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Automated marker makers (AMMs) are a class of decentralized exchanges that enable the automated trading of digital assets. They accept deposits of digital tokens from liquidity providers (LPs); tokens can be used by traders to execute trades, which generate fees for the investing LPs. The distinguishing feature of AMMs is that trade prices are determined algorithmically, unlike classical limit order books. Concentrated liquidity market makers (CLMMs) are a major class of AMMs that offer liquidity providers flexibility to decide not only \emph{how much} liquidity to provide, but \emph{in what ranges of prices} they want the liquidity to be used. This flexibility can complicate strategic planning, since fee rewards are shared among LPs. We formulate and analyze a game theoretic model to study the incentives of LPs in CLMMs. Our main results show that while our original formulation admits multiple Nash equilibria and has complexity quadratic in the number of price ticks in the contract, it can be reduced to a game with a unique Nash equilibrium whose complexity is only linear. We further show that the Nash equilibrium of this simplified game follows a waterfilling strategy, in which low-budget LPs use up their full budget, but rich LPs do not. Finally, by fitting our game model to real-world CLMMs, we observe that in liquidity pools with risky assets, LPs adopt investment strategies far from the Nash equilibrium. Under price uncertainty, they generally invest in fewer and wider price ranges than our analysis suggests, with lower-frequency liquidity updates. We show that across several pools, by updating their strategy to more closely match the Nash equilibrium of our game, LPs can improve their median daily returns by \$116, which corresponds to an increase of 0.009\% in median daily return on investment.

We demonstrate a machine learning based approach which can learn the time-dependent electronic excitation dynamics of small molecules subjected to ion irradiation. Ensembles of recurrent neural networks are trained on data generated by time-dependent density functional theory to relate atomic positions to occupations of molecular orbitals. New data is incrementally and efficiently added to the training data using an active learning process, thereby improving model accuracy. Predicted changes in orbital occupations made by the recurrent neural network ensemble are found to have errors and one standard deviation uncertainties which are two orders of magnitude smaller than the typical values of the orbital occupation numbers. The trained recurrent neural network ensembles demonstrate a limited ability to generalize to molecules not used to train the models. In such cases, the models are able to identify key qualitative features, but struggle to match the quantitative values. The machine learning procedure developed here is potentially broadly applicable and has the potential to enable study of broad ranges of both materials and dynamical processes by drastically lowering the computational cost and providing surrogate model for multiscale simulations., Comment: 8 pages, 6 figures

Quantum reservoir engineering aims to transform typically detrimental dissipations into advantageous resources. We present a versatile method for creating a squeezed thermal reservoir for quantum systems. By coupling the system to a lossy mode within a normal thermal environment, we can emulate the effect of a squeezed reservoir characterized by reduced fluctuations in one quadrature. We demonstrate this approach through two illustrative cases: one for two-level systems, such as qubits or atoms, and another for bosonic modes, like photons or phonons. This method leverages constant linear interactions within a normal thermal environment, ensuring experimental feasibility without requiring squeezed light inputs or time-dependent modulations. This technique holds promise for various applications, including the enhancement of dissipative squeezing, stabilization of entanglement, advancement of quantum simulations, exploration of quantum thermodynamics and phase transitions, and improvement of precision measurements., Comment: 6 pages, 3 figures

Visual State Space Model (VSS) has demonstrated remarkable performance in various computer vision tasks. However, in the process of development, backdoor attacks have brought severe challenges to security. Such attacks cause an infected model to predict target labels when a specific trigger is activated, while the model behaves normally on benign samples. In this paper, we conduct systematic experiments to comprehend on robustness of VSS through the lens of backdoor attacks, specifically how the state space model (SSM) mechanism affects robustness. We first investigate the vulnerability of VSS to different backdoor triggers and reveal that the SSM mechanism, which captures contextual information within patches, makes the VSS model more susceptible to backdoor triggers compared to models without SSM. Furthermore, we analyze the sensitivity of the VSS model to patch processing techniques and discover that these triggers are effectively disrupted. Based on these observations, we consider an effective backdoor for the VSS model that recurs in each patch to resist patch perturbations. Extensive experiments across three datasets and various backdoor attacks reveal that the VSS model performs comparably to Transformers (ViTs) but is less robust than the Gated CNNs, which comprise only stacked Gated CNN blocks without SSM., Comment: 11 pages, 9 figures, minor revise, under review

The potential impact of cation-substituted AlN-based materials, such as Al1-xScxN, Al1-xGaxN, and Al1-xBxN, with exceptional electronic, electromechanical, and dielectric properties has spurred research into this broad family of materials. Rare earth (RE) cations are particularly appealing as they could additionally impart optoelectronic or magnetic functionality. However, success in incorporating a significant level of RE cations into AlN has been limited so far because it is thermodynamically challenging to stabilize such heterostructural alloys. Using combinatorial co-sputtering, we synthesized Al1-xRExN (RE = Pr, Tb) thin films and performed a rapid survey of the composition-structure-property relationships as a function of RE alloying. Under our growth conditions, we observe that Al1-xPrxN maintains a phase-pure wurtzite structure until transitioning to amorphous for x>0.22. Al1-xTbxN exhibits a phase-pure wurtzite structure until x<0.15, then exhibits mixed wurtzite and rocksalt phases for 0.16

Pseudo-Hermitian system is a class of non-Hermitian system with Hamiltonian satisfying the condition $\eta^{-1}H^\dagger\eta=H$. We develop the in-in and Schwinger Keldysh formalism to calculate cosmological correlators for pseudo-Hermitian systems. We study a model consists of massive symplectic fermions coupled to the primordial curvature perturbation. The three-point function for the primordial curvature perturbation is computed up to one-loop and compared to earlier work where the loop correction comes from a massive scalar boson. The two results differ by a minus sign. Therefore, the one loop correction to the three-point function cannot be used to distinguished scalar bosons and symplectic fermions. To conclude, we discuss possibilities where the scalar bosons and symplectic fermions may be distinguished., Comment: 19 pages, 2 figures

AlN-based alloys find widespread application in high-power microelectronics, optoelectronics, and electromechanics. The realization of ferroelectricity in wurtzite AlN-based heterostructural alloys has opened up the possibility of directly integrating ferroelectrics with conventional microelectronics based on tetrahedral semiconductors such as Si, SiC and III-Vs, enabling compute-in-memory architectures, high-density data storage, and more. The discovery of AlN-based wurtzite ferroelectrics has been driven to date by chemical intuition and empirical explorations. Here, we demonstrate the computationally-guided discovery and experimental demonstration of new ferroelectric wurtzite Al$_{1-x}$Gd$_x$N alloys. First-principles calculations indicate that the minimum energy pathway for switching changes from a collective to an individual switching process with a lower overall energy barrier, at a rare-earth fraction $x$ of $x>$ 0.10$-$0.15. Experimentally, ferroelectric switching is observed at room temperature in Al$_{1-x}$Gd$_x$N films with $x>$ 0.12, which strongly supports the switching mechanisms in wurtzite ferroelectrics proposed previously (Lee et al., $\textit{Science Advances}$ 10, eadl0848, 2024). This is also the first demonstration of ferroelectricity in an AlN-based alloy with a magnetic rare-earth element, which could pave the way for additional functionalities such as multiferroicity and opto-ferroelectricity in this exciting class of AlN-based materials.

Recent advances in automatic speech recognition (ASR) often rely on large speech foundation models for generating high-quality transcriptions. However, these models can be impractical due to limited computing resources. The situation is even more severe in terms of more realistic or difficult scenarios, such as code-switching ASR (CS-ASR). To address this, we present a framework for developing more efficient models for CS-ASR through knowledge distillation using realistic speech-only data. Our proposed method, Leave No Knowledge Behind During Knowledge Distillation (K$^2$D), leverages both the teacher model's knowledge and additional insights from a small auxiliary model. We evaluate our approach on two in-domain and two out-domain datasets, demonstrating that K$^2$D is effective. By conducting K$^2$D on the unlabeled realistic data, we have successfully obtained a 2-time smaller model with 5-time faster generation speed while outperforming the baseline methods and the teacher model on all the testing sets. We have made our model publicly available on Hugging Face (https://huggingface.co/andybi7676/k2d-whisper.zh-en).

Semi-supervised learning (SSL) has achieved remarkable performance with a small fraction of labeled data by leveraging vast amounts of unlabeled data from the Internet. However, this large pool of untrusted data is extremely vulnerable to data poisoning, leading to potential backdoor attacks. Current backdoor defenses are not yet effective against such a vulnerability in SSL. In this study, we propose a novel method, Unlabeled Data Purification (UPure), to disrupt the association between trigger patterns and target classes by introducing perturbations in the frequency domain. By leveraging the Rate-Distortion-Perception (RDP) trade-off, we further identify the frequency band, where the perturbations are added, and justify this selection. Notably, UPure purifies poisoned unlabeled data without the need of extra clean labeled data. Extensive experiments on four benchmark datasets and five SSL algorithms demonstrate that UPure effectively reduces the attack success rate from 99.78% to 0% while maintaining model accuracy. Code is available here: \url{https://github.com/chengyi-chris/UPure}., Comment: Accepted by WACV 2025

Recent efforts in Spoken Dialogue Modeling aim to synthesize spoken dialogue without the need for direct transcription, thereby preserving the wealth of non-textual information inherent in speech. However, this approach faces a challenge when speakers talk simultaneously, requiring stereo dialogue data with speakers recorded on separate channels, a notably scarce resource. To address this, we have developed an innovative pipeline capable of transforming single-channel dialogue data into pseudo-stereo data. This expanded our training dataset from a mere 2,000 to an impressive 17,600 hours, significantly enriching the diversity and quality of the training examples available. The inclusion of this pseudo-stereo data has proven to be effective in improving the performance of spoken dialogue language models. Additionally, we explored the use of discrete units of different speech foundation models for spoken dialogue generation., Comment: submitted to interspeech 2024

In this paper, we provide a set of Hermitian interactions for quantum fields based on Elko, considering the recent achievements concerning the most general form of singular spinors and Wigner degeneracy. We consider Hermiticity and renormalizability a criterion to define the derivative Elko-Higgs interaction as the suitable candidate for a dark coupling. Then, the free parameters of the model are fixed by cosmological constraints on the dark matter abundance, bounds on the Higgs invisible decays, and limits on the electron-dark matter scattering mediated by the Higgs., Comment: 17 pages, 2 figures

Elko is a spin-half fermion with a two-fold Wigner degeneracy and Klein-Gordon dynamics. In this paper, we show that in a spatially flat FLRW space-time, slow-roll inflation can be initiated by the homogeneous Elko fields. The inflaton is a composite scalar field obtained by contracting the spinor field with its dual. This is possible because the background evolution as described by the Friedmann equation is completely determined by the scalar field. This approach has the advantage that we do not need to specify the initial conditions for every component of the spinor fields. We derive the equation of motion for the inflaton and also show that this solution is an attractor., Comment: 15 pages, 8 figures

Existing Maximum-Entropy (MaxEnt) Reinforcement Learning (RL) methods for continuous action spaces are typically formulated based on actor-critic frameworks and optimized through alternating steps of policy evaluation and policy improvement. In the policy evaluation steps, the critic is updated to capture the soft Q-function. In the policy improvement steps, the actor is adjusted in accordance with the updated soft Q-function. In this paper, we introduce a new MaxEnt RL framework modeled using Energy-Based Normalizing Flows (EBFlow). This framework integrates the policy evaluation steps and the policy improvement steps, resulting in a single objective training process. Our method enables the calculation of the soft value function used in the policy evaluation target without Monte Carlo approximation. Moreover, this design supports the modeling of multi-modal action distributions while facilitating efficient action sampling. To evaluate the performance of our method, we conducted experiments on the MuJoCo benchmark suite and a number of high-dimensional robotic tasks simulated by Omniverse Isaac Gym. The evaluation results demonstrate that our method achieves superior performance compared to widely-adopted representative baselines., Comment: Published at NeurIPS 2024. Code: https://github.com/ChienFeng-hub/meow

In this paper, we present a framework for convolutional coded Poisson receivers (CCPRs) that incorporates spatially coupled methods into the architecture of coded Poisson receivers (CPRs). We use density evolution equations to track the packet decoding process with the successive interference cancellation (SIC) technique. We derive outer bounds for the stability region of CPRs when the underlying channel can be modeled by a $\phi$-ALOHA receiver. The stability region is the set of loads that every packet can be successfully received with a probability of 1. Our outer bounds extend those of the spatially-coupled Irregular Repetition Slotted ALOHA (IRSA) protocol and apply to channel models with multiple traffic classes. For CCPRs with a single class of users, the stability region is reduced to an interval. Therefore, it can be characterized by a percolation threshold. We study the potential threshold by the potential function of the base CPR used for constructing a CCPR. In addition, we prove that the CCPR is stable under a technical condition for the window size. For the multiclass scenario, we recursively evaluate the density evolution equations to determine the boundaries of the stability region. Numerical results demonstrate that the stability region of CCPRs can be enlarged compared to that of CPRs by leveraging the spatially-coupled method. Moreover, the stability region of CCPRs is close to our outer bounds when the window size is large., Comment: Part of this work was presented in 2023 IEEE International Symposium on Information Theory (ISIT) [1] and 2024 IEEE International Symposium on Information Theory (ISIT) [2]

In the Lounesto classification, there are three types of regular spinors. They are classified by the condition that at least one of the scalar or pseudo scalar norms are non-vanishing. The Dirac spinors are regular spinors because their scalar and pseudo scalar norms are non-zero and zero respectively. We construct local and Lorentz-covariant fermionic fields from all three classes of regular spinors. By computing the invariants and bilinear covariants of the regular spinor fields, we show that they are physically equivalent to the Dirac fields in the sense that whatever interactions one writes down using the regular spinor fields, they can always be expressed in terms of the Dirac fields., Comment: 6 pages. Accepted for publication

Cosmelkology is the study of Elko in cosmology. Elko is a massive spin-half field of mass dimension one. Elko differs from the Dirac and Majorana fermions because it furnishes the irreducible representation of the extended Poincare group with a two-fold Wigner degeneracy where the particle and anti-particle states both have four degrees of freedom. Elko has a renormalizable quartic self interaction which makes it a candidate for self-interacting dark matter. We study Elko in the spatially flat FLRW space-time and find exact solutions in the de Sitter space. By choosing the appropriate solutions and phases, the fields satisfy the canonical anti-commutation relations and have the correct time evolutions in the flat space limit., Comment: 19 pages

Anthropogenic influences have been linked to tropical cyclone (TC) poleward migration, TC extreme precipitation, and an increased proportion of major hurricanes [1, 2, 3, 4]. Understanding past TC trends and variability is critical for projecting future TC impacts on human society considering the changing climate [5]. However, past trends of TC structure/energy remain uncertain due to limited observations; subjective-analyzed and spatiotemporal-heterogeneous "best-track" datasets lead to reduced confidence in the assessed TC repose to climate change [6, 7]. Here, we use deep learning to reconstruct past "observations" and yield an objective global TC wind profile dataset during 1981 to 2020, facilitating a comprehensive examination of TC structure/energy. By training with uniquely labeled data integrating best tracks and numerical model analysis of 2004 to 2018 TCs, our model converts multichannel satellite imagery to a 0-750-km wind profile of axisymmetric surface winds. The model performance is verified to be sufficient for climate studies by comparing it to independent satellite-radar surface winds. Based on the new homogenized dataset, the major TC proportion has increased by ~13% in the past four decades. Moreover, the proportion of extremely high-energy TCs has increased by ~25%, along with an increasing trend (> one standard deviation of the 40-y variability) of the mean total energy of high-energy TCs. Although the warming ocean favors TC intensification, the TC track migration to higher latitudes and altered environments further affect TC structure/energy. This new deep learning method/dataset reveals novel trends regarding TC structure extremes and may help verify simulations/studies regarding TCs in the changing climate., Comment: 41 pages

Not all complete set of spinors can be used as expansion coefficients of a quantum field. In fact, Steven Weinberg established the uniqueness of Dirac spinors for this purpose provided: (a) one paid due attention to the multiplicative phases for each of the spinors, and (b) one paired these to creation and annihilation operators in a specific manner. This is implicit in his implementation of the rotational symmetry for the spin half quantum field. Among the numerous complete set of spinors that are available to a physicist, Elko occupies a unique status that allows it to enter as expansion coefficients of a quantum field without violating Weinberg's no go theorem. How this paradigm changing claim arises is the primary subject of this communication. Weinberg's no go theorem is evaded by exploiting a uniquely special feature of Elko that allows us to introduce a doubling of the particle-antiparticle degrees of freedom from four to eight. Weinberg had dismissed this degeneracy on the ground that, "no examples are known of particles that furnish unconventional representations of inversions." Here we will find that this degeneracy, once envisioned by Eugene Wigner, in fact gives rise to a quantum field that has all the theoretical properties required of dark matter., Comment: 39 pages, no figures. Accepted for publication

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

Novel wide-band-gap semiconductors are needed for next-generation power electronic but there is a gap between a promising material and a functional device. Finding stable contacts is one of the major challenges, which is currently dealt with mainly via trial and error. Herein, we computationally investigate the thermochemistry and phase co-existence at the junction between three wide gap semiconductors, $\beta$-Ga$_{2}$O$_{3}$, GeO$_2$, and GaN, and possible contact materials. The pool of possible contacts includes 47 elemental metals and 4 common $n$-type transparent conducting oxides (ZnO, TiO$_2$, SnO$_2$, and In$_2$O$_3$). We use first-principles thermodynamics to model the Gibbs free energies of chemical reactions as a function of the gas pressure (p$_{\mathrm{O}_2}$/p$_{\mathrm{N}_2}$) and equilibrium temperature. We deduce whether a semiconductor/contact interface will be stable at relevant conditions, possibly influencing the long-term reliability and performance of devices. We generally find that most elemental metals tend to oxidize or nitridize and form various interface oxide/nitride layers. Exceptions include select late- and post-transition metals, and in case of GaN also the alkali metals, which are predicted to exhibit stable coexistence, although in many cases at relatively low gas partial pressures. Similar is true for the transparent conducting oxides, for which in most cases we predict a preference toward forming ternary oxides when in contact with $\beta$-Ga$_{2}$O$_{3}$ and GeO$_{2}$. The only exception is SnO$_2$, which can form stable contacts with both oxides. Finally, we show how the same approach can be used to predict gas partial pressure vs. temperature phase diagrams to help direct synthesis of ternary compounds. We believe these results provide a valuable guidance in selecting contact materials to wide-gap semiconductors and suitable growth conditions.

Knowing the rate at which particle radiation releases energy in a material, the stopping power, is key to designing nuclear reactors, medical treatments, semiconductor and quantum materials, and many other technologies. While the nuclear contribution to stopping power, i.e., elastic scattering between atoms, is well understood in the literature, the route for gathering data on the electronic contribution has for decades remained costly and reliant on many simplifying assumptions, including that materials are isotropic. We establish a method that combines time-dependent density functional theory (TDDFT) and machine learning to reduce the time to assess new materials to mere hours on a supercomputer and provides valuable data on how atomic details influence electronic stopping. Our approach uses TDDFT to compute the electronic stopping contributions to stopping power from first principles in several directions and then machine learning to interpolate to other directions at a cost of 10 million times fewer core-hours. We demonstrate the combined approach in a study of proton irradiation in aluminum and employ it to predict how the depth of maximum energy deposition, the "Bragg Peak," varies depending on incident angle -- a quantity otherwise inaccessible to modelers. The lack of any experimental information requirement makes our method applicable to most materials, and its speed makes it a prime candidate for enabling quantum-to-continuum models of radiation damage. The prospect of reusing valuable TDDFT data for training the model make our approach appealing for applications in the age of materials data science.

Understanding the key factors shaping environmental hazard exposures and their associated environmental injustice issues is vital for formulating equitable policy measures. Traditional perspectives on environmental injustice have primarily focused on the socioeconomic dimensions, often overlooking the influence of heterogeneous urban characteristics. This limited view may obstruct a comprehensive understanding of the complex nature of environmental justice and its relationship with urban design features. To address this gap, this study creates an interpretable machine learning model to examine the effects of various urban features and their non-linear interactions to the exposure disparities of three primary hazards: air pollution, urban heat, and flooding. The analysis trains and tests models with data from six metropolitan counties in the United States using Random Forest and XGBoost. The performance is used to measure the extent to which variations of urban features shape disparities in environmental hazard levels. In addition, the analysis of feature importance reveals features related to social-demographic characteristics as the most prominent urban features that shape hazard extent. Features related to infrastructure distribution and land cover are relatively important for urban heat and air pollution exposure respectively. Moreover, we evaluate the models' transferability across different regions and hazards. The results highlight limited transferability, underscoring the intricate differences among hazards and regions and the way in which urban features shape hazard exposures. The insights gleaned from this study offer fresh perspectives on the relationship among urban features and their interplay with environmental hazard exposure disparities, informing the development of more integrated urban design policies to enhance social equity and environmental injustice issues.