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The Hawkes process is a widely used model in many areas, such as finance, seismology, neuroscience, epidemiology, and social sciences. Estimation of the Hawkes process from continuous observations of a sample path is relatively straightforward using either the maximum likelihood or other methods. However, estimating the parameters of a Hawkes process from observations of a sample path at discrete time points only is challenging due to the intractability of the likelihood with such data. In this work, we introduce a method to estimate the Hawkes process from a discretely observed sample path. The method takes advantage of a state-space representation of the incomplete data problem and use the sequential Monte Carlo (aka particle filtering) to approximate the likelihood function. As an estimator of the likelihood function the SMC approximation is unbiased, and therefore it can be used together with the Metropolis-Hastings algorithm to construct Markov Chains to approximate the likelihood distribution, or more generally, the posterior distribution of model parameters. The performance of the methodology is assessed using simulation experiments and compared with other recently published methods. The proposed estimator is found to have a smaller mean square error than the two benchmark estimators. The proposed method has the additional advantage that confidence intervals for the parameters are easily available. We apply the proposed estimator to the analysis of weekly count data on measles cases in Tokyo Japan and compare the results to those by one of the benchmark methods., Comment: 23 page, 5 figures

Embodied agents equipped with GPT as their brains have exhibited extraordinary decision-making and generalization abilities across various tasks. However, existing zero-shot agents for vision-and-language navigation (VLN) only prompt GPT-4 to select potential locations within localized environments, without constructing an effective "global-view" for the agent to understand the overall environment. In this work, we present a novel map-guided GPT-based agent, dubbed MapGPT, which introduces an online linguistic-formed map to encourage global exploration. Specifically, we build an online map and incorporate it into the prompts that include node information and topological relationships, to help GPT understand the spatial environment. Benefiting from this design, we further propose an adaptive planning mechanism to assist the agent in performing multi-step path planning based on a map, systematically exploring multiple candidate nodes or sub-goals step by step. Extensive experiments demonstrate that our MapGPT is applicable to both GPT-4 and GPT-4V, achieving state-of-the-art zero-shot performance on R2R and REVERIE simultaneously (~10% and ~12% improvements in SR), and showcasing the newly emergent global thinking and path planning abilities of the GPT., Comment: LLM/VLM-based VLN Agents. Accepted to ACL 2024. Project: https://chen-judge.github.io/MapGPT/

Data slice finding is an emerging technique for validating machine learning (ML) models by identifying and analyzing subgroups in a dataset that exhibit poor performance, often characterized by distinct feature sets or descriptive metadata. However, in the context of validating vision models involving unstructured image data, this approach faces significant challenges, including the laborious and costly requirement for additional metadata and the complex task of interpreting the root causes of underperformance. To address these challenges, we introduce AttributionScanner, an innovative human-in-the-loop Visual Analytics (VA) system, designed for metadata-free data slice finding. Our system identifies interpretable data slices that involve common model behaviors and visualizes these patterns through an Attribution Mosaic design. Our interactive interface provides straightforward guidance for users to detect, interpret, and annotate predominant model issues, such as spurious correlations (model biases) and mislabeled data, with minimal effort. Additionally, it employs a cutting-edge model regularization technique to mitigate the detected issues and enhance the model's performance. The efficacy of AttributionScanner is demonstrated through use cases involving two benchmark datasets, with qualitative and quantitative evaluations showcasing its substantial effectiveness in vision model validation, ultimately leading to more reliable and accurate models., Comment: 12 pages, 12 figures, 3 tables. This manuscript is under review by the IEEE Transactions on Visualization and Computer Graphics (TVCG)

Phase-sensitive amplification of squeezed states is a technique to mitigate high detection loss, e.g. at 2-micrometre wavelengths. Our analytical model of amplified squeezed states expands on the effect of phase noise and derives two practical parameters: the effective measurable squeezing and the effective detection efficiency. A case study including realistic parameters demonstrates the benefit of phase-sensitive amplification. We identified the phase noise in the optical parametric amplifier (OPA) minimally affects the squeezing level, enabling increased gain of the OPA. This scheme is compatible with proposed gravitational-wave detectors and consistent with applications in quantum systems that are degraded by output coupling loss in optical waveguides., Comment: 9 pages, 6 figures, 1 table. Submitted to Classical Quantum Gravity

We introduce DiffSketch, a method for generating a variety of stylized sketches from images. Our approach focuses on selecting representative features from the rich semantics of deep features within a pretrained diffusion model. This novel sketch generation method can be trained with one manual drawing. Furthermore, efficient sketch extraction is ensured by distilling a trained generator into a streamlined extractor. We select denoising diffusion features through analysis and integrate these selected features with VAE features to produce sketches. Additionally, we propose a sampling scheme for training models using a conditional generative approach. Through a series of comparisons, we verify that distilled DiffSketch not only outperforms existing state-of-the-art sketch extraction methods but also surpasses diffusion-based stylization methods in the task of extracting sketches., Comment: 8 pages(main paper), 8 pages(supplementary material)

Communication among healthcare professionals (HCPs) is crucial for the quality of patient treatment. Surrounding each patient's treatment, communication among HCPs can be examined as temporal networks, constructed from Electronic Health Record (EHR) access logs. This paper introduces a visual analytics system designed to study the effectiveness and efficiency of temporal communication networks mediated by the EHR system. We present a method that associates network measures with patient survival outcomes and devises effectiveness metrics based on these associations. To analyze communication efficiency, we extract the latencies and frequencies of EHR accesses. Our visual analytics system is designed to assist in inspecting and understanding the composed communication effectiveness metrics and to enable the exploration of communication efficiency by encoding latencies and frequencies in an information flow diagram. We demonstrate and evaluate our system through multiple case studies and an expert review.

This article presents a novel multi-functional system for a sixth-generation (6G) wireless network with integrated sensing, communication, and powering (ISCAP), which unifies integrated sensing and communication (ISAC) and wireless information and power transfer (WIPT) techniques. The multi-functional ISCAP network promises to enhance resource utilization efficiency, reduce network costs, and improve overall performance through versatile operational modes. Specifically, a multi-functional base station (BS) can enable multi-functional transmission, by exploiting the same radio signals to perform target/environment sensing, wireless communication, and wireless power transfer (WPT), simultaneously. Besides, the three functions can be intelligently coordinated to pursue mutual benefits,i.e., wireless sensing can be leveraged to enable light-training or even training-free WIPT by providing side-channel information, and the BS can utilize WPT to wirelessly charge low-power devices for ensuring sustainable ISAC. Furthermore, multiple multi-functional BSs can cooperate in both transmission and reception phases for efficient interference management, multi-static sensing, and distributed energy beamforming. For these operational modes, we discuss the technical challenges and potential solutions, particularly focusing on the fundamental performance tradeoff limits, transmission protocol design, as well as waveform and beamforming optimization. Finally, interesting research directions are identified., Comment: 7 pages

Benefitting from the vast spatial degrees of freedom, the amalgamation of integrated sensing and communication (ISAC) and massive multiple-input multiple-output (MIMO) is expected to simultaneously improve spectral and energy efficiencies as well as the sensing capability. However, a large number of antennas deployed in massive MIMO-ISAC raises critical challenges in acquiring both accurate channel state information and target parameter information. To overcome these two challenges with a unified framework, we first analyze their underlying system models and then propose a novel tensor-based approach that addresses both the channel estimation and target sensing problems. Specifically, by parameterizing the high-dimensional communication channel exploiting a small number of physical parameters, we associate the channel state information with the sensing parameters of targets in terms of angular, delay, and Doppler dimensions. Then, we propose a shared training pattern adopting the same time-frequency resources such that both the channel estimation and target parameter estimation can be formulated as a canonical polyadic decomposition problem with a similar mathematical expression. On this basis, we first investigate the uniqueness condition of the tensor factorization and the maximum number of resolvable targets by utilizing the specific Vandermonde

We employ a deep learning method to deduce the \textit{bulk} spacetime from \textit{boundary} optical conductivity. We apply the neural ordinary differential equation technique, tailored for continuous functions such as the metric, to the typical class of holographic condensed matter models featuring broken translations: linear-axion models. We successfully extract the bulk metric from the boundary holographic optical conductivity. Furthermore, as an example for real material, we use experimental optical conductivity of $\text{UPd}_2\text{Al}_3$, a representative of heavy fermion metals in strongly correlated electron systems, and construct the corresponding bulk metric. To our knowledge, our work is the first illustration of deep learning bulk spacetime from \textit{boundary} holographic or experimental conductivity data., Comment: 30 pages, 8 figures

Recently, innovative model aggregation methods based on knowledge distillation (KD) have been proposed for federated learning (FL). These methods not only improved the robustness of model aggregation over heterogeneous learning environment, but also allowed training heterogeneous models on client devices. However, the scalability of existing methods is not satisfactory, because the training cost on the server increases with the number of clients, which limits their application in large scale systems. Furthermore, the ensemble of existing methods is built from a set of client models initialized from the same checkpoint, causing low diversity. In this paper, we propose a scalable and diversity-enhanced federated distillation scheme, FedSDD, which decouples the training complexity from the number of clients to enhance the scalability, and builds the ensemble from a set of aggregated models with enhanced diversity. In particular, the teacher model in FedSDD is an ensemble built by a small group of aggregated (global) models, instead of all client models, such that the computation cost will not scale with the number of clients. Furthermore, to enhance diversity, FedSDD only performs KD to enhance one of the global models, i.e., the \textit{main global model}, which improves the performance of both the ensemble and the main global model. While partitioning client model into more groups allow building an ensemble with more aggregated models, the convergence of individual aggregated models will be slow down. We introduce the temporal ensembling which leverage the issues, and provide significant improvement with the heterogeneous settings. Experiment results show that FedSDD outperforms other FL methods, including FedAvg and FedDF, on the benchmark datasets.

The integration of sensing and communication enables wireless communication systems to serve environment-aware applications. In this paper, we propose to leverage sensing to enhance physical layer security (PLS) in multiuser communication systems in the presence of a suspicious target. To this end, we develop a two-phase framework to first estimate the location of the potential eavesdropper by sensing and then utilize the estimated information to enhance PLS for communication. In particular, in the first phase, a dual-functional radar and communication (DFRC) base station (BS) exploits a sensing signal to mitigate the sensing information uncertainty of the potential eavesdropper. Then, in the second phase, to facilitate joint sensing and secure communication, the DFRC BS employs beamforming and artificial noise to enhance secure communication. The design objective is to maximize the system sum rate while alleviating the information leakage by jointly optimizing the time allocation and beamforming policy. Capitalizing on monotonic optimization theory, we develop a two-layer globally optimal algorithm to reveal the performance upper bound of the considered system. Simulation results show that the proposed scheme achieves a significant sum rate gain over two baseline schemes that adopt existing techniques. Moreover, our results unveil that ISAC is a promising paradigm for enhancing secure communication in wireless networks., Comment: 9 pages,3 figures

Consider a quadratic polynomial $Q(\xi_{1},\dots,\xi_{n})$ of independent Rademacher random variables $\xi_{1},\dots,\xi_{n}$. To what extent can $Q(\xi_{1},\dots,\xi_{n})$ concentrate on a single value? This quadratic version of the classical Littlewood--Offord problem was popularised by Costello, Tao and Vu in their study of symmetric random matrices. In this paper, we obtain an essentially optimal bound for this problem, as conjectured by Nguyen and Vu. Specifically, if $Q(\xi_{1},\dots,\xi_{n})$ "robustly depends on at least $m$ of the $\xi_{i}$" in the sense that there is no way to pin down the value of $Q(\xi_{1},\dots,\xi_{n})$ by fixing values for fewer than $m$ of the variables $\xi_{i}$, then we have $\Pr[Q(\xi_{1},\dots,\xi_{n})=0]\le O(1/\sqrt{m})$. This also implies a similar result in the case where $\xi_{1},\dots,\xi_{n}$ have arbitrary distributions. Our proof combines a number of ideas that may be of independent interest, including an inductive decoupling scheme that reduces quadratic anticoncentration problems to high-dimensional linear anticoncentration problems. Also, one application of our main result is the resolution of a conjecture of Alon, Hefetz, Krivelevich and Tyomkyn related to graph inducibility., Comment: 45 pages

Topological quantum phases have been largely understood in weakly correlated systems, which have identified various quantum phenomena such as spin Hall effect, protected transport of helical fermions, and topological superconductivity. Robust ferromagnetic order in correlated topological materials particularly attracts attention, as it can provide a versatile platform for novel quantum devices. Here, we report singular Hall response arising from a unique band structure of flat topological nodal lines in combination with electron correlation in an itinerant, van der Waals ferromagnetic semimetal, Fe3GaTe2, with a high Curie temperature of Tc=360 K. High anomalous Hall conductivity violating the conventional scaling, resistivity upturn at low temperature, and a large Sommerfeld coefficient are observed in Fe3GaTe2, which implies heavy fermion features in this ferromagnetic topological material. Our circular dichroism in angle-resolved photoemission spectroscopy and theoretical calculations support the original electronic features in the material. Thus, low-dimensional Fe3GaTe2 with electronic correlation, topology, and room-temperature ferromagnetic order appears to be a promising candidate for robust quantum devices.

This paper investigates the bit error rate (BER) minimum pre-coder design for an orthogonal time frequency space (OTFS)-based integrated sensing and communications (ISAC) system, which is considered as a promising technique for enabling future wireless networks. In particular, the BER minimum problem takes into account the maximized available transmission power and the required sensing performance. We devise the precoder from the perspective of delay-Doppler (DD) domain by exploiting the equivalent DD channel. To address the non-convex design problem, we resort to minimizing the lower bound of the derived average BER. Afterwards, we propose a computationally iterative method to solve the dual problem at low cost. Simulation results verify the effectiveness of our proposed precoder and reveal the interplay between sensing and communication for dual-functional precoder design.

In multi-agent settings with mixed incentives, methods developed for zero-sum games have been shown to lead to detrimental outcomes. To address this issue, opponent shaping (OS) methods explicitly learn to influence the learning dynamics of co-players and empirically lead to improved individual and collective outcomes. However, OS methods have only been evaluated in low-dimensional environments due to the challenges associated with estimating higher-order derivatives or scaling model-free meta-learning. Alternative methods that scale to more complex settings either converge to undesirable solutions or rely on unrealistic assumptions about the environment or co-players. In this paper, we successfully scale an OS-based approach to general-sum games with temporally-extended actions and long-time horizons for the first time. After analysing the representations of the meta-state and history used by previous algorithms, we propose a simplified version called Shaper. We show empirically that Shaper leads to improved individual and collective outcomes in a range of challenging settings from literature. We further formalize a technique previously implicit in the literature, and analyse its contribution to opponent shaping. We show empirically that this technique is helpful for the functioning of prior methods in certain environments. Lastly, we show that previous environments, such as the CoinGame, are inadequate for analysing temporally-extended general-sum interactions.

The two-dimensional (2D) material Cr$_2$Ge$_2$Te$_6$ is a member of the class of insulating van der Waals magnets. Here, using high resolution angle-resolved photoemission spectroscopy in a detailed temperature dependence study, we identify a clear response of the electronic structure to a dimensional crossover in the form of two distinct temperature scales marking onsets of modifications in the electronic structure. Specifically, we observe Te $p$-orbital-dominated bands to undergo changes at the Curie transition temperature T$_C$ while the Cr $d$-orbital-dominated bands begin evolving at a higher temperature scale. Combined with neutron scattering, density functional theory calculations, and Monte Carlo simulations, we find that the electronic system can be consistently understood to respond sequentially to the distinct temperatures at which in-plane and out-of-plane spin correlations exceed a characteristic length scale. Our findings reveal the sensitivity of the orbital-selective electronic structure for probing the dynamical evolution of local moment correlations in vdW insulating magnets., Comment: PRB, in press

We investigate in detail the $\nu=+1$ displacement-field-tuned interacting phase diagram of $L=3,4,5,6,7$ layer rhombohedral graphene aligned to hBN (R$L$G/hBN). Our calculations account for the 3D nature of the Coulomb interaction, the inequivalent stacking orientations $\xi=0,1$, the effects of the filled valence bands, and the choice of `interaction scheme' for specifying the many-body Hamiltonian. We show that the latter has a dramatic impact on the Hartree-Fock phase boundaries and the properties of the phases, including for pentalayers (R5G/hBN) with large displacement field $D$ where recent experiments observed a Chern insulator at $\nu=+1$ and fractional Chern insulators for $\nu<1$. In this large $D$ regime, the low-energy conduction bands are polarized away from the aligned hBN layer, and are hence well-described by the folded bands of moir\'eless rhombohedral graphene at the non-interacting level. Despite this, the filled valence bands develop moir\'e-periodic charge density variations which can generate an effective moir\'e potential, thereby explicitly breaking the approximate continuous translation symmetry in the conduction bands, and leading to contrasting electronic topology in the ground state for the two stacking arrangements. Within time-dependent Hartree-Fock theory, we further characterize the strength of the moir\'e pinning potential in the Chern insulator phase by computing the low-energy $\mathbf{q}=0$ collective mode spectrum, where we identify competing gapped pseudophonon and valley magnon excitations. Our results emphasize the importance of careful examination of both the single-particle and interaction model for a proper understanding of the correlated phases in R$L$G/hBN.

Information Pursuit (IP) is an explainable prediction algorithm that greedily selects a sequence of interpretable queries about the data in order of information gain, updating its posterior at each step based on observed query-answer pairs. The standard paradigm uses hand-crafted dictionaries of potential data queries curated by a domain expert or a large language model after a human prompt. However, in practice, hand-crafted dictionaries are limited by the expertise of the curator and the heuristics of prompt engineering. This paper introduces a novel approach: learning a dictionary of interpretable queries directly from the dataset. Our query dictionary learning problem is formulated as an optimization problem by augmenting IP's variational formulation with learnable dictionary parameters. To formulate learnable and interpretable queries, we leverage the latent space of large vision and language models like CLIP. To solve the optimization problem, we propose a new query dictionary learning algorithm inspired by classical sparse dictionary learning. Our experiments demonstrate that learned dictionaries significantly outperform hand-crafted dictionaries generated with large language models.

New generations of ultracold-atom experiments are continually raising the demand for efficient solutions to optimal control problems. Here, we apply Bayesian optimization to improve a state-preparation protocol recently implemented in an ultracold-atom system to realize a two-particle fractional quantum Hall state. Compared to manual ramp design, we demonstrate the superior performance of our optimization approach in a numerical simulation - resulting in a protocol that is 10x faster at the same fidelity, even when taking into account experimentally realistic levels of disorder in the system. We extensively analyze and discuss questions of robustness and the relationship between numerical simulation and experimental realization, and how to make the best use of the surrogate model trained during optimization. We find that numerical simulation can be expected to substantially reduce the number of experiments that need to be performed with even the most basic transfer learning techniques. The proposed protocol and workflow will pave the way toward the realization of more complex many-body quantum states in experiments., Comment: 6 + 6 pages

Online learning is a rapidly growing industry. However, a major doubt about online learning is whether students are as engaged as they are in face-to-face classes. An engagement recognition system can notify the instructors about the students condition and improve the learning experience. Current challenges in engagement detection involve poor label quality, extreme data imbalance, and intra-class variety - the variety of behaviors at a certain engagement level. To address these problems, we present the CMOSE dataset, which contains a large number of data from different engagement levels and high-quality labels annotated according to psychological advice. We also propose a training mechanism MocoRank to handle the intra-class variety and the ordinal pattern of different degrees of engagement classes. MocoRank outperforms prior engagement detection frameworks, achieving a 1.32% increase in overall accuracy and 5.05% improvement in average accuracy. Further, we demonstrate the effectiveness of multi-modality in engagement detection by combining video features with speech and audio features. The data transferability experiments also state that the proposed CMOSE dataset provides superior label quality and behavior diversity., Comment: 11 pages

Weak gravitational lensing convergence peaks, the local maxima in weak lensing convergence maps, have been shown to contain valuable cosmological information complementary to commonly used two-point statistics. To exploit the full power of weak lensing for cosmology, we must model baryonic feedback processes because these reshape the matter distribution on non-linear and mildly non-linear scales. We study the impact of baryonic physics on the number density of weak lensing peaks using the FLAMINGO cosmological hydrodynamical simulation suite. We generate ray-traced full-sky convergence maps mimicking the characteristics of a Stage IV weak lensing survey. We compare the number densities of peaks in simulations that have been calibrated to reproduce the observed galaxy mass function and cluster gas fraction or to match a shifted version of these, and that use either thermally driven or jet AGN feedback. We show that the differences induced by realistic baryonic feedback prescriptions (typically $5 - 30\%$ for $\kappa = 0.1 - 0.4$) are smaller than those induced by reasonable variations in cosmological parameters ($20 - 60\%$ for $\kappa = 0.1 - 0.4$) but must be modeled carefully to obtain unbiased results. The reasons behind these differences can be understood by considering the impact of feedback on halo masses, or by considering the impact of different cosmological parameters on the halo mass function. Our analysis demonstrates that, for the range of models we investigated, the baryonic suppression is insensitive to changes in cosmology up to $\kappa \approx 0.4$ and that the higher $\kappa$ regime is dominated by Poisson noise and cosmic variance., Comment: 18 pages, 12 figures (including the appendix), Accepted for publication in MNRAS

Galaxy clusters are important probes for both cosmology and galaxy formation physics. We test the cosmological, hydrodynamical FLAMINGO simulations by comparing to observations of the gaseous properties of clusters measured from X-ray observations. FLAMINGO contains unprecedented numbers of massive galaxy groups ($>10^6$) and clusters ($>10^5$) and includes variations in both cosmology and galaxy formation physics. We predict the evolution of cluster scaling relations as well as radial profiles of the temperature, density, pressure, entropy, and metallicity for different masses and redshifts. We show that the differences between volume-, and X-ray-weighting of particles in the simulations, and between cool-core non cool-core samples, are similar in size as the differences between simulations for which the stellar and AGN feedback has been calibrated to produce significantly different gas fractions. Compared to thermally-driven AGN feedback, kinetic jet feedback calibrated to produce the same gas fraction at $R_{\rm 500c}$ yields a hotter core with higher entropies and lower densities, which translates into a smaller fraction of cool-core clusters. Stronger feedback, calibrated to produce lower gas fractions and hence lower gas densities, results in higher temperatures, entropies, and metallicities, but lower pressures. The scaling relations and thermodynamic profiles show almost no evolution with respect to self-similar expectations, except for the metallicity decreasing with redshift. We find that the temperature, density, pressure, and entropy profiles of clusters in the fiducial FLAMINGO simulation are in excellent agreement with observations, while the metallicities in the core are too high., Comment: 21 pages, 14 figures (including appendix). Accepted for publication in MNRAS. Moved Section 3 to Appendix A, new Section 3 compares scaling relations for different physics models. Added lower mass bin for physics model comparison of thermodynamic profiles. Conclusions unchanged

Brain-to-speech technology represents a fusion of interdisciplinary applications encompassing fields of artificial intelligence, brain-computer interfaces, and speech synthesis. Neural representation learning based intention decoding and speech synthesis directly connects the neural activity to the means of human linguistic communication, which may greatly enhance the naturalness of communication. With the current discoveries on representation learning and the development of the speech synthesis technologies, direct translation of brain signals into speech has shown great promise. Especially, the processed input features and neural speech embeddings which are given to the neural network play a significant role in the overall performance when using deep generative models for speech generation from brain signals. In this paper, we introduce the current brain-to-speech technology with the possibility of speech synthesis from brain signals, which may ultimately facilitate innovation in non-verbal communication. Also, we perform comprehensive analysis on the neural features and neural speech embeddings underlying the neurophysiological activation while performing speech, which may play a significant role in the speech synthesis works., Comment: 4 pages

The splashback radius, coinciding with the minimum in the dark matter radial density gradient, is thought to be a universal definition of the edge of a dark matter halo. Observational methods to detect it have traced the dark matter using weak gravitational lensing or galaxy number counts. Recent attempts have also claimed the detection of a similar feature in Sunyaev-Zel'dovich (SZ) observations of the hot intracluster gas. Here, we use the FLAMINGO simulations to investigate whether an extremum gradient in a similar position to the splashback radius is predicted to occur in the cluster gas profiles. We find that the minimum in the gradient of the stacked 3D gas density and pressure profiles, and the maximum in the gradient of the entropy profile, broadly align with the splashback feature though there are significant differences. While the dark matter splashback radius varies with specific mass accretion rate, in agreement with previous work, the radial position of the deepest minimum in the log-slope of the gas density is more sensitive to halo mass. In addition, we show that a similar minimum is also present in projected 2D pseudo-observable profiles: emission measure (X-ray); Compton-$y$ (SZ) and surface mass density (weak lensing). We find that the latter traces the dark matter results reasonably well albeit the minimum occurs at a slightly smaller radius. While results for the gas profiles are largely insensitive to accretion rate and various observable proxies for dynamical state, they do depend on the strength of the feedback processes., Comment: Accepted by MNRAS

Redshift space distortions are an important probe of the growth of large-scale structure and for constraining cosmological parameters in general. As galaxy redshift surveys approach percent level precision in their observations of the two point clustering statistics, it is timely to review what effects baryons and associated processes such as feedback may have on small-scale clustering in redshift space. Contrary to previous studies in the literature, we show using the large-volume BAHAMAS hydrodynamic simulations that the effect of baryons can be as much as 1% in the k ~ 0.1 h/Mpc range for the monopole and 5% for quadrupole, and that this could rise to as much as 10% at k~10 h/Mpc in both measurements. For the halo power spectra, this difference can be as much 3-4% in the monopole on scales of 0.05 < k < 0.3 h/Mpc for 10^{13} M_sun/h haloes. We find that these deviations can be mitigated to the sub-percent level in the both the monopole and quadrupole up to k ~ 0.3 h/Mpc if the baryon corrected halo masses are used to calculate the redshift space power spectra. Finally, we use the cosmo-OWLS simulation suite to explore the changes in the redshift space power spectra with different feedback prescriptions, finding that there is a maximum of 15-20% difference between the redshift space monopole and quadrupole with and without baryons at k ~1-2 h/Mpc within these models., Comment: 16 pages, 11 figues, replaced with accepted version, fixed typos

The inertia bound and ratio bound (also known as the Cvetkovi\'c bound and Hoffman bound) are two fundamental inequalities in spectral graph theory, giving upper bounds on the independence number $\alpha(G)$ of a graph $G$ in terms of spectral information about a weighted adjacency matrix of $G$. For both inequalities, given a graph $G$, one needs to make a judicious choice of weighted adjacency matrix to obtain as strong a bound as possible. While there is a well-established theory surrounding the ratio bound, the inertia bound is much more mysterious, and its limits are rather unclear. In fact, only recently did Sinkovic find the first example of a graph for which the inertia bound is not tight (for any weighted adjacency matrix), answering a longstanding question of Godsil. We show that the inertia bound can be extremely far from tight, and in fact can significantly underperform the ratio bound: for example, one of our results is that for infinitely many $n$, there is an $n$-vertex graph for which even the unweighted ratio bound can prove $\alpha(G)\leq 4n^{3/4}$, but the inertia bound is always at least $n/4$. In particular, these results address questions of Rooney, Sinkovic, and Wocjan--Elphick--Abiad., Comment: 14 pages

This paper studies a secure cell-free integrated sensing and communication (ISAC) system, in which multiple ISAC transmitters collaboratively send confidential information to multiple communication users (CUs) and concurrently conduct target detection. Different from prior works investigating communication security against potential information eavesdropping, we consider the security of both communication and sensing in the presence of both information and sensing eavesdroppers that aim to intercept confidential communication information and extract target information, respectively. Towards this end, we optimize the joint information and sensing transmit beamforming at these ISAC transmitters for secure cell-free ISAC. Our objective is to maximize the detection probability over a designated sensing area while ensuring the minimum signal-to-interference-plus-noise-ratio (SINR) requirements at CUs. Our formulation also takes into account the maximum tolerable signal-to-noise ratio (SNR) at information eavesdroppers for ensuring the confidentiality of information transmission, and the maximum detection probability constraints at sensing eavesdroppers for preserving sensing privacy. The formulated secure joint transmit beamforming problem is highly non-convex due to the intricate interplay between the detection probabilities, beamforming vectors, and SINR constraints. Fortunately, through strategic manipulation and via applying the semidefinite relaxation (SDR) technique, we successfully obtain the globally optimal solution to the design problem by rigorously verifying the tightness of SDR. Furthermore, we present two alternative joint beamforming designs based on the sensing SNR maximization over the specific sensing area and the coordinated beamforming, respectively. Numerical results reveal the benefits of our proposed design over these alternative benchmarks., Comment: 13 pages

The Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state, an unusual superconducting state, defies high magnetic fields beyond the Pauli paramagnetic limit. It exhibits a spatial modulation of the superconducting order parameter in real space and is exceptionally rare. Recently, an even more exotic variant - the orbital FFLO state - was predicted and identified in the transition metal dichalcogenide superconductor 2H-NbSe2. This state emerges in thin samples with thicknesses below ~40 nm, at the boundary between two and three dimensions. The complex interplay between Ising spin orbit coupling and the Pauli paramagnetic effect can lead to a stabilization of the FFLO state in a relatively large range of the magnetic phase diagram, even well below the Pauli limit. In this study, we present experimental evidence of the formation of this orbital FFLO state in bulk 2H-NbSe2 samples. This evidence was obtained using high-resolution DC magnetization and magnetic torque experiments in magnetic fields applied strictly parallel to the NbSe2 basal plane. Both quantities display a crossover to a discontinuous first-order superconducting transition at the normal state boundary in magnetic fields of 4 T and above. This is usually seen as a sign that Pauli paramagnetic pair breaking effects affect the superconducting state. The magnetic torque reveals a small step-like reversible anomaly, indicating a magnetic field-induced thermodynamic phase transition within the superconducting state. This anomaly bears many similarities to the FFLO transitions in other FFLO superconductors, suggesting the potential existence of an orbital FFLO state in bulk 2H-NbSe2 samples. Additionally, we observe a pronounced in-plane 6-fold symmetry of the upper critical field in the field range above this phase transition, which has previously been interpreted as a hallmark of the orbital FFLO state in thin 2H-NbSe2.

We prove the convergence of a Wasserstein gradient flow of a free energy in an inhomogeneous media. Both the energy and media can depend on the spatial variable in a fast oscillatory manner. In particular, we show that the gradient flow structure is preserved in the limit which is expressed in terms of an effective energy and Wasserstein metric. The gradient flow and its limiting behavior is analyzed through an energy dissipation inequality (EDI). The result is consistent with asymptotic analysis in the realm of homogenization. However, we note that the effective metric is in general different from that obtained from the Gromov-Hausdorff convergence of metric spaces. We apply our framework to a linear Fokker-Planck equation but we believe the approach is robust enough to be applicable in a broader context.

This is a sequel to our previous articles \cite{Kw23, Kw23a+}. In this work, we apply recent techniques that fall under the banner of `Period Reciprocity' to study moments of $GL(3)\times GL(2)$ $L$-functions in the non-archimedean aspects, with a view towards the `Twisted Moment Conjectures' formulated by CFKRS., Comment: Minor edits. Accepted for publication

In this paper, we introduce DiffusionMat, a novel image matting framework that employs a diffusion model for the transition from coarse to refined alpha mattes. Diverging from conventional methods that utilize trimaps merely as loose guidance for alpha matte prediction, our approach treats image matting as a sequential refinement learning process. This process begins with the addition of noise to trimaps and iteratively denoises them using a pre-trained diffusion model, which incrementally guides the prediction towards a clean alpha matte. The key innovation of our framework is a correction module that adjusts the output at each denoising step, ensuring that the final result is consistent with the input image's structures. We also introduce the Alpha Reliability Propagation, a novel technique designed to maximize the utility of available guidance by selectively enhancing the trimap regions with confident alpha information, thus simplifying the correction task. To train the correction module, we devise specialized loss functions that target the accuracy of the alpha matte's edges and the consistency of its opaque and transparent regions. We evaluate our model across several image matting benchmarks, and the results indicate that DiffusionMat consistently outperforms existing methods. Project page at~\url{https://cnnlstm.github.io/DiffusionMat

The reconstruction method has been widely employed to improve the Baryon Acoustic Oscillations (BAO) measurement in spectroscopic survey data analysis. In this study, we explore the reconstruction of the BAO signals in the realm of photometric data. By adapting the Zel'dovich reconstruction technique, we develop a formalism to reconstruct the transverse BAO in the presence of photo-$z$ uncertainties \change{under the plane-parallel approximation}. We access the performance of the BAO reconstruction through comoving $N$-body simulations. The transverse reconstruction potential can be derived by solving a 2D potential equation, with the surface density and the radial potential contribution acting as the source terms. The solution is predominantly determined by the surface density. As is evident in dense samples, such as the matter field, the transverse BAO reconstruction can enhance both the strength of the BAO signals and their cross correlation with the initial conditions. At $z=0$, the cross correlation is increased by a factor of 1.2 at $ k_\perp = 0.2 \, \mathrm{Mpc}^{-1}h $ and 1.4 at $ k_\perp = 0.3 \, \mathrm{Mpc}^{-1}h $, respectively. We contrast the 2D potential results with the 3D Poisson equation solution, wherein we directly solve the potential equation using the position in photo-$z$ space, and find good agreement. Additionally, we examine the impact of various conditions, such as the smoothing scales and the level of photo-$z$ uncertainties, on the reconstruction results. We envision the straightforward application of this method to survey data., Comment: 15 pages, 13 figures, matched to the published version

The amalgamation of cell-free networks and reconfigurable intelligent surface (RIS) has become a prospective technique for future sixth-generation wireless communication systems. In this paper, we focus on the precoding and beamforming design for a downlink RIS-aided cell-free network. The design is formulated as a non-convex optimization problem by jointly optimizing the combining vector, active precoding, and passive RIS beamforming for minimizing the weighted sum of users' mean square error. A novel joint distributed precoding and beamforming framework is proposed to decentralize the alternating optimization method for acquiring a suboptimal solution to the design problem. Finally, numerical results validate the effectiveness of the proposed distributed precoding and beamforming framework, showing its low-complexity and improved scalability compared with the centralized method.

Tour itinerary recommendation involves planning a sequence of relevant Point-of-Interest (POIs), which combines challenges from the fields of both Operations Research (OR) and Recommendation Systems (RS). As an OR problem, there is the need to maximize a certain utility (e.g., popularity of POIs in the tour) while adhering to some constraints (e.g., maximum time for the tour). As a RS problem, it is heavily related to problem or filtering or ranking a subset of POIs that are relevant to a user and recommending it as part of an itinerary. In this paper, we explore the use of language models for the task of tour itinerary recommendation and planning. This task has the unique requirement of recommending personalized POIs relevant to users and planning these POIs as an itinerary that satisfies various constraints. We discuss some approaches in this area, such as using word embedding techniques like Word2Vec and GloVe for learning POI embeddings and transformer-based techniques like BERT for generating itineraries., Comment: PMAI23 @IJCAI 2023 2nd International Workshop on Process Management in the AI era

Cosmology is poised to measure the neutrino mass sum $M_\nu$ and has identified several smaller-scale observables sensitive to neutrinos, necessitating accurate predictions of neutrino clustering over a wide range of length scales. The FlowsForTheMasses non-linear perturbation theory for the massive neutrino power spectrum, $\Delta^2_\nu(k)$, agrees with its companion N-body simulation at the $10\%-15\%$ level for $k \leq 1~h/$Mpc. Building upon the Mira-Titan IV emulator for the cold matter, we use FlowsForTheMasses to construct an emulator for $\Delta^2_\nu(k)$ covering a large range of cosmological parameters and neutrino fractions $\Omega_{\nu,0} h^2 \leq 0.01$, which corresponds to $M_\nu \leq 0.93$~eV. Consistent with FlowsForTheMasses at the $3.5\%$ level, it returns a power spectrum in milliseconds. Ranking the neutrinos by initial momenta, we also emulate the power spectra of momentum deciles, providing information about their perturbed distribution function. Comparing a $M_\nu=0.15$~eV model to a wide range of N-body simulation methods, we find agreement to $3\%$ for $k \leq 3 k_\mathrm{FS} = 0.17~h/$Mpc and to $19\%$ for $k \leq 0.4~h/$Mpc. We find that the enhancement factor, the ratio of $\Delta^2_\nu(k)$ to its linear-response equivalent, is most strongly correlated with $\Omega_{\nu,0} h^2$, and also with the clustering amplitude $\sigma_8$. Furthermore, non-linearities enhance the free-streaming-limit scaling $\partial \log(\Delta^2_\nu / \Delta^2_{\rm m}) / \partial \log(M_\nu)$ beyond its linear value of 4, increasing the $M_\nu$-sensitivity of the small-scale neutrino density., Comment: 17 pages, 14 figures, 3 tables. Emulator code available at: https://github.com/upadhye/Cosmic-Enu

When traveling to an unfamiliar city for holidays, tourists often rely on guidebooks, travel websites, or recommendation systems to plan their daily itineraries and explore popular points of interest (POIs). However, these approaches may lack optimization in terms of time feasibility, localities, and user preferences. In this paper, we propose the SBTRec algorithm: a BERT-based Trajectory Recommendation with sentiment analysis, for recommending personalized sequences of POIs as itineraries. The key contributions of this work include analyzing users' check-ins and uploaded photos to understand the relationship between POI visits and distance. We introduce SBTRec, which encompasses sentiment analysis to improve recommendation accuracy by understanding users' preferences and satisfaction levels from reviews and comments about different POIs. Our proposed algorithms are evaluated against other sequence prediction methods using datasets from 8 cities. The results demonstrate that SBTRec achieves an average F1 score of 61.45%, outperforming baseline algorithms. The paper further discusses the flexibility of the SBTRec algorithm, its ability to adapt to different scenarios and cities without modification, and its potential for extension by incorporating additional information for more reliable predictions. Overall, SBTRec provides personalized and relevant POI recommendations, enhancing tourists' overall trip experiences. Future work includes fine-tuning personalized embeddings for users, with evaluation of users' comments on POIs,~to further enhance prediction accuracy.

Next-generation wireless networks are expected to utilize the limited radio frequency (RF) resources more efficiently with the aid of intelligent transceivers. To this end, we propose a promising transceiver architecture relying on stacked intelligent metasurfaces (SIM). An SIM is constructed by stacking an array of programmable metasurface layers, where each layer consists of a massive number of low-cost passive meta-atoms that individually manipulate the electromagnetic (EM) waves. By appropriately configuring the passive meta-atoms, an SIM is capable of accomplishing advanced computation and signal processing tasks, such as multiple-input multiple-output (MIMO) precoding/combining, multi-user interference mitigation, and radar sensing, as the EM wave propagates through the multiple layers of the metasurface, which effectively reduces both the RF-related energy consumption and processing delay. Inspired by this, we provide an overview of the SIM-aided MIMO transceiver design, which encompasses its hardware architecture and its potential benefits over state-of-the-art solutions. Furthermore, we discuss promising application scenarios and identify the open research challenges associated with the design of advanced SIM architectures for next-generation wireless networks. Finally, numerical results are provided for quantifying the benefits of wave-based signal processing in wireless systems., Comment: 9 pages, 5 figures, 1 table

The concept of non-Hermiticity has expanded the understanding of band topology leading to the emergence of counter-intuitive phenomena. One example is the non-Hermitian skin effect (NHSE), which involves the concentration of eigenstates at the boundary. However, despite the potential insights that can be gained from high-dimensional non-Hermitian quantum systems in areas like curved space, high-order topological phases, and black holes, the realization of this effect in high dimensions remains unexplored. Here, we create a two-dimensional (2D) non-Hermitian topological band for ultracold fermions in spin-orbit-coupled optical lattices with tunable dissipation, which exhibits the NHSE. We first experimentally demonstrate pronounced nonzero spectral winding numbers in the complex energy plane with non-zero dissipation, which establishes the existence of 2D skin effect. Further, we observe the real-space dynamical signature of NHSE in real space by monitoring the center of mass motion of atoms. Finally, we also demonstrate that a pair of exceptional points (EPs) are created in the momentum space, connected by an open-ended bulk Fermi arc, in contrast to closed loops found in Hermitian systems. The associated EPs emerge and shift with increasing dissipation, leading to the formation of the Fermi arc. Our work sets the stage for further investigation into simulating non-Hermitian physics in high dimensions and paves the way for understanding the interplay of quantum statistics with NHSE., Comment: 13 pages, 5 figures with supplementary information

Achieving robust and electrically controlled valley polarization in monolayer transition metal dichalcogenides (ML-TMDs) is a frontier challenge for realistic valleytronic applications. Theoretical investigations show that integration of 2D materials with ferroelectrics is a promising strategy; however, its experimental demonstration has remained elusive. Here, we fabricate ferroelectric field-effect transistors using a ML-WSe2 channel and a AlScN ferroelectric dielectric, and experimentally demonstrate efficient tuning as well as non-volatile control of valley polarization. We measured a large array of transistors and obtained a maximum valley polarization of ~27% at 80 K with stable retention up to 5400 secs. The enhancement in the valley polarization was ascribed to the efficient exciton-to-trion (X-T) conversion and its coupling with an out-of-plane electric field, viz. the quantum-confined Stark effect. This changes the valley depolarization pathway from strong exchange interactions to slow spin-flip intervalley scattering. Our research demonstrates a promising approach for achieving non-volatile control over valley polarization and suggests new design principles for practical valleytronic devices., Comment: Manuscript (22 pages and 5 figures), supporting information

For a multiple-input multiple-output (MIMO) system with unknown channel state information (CSI), a novel low-density parity check (LDPC)-coded transmission (LCT) scheme with joint pilot and data channel estimation is proposed. To fine-tune the CSI, a method based on the constraints introduced by the coded data from an LDPC code is designed such that the MIMO detector exploits the fine-tuned CSI. For reducing the computational burden, a coordinate ascent algorithm is employed along with several approximation methods, effectively reducing the required times of MIMO detection and computational complexity to achieve a satisfying performance. Simulation results utilizing WiMAX standard LDPC codes and quadrature phase-shift keying (QPSK) modulation demonstrate gains of up to 1.3 dB at a frame error rate (FER) of $10^{-4}$ compared to pilot-assisted transmission (PAT) over Rayleigh block-fading channels., Comment: This paper has been accepted by IEEE Wireless Communications Letters

The advent of the sixth-generation (6G) of wireless communications has given rise to the necessity to connect vast quantities of heterogeneous wireless devices, which requires advanced system capabilities far beyond existing network architectures. In particular, such massive communication has been recognized as a prime driver that can empower the 6G vision of future ubiquitous connectivity, supporting Internet of Human-Machine-Things for which massive access is critical. This paper surveys the most recent advances toward massive access in both academic and industry communities, focusing primarily on the promising compressive sensing-based grant-free massive access paradigm. We first specify the limitations of existing random access schemes and reveal that the practical implementation of massive communication relies on a dramatically different random access paradigm from the current ones mainly designed for human-centric communications. Then, a compressive sensing-based grant-free massive access roadmap is presented, where the evolutions from single-antenna to large-scale antenna array-based base stations, from single-station to cooperative massive multiple-input multiple-output systems, and from unsourced to sourced random access scenarios are detailed. Finally, we discuss the key challenges and open issues to shed light on the potential future research directions of grant-free massive access., Comment: Accepted by IEEE IoT Journal

This paper investigates the sensing performance of two intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) sensing systems with fully-passive and semi-passive IRSs, respectively. In particular, we consider a fundamental setup with one base station (BS), one uniform linear array (ULA) IRS, and one point target in the NLoS region of the BS. Accordingly, we analyze the sensing signal-to-noise ratio (SNR) performance for a target detection scenario and the estimation Cram\'er-Rao bound (CRB) performance for a target's direction-of-arrival (DoA) estimation scenario, in cases where the transmit beamforming at the BS and the reflective beamforming at the IRS are jointly optimized. First, for the target detection scenario, we characterize the maximum sensing SNR when the BS-IRS channels are line-of-sight (LoS) and Rayleigh fading, respectively. It is revealed that when the number of reflecting elements $N$ equipped at the IRS becomes sufficiently large, the maximum sensing SNR increases proportionally to $N^2$ for the semi-passive-IRS sensing system, but proportionally to $N^4$ for the fully-passive-IRS counterpart. Then, for the target's DoA estimation scenario, we analyze the minimum CRB performance when the BS-IRS channel follows Rayleigh fading. Specifically, when $N$ grows, the minimum CRB decreases inversely proportionally to $N^4$ and $N^6$ for the semi-passive and fully-passive-IRS sensing systems, respectively. Finally, numerical results are presented to corroborate our analysis across various transmit and reflective beamforming design schemes under general channel setups. It is shown that the fully-passive-IRS sensing system outperforms the semi-passive counterpart when $N$ exceeds a certain threshold. This advantage is attributed to the additional reflective beamforming gain in the IRS-BS path, which efficiently compensates for the path loss for a large $N$., Comment: 13 pages,7 figures

This correspondence presents a novel sensing-assisted sparse channel recovery approach for massive antenna wireless communication systems. We focus on a fundamental configuration with one massive-antenna base station (BS) and one single-antenna communication user (CU). The wireless channel exhibits sparsity and consists of multiple paths associated with scatterers detectable via radar sensing. Under this setup, the BS first sends downlink pilots to the CU and concurrently receives the echo pilot signals for sensing the surrounding scatterers. Subsequently, the CU sends feedback information on its received pilot signal to the BS. Accordingly, the BS determines the sparse basis based on the sensed scatterers and proceeds to recover the wireless channel, exploiting the feedback information based on advanced compressive sensing (CS) algorithms. Numerical results show that the proposed sensing-assisted approach significantly increases the overall achievable rate than the conventional design relying on a discrete Fourier transform (DFT)-based sparse basis without sensing, thanks to the reduced training overhead and enhanced recovery accuracy with limited feedback., Comment: 5 pages, 4 figs