Search

The structure tensor method is often used for 2D and 3D analysis of imaged structures, but its results are in many cases very dependent on the user's choice of method parameters. We simplify this parameter choice in first order structure tensor scale-space by directly connecting the width of the derivative filter to the size of image features. By introducing a ring-filter step, we substitute the Gaussian integration/smoothing with a method that more accurately shifts the derivative filter response from feature edges to their center. We further demonstrate how extracted structural measures can be used to correct known inaccuracies in the scale map, resulting in a reliable representation of the feature sizes both in 2D and 3D. Compared to the traditional first order structure tensor, or previous structure tensor scale-space approaches, our solution is much more accurate and can serve as an out-of-the-box method for extracting a wide range of structural parameters with minimal user input.

Thermalization of isolated and open quantum systems has been studied extensively. However, being the subject of investigation by different scientific communities and being analysed using different mathematical tools, the connection between the isolated (IQS) and open (OQS) approaches to thermalization has remained opaque. Here we demonstrate that the fundamental difference between the two paradigms is the order in which the long time and the thermodynamic limits are taken. This difference implies that they describe physics on widely different time and length scales. Our analysis is carried out numerically for the case of a double quantum dot (DQD) coupled to a fermionic lead. We show how both OQS and IQS thermalization can be explored in this model on equal footing, allowing a fair comparison between the two. We find that while the quadratically coupled (free) DQD experiences no isolated thermalization, it of course does experience open thermalization. For the non-linearly interacting DQD coupled to fermionic lead, we show by characterizing its spectral form factor and level spacing distribution, that the system falls in the twilight zone between integrable and non-integrable regimes, which we call partially non-integrable. We further evidence that, despite being only partially non-integrable and thereby falling outside the remit of the standard eigenstate thermalization hypothesis, it nevertheless experiences IQS as well as OQS thermalization.

We prove that Schubert varieties in flag manifolds are uniquely determined by their equivariant cohomology classes, as well as a stronger result that replaces Schubert varieties with closures of Bialynicki-Birula cells under suitable conditions. This is used to prove that any two-pointed curve neighborhood representing a quantum cohomology product with a Seidel class is a Schubert variety.

Rocky planets may acquire a primordial atmosphere by outgassing of volatiles from their magma ocean. The distribution of O between H$_2$O, CO and CO$_2$ in chemical equilibrium subsequently changes significantly with decreasing temperature. We explore here two chemical models: one where CH$_4$ and NH$_3$ are assumed to be irrevocably destroyed by photolysis, and one where these molecules persist. In the first case, we show that CO cannot co-exist with H$_2$O, since CO oxidizes at low temperatures to form CO$_2$ and H$_2$. In both cases, H escapes from the thermosphere within a few ten million years by absorption of stellar XUV radiation. This escape drives an atmospheric self-oxidation process whereby rocky planet atmospheres become dominated by CO$_2$ and H$_2$O, regardless of their initial oxidation state at outgassing. HCN is considered a potential precursor of prebiotic compounds and RNA. Our oxidizing atmospheres are inefficient at producing HCN by lightning. Instead, we demonstrate that lightning-produced NO, which dissolves as nitrate in the oceans, and interplanetary dust particles may be the main sources of fixed nitrogen to emerging biospheres. Our results highlight the need for origin-of-life scenarios where the first metabolism fixes its C from CO$_2$, rather than from HCN and CO., Comment: Accepted for publication in Astrobiology

We present here results of numerical simulations of the formation and early evolution of rocky planets through pebble accretion, with an with an emphasis on hydrogen envelope longevity and the composition of the outgassed atmosphere. We model planets with a range in mass from 0.1 to 5 Earth masses that orbit between 0.7 and 1.7 AU. The composition of the outgassed atmosphere is calculated with the partial pressure of free oxygen fit to geophysical models of magma ocean self-oxidation. XUV radiation powered photoevaporation is considered as the main driver of atmospheric escape. We model planets that remain below the pebble isolation mass and hence accrete tenuous envelopes only. We consider slow, medium or fast initial stellar rotation for the temporal evolution of the XUV flux. The loss of the envelope is a key event that allows the magma ocean to crystallise and outgas its bulk volatiles. The atmospheric composition of the majority of our simulated planets is dominated by CO$_2$. Our planets accrete a total of 11.6 Earth oceans of water, the majority of which enters the core. The hydrospheres of planets lighter than the Earth reach several times the mass of the Earth's modern oceans, while the hydrospheres of planets ranging from 1 to 3.5 Earth masses are comparable to those of our planet. However, planets of 4-5 Earth masses have smaller hydrospheres due to trapping of volatiles in their massive mantles. Overall, our simulations demonstrate that hydrogen envelopes are easily lost from rocky planets and that this envelope loss triggers the most primordial partitioning of volatiles between the solid mantle and the atmosphere., Comment: Accepted for Astronomy & Astrophysics

A homology class $d \in H_2(X)$ of a complex flag variety $X = G/P$ is called a line degree if the moduli space $\overline{M}_{0,0}(X,d)$ of 0-pointed stable maps to $X$ of degree $d$ is also a flag variety $G/P'$. We prove a quantum equals classical formula stating that any $n$-pointed (equivariant, K-theoretic, genus zero) Gromov-Witten invariant of line degree on $X$ is equal to a classical intersection number computed on the flag variety $G/P'$. We also prove an $n$-pointed analogue of the Peterson comparison formula stating that these invariants coincide with Gromov-Witten invariants of the variety of complete flags $G/B$. Our formulas make it straightforward to compute the big quantum K-theory ring of $X$ modulo degrees larger than line degrees., Comment: To appear in the proceedings of the conference GLSM@30 (Stony Brook, May 2023)

Studying and building datasets for dialogue tasks is both expensive and time-consuming due to the need to recruit, train, and collect data from study participants. In response, much recent work has sought to use large language models (LLMs) to simulate both human-human and human-LLM interactions, as they have been shown to generate convincingly human-like text in many settings. However, to what extent do LLM-based simulations \textit{actually} reflect human dialogues? In this work, we answer this question by generating a large-scale dataset of 100,000 paired LLM-LLM and human-LLM dialogues from the WildChat dataset and quantifying how well the LLM simulations align with their human counterparts. Overall, we find relatively low alignment between simulations and human interactions, demonstrating a systematic divergence along the multiple textual properties, including style and content. Further, in comparisons of English, Chinese, and Russian dialogues, we find that models perform similarly. Our results suggest that LLMs generally perform better when the human themself writes in a way that is more similar to the LLM's own style.

The choice of how to represent an abstract type can have a major impact on the performance of a program, yet mainstream compilers cannot perform optimizations at such a high level. When dealing with optimizations of data type representations, an important feature is having extensible representation-flexible data types; the ability for a programmer to add new abstract types and operations, as well as concrete implementations of these, without modifying the compiler or a previously defined library. Many research projects support high-level optimizations through static analysis, instrumentation, or benchmarking, but they are all restricted in at least one aspect of extensibility. This paper presents a new approach to representation-flexible data types without such restrictions and which still finds efficient optimizations. Our approach centers around a single built-in type $\texttt{repr}$ and function overloading with cost annotations for operation implementations. We evaluate our approach (i) by defining a universal collection type as a library, a single type for all conventional collections, and (ii) by designing and implementing a representation-flexible graph library. Programs using $\texttt{repr}$ types are typically faster than programs with idiomatic representation choices -- sometimes dramatically so -- as long as the compiler finds good implementations for all operations. Our compiler performs the analysis efficiently by finding optimized solutions quickly and by reusing previous results to avoid recomputations., Comment: 25 pages, 11 figures

An oriented multigraph is a directed multigraph without directed 2-cycles. Let ${\rm fas}(D)$ denote the minimum size of a feedback arc set in an oriented multigraph $D$. The degree of a vertex is the sum of its out- and in-degrees. In several papers, upper bounds for ${\rm fas}(D)$ were obtained for oriented multigraphs $D$ with maximum degree upper-bounded by a constant. Hanauer (2017) conjectured that ${\rm fas}(D)\le 2.5n/3$ for every oriented multigraph $D$ with $n$ vertices and maximum degree at most 5. We prove a strengthening of the conjecture: ${\rm fas}(D)\le m/3$ holds for every oriented multigraph $D$ with $m$ arcs and maximum degree at most 5. This bound is tight and improves a bound of Berger and Shor (1990,1997). It would be interesting to determine $c$ such that ${\rm fas}(D)\le cn$ for every oriented multigraph $D$ with $n$ vertices and maximum degree at most 5 such that the bound is tight. We show that $\frac{5}{7}\le c \le \frac{24}{29} < \frac{2.5}{3}$.

A run of the deferred acceptance (DA) algorithm may contain proposals that are sure to be rejected. We introduce the accelerated deferred acceptance algorithm that proceeds in a similar manner to DA but with sure-to-be rejected proposals ruled out. Accelerated deferred acceptance outputs the same stable matching as DA but does so more efficiently: it terminates in weakly fewer rounds, requires weakly fewer proposals, and final pairs match no later. Computational experiments show that these efficiency savings can be strict.

We perform an analysis of a number of approximations and methods used in numerical simulations of real-time Kadanoff-Baym equations based on truncations of the 2PI effective action. We compare the loop expansion to the 1/N expansion and compare their classical limit to classical-statistical simulations. We also compare implementations based on a space-time lattice discretization at the level of the action to an ad hoc momentum discretization at the level of the equations of motions. We extract some rules of thumb for performing 2PI-simulations of out-of-equilibrium systems., Comment: 21 pages, 25 figures

Hybrid double-network hydrogels are a class of material that comprise transiently and permanently crosslinked polymer networks and exhibit an enhanced toughness that is believed to be governed by the yielding of the transient polymer network. The precise role of the two polymer networks in this yielding transition and their interplay remains an open question that we address here through constructing a series of hydrogel designs in which the interaction within and between the two polymer networks are systematically inhibited or enhanced. We characterise each of the hydrogel designs using large amplitude oscillatory shear rheology (LAOS). Inspecting yielding through elastic stress across hydrogel designs, we elucidate that the hybrid double-network hydrogel exhibits a two-step yielding behaviour that originates from to the presence of transient crosslinks. Examining the rheological response within each oscillatory cycle and across the hydrogel designs, we show that the micro-structural changes in the transient network are crucial in the second stage of this yielding. We surmise that the first step of yielding is determined by the intermolecular interactions between the two polymer networks by systematically altering the strength of the interactions. These interactions also influence the second step of yielding, which we show is governed by the transient intermolecular interactions within the polymer networks. Our study therefore reveals that the interactions between the polymer networks are as crucial as within the polymer networks and therefore provides insights into how the yielding mechanisms in soft composite materials can be identified, adjusted, and controlled.

This paper formulates adaptive controller design as a minimax dual control problem. The objective is to design a controller that minimizes the worst-case performance over a set of uncertain systems. The uncertainty is described by a set of linear time-invariant systems with unknown parameters. The main contribution is a common framework for both state feedback and output feedback control. We show that for finite uncertainty sets, the minimax dual control problem admits a finite-dimensional information state. This information state can be used to design adaptive controllers that ensure that the closed-loop has finite gain. The controllers are derived from a set of Bellman inequalities that are amenable to numerical solutions. The proposed framework is illustrated on a challenging numerical example., Comment: 38 pages, 9 figures, included in the first author's PhD Thesis. Code: https://github.com/kjellqvist/MinimaxAdaptiveControl.jl

We present experiences and lessons learned from increasing data readiness of heterogeneous data for artificial intelligence projects using visual analysis methods. Increasing the data readiness level involves understanding both the data as well as the context in which it is used, which are challenges well suitable to visual analysis. For this purpose, we contribute a mapping between data readiness aspects and visual analysis techniques suitable for different data types. We use the defined mapping to increase data readiness levels in use cases involving time-varying data, including numerical, categorical, and text. In addition to the mapping, we extend the data readiness concept to better take aspects of the task and solution into account and explicitly address distribution shifts during data collection time. We report on our experiences in using the presented visual analysis techniques to aid future artificial intelligence projects in raising the data readiness level.

In this paper, we examine the energy efficiency (EE) of a base station (BS) with multiple antennas. We use a state-of-the-art power consumption model, taking into account the passive and active parts of the transceiver circuitry, including the effects of radiated power, signal processing, and passive consumption. The paper treats the transmit power, bandwidth, and number of antennas as the optimization variables. We provide novel closed-form solutions for the optimal ratios of power per unit bandwidth and power per transmit antenna. We present a novel algorithm that jointly optimizes these variables to achieve maximum EE, while fulfilling constraints on the variable ranges. We also discover a new relationship between the radiated power and the passive transceiver power consumption. We provide analytical insight into whether using maximum power or bandwidth is optimal and how many antennas a BS should utilize., Comment: 6 pages, 4 figures

This work studies offline Reinforcement Learning (RL) in a class of non-Markovian environments called Regular Decision Processes (RDPs). In RDPs, the unknown dependency of future observations and rewards from the past interactions can be captured by some hidden finite-state automaton. For this reason, many RDP algorithms first reconstruct this unknown dependency using automata learning techniques. In this paper, we show that it is possible to overcome two strong limitations of previous offline RL algorithms for RDPs, notably RegORL. This can be accomplished via the introduction of two original techniques: the development of a new pseudometric based on formal languages, which removes a problematic dependency on $L_\infty^\mathsf{p}$-distinguishability parameters, and the adoption of Count-Min-Sketch (CMS), instead of naive counting. The former reduces the number of samples required in environments that are characterized by a low complexity in language-theoretic terms. The latter alleviates the memory requirements for long planning horizons. We derive the PAC sample complexity bounds associated to each of these techniques, and we validate the approach experimentally., Comment: To appear in EWRL 2024

Research in ML4VIS investigates how to use machine learning (ML) techniques to generate visualizations, and the field is rapidly growing with high societal impact. However, as with any computational pipeline that employs ML processes, ML4VIS approaches are susceptible to a range of ML-specific adversarial attacks. These attacks can manipulate visualization generations, causing analysts to be tricked and their judgments to be impaired. Due to a lack of synthesis from both visualization and ML perspectives, this security aspect is largely overlooked by the current ML4VIS literature. To bridge this gap, we investigate the potential vulnerabilities of ML-aided visualizations from adversarial attacks using a holistic lens of both visualization and ML perspectives. We first identify the attack surface (i.e., attack entry points) that is unique in ML-aided visualizations. We then exemplify five different adversarial attacks. These examples highlight the range of possible attacks when considering the attack surface and multiple different adversary capabilities. Our results show that adversaries can induce various attacks, such as creating arbitrary and deceptive visualizations, by systematically identifying input attributes that are influential in ML inferences. Based on our observations of the attack surface characteristics and the attack examples, we underline the importance of comprehensive studies of security issues and defense mechanisms as a call of urgency for the ML4VIS community., Comment: This is the author's version of the article that has been accepted by the Journal of Visualization

The cores of stars are the cosmic furnaces where light elements are fused into heavier nuclei. The fusion of hydrogen to helium initially powers all stars. The ashes of the fusion reactions are then predicted to serve as fuel in a series of stages, eventually transforming massive stars into a structure of concentric shells. These are composed of natal hydrogen on the outside, and consecutively heavier compositions inside, predicted to be dominated by helium, carbon/oxygen, oxygen/neon/magnesium, and oxygen/silicon/sulphur. Silicon and sulphur are fused into inert iron, leading to the collapse of the core and either a supernova explosion or the direct formation of a black hole. Stripped stars, where the outer hydrogen layer has been removed and the internal He-rich layer (in Wolf-Rayet WN stars) or even the C/O layer below it (in Wolf-Rayet WC/WO stars) are exposed, provide evidence for this shell structure, and the cosmic element production mechanism it reflects. The types of supernova explosions that arise from stripped stars embedded in shells of circumstellar material (most notably Type Ibn supernovae from stars with outer He layers, and Type Icn supernovae from stars with outer C/O layers) confirm this scenario. However, direct evidence for the most interior shells, which are responsible for the production of elements heavier than oxygen, is lacking. Here, we report the discovery of the first-of-its-kind supernova arising from a star peculiarly stripped all the way to the silicon and sulphur-rich internal layer. Whereas the concentric shell structure of massive stars is not under debate, it is the first time that such a thick, massive silicon and sulphur-rich shell, expelled by the progenitor shortly before the SN explosion, has been directly revealed., Comment: 48 pages, 12 figures and 10 tables. Submitted to a high-impact journal. The reduced spectra and photometry will be made available via the journal webpage and the WISeREP archive after the acceptance of the paper

Classifying breast cancer molecular subtypes is crucial for tailoring treatment strategies. While immunohistochemistry (IHC) and gene expression profiling are standard methods for molecular subtyping, IHC can be subjective, and gene profiling is costly and not widely accessible in many regions. Previous approaches have highlighted the potential application of deep learning models on H&E-stained whole slide images (WSI) for molecular subtyping, but these efforts vary in their methods, datasets, and reported performance. In this work, we investigated whether H&E-stained WSIs could be solely leveraged to predict breast cancer molecular subtypes (luminal A, B, HER2-enriched, and Basal). We used 1,433 WSIs of breast cancer in a two-step pipeline: first, classifying tumor and non-tumor tiles to use only the tumor regions for molecular subtyping; and second, employing a One-vs-Rest (OvR) strategy to train four binary OvR classifiers and aggregating their results using an eXtreme Gradient Boosting (XGBoost) model. The pipeline was tested on 221 hold-out WSIs, achieving an overall macro F1 score of 0.95 for tumor detection and 0.73 for molecular subtyping. Our findings suggest that, with further validation, supervised deep learning models could serve as supportive tools for molecular subtyping in breast cancer. Our codes are made available to facilitate ongoing research and development., Comment: 16 pages, 5 figures (+4 supplementary figures), 4 tables

Voice conversion has gained increasing popularity within the field of audio manipulation and speech synthesis. Often, the main objective is to transfer the input identity to that of a target speaker without changing its linguistic content. While current work provides high-fidelity solutions they rarely focus on model simplicity, high-sampling rate environments or stream-ability. By incorporating speech representation learning into a generative timbre transfer model, traditionally created for musical purposes, we investigate the realm of voice conversion generated directly in the time domain at high sampling rates. More specifically, we guide the latent space of a baseline model towards linguistically relevant representations and condition it on external speaker information. Through objective and subjective assessments, we demonstrate that the proposed solution can attain levels of naturalness, quality, and intelligibility comparable to those of a state-of-the-art solution for seen speakers, while significantly decreasing inference time. However, despite the presence of target speaker characteristics in the converted output, the actual similarity to unseen speakers remains a challenge., Comment: Accepted for publication in Proceedings of the 27th International Conference on Digital Audio Effects (DAFx24), Guildford, United Kingdom, 3 - 7 September 2024

In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This comprehensive review examines state-of-the-art (SOTA) pre-trained models and foundation models in music, spanning from representation learning, generative learning and multimodal learning. We first contextualise the significance of music in various industries and trace the evolution of AI in music. By delineating the modalities targeted by foundation models, we discover many of the music representations are underexplored in FM development. Then, emphasis is placed on the lack of versatility of previous methods on diverse music applications, along with the potential of FMs in music understanding, generation and medical application. By comprehensively exploring the details of the model pre-training paradigm, architectural choices, tokenisation, finetuning methodologies and controllability, we emphasise the important topics that should have been well explored, like instruction tuning and in-context learning, scaling law and emergent ability, as well as long-sequence modelling etc. A dedicated section presents insights into music agents, accompanied by a thorough analysis of datasets and evaluations essential for pre-training and downstream tasks. Finally, by underscoring the vital importance of ethical considerations, we advocate that following research on FM for music should focus more on such issues as interpretability, transparency, human responsibility, and copyright issues. The paper offers insights into future challenges and trends on FMs for music, aiming to shape the trajectory of human-AI collaboration in the music realm.

The nitrogen-vacancy (NV) center in diamond is a promising candidate for various quantum applications, such as quantum sensing. High sensitivity in NV-based magnetic sensing requires a diamond sample with a high density of NV centers and a long electron spin dephasing time. In this work, we propose a systematic measurement method for determining the electron spin dephasing time of NV center ensembles and analyze the contributions to the dephasing time from various sources, including NV-NV interactions, strain distribution, $^{13}C$ nuclear spin, and P1 electron spin. We demonstrate the effectiveness of our method on a series of high-performance diamond samples and provide a comprehensive understanding of dephasing sources, enabling the optimization of NV-based quantum sensing applications.

There has been a transition from broad to more specific research questions in the practice of network meta-analysis (NMA). Such convergence is also taking place in the context of individual registrational trials, following the recent introduction of the estimand framework, which is impacting the design, data collection strategy, analysis and interpretation of clinical trials. The language of estimands has much to offer to NMA, particularly given the "narrow" perspective of treatments and target populations taken in health technology assessment., Comment: 6 pages, discussion paper based on Ades et al. (2024), accepted for publication by Research Synthesis Methods

We study the Heisenberg $S=1/2$ chain with random ferro- and antiferromagnetic couplings, using quantum Monte Carlo simulations at ultra-low temperatures, converging to the ground state. Finite-size scaling of correlation functions and excitation gaps demonstrate an exotic critical state in qualitative agreement with previous strong-disorder renormalization group calculations, but with scaling exponents depending on the coupling distribution. We find dual scaling regimes of the transverse correlations versus the distance, with an $L$ independent form $C(r)=r^{-\mu}$ for $r \ll L$ and $C(r,L)=L^{-\eta}f(r/L)$ for $r/L > 0$, where $\mu > \eta$ and the scaling function is delivered by our analysis. These results are at variance with previous spin-wave and density-matrix renormalization group calculations, thus highlighting the power of unbiased quantum Monte Carlo simulations., Comment: 8 pages, 10 figures

Ultrafast spectroscopy paved the way for probing transient states of matter produced through photoexcitation. Despite significant advances, the microscopic processes governing the formation of these states remain largely unknown. This study discloses the nuclear quantum dynamics of $\mathrm{SrTiO_3}$ when excited by THz laser pumping. We use a first-principles machine-learning approach accounting for all atomistic degrees of freedom to examine the time-resolved energy flow across phonon modes following the photoexcitation, revealing the mechanism underpinning the observed phonon upconversion and quantifying the lifetime of the out-of-equilibrium motion. Crucially, our simulations predict that THz pump pulses can generate persistent out-of-equilibrium stress capable of inducing polar order. We observe a correlation between the experimentally measured lifetime of the transient inversion-symmetry-broken state and the duration of the out-of-equilibrium nuclear state. This work not only explains the experimental results on $\mathrm{SrTiO_3}$ but also establishes a framework for simulating the photoexcited quantum dynamics of nuclei from first principles without any empirical input. It lays the groundwork for systematic explorations of complex materials sensitive to photoexcitation.

Recent observational constraints on the internal structure of Jupiter and Saturn suggest that these planets have ``fuzzy" cores, i.e., radial gradients of the concentration of heavy elements that might span $50\%$ to $70\%$ of each planet's radius. These cores could be composed of a semi-convective staircase, i.e., multiple convective layers separated by diffusive interfaces arising from double-diffusive instabilities. However, to date, no study has demonstrated how such staircases can avoid layer mergers and persist over evolutionary time scales. In fact, previous work has found that these mergers occur rapidly, quickly leading to only a single convective layer. Using 3D simulations of convective staircases in non-rotating and rotating flows, we demonstrate that rotation prolongs the lifetime of a convective staircase by increasing the timescale for both layer merger and erosion of the interface between the final two layers. We present an analytic model for the erosion phase, predicting that rotation increases the erosion time by a factor of approximately $\mathrm{Ro}^{-1/2}$, where $\mathrm{Ro}$ is the Rossby number of the convective flows (the ratio of the rotation period to the convective turnover time). For Jovian conditions at early times after formation (when convection is vigorous enough to mix a large fraction of the planet), we find the erosion time to be roughly $10^{9}~\mathrm{yrs}$ in the non-rotating case and $10^{11}~\mathrm{yrs}$ in the rotating case. Thus, the current existence of convective staircases within the deep interiors of giant planets is a strong possibility, and rotation could be an important factor in the preservation of their fuzzy cores., Comment: Submitted to AAS Journals, Comments welcome

Topological insulators were originally discovered for electron waves in condensed matter systems. Recently this concept has been transferred to bosonic systems such as photons and phonons, which propagate in materials patterned with artificial lattices that emulate spin-Hall physics. This work has been motivated, in part, by the prospect of topologically protected transport along edge channels in on-chip circuits. Importantly, even in principle, topology protects propagation against backscattering, but not against loss, which has remained limited to the dB/cm-level for phonon waveguides, be they topological or not. Here, we combine advanced dissipation engineering, in particular the recently introduced method of soft-clamping, with the concept of a valley-Hall topological insulator for phonons. This enables on-chip phononic waveguides with propagation losses of 3 dB/km at room temperature, orders of magnitude below any previous chip-scale devices. For the first time, the low losses also allow us to accurately quantify backscattering protection in a topological phonon waveguide, using high-resolution ultrasound spectroscopy. We infer that phonons follow a sharp, 120 degree-bend with a 99.99%-probability instead of being scattered back, and less than one phonon in a million is lost. The extraordinary combination of features of this novel platform suggest applications in classical and quantum signal routing, processing, and storage., Comment: Main manuscript (16 pages, 4 figures) and supplementary information (19 pages, 10 figures)

Electroencephalography (EEG) research typically focuses on tasks with narrowly defined objectives, but recent studies are expanding into the use of unlabeled data within larger models, aiming for a broader range of applications. This addresses a critical challenge in EEG research. For example, Kostas et al. (2021) show that self-supervised learning (SSL) outperforms traditional supervised methods. Given the high noise levels in EEG data, we argue that further improvements are possible with additional preprocessing. Current preprocessing methods often fail to efficiently manage the large data volumes required for SSL, due to their lack of optimization, reliance on subjective manual corrections, and validation processes or inflexible protocols that limit SSL. We propose a Python-based EEG preprocessing pipeline optimized for self-supervised learning, designed to efficiently process large-scale data. This optimization not only stabilizes self-supervised training but also enhances performance on downstream tasks compared to training with raw data., Comment: To appear in proceedings of 2024 IEEE International workshop on Machine Learning for Signal Processing

Ascent sequences play a key role in the combinatorics of Fishburn structures. Difference ascent sequences are a natural generalization obtained by replacing ascents with $d$-ascents. We have recently extended the so-called hat map to difference ascent sequences, and self-modified difference ascent sequences are the fixed points under this map. We characterize self-modified difference ascent sequences and enumerate them in terms of certain generalized Fibonacci polynomials. Furthermore, we describe the corresponding subset of $d$-Fishburn permutations., Comment: 22 pages, 2 tables

We study the excitation spectrum of the $S=1/2$ Heisenberg model on the randomly diluted square lattice by analytic continuation of QMC data. At dilution fractions $p=1/16$ and $p=1/8$, the dynamic structure factor $S({\bf q},\omega)$ exhibits a damped magnon peak with anomalous dispersion near ${\bf q}=(0,0)$ and $(\pi,\pi)$, a non-dispersive low-energy localization peak, and a second dispersive peak between these two features. A magnon with anomalous dispersion, close to our result, was predicted in spin wave and $T$-matrix theory [A. Chernyshev et al., PRB {\bf 65}, 104407 (2002)], above the localization energy. However, no intermediate dispersive mode was predicted. Analyzing spectral functions in real space for individual vacancy realizations by energy tomography, we find that these excitations are concentrated on a small subset of the spins adjacent to vacancies. We argue that the low-energy excitations are those of a sparse random network of effective moments at a fraction of the vacancies. There is a shift in magnon spectral weight distribution, from the spins away from vacancies at high energy to those adjacent to vacancies at lower energy. We also analyze the Anderson quantum rotor excitation at $\omega \propto N^{-1}$ (with $N=L^2$ the system size), which in the clean system is visible in $S({\bf q},\omega)$ only at ${\bf q}=(\pi,\pi)$ but spreads through the Brillouin zone when $p>0$. Weight close to ${\bf q}=(0,0)$ and $(\pi,\pi)$ is explained by local sublattice imbalance within a dimer-monomer model but there is also structure arising from correlated singlet fluctuations, which we demonstrate by enhancing said fluctuations with four-spin couplings. All spectral features found here should be observable by elastic neutron scattering experiments on layered quantum antiferromagnets doped with nonmagnetic impurities., Comment: 35 pages, 37 figures

Given a pointed metric space $(X,\mathsf{dist}, w)$ on $n$ points, its Gromov's approximating tree is a 0-hyperbolic pseudo-metric space $(X,\mathsf{dist}_T)$ such that $\mathsf{dist}(x,w)=\mathsf{dist}_T(x,w)$ and $\mathsf{dist}(x, y)-2 \delta \log_2n \leq \mathsf{dist}_T (x, y) \leq \mathsf{dist}(x, y)$ for all $x, y \in X$ where $\delta$ is the Gromov hyperbolicity of $X$. On the other hand, the all pairs bottleneck paths (APBP) problem asks, given an undirected graph with some capacities on its edges, to find the maximal path capacity between each pair of vertices. In this note, we prove: $\bullet$ Computing Gromov's approximating tree for a metric space with $n+1$ points from its matrix of distances reduces to solving the APBP problem on an connected graph with $n$ vertices. $\bullet$ There is an explicit algorithm that computes Gromov's approximating tree for a graph from its adjacency matrix in quadratic time. $\bullet$ Solving the APBP problem on a weighted graph with $n$ vertices reduces to finding Gromov's approximating tree for a metric space with $n+1$ points from its distance matrix., Comment: Version 1. Comments are welcome

We determine the three-loop $\overline{\text{MS}}$ quartic $ \beta $-function for the most general renormalisable four-dimensional theories. A general parametrization of the $ \beta $-function is compared to known $ \beta $-functions for specific theories to fix all coefficients. Three-loop $ \beta $-functions for the cubic coupling and scalar mass terms also follow from the result, which is made available in software packages., Comment: 30 pages

Congenital malformations of the brain are among the most common fetal abnormalities that impact fetal development. Previous anomaly detection methods on ultrasound images are based on supervised learning, rely on manual annotations, and risk missing underrepresented categories. In this work, we frame fetal brain anomaly detection as an unsupervised task using diffusion models. To this end, we employ an inpainting-based Noise Agnostic Anomaly Detection approach that identifies the abnormality using diffusion-reconstructed fetal brain images from multiple noise levels. Our approach only requires normal fetal brain ultrasound images for training, addressing the limited availability of abnormal data. Our experiments on a real-world clinical dataset show the potential of using unsupervised methods for fetal brain anomaly detection. Additionally, we comprehensively evaluate how different noise types affect diffusion models in the fetal anomaly detection domain., Comment: Accepted at ASMUS@MICCAI 2024

We introduce the Consistent Reasoning Paradox (CRP). Consistent reasoning, which lies at the core of human intelligence, is the ability to handle tasks that are equivalent, yet described by different sentences ('Tell me the time!' and 'What is the time?'). The CRP asserts that consistent reasoning implies fallibility -- in particular, human-like intelligence in AI necessarily comes with human-like fallibility. Specifically, it states that there are problems, e.g. in basic arithmetic, where any AI that always answers and strives to mimic human intelligence by reasoning consistently will hallucinate (produce wrong, yet plausible answers) infinitely often. The paradox is that there exists a non-consistently reasoning AI (which therefore cannot be on the level of human intelligence) that will be correct on the same set of problems. The CRP also shows that detecting these hallucinations, even in a probabilistic sense, is strictly harder than solving the original problems, and that there are problems that an AI may answer correctly, but it cannot provide a correct logical explanation for how it arrived at the answer. Therefore, the CRP implies that any trustworthy AI (i.e., an AI that never answers incorrectly) that also reasons consistently must be able to say 'I don't know'. Moreover, this can only be done by implicitly computing a new concept that we introduce, termed the 'I don't know' function -- something currently lacking in modern AI. In view of these insights, the CRP also provides a glimpse into the behaviour of Artificial General Intelligence (AGI). An AGI cannot be 'almost sure', nor can it always explain itself, and therefore to be trustworthy it must be able to say 'I don't know'., Comment: 12 pages and 50 pages of supplementary material, 7 figures

Given an undirected, weighted $n$-vertex graph $G = (V, E, w)$, a Gomory-Hu tree $T$ is a weighted tree on $V$ such that for any pair of distinct vertices $s, t \in V$, the Min-$s$-$t$-Cut on $T$ is also a Min-$s$-$t$-Cut on $G$. Computing a Gomory-Hu tree is a well-studied problem in graph algorithms and has received considerable attention. In particular, a long line of work recently culminated in constructing a Gomory-Hu tree in almost linear time [Abboud, Li, Panigrahi and Saranurak, FOCS 2023]. We design a differentially private (DP) algorithm that computes an approximate Gomory-Hu tree. Our algorithm is $\varepsilon$-DP, runs in polynomial time, and can be used to compute $s$-$t$ cuts that are $\tilde{O}(n/\varepsilon)$-additive approximations of the Min-$s$-$t$-Cuts in $G$ for all distinct $s, t \in V$ with high probability. Our error bound is essentially optimal, as [Dalirrooyfard, Mitrovi\'c and Nevmyvaka, NeurIPS 2023] showed that privately outputting a single Min-$s$-$t$-Cut requires $\Omega(n)$ additive error even with $(1, 0.1)$-DP and allowing for a multiplicative error term. Prior to our work, the best additive error bounds for approximate all-pairs Min-$s$-$t$-Cuts were $O(n^{3/2}/\varepsilon)$ for $\varepsilon$-DP [Gupta, Roth and Ullman, TCC 2012] and $O(\sqrt{mn} \cdot \text{polylog}(n/\delta) / \varepsilon)$ for $(\varepsilon, \delta)$-DP [Liu, Upadhyay and Zou, SODA 2024], both of which are implied by differential private algorithms that preserve all cuts in the graph. An important technical ingredient of our main result is an $\varepsilon$-DP algorithm for computing minimum Isolating Cuts with $\tilde{O}(n / \varepsilon)$ additive error, which may be of independent interest.

MoonLITE (Lunar InTerferometry Explorer) is an Astrophysics Pioneers proposal to develop, build, fly, and operate the first separated-aperture optical interferometer in space, delivering sub-mas science results. MoonLITE will leverage the Pioneers opportunity for utilizing NASA's Commercial Lunar Payload Services (CLPS) to deliver an optical interferometer to the lunar surface, enabling unprecedented discovery power by combining high spatial resolution from optical interferometry with deep sensitivity from the stability of the lunar surface. Following landing, the CLPS-provided rover will deploy the pre-loaded MoonLITE outboard optical telescope 100 meters from the lander's inboard telescope, establishing a two-element interferometric observatory with a single deployment. MoonLITE will observe targets as faint as 17th magnitude in the visible, exceeding ground-based interferometric sensitivity by many magnitudes, and surpassing space-based optical systems resolution by a factor of 50 times. The capabilities of MoonLITE open a unique discovery space that includes direct size measurements of the smallest, coolest stars and substellar brown dwarfs; searches for close-in stellar companions orbiting exoplanet-hosting stars that could confound our understanding and characterization of the frequency of Earth-like planets; direct size measurements of young stellar objects and characterization of the terrestrial planet-forming regions of these young stars; measurements of the inner regions and binary fraction of active galactic nuclei; and a probe of the very nature of spacetime foam itself. A portion of the observing time will also be made available to the broader community via a guest observer program. MoonLITE takes advantage of the CLPS opportunity and delivers an unprecedented combination of sensitivity and angular resolution at the remarkably affordable cost point of Pioneers., Comment: 13 pages, 8 figures, SPIE Astronomical Telescopes + Instrumentation 2024

The Big Fringe Telescope (BFT) is a facility concept under development for a next-generation, kilometer-scale optical interferometer. Observations over the past two decades from routinely operational facilities such as CHARA and VLTI have produced groundbreaking scientific results, reflecting the mature state of the techniques in optical interferometry. However, routine imaging of bright main sequence stars remains a surprisingly unexplored scientific realm. Additionally, the three-plus decade old technology infrastructure of these facilities leads to high operations \& maintenance costs, and limits performance. We are developing the BFT, based upon robust, modern, commercially-available, automated technologies with low capital construction and O\&M costs, in support of kilometer-scale optical interferometers that will open the door to regular `snapshot' imaging of main sequence stars. Focusing on extreme angular resolution for bright objects leads to substantial reductions in expected costs through use of COTS elements and simplified infrastructure., Comment: 11 pages, 3 figures, SPIE Astronomical Telescopes + Instrumentation 2024

Large Language Models are expressive tools that enable complex tasks of text understanding within Computational Social Science. Their versatility, while beneficial, poses a barrier for establishing standardized best practices within the field. To bring clarity on the values of different strategies, we present an overview of the performance of modern LLM-based classification methods on a benchmark of 23 social knowledge tasks. Our results point to three best practices: select models with larger vocabulary and pre-training corpora; avoid simple zero-shot in favor of AI-enhanced prompting; fine-tune on task-specific data, and consider more complex forms instruction-tuning on multiple datasets only when only training data is more abundant., Comment: 5 pages, 1 table

The rapid advancements in ultrafast laser technology have paved the way for pumping and probing the out-of-equilibrium dynamics of nuclei in crystals. However, interpreting these experiments is extremely challenging due to the complex nonlinear responses in systems where lattice excitations interact, particularly in crystals composed of light atoms or at low temperatures where the quantum nature of ions becomes significant. In this work, we address the nonequilibrium quantum ionic dynamics from first principles. Our approach is general and can be applied to simulate any crystal, in combination with a first-principles treatment of electrons or external machine-learning potentials. It is implemented by leveraging the nonequilibrium time-dependent self-consistent harmonic approximation (TD-SCHA), with a stable, energy-conserving, correlated stochastic integration scheme that achieves an accuracy of $\mathcal{O}(dt^3)$. We benchmark the method with both a simple one-dimensional model to test its accuracy and a realistic 40-atom cell of SrTiO3 under THz laser pump, paving the way for simulations of ultrafast THz-Xray pump-probe spectroscopy like those performed in synchrotron facilities.

A Cayley permutation is a word of positive integers such that if a letter appears in this word, then all positive integers smaller than that letter also appear. We initiate a systematic study of pattern avoidance on Cayley permutations adopting a combinatorial species approach. Our methods lead to species equations, generating series, and counting formulas for Cayley permutations avoiding any pattern of length at most three. We also introduce the species of primitive structures as a generalization of Cayley permutations with no "flat steps". Finally, we explore various notions of Wilf equivalence arising in this context., Comment: 32 pages, 3 figures, 1 table

An optimal control problem on finite-dimensional positive cones is stated. Under a critical assumption on the cone, the corresponding Bellman equation is satisfied by a linear function, which can be computed by convex optimization. A separate theorem relates the assumption on the cone to the existence of minimal elements in certain subsets of the dual cone. Three special cases are derived as examples. The first one, where the positive cone is the set of positive semi-definite matrices, reduces to standard linear quadratic control. The second one, where the positive cone is a polyhedron, reduces to a recent result on optimal control of positive systems. The third special case corresponds to linear quadratic control with additional structure, such as spatial invariance., Comment: 16 pages, to be published in the proceedings for the 2024 Conference on Decision and Control (CDC)

In this paper, we present a nonlinear model predictive control (NMPC) algorithm for systems modeled by semi-explicit stochastic differential-algebraic equations (DAEs) of index 1. The NMPC combines a continuous-discrete extended Kalman filter (CD-EKF) with an optimal control problem (OCP) for setpoint tracking. We discretize the OCP using direct multiple shooting. We apply an explicit singly diagonal implicit Runge-Kutta (ESDIRK) integration scheme to solve systems of DAEs, both for the one-step prediction in the CD-EKF and in each shooting interval of the discretized OCP. The ESDIRK methods use an iterated internal numerical differentiation approach for precise sensitivity computations. These sensitivities are used to provide accurate gradient information in the OCP and to efficiently integrate the covariance information in the CD-EKF. Subsequently, we present a simulation case study where we apply the NMPC to a simple alkaline electrolyzer stack model. We use the NMPC to track a time-varying setpoint for the stack temperature subject to input bound constraints.

This study presents the design, discretization and implementation of the continuous-time linear-quadratic model predictive control (CT-LMPC). The control model of the CT-LMPC is parameterized as transfer functions with time delays, and they are separated into deterministic and stochastic parts for relevant control and filtering algorithms. We formulate time-delay, finite-horizon CT linear-quadratic optimal control problems (LQ-OCPs) for the CT-LMPC. By assuming piece-wise constant inputs and constraints, we present the numerical discretization of the proposed LQ-OCPs and show how to convert the discrete-time (DT) equivalent into a standard quadratic program. The performance of the CT-LMPC is compared with the conventional DT-LMPC algorithm. Our numerical experiments show that, under fixed tunning parameters, the CT-LMPC shows better closed-loop performance as the sampling time increases than the conventional DT-LMPC.

Spherical or omni-directional images offer an immersive visual format appealing to a wide range of computer vision applications. However, geometric properties of spherical images pose a major challenge for models and metrics designed for ordinary 2D images. Here, we show that direct application of Fr\'echet Inception Distance (FID) is insufficient for quantifying geometric fidelity in spherical images. We introduce two quantitative metrics accounting for geometric constraints, namely Omnidirectional FID (OmniFID) and Discontinuity Score (DS). OmniFID is an extension of FID tailored to additionally capture field-of-view requirements of the spherical format by leveraging cubemap projections. DS is a kernel-based seam alignment score of continuity across borders of 2D representations of spherical images. In experiments, OmniFID and DS quantify geometry fidelity issues that are undetected by FID., Comment: Accepted at ECCV 2024

Tests based on the $2$- and $\infty$-norm have received considerable attention in high-dimensional testing problems, as they are powerful against dense and sparse alternatives, respectively. The power enhancement principle of Fan et al. (2015) combines these two norms to construct tests that are powerful against both types of alternatives. Nevertheless, the $2$- and $\infty$-norm are just two out of the whole spectrum of $p$-norms that one can base a test on. In the context of testing whether a candidate parameter satisfies a large number of moment equalities, we construct a test that harnesses the strength of all $p$-norms with $p\in[2, \infty]$. As a result, this test consistent against strictly more alternatives than any test based on a single $p$-norm. In particular, our test is consistent against more alternatives than tests based on the $2$- and $\infty$-norm, which is what most implementations of the power enhancement principle target. We illustrate the scope of our general results by using them to construct a test that simultaneously dominates the Anderson-Rubin test (based on $p=2$) and tests based on the $\infty$-norm in terms of consistency in the linear instrumental variable model with many (weak) instruments.

An oriented graph $D$ is converse invariant if, for any tournament $T$, the number of copies of $D$ in $T$ is equal to that of its converse $-D$. El Sahili and Ghazo Hanna [J. Graph Theory 102 (2023), 684-701] showed that any oriented graph $D$ with maximum degree at most 2 is converse invariant. They proposed a question: Can we characterize all converse invariant oriented graphs? In this paper, we introduce a digraph polynomial and employ it to give a necessary condition for an oriented graph to be converse invariant. This polynomial serves as a cornerstone in proving all the results presented in this paper. In particular, we characterize all orientations of trees with diameter at most 3 that are converse invariant. We also show that all orientations of regular graphs are not converse invariant if $D$ and $-D$ have different degree sequences. In addition, in contrast to the findings of El Sahili and Ghazo Hanna, we prove that every connected graph $G$ with maximum degree at least $3$, admits an orientation $D$ of $G$ such that $D$ is not converse invariant. We pose one conjecture.

Fast decoding algorithms are decisive for real-time quantum error correction and for analyzing properties of error correction codes. Here, we develop variants of the union-find decoder that simplify its implementation and provide potential decoding speed advantages. Furthermore, we show how these methods can be adapted to decode non-topological quantum low-density-parity-check (qLDPC) codes. All the developed decoders can directly include both qubit erasures and Pauli errors in the decoding step, thus addressing the dominant noise mechanisms for photonic quantum computing. We investigate the strengths and weaknesses of the different decoder variants, benchmark their speed and threshold error rates on several codes, and provide the used source code.

As the number of people affected by diseases in the gastrointestinal system is ever-increasing, a higher demand on preventive screening is inevitable. This will significantly increase the workload on gastroenterologists. To help reduce the workload, tools from computer vision may be helpful. In this paper, we investigate the possibility of constructing 3D models of whole sections of the human colon using image sequences from wireless capsule endoscope video, providing enhanced viewing for gastroenterologists. As capsule endoscope images contain distortion and artifacts non-ideal for many 3D reconstruction algorithms, the problem is challenging. However, recent developments of virtual graphics-based models of the human gastrointestinal system, where distortion and artifacts can be enabled or disabled, makes it possible to ``dissect'' the problem. The graphical model also provides a ground truth, enabling computation of geometric distortion introduced by the 3D reconstruction method. In this paper, most distortions and artifacts are left out to determine if it is feasible to reconstruct whole sections of the human gastrointestinal system by existing methods. We demonstrate that 3D reconstruction is possible using simultaneous localization and mapping. Further, to reconstruct the gastrointestinal wall surface from resulting point clouds, varying greatly in density, Poisson surface reconstruction is a good option. The results are promising, encouraging further research on this problem., Comment: 11 pages, 12 figures

Large language models (LLMs) are very performant connectionist systems, but do they exhibit more compositionality? More importantly, is that part of why they perform so well? We present empirical analyses across four LLM families (12 models) and three task categories, including a novel task introduced below. Our findings reveal a nuanced relationship in learning of compositional strategies by LLMs -- while scaling enhances compositional abilities, instruction tuning often has a reverse effect. Such disparity brings forth some open issues regarding the development and improvement of large language models in alignment with human cognitive capacities., Comment: Accepted to ICML 2024 Workshop on LLMs & Cognition

There is a broad consensus that artificial intelligence (AI) will be a defining component of the sixth-generation (6G) networks. As a specific instance, AI-empowered sensing will gather and process environmental perception data at the network edge, giving rise to integrated sensing and edge AI (ISEA). Many applications, such as autonomous driving and industrial manufacturing, are latency-sensitive and require end-to-end (E2E) performance guarantees under stringent deadlines. However, the 5G-style ultra-reliable and low-latency communication (URLLC) techniques designed with communication reliability and agnostic to the data may fall short in achieving the optimal E2E performance of perceptive wireless systems. In this work, we introduce an ultra-low-latency (ultra-LoLa) inference framework for perceptive networks that facilitates the analysis of the E2E sensing accuracy in distributed sensing by jointly considering communication reliability and inference accuracy. By characterizing the tradeoff between packet length and the number of sensing observations, we derive an efficient optimization procedure that closely approximates the optimal tradeoff. We validate the accuracy of the proposed method through experimental results, and show that the proposed ultra-Lola inference framework outperforms conventional reliability-oriented protocols with respect to sensing performance under a latency constraint.