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The emergence of deep learning models has revolutionized various industries over the last decade, leading to a surge in connected devices and infrastructures. However, these models can be tricked into making incorrect predictions with high confidence, leading to disastrous failures and security concerns. To this end, we explore the impact of adversarial attacks on multivariate time-series forecasting and investigate methods to counter them. Specifically, we employ untargeted white-box attacks, namely the Fast Gradient Sign Method (FGSM) and the Basic Iterative Method (BIM), to poison the inputs to the training process, effectively misleading the model. We also illustrate the subtle modifications to the inputs after the attack, which makes detecting the attack using the naked eye quite difficult. Having demonstrated the feasibility of these attacks, we develop robust models through adversarial training and model hardening. We are among the first to showcase the transferability of these attacks and defenses by extrapolating our work from the benchmark electricity data to a larger, 10-year real-world data used for predicting the time-to-failure of hard disks. Our experimental results confirm that the attacks and defenses achieve the desired security thresholds, leading to a 72.41% and 94.81% decrease in RMSE for the electricity and hard disk datasets respectively after implementing the adversarial defenses., Comment: 17 pages, 32 figures

Fine-tuning large language models (LLMs) on downstream tasks requires substantial computational resources. A class of parameter-efficient fine-tuning (PEFT) aims to mitigate these computational challenges by selectively fine-tuning only a small fraction of the model parameters. Although computationally efficient, these techniques often fail to match the performance of fully fine-tuned models, primarily due to inherent biases introduced during parameter selection. Traditional selective PEFT techniques use a fixed set of parameters based on a predefined budget (a process also known as unmasking), failing to capture parameter importance dynamically and often ending up exceeding the budget. We introduce $\text{ID}^3$, a novel selective PEFT method that calculates parameter importance continually and dynamically unmasks parameters by balancing exploration and exploitation in parameter selection. Our empirical study on 15 tasks spanning natural language understanding and generative tasks demonstrates the effectiveness of our method compared to fixed-masking-based PEFT techniques. We analytically show that $\text{ID}^3$ reduces the number of gradient updates by a factor of two, enhancing computational efficiency. $\text{ID}^3$ is robust to random initialization of neurons and, therefore, can be seamlessly integrated into existing additive and reparametrization-based PEFT modules such as adapters and LoRA for dynamic sparsification., Comment: 15 pages, 7 tables, 9 figures

We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in particle physics. These datasets encompass a diverse range of jet sources, including quark/gluon jets, jets from the decay of W bosons, top quarks, and heavy new-physics particles. The datasets provide detailed substructure information, including jet kinematics, constituent kinematics, and track displacement details, enabling a wide range of applications in jet tagging, anomaly detection, and generative modelling., Comment: The datasets are available on Zenodo at https://doi.org/10.5281/zenodo.12793616

Monocular depth estimation in colonoscopy video aims to overcome the unusual lighting properties of the colonoscopic environment. One of the major challenges in this area is the domain gap between annotated but unrealistic synthetic data and unannotated but realistic clinical data. Previous attempts to bridge this domain gap directly target the depth estimation task itself. We propose a general pipeline of structure-preserving synthetic-to-real (sim2real) image translation (producing a modified version of the input image) to retain depth geometry through the translation process. This allows us to generate large quantities of realistic-looking synthetic images for supervised depth estimation with improved generalization to the clinical domain. We also propose a dataset of hand-picked sequences from clinical colonoscopies to improve the image translation process. We demonstrate the simultaneous realism of the translated images and preservation of depth maps via the performance of downstream depth estimation on various datasets., Comment: 12 pages, 7 figures, accepted at MICCAI 2024

The JETSCAPE Collaboration reports a new determination of the jet transport parameter $\hat{q}$ in the Quark-Gluon Plasma (QGP) using Bayesian Inference, incorporating all available inclusive hadron and jet yield suppression data measured in heavy-ion collisions at RHIC and the LHC. This multi-observable analysis extends the previously published JETSCAPE Bayesian Inference determination of $\hat{q}$, which was based solely on a selection of inclusive hadron suppression data. JETSCAPE is a modular framework incorporating detailed dynamical models of QGP formation and evolution, and jet propagation and interaction in the QGP. Virtuality-dependent partonic energy loss in the QGP is modeled as a thermalized weakly-coupled plasma, with parameters determined from Bayesian calibration using soft-sector observables. This Bayesian calibration of $\hat{q}$ utilizes Active Learning, a machine--learning approach, for efficient exploitation of computing resources. The experimental data included in this analysis span a broad range in collision energy and centrality, and in transverse momentum. In order to explore the systematic dependence of the extracted parameter posterior distributions, several different calibrations are reported, based on combined jet and hadron data; on jet or hadron data separately; and on restricted kinematic or centrality ranges of the jet and hadron data. Tension is observed in comparison of these variations, providing new insights into the physics of jet transport in the QGP and its theoretical formulation., Comment: 20 pages, 10 figures, 2 tables, submitted to PRC; updated acknowledgements

Distribution functions of collisionless systems are known to show non-thermal power law tails. Interestingly, collisionless plasmas in various physical scenarios, (e.g., the ion population of the solar wind) feature a $v^{-5}$ tail in the velocity ($v$) distribution, whose origin has been a long-standing mystery. We show this power law tail to be a natural outcome of the self-consistent collisionless relaxation of driven electrostatic plasmas. We perform a quasilinear analysis of the perturbed Vlasov-Poisson equations to show that the coarse-grained mean distribution function (DF), $f_0$, follows a quasilinear diffusion equation with a diffusion coefficient $D(v)$ that depends on $v$ through the plasma dielectric constant. If the plasma is isotropically forced on scales much larger than the Debye length with a white noise-like electric field, then $D(v)\sim v^4$ for $\sigma

The Segment Anything Model (SAM) has demonstrated impressive performance in zero-shot promptable segmentation on natural images. The recently released Segment Anything Model 2 (SAM 2) claims to outperform SAM on images and extends the model's capabilities to video segmentation. Evaluating the performance of this new model in medical image segmentation, specifically in a zero-shot promptable manner, is crucial. In this work, we conducted extensive studies using multiple datasets from various imaging modalities to compare the performance of SAM and SAM 2. We employed two point-prompt strategies: (i) multiple positive prompts where one prompt is placed near the centroid of the target structure, while the remaining prompts are randomly placed within the structure, and (ii) combined positive and negative prompts where one positive prompt is placed near the centroid of the target structure, and two negative prompts are positioned outside the structure, maximizing the distance from the positive prompt and from each other. The evaluation encompassed 24 unique organ-modality combinations, including abdominal structures, cardiac structures, fetal head images, skin lesions and polyp images across 11 publicly available MRI, CT, ultrasound, dermoscopy, and endoscopy datasets. Preliminary results based on 2D images indicate that while SAM 2 may perform slightly better in a few cases, it does not generally surpass SAM for medical image segmentation. Notably, SAM 2 performs worse than SAM in lower contrast imaging modalities, such as CT and ultrasound. However, for MRI images, SAM 2 performs on par with or better than SAM. Like SAM, SAM 2 also suffers from over-segmentation issues, particularly when the boundaries of the target organ are fuzzy.

While the minimal anomaly-mediated SUSY breaking model (mAMSB) seems ruled out by constraints on Higgs mass, naturalness and wino dark matter, a slightly generalized version dubbed natural AMSB (nAMSB) remains both viable and compelling. Like mAMSB, nAMSB features winos as the lightest gauginos, but unlike mAMSB, nAMSB allows a small mu parameter so that higgsinos are the lightest of electroweakinos (EWinos). nAMSB spectra depend on the input value of gravitino mass m_{3/2}, where the lower range of m_{3/2} is excluded by LHC gluino pair searches while a higher m_{3/2} band is excluded by LHC limits on wino pair production followed by boosted hadronic wino decays. A remaining intermediate gap in m_{3/2} values remains allowed by present LHC searches, but appears to be completely explorable by high luminosity ugrades of LHC (HL-LHC). We explore a variety of compelling discovery channels that may allow one to close the intermediate gap in m_{3/2} values: 1. same-sign diboson +MET (SSdB) production arising from wino pair production, leading to same-sign dileptons plus MET, 2. trilepton production arising from wino pair production and 3. soft dilepton plus jet events from higgsino pair production, 4. top-squark pair production. From our signal-to-background analysis along a nAMSB model line, we expect HL-LHC to either discover or rule out the nAMSB model with 3000 fb^{-1} of integrated luminosity., Comment: 25 pages with 10 figures

We study a set of many-body wave-functions of Fermions that are naturally written using momentum space basis and allow for quantum superposition of Fermion occupancy, $\{n_{\bf k}\}$. This {enables} us to capture the fluctuations of the Fermi-surface {(FS)} -- the singularly most important signature of a metal. We bench-mark our results in one spatial dimensions (1D) to show that these wave-functions allow for quantitative understanding of the Tomonaga-Luttinger liquid (TLL); computations of certain correlators using them can in fact be extended to larger systems sizes compared to conventional exact diagonalization (ED) allowing for a more systematic comparison with bosonization techniques. Finally we show that this basis may be useful for obtaining fixed-point wave-function for strongly correlated metals {in dimensions greater that one}. In particular, we study the case of coherent (equal) superposition of elliptical FS {in continuum (2D) and on a} square lattice{. In case of the former, our variational wave-function systematically interpolates between the phenomenology of the Fermi liquid ground state, i.e., finite single-Fermion residue at a sharp FS, to a non-Fermi liquid (NFL) with zero residue. In the NFL the jump in $\langle n_{\bf k}\rangle$ at the FS is replaced by a point of inflection (similar to a 1D TLL) whose contour is consistent with the Luttinger Theorem. In case of the square lattice, we} find highly anisotropic distribution of the quasi-particle residue, which, at finite resolution has an uncanny resemblance to the Fermi-arcs{, albeit at zero temperature,} seen in the pseudo-gap state of the cuprates.

Surface-adhesion and stiffness of underlying substrates mediate geometry, mechanics and self-organization of bacterial colonies. Recent studies have qualitatively indicted that stiffness may impact bacterial attachment, yet the variation of cell-to-surface adhesion with substrate stiffness remains to be quantified. Here, by developing a cell-level Force Distance Spectroscopy (FDS) technique based on Atomic Force Microscopy (AFM), we simultaneously quantify the cell-surface adhesion alongside stiffness of the underlying substrates to reveal stiffness-dependent adhesion in phototrophic bacterium Chromatium okenii. As stiffness of the soft substrate, modelled via low-melting-point (LMP) agarose pad, was varied between 20 kPa and 120 kPa by changing agarose concentrations, we observe a progressive increase of the mean adhesion force by over an order of magnitude, from 0.21 (+/-0.10) nN to 2.42 (+/-1.16) nN. In contrast, passive polystyrene (PS) microparticles of comparable dimensions showed no perceptible change in their surface adhesion. Furthermore, for Escherichia coli, the cell-surface adhesion varied between 0.29 (+/-0.17) nN to 0.39 (+/-0.20) nN, showing a weak dependence on the substrate stiffness, thus suggesting that the stiffness-modulated adhesion is a species-specific trait. Finally, by quantifying the adhesion of C. okenii populations across growth stages, we report an emergent co-existence of weak and strongly adherent sub-populations, demonstrating a diversification of adherent phenotypes over time. Taken together, these findings suggest that bacteria, depending on the species and their physiological stage, actively modulate cell-to-surface adhesion in response to substrate stiffness, and leverage it as a functional trait to modulate initial attachment and colonization on soft substrates during early stages of biofilm development., Comment: 37 pages, 10 figures

Periodically driven closed quantum systems are expected to eventually heat up to infinite temperature reaching a steady state described by a circular orthogonal ensemble (COE). However, such finite driven systems may exhibit sufficiently long prethermal regimes; their properties in these regimes are qualitatively different from that in their infinite temperature steady states. These, often experimentally relevant, prethermal regimes host a wide range of phenomena; they may exhibit dynamical localization and freezing, host Floquet scars, display signatures of Hilbert space fragmentation, and exhibit time crystalline phases. Such phenomena are often accompanied by emergent approximate dynamical symmetries which have no analogue in equilibrium systems. In this review, we provide a pedagogical introduction to the origin and nature of these symmetries and discuss their role in shaping the prethermal phases of a class of periodically driven closed quantum systems., Comment: Invited review article to be submitted to JPCM

We introduce Resonant Anomaly Detection with Optimal Transport (RAD-OT), a method for generating signal templates in resonant anomaly detection searches. RAD-OT leverages the fact that the conditional probability density of the target features vary approximately linearly along the optimal transport path connecting the resonant feature. This does not assume that the conditional density itself is linear with the resonant feature, allowing RAD-OT to efficiently capture multimodal relationships, changes in resolution, etc. By solving the optimal transport problem, RAD-OT can quickly build a template by interpolating between the background distributions in two sideband regions. We demonstrate the performance of RAD-OT using the LHC Olympics R\&D dataset, where we find comparable sensitivity and improved stability with respect to deep learning-based approaches., Comment: 14 pages, 7 figures, 1 table

A new framework, called x-scape, for the combined study of both hard and soft transverse momentum sectors in high energy proton-proton ($p$-$p$) and proton-nucleus ($p$-$A$) collisions is set up. A dynamical initial state is set up using the 3d-Glauber model with transverse locations of hotspots within each incoming nucleon. A hard scattering that emanates from two colliding hotspots is carried out using the Pythia generator. Initial state radiation from the incoming hard partons is carried out in a new module called I-matter, which includes the longitudinal location of initial splits. The energy-momentum of both the initial hard partons and their associated beam remnants is removed from the hot spots, depleting the energy-momentum available for the formation of the bulk medium. Outgoing showers are simulated using the matter generator, and results are presented for both cases, allowing for and not allowing for energy loss. First comparisons between this hard-soft model and single inclusive hadron and jet data from $p$-$p$ and minimum bias $p$-$Pb$ collisions are presented. Single hadron spectra in $p$-$p$ are used to carry out a limited (in number of parameters) Bayesian calibration of the model. Fair comparisons with data are indicative of the utility of this new framework. Theoretical studies of the correlation between jet $p_T$ and event activity at mid and forward rapidity are carried out., Comment: 18 pages, 15 figures

Calabi-Yau compactifications have typically a large number of complex structure and/or K\"ahler moduli that have to be stabilised in phenomenologically-relevant vacua. The former can in principle be done by fluxes in type IIB solutions. However, the tadpole conjecture proposes that the number of stabilised moduli can at most grow linearly with the tadpole charge of the fluxes required for stabilisation. We scrutinise this conjecture in the $2^6$ Gepner model: a non-geometric background mirror dual to a rigid Calabi-Yau manifold, in the deep interior of moduli space. By constructing an extensive set of supersymmetric Minkowski flux solutions, we spectacularly confirm the linear growth, while achieving a slightly higher ratio of stabilised moduli to flux charge than the conjectured upper bound. As a byproduct, we obtain for the first time a set of solutions within the tadpole bound where all complex structure moduli are massive. Since the $2^6$ model has no K\"ahler moduli, these show that the massless Minkowski conjecture does not hold beyond supergravity., Comment: 33 pages, 2 figures. Bibliography contains a GitHub link to the accompanying codes and dataset

We study moduli stabilization via fluxes in the $2^6$ Landau-Ginzburg model. Fluxes not only give masses to scalar fields but can also induce higher order couplings that stabilize massless fields. We investigate this for several different flux choices in the $2^6$ model and find two examples that are inconsistent with the Refined Tadpole Conjecture. We also present, to our knowledge, the first 4d $\mathcal{N}=1$ Minkowski solution in string theory without any flat direction., Comment: 33 pages, 1 figure

Phase transitions in many-body systems are often associated with the emergence of spatial inhomogeneities. Such features may develop at microscopic lengthscales and are not necessarily evident in measurements of macroscopic quantities. In this work, we address the topic of distribution of current paths in superconducting films. Typical lengthscales associated with superconductivity are in the range of nanometres. Accordingly, measurements of electrical resistance over much larger distances are supposed to be insensitive to details of spatial inhomogeneities of electronic properties. We observe that, contrary to expectations, current paths adopt a highly non-uniform distribution at the onset of the superconducting transition which is manifested in the development of a finite transverse resistance. The anisotropic distribution of current density is unrelated to the structural properties of the superconducting films, and indicates the emergence of electronic inhomogeneities perceivable over macroscopic distances. Our experiments reveal the ubiquitous nature of this phenomenon in conventional superconductors.

In order to unravel the present situation of the PBH overproduction problem, our study emphasizes the critical role played by the equation of state (EoS) parameter $w$ within the framework of effective field theory (EFT) of non-singular bounce. Our analysis focuses on a wide range of EoS parameter values that are still optimal for explaining the latest data from the pulsar timing array (PTA). As a result of our study, the most advantageous window, $0.31 \leq w \leq 1/3$, is identified as the location of a substantial PBH abundance, $f_{\rm PBH} \in (10^{-3},1)$ with large mass PBHs, $M_{\rm PBH}\sim {\cal O}(10^{-7}-10^{-3})M_{\odot}$, in the SIGW interpretation of the PTA signal. When confronted with PTA, we find that the overproduction avoiding circumstances are between $1\sigma-2\sigma$, while the EoS parameter lies inside the narrow window, $0.31

Aligning future system design with the ever-increasing compute needs of large language models (LLMs) is undoubtedly an important problem in today's world. Here, we propose a general performance modeling methodology and workload analysis of distributed LLM training and inference through an analytical framework that accurately considers compute, memory sub-system, network, and various parallelization strategies (model parallel, data parallel, pipeline parallel, and sequence parallel). We validate our performance predictions with published data from literature and relevant industry vendors (e.g., NVIDIA). For distributed training, we investigate the memory footprint of LLMs for different activation re-computation methods, dissect the key factors behind the massive performance gain from A100 to B200 ($\sim$ 35x speed-up closely following NVIDIA's scaling trend), and further run a design space exploration at different technology nodes (12 nm to 1 nm) to study the impact of logic, memory, and network scaling on the performance. For inference, we analyze the compute versus memory boundedness of different operations at a matrix-multiply level for different GPU systems and further explore the impact of DRAM memory technology scaling on inference latency. Utilizing our modeling framework, we reveal the evolution of performance bottlenecks for both LLM training and inference with technology scaling, thus, providing insights to design future systems for LLM training and inference., Comment: 12 pages, 9 figures

Many-body systems which saturate the quantum bound on chaos are attracting interest across a wide range of fields. Notable examples include the Sachdev-Ye-Kitaev model and its variations, all characterised by some form or randomness and all to all couplings. Here we study many-body quantum chaos in a quantum impurity model showing Non-Fermi-Liquid physics, the overscreened multichannel $SU(N)$ Kondo model. We compute exactly the low-temperature behavior of the out-of time order correlator in the limit of large $N$ and large number of channels $K$, at fixed ratio $\gamma=K/N$. Due to strong correlations at the impurity site the spin fractionalizes in auxiliary fermions and bosons. We show that all the degrees of freedom of our theory acquire a Lyapunov exponent which is linear in temperature as $T\rightarrow 0$, with a prefactor that depends on $\gamma$. Remarkably, for $N=K$ the impurity spin displays maximal chaos, while bosons and fermions only get up to half of the maximal Lyapunov exponent. Our results highlights two new features: a non-disordered model which is maximally chaotic due to strong correlations at its boundary and a fractionalization of quantum chaos., Comment: 16 pages, 7 figures

Skyrmions are topological magnetic textures, mostly treated classically, studied extensively due to their potential spintronics applications due to their topological stability. However, it remains unclear what physical phenomena differentiate a classical from a quantum skyrmion. We present numerical evidence for the existence of a quantum skyrmion liquid (SkL) phase in quasi-one-dimensional lattices which has no classical counterpart. The transition from a conventional quantum skyrmion crystal (SkX) to a field-polarized phase (FP) is found to be of second order while the analogous classical transition near zero temperature is first-order due to a missing SkL phase. As an indicator of the quantum mechanical origin of the SkL phase, we find concentrated entanglement (indicated by the concurrence) around the skyrmion center, which we attribute to the uncertainty in the skyrmion position resulting from the non-commutativity of the skyrmion coordinate operators. The latter also gives rise to a nontrivial kinetic energy in the presence of an atomic lattice. The SkL phase emerges when the kinetic energy dominates over the skyrmion-skyrmion interaction energy. It is tied to the breaking of discrete translational invariance of the skyrmion crystal and occurs when the skyrmion radius is comparable with the size of the magnetic unit cell. In contrast to the long-range order present in the SkX phase, spin-spin correlations in the SkL phase exponentially decay with distance, indicating the fluid-like behavior of uncorrelated skyrmions. The emergence of kinetic energy-induced quantum SkL phase serves as a strong indication of the possible Bose-Einstein condensation of skyrmions in higher-dimensional systems. Our findings are effectively explained by microscopic theories like collective coordinate formalism and trial wave functions, effectively enhancing our understanding of the numerical findings., Comment: 15 pages, 13 figures

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

We present an efficient method for adapting a monolingual Large Language Model (LLM) to another language, addressing challenges of catastrophic forgetting and tokenizer limitations. We focus this study on adapting Llama 2 to Arabic. Our two-stage approach begins with expanding the vocabulary and training only the embeddings matrix, followed by full model continual pre-training on a bilingual corpus. By continually pre-training on a mix of Arabic and English corpora, the model retains its proficiency in English while acquiring capabilities in Arabic. Our approach results in significant improvements in Arabic and slight enhancements in English, demonstrating cost-effective cross-lingual transfer. We perform ablations on embedding initialization techniques, data mix ratios, and learning rates and release a detailed training recipe. To demonstrate generalizability of this approach we also adapted Llama 3 8B to Arabic and Llama 2 13B to Hindi.

Autonomous driving holds great promise in addressing traffic safety concerns by leveraging artificial intelligence and sensor technology. Multi-Object Tracking plays a critical role in ensuring safer and more efficient navigation through complex traffic scenarios. This paper presents a novel deep learning-based method that integrates radar and camera data to enhance the accuracy and robustness of Multi-Object Tracking in autonomous driving systems. The proposed method leverages a Bi-directional Long Short-Term Memory network to incorporate long-term temporal information and improve motion prediction. An appearance feature model inspired by FaceNet is used to establish associations between objects across different frames, ensuring consistent tracking. A tri-output mechanism is employed, consisting of individual outputs for radar and camera sensors and a fusion output, to provide robustness against sensor failures and produce accurate tracking results. Through extensive evaluations of real-world datasets, our approach demonstrates remarkable improvements in tracking accuracy, ensuring reliable performance even in low-visibility scenarios., Comment: Published in IEEE Transactions on Intelligent Transportation Systems

This paper introduces a collaborative, human-centered taxonomy of AI, algorithmic and automation harms. We argue that existing taxonomies, while valuable, can be narrow, unclear, typically cater to practitioners and government, and often overlook the needs of the wider public. Drawing on existing taxonomies and a large repository of documented incidents, we propose a taxonomy that is clear and understandable to a broad set of audiences, as well as being flexible, extensible, and interoperable. Through iterative refinement with topic experts and crowdsourced annotation testing, we propose a taxonomy that can serve as a powerful tool for civil society organisations, educators, policymakers, product teams and the general public. By fostering a greater understanding of the real-world harms of AI and related technologies, we aim to increase understanding, empower NGOs and individuals to identify and report violations, inform policy discussions, and encourage responsible technology development and deployment.

Variation and imbalance in demand poses significant challenges to Urban Air Mobility (UAM) operations, affecting strategic decisions such as fleet sizing. To study the implications of demand variation on UAM fleet operations, we propose a stochastic passenger arrival time generation model that uses real-world data to infer demand distributions, and two integer programs that compute the zero-spill fleet size and the spill-minimizing flight schedules and charging policies, respectively. Our numerical experiment on a two-vertiport network shows that spill in relatively inelastic to fleet size and that the driving factor behind spill is the imbalance in demand.

In this paper we investigate the scattering amplitudes of spin-2 Kaluza-Klein (KK) states in Randall-Sundrum models with brane-localized curvature terms. We show that the presence of brane-localized curvature interactions modifies the properties of (4D) scalar fluctuations of the metric, resulting in scattering amplitudes of the massive spin-2 KK states which grow as ${\cal O}(s^3)$ instead of ${\cal O}(s)$. We discuss the constraints on the size of the brane-localized curvature interactions based on the consistency of the Sturm-Liouville mode systems of the spin-2 and spin-0 metric fluctuations. We connect the properties of the scattering amplitudes to the diffeomorphism invariance of the compactified KK theory with brane-localized curvature interactions. We verify that the scattering amplitudes involving brane-localized external sources (matter) are diffeomorphism-invariant, but show that those for matter localized at an arbitrary point in the bulk are not. We demonstrate that, in Feynman gauge, the spin-0 Goldstone bosons corresponding to helicity-0 states of the massive spin-2 KK bosons behave as a tower of Galileons, and that it is their interactions that produce the high-energy behavior of the scattering amplitudes. We also outline the correspondence between our results and those in the Dvali-Gabadadze-Porrati (DGP) model. In an appendix we discuss the analogous issue in extra-dimensional gauge theory, and show that the presence of a brane-localized gauge kinetic-energy term does not change the high-energy behavior of corresponding KK vector boson scattering amplitudes., Comment: 36 pages, 2 figures. Minor changes, new reference added

The number of quantifiers needed to express first-order (FO) properties is captured by two-player combinatorial games called multi-structural games. We analyze these games on binary strings with an ordering relation, using a technique we call parallel play, which significantly reduces the number of quantifiers needed in many cases. Ordered structures such as strings have historically been notoriously difficult to analyze in the context of these and similar games. Nevertheless, in this paper, we provide essentially tight upper bounds on the number of quantifiers needed to characterize different-sized subsets of strings. The results immediately give bounds on the number of quantifiers necessary to define several different classes of Boolean functions. One of our results is analogous to Lupanov's upper bounds on circuit size and formula size in propositional logic: we show that every Boolean function on $n$-bit inputs can be defined by a FO sentence having $(1 + \varepsilon)n\log(n) + O(1)$ quantifiers, and that this is essentially tight. We reduce this number to $(1 + \varepsilon)\log(n) + O(1)$ when the Boolean function in question is sparse., Comment: Full version of version that is to appear in Proceedings of 49th International Symposium on Mathematical Foundations of Computer Science, 2024. arXiv admin note: substantial text overlap with arXiv:2402.10293

Completely positive trace preserving maps are widely used in quantum information theory. These are mostly studied using the master equation perspective. A central part in this theory is to study whether a given system of dynamical maps $\{\Lambda_t: t \ge 0\}$ is Markovian or non-Markovian. We study the problem when the underlying Hilbert space is of infinite dimensional. We construct a sufficient condition for checking P (resp. CP) divisibility of dynamical maps. We construct several examples where the underlying Hilbert space may not be of finite dimensional. We also give a special emphasis on Gaussian dynamical maps and get a version of our result in it.

We develop novel LASSO-based methods for coefficient testing and confidence interval construction in the Gaussian linear model with $n\ge d$. Our methods' finite-sample guarantees are identical to those of their ubiquitous ordinary-least-squares-$t$-test-based analogues, yet have substantially higher power when the true coefficient vector is sparse. In particular, our coefficient test, which we call the $\ell$-test, performs like the one-sided $t$-test (despite not being given any information about the sign) under sparsity, and the corresponding confidence intervals are more than 10% shorter than the standard $t$-test based intervals. The nature of the $\ell$-test directly provides a novel exact adjustment conditional on LASSO selection for post-selection inference, allowing for the construction of post-selection p-values and confidence intervals. None of our methods require resampling or Monte Carlo estimation. We perform a variety of simulations and a real data analysis on an HIV drug resistance data set to demonstrate the benefits of the $\ell$-test. We end with a discussion of how the $\ell$-test may asymptotically apply to a much more general class of parametric models.

This paper presents a novel approach for directly generating full events at detector-level from parton-level information, leveraging cutting-edge machine learning techniques. To address the challenge of multiplicity variations between parton and reconstructed object spaces, we employ transformers, score-based models and normalizing flows. Our method tackles the inherent complexities of the stochastic transition between these two spaces and achieves remarkably accurate results. The combination of innovative techniques and the achieved accuracy demonstrates the potential of our approach in advancing the field and opens avenues for further exploration. This research contributes to the ongoing efforts in high-energy physics and generative modelling, providing a promising direction for enhanced precision in fast detector simulation.

Exoplanet direct imaging using adaptive optics (AO) is often limited by non-common path aberrations (NCPAs) and aberrations that are invisible to traditional pupil-plane wavefront sensors (WFSs). This can be remedied by focal-plane (FP) WFSs that characterize aberrations directly from a final science image. Photonic lanterns (PLs) can act as low-order FPWFSs with the ability to direct some light to downstream science instruments. Using a PL on the SEAL (Santa Cruz Extreme AO Laboratory) high-contrast imaging testbed, we demonstrate (1) linear ranges and (2) closed-loop control. Additionally, we simulate the use of the PL in a multi-wavefront sensor AO system, in which multiple WFSs feed back to the same common-path deformable mirror. Building on previous multi-WFS AO demonstrations on SEAL, we simulate a modulated pyramid WFS to sense aberrations of high spatial order and large amplitude, and the PL to sense low order aberrations including NCPAs. We assess adaptive optics performance in this setting using three different PL wavefront reconstruction algorithms. We also provide a new method to experimentally identify the propagation matrix of a PL, making advanced model-based algorithms practical. This work demonstrates the role of photonic technologies and multi-stage wavefront sensing in the context of extreme AO and high contrast imaging., Comment: submitted to SPIE Astronomical Telescopes and Instrumentation 2024, paper number 13097-55

We theoretically propose and experimentally demonstrate the use of a configurable four-qubit photonic system to generate a publicly verifiable quantum random numbers, to perform entanglement verification, and to generate a secure public and private key. Quantum circuits, to generate the desired four-qubit states and its experimental realization in the photonic architecture is carried out using photon pairs entangled in polarization and path degree of freedom. By performing measurements on the four-qubit system and accessing partial information of the four-qubit state for public verification, we generate publicly verified and purely secured random bits at the rate of 370 kbps. When the system is used for generating public and private keys, an equal number of public and private keys are generated simultaneously. We also record about 97.9\% of sampled bits from four-qubit states passing entanglement verification. The theoretical model of noise on the four-qubit state and its effect on the generation rate of verified and secured bits are in perfect agreement with the experimental results. This demonstrates the practical use of the small-scale multi-qubit photonic system for quantum-safe applications by providing the option for real-time verification of the security feature of the quantum system., Comment: 11 pages, 8 figures

Melting of a solid is one of the most ubiquitous phenomena observed in nature. Most solids, when heated, melt from a crystalline state to an isotropic liquid at a characteristic temperature. There are however situations where increase in temperature can induce a transition to a more ordered state. Broadly termed as "inverse melting", experimental realisations of such situations are rare. Here, we report such a phenomenon in the 2-dimensional vortex liquid that forms in a moderately pinned amorphous Re6Zr (a-ReZr) thin film, from direct imaging of the vortex lattice using a scanning tunnelling microscope. At low temperature and magnetic fields, we find that the vortices form a "pinned liquid" , that is characterised by a low mobility of the vortices and vortex density that is spatially inhomogeneous. As the temperature or magnetic field is increased the vortices become more ordered, eventually forming a nearly perfectly ordered vortex lattice. Above this temperature/magnetic field, the ordered vortex lattice melts again into a vortex liquid. This re-entrant transformation from a liquid to solid-like state and then back to a liquid also leaves distinct signature in the magnetotransport properties of the superconductor.

Mitigating biases in generative AI and, particularly in text-to-image models, is of high importance given their growing implications in society. The biased datasets used for training pose challenges in ensuring the responsible development of these models, and mitigation through hard prompting or embedding alteration, are the most common present solutions. Our work introduces a novel approach to achieve diverse and inclusive synthetic images by learning a direction in the latent space and solely modifying the initial Gaussian noise provided for the diffusion process. Maintaining a neutral prompt and untouched embeddings, this approach successfully adapts to diverse debiasing scenarios, such as geographical biases. Moreover, our work proves it is possible to linearly combine these learned latent directions to introduce new mitigations, and if desired, integrate it with text embedding adjustments. Furthermore, text-to-image models lack transparency for assessing bias in outputs, unless visually inspected. Thus, we provide a tool to empower developers to select their desired concepts to mitigate. The project page with code is available online., Comment: Accepted at CVPR workshop 2024, proceedings of ReGenAI: First Workshop on Responsible Generative AI

In: {\it "Three-dimensional direct numerical simulation (DNS) of Rayleigh-Taylor instability (RTI) trigerred by acoustic excitation -- Sengupta et al. {\bf 34},054108 (2022)"} the receptivity of RTI to pressure pulses have been established. It has also been shown that at the onset of RTI these pulses are one-dimensional and the dissipation of the pressure pulses are governed by a dissipative wave equation. The propagation of these infrasonic to ultrasonic pressure pulses have been studied theoretically and numerically by a high fidelity numerical procedure in the physical plane. The numerical results are consistent with the theoretical analysis and the DNS of RTI noted above. The properties of pulse propagation in a quiescent dissipative ambience have been theoretically obtained from the linearized compressible Navier-Stokes equation, without Stokes' hypothesis. This analysis is extended here for a special class of excitation, with combination of wavenumbers and circular frequencies for which the phase shift results in an imposed time period is integral multiple of $\pi$, and the signal amplification is by a real factor. Here, the governing partial differential equation (PDE) for the free-field propagation of pulses is solved by the Bromwich contour integral method in the spectral plane. This method, for an input Gaussian pulse excited at a fixed frequency, is the so-called signal problem. Responses for the specific phase shifts integral multiple of $\pi$ can reinforce each other due to the phase coherence. It is shown that these combinations occur at a fixed wavenumber, with higher frequencies attenuated more in such a sequence., Comment: 25 pages, 7 figures

Recent work on flux compactifications suggests that the tadpole constraint generically allows only a limited number of complex structure moduli to become massive, i.e., be stabilized at quadratic order in the spacetime superpotential. We study the effects of higher-order terms systematically around the Fermat point in the $1^9$ Landau-Ginzburg model. This model lives at strong coupling and features no K\"ahler moduli. We show that, depending on the flux, several massless fields can indeed be stabilized in this fashion, and argue that this paves the way to explicit ${\mathcal N}=1$ Minkowski vacua without flat directions. Along the way, we complete the classification of integral flux vectors with small tadpole contribution. Thereby we are closing in on a future complete understanding of all possible flux configurations in the $1^9$ Landau-Ginzburg model., Comment: 41 pages, 2 figures

Spiking neural networks (SNNs) are posited as a biologically plausible alternative to conventional neural architectures, with their core computational framework resting on the extensively studied leaky integrate-and-fire (LIF) neuron design. The stateful nature of LIF neurons has spurred ongoing discussions about the ability of SNNs to process sequential data, akin to recurrent neural networks (RNNs). Despite this, there remains a significant gap in the exploration of current SNNs within the realm of long-range dependency tasks. In this study, to extend the analysis of neuronal dynamics beyond simplistic LIF mechanism, we present a novel class of stochastic spiking neuronal model grounded in state space models. We expand beyond the scalar hidden state representation of LIF neurons, which traditionally comprises only the membrane potential, by proposing an n-dimensional hidden state. Additionally, we enable fine-tuned formulation of neuronal dynamics across each layer by introducing learnable parameters, as opposed to the fixed dynamics in LIF neurons. We also develop a robust framework for scaling these neuronal models to deep SNN-based architectures, ensuring efficient parallel training while also adeptly addressing the challenge of non-differentiability of stochastic spiking operation during the backward phase. Our models attain state-of-the-art performance among SNN models across diverse long-range dependency tasks, encompassing the Long Range Arena benchmark, permuted sequential MNIST, and the Speech Command dataset. Moreover, we provide an analysis of the energy efficiency advantages, emphasizing the sparse activity pattern intrinsic to this spiking model., Comment: Under Review

We report a microscopic mechanism for stabilization of biskyrmions in nature by investigating a minimal classical spin lattice model with nearest neighbour ferromagnetic Heisenberg exchange and static chiral magnetic interaction on the triangular lattice. The model is physically motivated model from the Mott insulators with broken time-reversal symmetry, that is, in the large-$U$ limit of the one band Hubbard model at half-filling on a two-dimensional lattice in the presence of external magnetic field. At order $1/U^{2}$, the external magnetic field can induce a chiral interaction between the three neighbouring spins. We demonstrate that the chiral magnetic interaction results in biskyrmion states above a critical value and its strength affects the size of biskyrmions forming.

Bias correction is a common pre-processing step applied to climate model data before it is used for further analysis. This article introduces an efficient adaptation of a well-established bias-correction method - quantile mapping - for global horizontal irradiance (GHI) that ensures corrected data is physically plausible through incorporating measurements of clearsky GHI. The proposed quantile mapping method is fit on reanalysis data to first bias correct for regional climate models (RCMs) and is tested on RCMs forced by general circulation models (GCMs) to understand existing biases directly from GCMs. Additionally, we adapt a functional analysis of variance methodology that analyzes sources of remaining biases after implementing the proposed quantile mapping method and considered biases by climate region. This analysis is applied to four sets of climate model output from NA-CORDEX and compared against data from the National Solar Radiation Database produced by the National Renewable Energy Lab., Comment: 28 pages, 15 figures

Reinforcement learning (RL) in large or infinite state spaces is notoriously challenging, both theoretically (where worst-case sample and computational complexities must scale with state space cardinality) and experimentally (where function approximation and policy gradient techniques often scale poorly and suffer from instability and high variance). One line of research attempting to address these difficulties makes the natural assumption that we are given a collection of heuristic base or $\textit{constituent}$ policies upon which we would like to improve in a scalable manner. In this work we aim to compete with the $\textit{max-following policy}$, which at each state follows the action of whichever constituent policy has the highest value. The max-following policy is always at least as good as the best constituent policy, and may be considerably better. Our main result is an efficient algorithm that learns to compete with the max-following policy, given only access to the constituent policies (but not their value functions). In contrast to prior work in similar settings, our theoretical results require only the minimal assumption of an ERM oracle for value function approximation for the constituent policies (and not the global optimal policy or the max-following policy itself) on samplable distributions. We illustrate our algorithm's experimental effectiveness and behavior on several robotic simulation testbeds.

We study two dimensional systems with extended conformal symmetry generated by the ${\mathcal W}_3$ algebra. These are expected to have an infinite number of commuting conserved charges, which we refer to as the quantum Boussinesq charges. We compute the eigenvalues of the quantum Boussinesq charges in both the vacuum and first excited states of the higher spin module through the ODE/IM correspondence. By studying the higher spin conformal field theory on the torus, we also calculate thermal correlators involving the energy-momentum tensor and the spin-3 current by making use of the Zhu recursion relations. By combining these results, we show that it is possible to derive the current densities, whose integrals are the quantum Boussinesq charges. We also evaluate the thermal expectation values of the conserved charges, and show that these are quasi-modular differential operators acting on the character of the higher spin module., Comment: 47 pages

We present SkyCURTAINs, a data driven and model agnostic method to search for stellar streams in the Milky Way galaxy using data from the Gaia telescope. SkyCURTAINs is a weakly supervised machine learning algorithm that builds a background enriched template in the signal region by leveraging the correlation of the source's characterising features with their proper motion in the sky. This allows for a more representative template of the background in the signal region, and reduces the false positives in the search for stellar streams. The minimal model assumptions in the SkyCURTAINs method allow for a flexible and efficient search for various kinds of anomalies such as streams, globular clusters, or dwarf galaxies directly from the data. We test the performance of SkyCURTAINs on the GD-1 stream and show that it is able to recover the stream with a purity of 75.4% which is an improvement of over 10% over existing machine learning based methods while retaining a signal efficiency of 37.9%.

This paper investigates the projective closure of simplicial affine semigroups in $\mathbb{N}^{d}$, $d \geq 2$. We present a characterization of the Cohen-Macaulay property for the projective closure of these semigroups using Gr\"{o}bner bases. Additionally, we establish a criterion, based on Gr\"{o}bner bases, for determining the Buchsbaum property of non-Cohen-Macaulay projective closures of numerical semigroup rings. Lastly, we introduce the concept of $k$-lifting for simplicial affine semigroups in $\mathbb{N}^d$, and investigate its relationship with the original simplicial affine semigroup.

Environmental factors pose a significant challenge to the operational efficiency and safety of advanced air mobility (AAM) networks. This paper presents a simulation-optimization framework that dynamically integrates wind variability into AAM operations. We employ a nonlinear charging model within a multi-vertiport environment to optimize fleet size and scheduling. Our framework assesses the impact of wind on operational parameters, providing strategies to enhance the resilience of AAM ecosystems. The results demonstrate that wind conditions exert significant influence on fleet size even for short-distance flights, their impact on fleet size and energy requirements becomes more pronounced over longer distances. Efficient management of fleet size and charging policies, particularly for long-distance networks, is needed to accommodate the variability of wind conditions effectively., Comment: Accepted to ICRAT 2024

Global and regional climate model projections are useful for gauging future patterns of climate variables, including solar radiation, but data from these models is often too coarse to assess local impacts. Within the context of solar radiation, the changing climate may have an effect on photovoltaic (PV) production, especially as the PV industry moves to extend plant lifetimes to 50 years. Predicting PV production while taking into account a changing climate requires data at a resolution that is useful for building PV plants. Although temporal and spatial downscaling of solar radiation data is widely studied, we present a novel method to downscale solar radiation data from daily averages to hourly profiles, while maintaining spatial correlation of parameters characterizing the diurnal profile of solar radiation. The method focuses on the use of a diurnal template which can be shifted and scaled according to the time or year and location and the use of thin plate splines for spatial downscaling. This analysis is applied to data from the National Solar Radiation Database housed at the National Renewable Energy Lab and a case study of the mentioned methods over several sub-regions of continental United States is presented., Comment: 35 pages, 14 figures

We present a complete and consistent exposition of the regularization, renormalization, and resummation procedures in the setup of having a contraction and then non-singular bounce followed by inflation with a sharp transition from slow-roll (SR) to ultra-slow roll (USR) phase for generating primordial black holes (PBHs). We consider following an effective field theory (EFT) approach and study the quantum loop corrections to the power spectrum from each phase. We demonstrate the complete removal of quadratic UV divergences after renormalization and softened logarithmic IR divergences after resummation and illustrate the scheme-independent nature of our renormalization approach. We further show that the addition of a contracting and bouncing phase allows us to successfully generate PBHs of solar-mass order, $M_{\rm PBH}\sim {\cal O}(M_{\odot})$, by achieving the minimum e-folds during inflation to be $\Delta N_{\rm Total}\sim {\cal O}(60)$ and in this process successfully evading the strict no-go theorem. We notice that varying the effective sound speed between $0.88\leq c_{s}\leq 1$, allows the peak spectrum amplitude to lie within $10^{-3}\leq A \leq 10^{-2}$, indicating that causality and unitarity remain protected in the theory. We analyse PBHs in the extremely small, $M_{\rm PBH}\sim {\cal O}(10^{-33}-10^{-27})M_{\odot}$, and the large, $M_{\rm PBH}\sim {\cal O}(10^{-6}-10^{-1})M_{\odot}$, mass limits and confront the PBH abundance results with the latest microlensing constraints. We also study the cosmological beta functions across all phases and find their interpretation consistent in the context of bouncing and inflationary scenarios while satisfying the pivot scale normalization requirement. Further, we estimate the spectral distortion effects and shed light on controlling PBH overproduction., Comment: 103 pages, 17 figures, Reference updated and typos corrected, Comments are welcome

Spatio-temporal evolution of large amplitude upper hybrid oscillations in a cold homogeneous plasma in the presence of an inhomogeneous magnetic field is studied analytically and numerically using the Dawson sheet model. It is observed that the inhomogeneity in magnetic field which causes the upper hybrid frequency to acquire a spatial dependence, results in phase mixing and subsequent breaking of the upper hybrid oscillations at arbitrarily low amplitudes. This result is in sharp contrast to the usual upper hybrid oscillations in a homogeneous magnetic field where the oscillations break within a fraction of a period when the amplitude exceeds a certain critical value. Our perturbative calculations show that the phase mixing (wave breaking) time scales inversely with the amplitude of magnetic field inhomogeneity ($\Delta$) and amplitude of imposed density perturbation ($\delta$), and scales directly with the ratio of magnetic field inhomogeneity scale length to imposed density perturbation scale length ($(\alpha/k_L)^{-1}$ ) as $\omega_{pe}\tau_{mix} \sim \left( 1+\beta^2 \right) ^{3/2}k_L/(\beta^2\delta\Delta\alpha)$, where $\beta$ is the ratio of electron cyclotron frequency to electron plasma frequency. Further phase mixing time measured in simulations, performed using a 1-1/2 D code based on Dawson sheet model, shows good agreement with the above mentioned scaling. This result may be of relevance to plasma based particle acceleration experiments in the presence of a transverse inhomogeneous magnetic field.

We consider the problem of reconstructing an undirected graph $G$ on $n$ vertices given multiple random noisy subgraphs or "traces". Specifically, a trace is generated by sampling each vertex with probability $p_v$, then taking the resulting induced subgraph on the sampled vertices, and then adding noise in the form of either (a) deleting each edge in the subgraph with probability $1-p_e$, or (b) deleting each edge with probability $f_e$ and transforming a non-edge into an edge with probability $f_e$. We show that, under mild assumptions on $p_v$, $p_e$ and $f_e$, if $G$ is selected uniformly at random, then $O(p_e^{-1} p_v^{-2} \log n)$ or $O((f_e-1/2)^{-2} p_v^{-2} \log n)$ traces suffice to reconstruct $G$ with high probability. In contrast, if $G$ is arbitrary, then $\exp(\Omega(n))$ traces are necessary even when $p_v=1, p_e=1/2$., Comment: 6 pages, to appear in ISIT 2024