash trading activities beyond surface-level transactions. Our approach integrates NFT ownership traces with the Ethereum Transaction Network, encompassing the complete historical record of all Ethereum account normal transactions. By analyzing both networks, our method offers a notable advancement over techniques proposed by existing research. We analyzed the wash trading activity of 7 notable NFT collections. Our results show that wash trading in unregulated NFT markets is an underestimated concern and is much more widespread both in terms of frequency as well as volume. Excluding the Meebits collection, which emerged as an outlier, we found that wash trading constituted up to 25% of the total trading volume. Specifically, for the Meebits collection, a staggering 93% of its total trade volume was attributed to wash trading.
Brunnian planar braids and simplicial groups

Twin groups are planar analogues of Artin braid groups and play a crucial role in the Alexander-Markov correspondence for the isotopy classes of immersed circles on the 2-sphere without triple and higher intersections. These groups admit diagrammatic representations, leading to maps obtained by the addition and deletion of strands. This paper explores Brunnian twin groups, which are subgroups of twin groups composed of twins that become trivial when any of their strands are deleted. We establish that Brunnian twin groups consisting of more than two strands are free groups. Furthermore, we provide a necessary and sufficient condition for a Brunnian doodle on the 2-sphere to be the closure of a Brunnian twin. Additionally, we delve into two generalizations of Brunnian twins, namely, $k$-decomposable twins and Cohen twins, and prove some structural results about these groups. We also investigate a simplicial structure on pure twin groups that admits a simplicial homomorphism from Milnor's construction of the simplicial 2-sphere. This gives a possibility to provide a combinatorial description of homotopy groups of the 2-sphere in terms of pure twins.
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional Understanding

Vision-Language Models (VLMs), such as CLIP, exhibit strong image-text comprehension abilities, facilitating advances in several downstream tasks such as zero-shot image classification, image-text retrieval, and text-to-image generation. However, the compositional reasoning abilities of existing VLMs remains subpar. The root of this limitation lies in the inadequate alignment between the images and captions in the pretraining datasets. Additionally, the current contrastive learning objective fails to focus on fine-grained grounding components like relations, actions, and attributes, resulting in"bag-of-words"representations. We introduce a simple and effective method to improve compositional reasoning in VLMs. Our method better leverages available datasets by refining and expanding the standard image-text contrastive learning framework. Our approach does not require specific annotations and does not incur extra parameters. When integrated with CLIP, our technique yields notable improvement over state-of-the-art baselines across five vision-language compositional benchmarks. We open-source our code at https://github.com/lezhang7/Enhance-FineGrained.
Gravitational Wave Signatures of a Chiral Fermion Dark Matter Model

Theories in which the dark matter (DM) candidate is a fermion transforming chirally under a gauge symmetry are attractive, as the gauge symmetry would protect the DM mass. In such theories, the universe would have undergone a phase transition at early times that generated the DM mass upon spontaneous breaking of the gauge symmetry. In this paper, we explore the gravitational wave signals of a simple such theory based on an $\mathrm{SU}(2)_\mathrm{D}$ dark sector with a dark isospin-$3/2$ fermion serving as the DM candidate. This is arguably the simplest chiral theory possible. The scalar sector consists of a dark isospin-$3$ multiplet which breaks the $\mathrm{SU}(2)_\mathrm{D}$ gauge symmetry and also generates the DM mass. We construct the full thermal potential of the model and identify regions of parameter space which lead to detectable gravitational wave signals, arising from a strong first-order $\mathrm{SU}(2)_\mathrm{D}$ phase transition, in various planned space-based interferometers, while also being consistent with dark matter relic abundance. Bulk of the parameter space exhibiting detectable gravitational wave signals in the model also has large WIMP-nucleon scattering cross sections, $\sigma_{\rm SI}$, which could be probed in upcoming direct detection experiments.
Representing and Modeling Inconsistent, Impossible, and Incoherent Shapes and Scenes with 2D Non-Conservative Vector Fields mapped on 2-Complexes

In this paper, we present a framework to represent mock 3D objects and scenes, which are not 3D but appear 3D. In our framework, each mock-3D object is represented using 2D non-conservative vector fields and thickness information that are mapped on 2-complexes. Mock-3D scenes are simply scenes consisting of more than one mock-3D object. We demonstrated that using this representation, we can dynamically compute a 3D shape using rays emanating from any given point in 3D. These mock-3D objects are view-dependent since their computed shapes depend on the positions of ray centers. Using these dynamically computed shapes, we can compute shadows, reflections, and refractions in real time. This representation is mainly useful for 2D artistic applications to model incoherent, inconsistent, and impossible objects. Using this representation, it is possible to obtain expressive depictions with shadows and global illumination effects. The representation can also be used to convert existing 2D artworks into a Mock-3D form that can be interactively re-rendered.
Accretion flows around spinning compact objects in the post-Newtonian regime

We present the structure of a low angular momentum accretion flows around rotating compact objects incorporating relativistic corrections up to the leading post-Newtonian order. To begin with, we formulate the governing post-Newtonian hydrodynamic equations for the mass and energy-momentum flux without imposing any symmetries. However, for the sake of simplicity, we consider the flow to be stationary, axisymmetric, and inviscid. Toward this, we adapt the polytropic equation of state (EoS) and analyze the geometrically thin accretion flow confined to the equatorial plane. The spin-orbit effects manifest themselves in the disk structure. This is a relativistic interaction between the body's spin and the motion of fluid elements inside the gravitational potential of the body. In the present analysis, we focus on global transonic accretion solutions, where a subsonic flow enters far away from the compact object and gradually gains radial velocity as it moves inwards. Thus, the flow becomes supersonic after reaching a certain radius, known as the critical point. For a better understanding of the transonic solutions, we classify the post-Newtonian equations into semi-relativistic (SR), semi-Newtonian (SN), and non-relativistic (NR) limits and compare the accretion solutions and their corresponding flow variables. With these, we find that SR and SN flow are in good agreement all throughout, although they deviate largely from the NR ones. Interestingly, the density profile seems to follow the profile $\rho \propto r^{-3/2}$ in the post-Newtonian regime. The present study has the potential to connect Newtonian and GR descriptions of accretion disks.