Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal/Nearly-optimal simulation of multi-periodic time-dependent Hamiltonians

Simulating Hamiltonian dynamics is one of the most fundamental and significant tasks for characterising quantum materials. Recently, a series of quantum algorithms employing block-encoding of Hamiltonians have succeeded in providing efficient simulation of time-evolution operators on quantum computers. While time-independent Hamiltonians can be simulated by the quantum eigenvalue transformation (QET) or quantum singularvalue transformation with the optimal query complexity in time $t$ and desirable accuracy $\varepsilon$, generic time-dependent Hamiltonians face at larger query complexity and more complicated oracles due to the difficulty of handling time-dependency. In this paper, we establish a QET-based approach for simulating time-dependent Hamiltonians with multiple time-periodicity. Such time-dependent Hamiltonians involve a variety of nonequilibrium systems such as time-periodic systems (Floquet systems) and time-quasiperiodic systems. Overcoming the difficulty of time-dependency, our protocol can simulate the dynamics under multi-periodic time-dependent Hamiltonians with optimal/nearly-optimal query complexity both in time $t$ and desirable accuracy $\varepsilon$, and simple oracles as well as the optimal algorithm for time-independent cases.
Clustered Relational Thread-Modular Abstract Interpretation with Local Traces

We construct novel thread-modular analyses that track relational information for potentially overlapping clusters of global variables - given that they are protected by common mutexes. We provide a framework to systematically increase the precision of clustered relational analyses by splitting control locations based on abstractions of local traces. As one instance, we obtain an analysis of dynamic thread creation and joining. Interestingly, tracking less relational information for globals may result in higher precision. We consider the class of 2-decomposable domains that encompasses many weakly relational domains (e.g., Octagons). For these domains, we prove that maximal precision is attained already for clusters of globals of sizes at most 2.
Quasinormal modes from Penrose limits

We use Penrose limits to approximate quasinormal modes (QNMs) with large real frequencies. The Penrose limit associates a plane wave to a region of spacetime near a null geodesic. This plane wave can be argued to geometrically realize the geometrical optics approximation. Therefore, when applied to the bound null orbits around black holes, the Penrose limit can be used to study QNMs. For instance, this Penrose limit point of view makes manifest the symmetry that emerges in the geometrical optics approximation of QNMs in terms of isometries of the resulting plane wave spacetime. We apply the procedure to warped AdS3 , Schwarzschild black holes and Kerr black holes. In the former we show explicitly how the symmetry algebra contracts to that of the limiting plane wave while in the latter we find the expected agreement with numerically computed QNMs for large (real) frequencies.
Spatiotemporal relative risk distribution of porcine reproductive and respiratory syndrome virus in the United States

Porcine reproductive and respiratory syndrome virus (PRRSV) remains widely distributed across the U.S. swine industry. Between-farm movements of animals and transportation vehicles, along with local transmission are the primary routes by which PRRSV is spread. Given the farm-to-farm proximity in high pig production areas, local transmission is an important pathway in the spread of PRRSV; however, there is limited understanding of the role local transmission plays in the dissemination of PRRSV, specifically, the distance at which there is increased risk for transmission from infected to susceptible farms. We used a spatial and spatiotemporal kernel density approach to estimate PRRSV relative risk and utilized a Bayesian spatiotemporal hierarchical model to assess the effects of environmental variables, between-farm movement data and on-farm biosecurity features on PRRSV outbreaks. The maximum spatial distance calculated through the kernel density approach was 15.3 km in 2018, 17.6 km in 2019, and 18 km in 2020. Spatiotemporal analysis revealed greater variability throughout the study period, with significant differences between the different farm types. We found that downstream farms (i.e., finisher and nursery farms) were located in areas of significant-high relative risk of PRRSV. Factors associated with PRRSV outbreaks were farms with higher number of access points to barns, higher numbers of outgoing movements of pigs, and higher number of days where temperatures were between 4°C and 10°C. Results obtained from this study may be used to guide the reinforcement of biosecurity and surveillance strategies to farms and areas within the distance threshold of PRRSV positive farms.
Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities

As automated decision making and decision assistance systems become common in everyday life, research on the prevention or mitigation of potential harms that arise from decisions made by these systems has proliferated. However, various research communities have independently conceptualized these harms, envisioned potential applications, and proposed interventions. The result is a somewhat fractured landscape of literature focused generally on ensuring decision-making algorithms"do the right thing". In this paper, we compare and discuss work across two major subsets of this literature: algorithmic fairness, which focuses primarily on predictive systems, and ethical decision making, which focuses primarily on sequential decision making and planning. We explore how each of these settings has articulated its normative concerns, the viability of different techniques for these different settings, and how ideas from each setting may have utility for the other.
Deep Learning Models to Study Sentence Comprehension in the Human Brain

Recent artificial neural networks that process natural language achieve unprecedented performance in tasks requiring sentence-level understanding. As such, they could be interesting models of the integration of linguistic information in the human brain. We review works that compare these artificial language models with human brain activity and we assess the extent to which this approach has improved our understanding of the neural processes involved in natural language comprehension. Two main results emerge. First, the neural representation of word meaning aligns with the context-dependent, dense word vectors used by the artificial neural networks. Second, the processing hierarchy that emerges within artificial neural networks broadly matches the brain, but is surprisingly inconsistent across studies. We discuss current challenges in establishing artificial neural networks as process models of natural language comprehension. We suggest exploiting the highly structured representational geometry of artificial neural networks when mapping representations to brain data.
Chemical treatments of Monolayer Transition Metal Dichalcogenides and Their Prospect in Optoelectronic Applications

The interest in obtaining high-quality monolayer transition metal dichalcogenides (TMDs) for optoelectronic device applications has been growing dramatically. However, the prevalence of defects and unwanted doping in these materials remains a challenge, as they both limit optical properties and device performance. Surface chemical treatments of monolayer TMDs have been effective in improving their photoluminescence yield and charge transport properties. In this scenario, a systematic understanding of the underlying mechanism of chemical treatments will lead to a rational design of passivation strategies in future research, ultimately taking a step toward practical optoelectronic applications. We will therefore describe in this review the strategies, progress, mechanisms, and prospects of chemical treatments to passivate and improve the optoelectronic properties of TMDs.
Metallurgical behavior of Sm(Co,Fe,Cu,Zr)z alloys

A model, based on the Sm‐Co system between SmCo5 (1:5) and Sm2Co17 (2:17 R and 2:17 H), is proposed to describe the metallurgical behavior of Sm(Co,Fe,Cu,Zr)z alloys between 6.8 10 a/o) destabilize the 2:17 R phase due to Fe‐Fe pairs which preferentially occupy the dumbbell sites (6c in R3m); (5) Zr‐vacancy pairs stabilize the 2:17 R phase at high Fe concentrations by displacing Fe‐F.
Probability distributions of travel time and intensity of the earliest arrivals of a short pulse backscattered by a rough surface

Abstract Precise measurements of the travel times of backscattered waves, and especially the travel times of the first (i.e., earliest) arrivals, underlie a number of geophysical remote sensing techniques. In this paper, statistical properties of the travel time and intensity of pulses backscattered by a two-dimensional rough surface are investigated within the geometric optics approximation by adopting a method originally developed in the theory of excursions of a stochastic process. We assume a wave source located sufficiently far from a rough surface with Gaussian statistics, and show that the probability distribution functions of the normalized deviation of the travel time of the first and second backscattered pulses from the travel time in the absence of roughness, are functions of a single dimensionless parameter, T=γ2 0 H/(2πσ), where σ2 and γ2 0 are the variances of the rough surface elevation and slope, and H is the source altitude. Signals from the rough surface return to the source location earlier than from the mean plane by O(2σ/c), where c is the velocity of wave propagation. On average, the travel times of the first and second arrivals decrease as parameter T increases, with the travel time shift being proportional to . The time delay between the first and the second arrivals is inversely proportional to . The joint probability density functions (PDF) of the travel times and the intensities of the first two backscattered pulses are derived. This allows us to obtain the travel time PDF for signals exceeding the given intensity threshold. It is shown that the travel time and the intensity are strongly correlated: on average, earlier arrivals have smaller amplitudes.
Triad interactions investigated by dual vortex shedding

The study of the exchange of momentum and energy between wave components of the turbulent velocity field, the so-called triad interactions, offers a unique way of visualizing and describing turbulence. Most often, this study has been carried out by Direct Numerical Simulations or by power spectral measurements. Due to the complexity of the problem and the great range of velocity scales in high Reynolds number developed turbulence, direct measurements of the interaction between the individual wave components have been rare. In the present work, we therefore present measurements and related computer simulations of triad interactions between controlled wave components injected into an approximately laminar and uniform flow from an open wind tunnel by vortex shedding from two rods suspended into the flow. This well-defined vortex shedding approximates well a two-dimensional flow, which makes the analysis of the triadic interactions considerably less complex to analyze than in a fully developed spatially three-dimensional flow. With the information obtained from the simulations, we are hereby able to isolate and display the individual triad interactions taking place as the flow develops downstream as well as the strengths of these interactions. The experiments provide the time constants governing the development of higher order frequency components. The combination of these experiments and simulations provide unique insight into the inner workings of turbulence and shows how the nonlinear term in the Navier-Stokes equation on average forces the energy towards higher frequencies, which is the reason for the so-called energy cascade.
Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models

The ability to quickly learn a new task with minimal instruction - known as few-shot learning - is a central aspect of intelligent agents. Classical few-shot benchmarks make use of few-shot samples from a single modality, but such samples may not be sufficient to characterize an entire concept class. In contrast, humans use cross-modal information to learn new concepts efficiently. In this work, we demonstrate that one can indeed build a better visual dog classifier by reading about dogs and listening to them bark. To do so, we exploit the fact that recent multimodal foundation models such as CLIP are inherently cross-modal, mapping different modalities to the same representation space. Specifically, we propose a simple cross-modal adaptation approach that learns from few-shot examples spanning different modalities. By repurposing class names as additional one-shot training samples, we achieve SOTA results with an embarrassingly simple linear classifier for vision-language adaptation. Furthermore, we show that our approach can benefit existing methods such as prefix tuning, adapters, and classifier ensembling. Finally, to explore other modalities beyond vision and language, we construct the first (to our knowledge) audiovisual few-shot benchmark and use cross-modal training to improve the performance of both image and audio classification. Project site at link.
Unified scatter model for rough surfaces at large incident and scatter angles

A new unified surface scatter model has been developed that produces accurate results for rougher surfaces than the classical Rayleigh-Rice vector perturbation theory and for larger incident and scattered angles than the classical Beckmann-Kirchhoff theory. It is a generalization of the linear systems formulation developed by Harvey and Shack in the mid 1970s. The scattered light behavior is characterized by a two-parameter family of surface transfer functions. The rather computationally intensive process of calculating the scattered intensity from rough surfaces at large incident angles is described and shown to agree well with experimental data. Furthermore, the smooth-surface approximation to this new generalized surface scatter theory yields an improved solution to the inverse scattering problem of characterizing optical surfaces from BRDF measurements. The new obliquity factor greatly reduces the ubiquitous and annoying "hook" at the high spatial frequency end of surface PSDs predicted by the Rayleigh-Rice theory. Comparisons of this new theory and the Rayleigh-Rice theory will be made with experimental BRDF measurements provided by John Stover, author of the companion paper entitled "Limitations of Rayleigh-Rice perturbation theory for describing surface scatter".
UL-DL duality for cell-free massive MIMO with per-AP power and information constraints

We derive a novel uplink-downlink duality principle for optimal joint precoding design under per-transmitter power and information constraints in fading channels. The information constraints model limited sharing of channel state information and data bearing signals across the transmitters. The main application is to cell-free networks, where each access point (AP) must typically satisfy an individual power constraint and form its transmit signal using limited cooperation capabilities. Our duality principle applies to ergodic achievable rates given by the popular hardening bound, and it can be interpreted as a nontrivial generalization of a previous result by Yu and Lan for deterministic channels. This generalization allows us to study involved information constraints going beyond the simple case of cluster-wise centralized precoding covered by previous techniques. Specifically, we show that the optimal joint precoders are, in general, given by an extension of the recently developed team minimum mean-square error method. As a particular yet practical example, we then solve the problem of optimal local precoding design in user-centric cell-free massive MIMO networks subject to per-AP power constraints.
Multiple-Antenna Aided Aeronautical Communications in Air-Ground Integrated Networks: Channel Estimation, Reliable Transmission, and Multiple Access

To provide seamless coverage during all flight phases, aeronautical communications systems (ACS) have to integrate space-based, air-based, as well as ground-based platforms to formulate aviation-oriented space-air-ground integrated networks (SAGINs). In continental areas, L-band aeronautical broadband communications (ABC) are gaining popularity for supporting air traffic management (ATM) modernization. However, L-band ABC faces the challenges of spectrum congestion and severe interference due to the legacy systems. To circumvent these, we propose a novel multiple-antenna aided L-band ABC paradigm to tackle the key issues of reliable and high-rate air-to-ground (A2G) transmissions. Specifically, we first introduce the development roadmap of the ABC. Furthermore, we discuss the peculiarities of the L-band ABC propagation environment and the distinctive challenges of the associated multiple-antenna techniques. To overcome these challenges, we propose an advanced multiple-antenna assisted L-band ABC paradigm from the perspective of channel estimation, reliable transmission, and multiple access. Finally, we shed light on the compelling research directions of the aviation component of SAGINs.
Two Stage Contextual Word Filtering for Context bias in Unified Streaming and Non-streaming Transducer

It is difficult for an E2E ASR system to recognize words such as entities appearing infrequently in the training data. A widely used method to mitigate this issue is feeding contextual information into the acoustic model. Previous works have proven that a compact and accurate contextual list can boost the performance significantly. In this paper, we propose an efficient approach to obtain a high quality contextual list for a unified streaming/non-streaming based E2E model. Specifically, we make use of the phone-level streaming output to first filter the predefined contextual word list then fuse it into non-casual encoder and decoder to generate the final recognition results. Our approach improve the accuracy of the contextual ASR system and speed up the inference process. Experiments on two datasets demonstrates over 20% CER reduction comparing to the baseline system. Meanwhile, the RTF of our system can be stabilized within 0.15 when the size of the contextual word list grows over 6,000.
GAR: Generalized Autoregression for Multi-Fidelity Fusion

In many scientific research and engineering applications where repeated simulations of complex systems are conducted, a surrogate is commonly adopted to quickly estimate the whole system. To reduce the expensive cost of generating training examples, it has become a promising approach to combine the results of low-fidelity (fast but inaccurate) and high-fidelity (slow but accurate) simulations. Despite the fast developments of multi-fidelity fusion techniques, most existing methods require particular data structures and do not scale well to high-dimensional output. To resolve these issues, we generalize the classic autoregression (AR), which is wildly used due to its simplicity, robustness, accuracy, and tractability, and propose generalized autoregression (GAR) using tensor formulation and latent features. GAR can deal with arbitrary dimensional outputs and arbitrary multifidelity data structure to satisfy the demand of multi-fidelity fusion for complex problems; it admits a fully tractable likelihood and posterior requiring no approximate inference and scales well to high-dimensional problems. Furthermore, we prove the autokrigeability theorem based on GAR in the multi-fidelity case and develop CIGAR, a simplified GAR with the exact predictive mean accuracy with computation reduction by a factor of d 3, where d is the dimensionality of the output. The empirical assessment includes many canonical PDEs and real scientific examples and demonstrates that the proposed method consistently outperforms the SOTA methods with a large margin (up to 6x improvement in RMSE) with only a couple high-fidelity training samples.
Interaction potentials for two-particle states with nonzero total momenta in lattice QCD

In this study, we extend the HAL QCD method to a case where a total momentum of a two-particle system is non-zero and apply it to the $I=2$ S-wave $\pi\pi$ scattering in order to confirm its validity. We derive a fundamental relation of an energy-independent non-local potential defined in the center of mass frame with NBS wave functions in a laboratory frame. Based on the relation, we propose the time-dependent method to extract potentials, often used in practice for the HALQCD method in the center of mass frame. For numerical simulations in the $I=2$ $\pi\pi$ system, we employ (2+1)-flavor gauge configurations on a $32^3 \times 64$ lattice at the lattice spacing $a \approx 0.0907$ fm and $m_{\pi} \approx 700$ MeV. Both effective leading order (LO) potentials and corresponding phase shifts obtained in laboratory frames agree with those obtained in the center-of-mass frame by the conventional HAL QCD method within somewhat larger statistical errors. In addition, we observe a consistency in scattering phase shifts between ours and results by the finite-volume method as well. The HAL QCD method with non-zero total momenta, established in this study, brings more flexibility to the HAL QCD method, which enables us to handle systems having the same quantum numbers with a vacuum or to access energy regions prohibited in the center of mass frame.
Statistical inference for the logarithmic spatial heteroskedasticity model with exogenous variables

The spatial dependence in mean has been well studied by plenty of models in a large strand of literature, however, the investigation of spatial dependence in variance is lagging significantly behind. The existing models for the spatial dependence in variance are scarce, with neither probabilistic structure nor statistical inference procedure being explored. To circumvent this deficiency, this paper proposes a new generalized logarithmic spatial heteroscedasticity model with exogenous variables (denoted by the log-SHE model) to study the spatial dependence in variance. For the log-SHE model, its spatial near-epoch dependence (NED) property is investigated, and a systematic statistical inference procedure is provided, including the maximum likelihood and generalized method of moments estimators, the Wald, Lagrange multiplier and likelihood-ratio-type D tests for model parameter constraints, and the overidentification test for the model diagnostic checking. Using the tool of spatial NED, the asymptotics of all proposed estimators and tests are established under regular conditions. The usefulness of the proposed methodology is illustrated by simulation results and a real data example on the house selling price.