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We study the potential of a set of future detectors, proposed to be located at the European Spallation Source (ESS), to probe neutrino millicharges through coherent elastic neutrino-nucleus scattering. In particular, we focus on detectors with similar characteristics as those that are under development for operation at the ESS, including detection technologies based on cesium iodine, germanium, and noble gases. Under the considered conditions, we show that the Ge detector, with a lighter nuclear target mass with respect to CsI and to a noble gas like Xe, is more efficient to constrain neutrino millicharges, reaching a sensitivity of $\sim 10^{-9}e$ for diagonal neutrino millicharges, and $\sim 10^{-8}e$ for the transition ones. In addition, we study the effects of including electron scattering processes for the CsI detector, achieving an expected sensitivity of $\sim 10^{-10} e$ for the diagonal millicharges., Comment: 21 pages, 6 Figures

In this work, we explore the use and reliability of Large Language Models (LLMs) in musicology. From a discussion with experts and students, we assess the current acceptance and concerns regarding this, nowadays ubiquitous, technology. We aim to go one step further, proposing a semi-automatic method to create an initial benchmark using retrieval-augmented generation models and multiple-choice question generation, validated by human experts. Our evaluation on 400 human-validated questions shows that current vanilla LLMs are less reliable than retrieval augmented generation from music dictionaries. This paper suggests that the potential of LLMs in musicology requires musicology driven research that can specialized LLMs by including accurate and reliable domain knowledge.

This work presents the results of modelling the ion dynamics in the ART-MS (Autoresonant Trap Mass Spectrometry) device in the quasi-static approximation. This instrument utilizes an anharmonic, purely electrostatic trap for ion confinement and a radio frequency (RF) voltage source with decrementally varying frequency for selective ion extraction. The autoresonant interaction between the oscillatory motion of the ion and the RF voltage causes an increase in the amplitude of certain confined ions, allowing their selective extraction. Numerical modelling shows that the extraction of ions with a given mass occurs not only at the fundamental frequency but also at its harmonics. This effect reduces the selective properties of devices of this type because along with the main mass component for a given frequency, it is possible to enter the detector channel of ions with another mass, for which this frequency corresponds to the second or higher harmonics, even a superposition of some of these harmonics of different ions., Comment: 9 pages, 6 figures

Ptychography is a popular imaging technique that combines diffractive imaging with scanning microscopy. The technique consists of a coherent beam that is scanned across an object in a series of overlapping positions, leading to reliable and improved reconstructions. Ptychographic microscopes allow for large fields to be imaged at high resolution at additional computational expense. In this work, we explore the use of the fast Partial Fourier Transforms (PFTs), which efficiently compute Fourier coefficients corresponding to low frequencies. The core idea is to use the PFT in a plug-and-play manner to warm-start existing ptychography algorithms such as the ptychographic iterative engine (PIE). This approach reduces the computational budget required to solve the ptychography problem. Our numerical results show that our scheme accelerates the convergence of traditional solvers without sacrificing quality of reconstruction.

Estimating music piece difficulty is important for organizing educational music collections. This process could be partially automatized to facilitate the educator's role. Nevertheless, the decisions performed by prevalent deep-learning models are hardly understandable, which may impair the acceptance of such a technology in music education curricula. Our work employs explainable descriptors for difficulty estimation in symbolic music representations. Furthermore, through a novel parameter-efficient white-box model, we outperform previous efforts while delivering interpretable results. These comprehensible outcomes emulate the functionality of a rubric, a tool widely used in music education. Our approach, evaluated in piano repertoire categorized in 9 classes, achieved 41.4% accuracy independently, with a mean squared error (MSE) of 1.7, showing precise difficulty estimation. Through our baseline, we illustrate how building on top of past research can offer alternatives for music difficulty assessment which are explainable and interpretable. With this, we aim to promote a more effective communication between the Music Information Retrieval (MIR) community and the music education one.

An elusive goal in navigation research is to build an intelligent agent that can understand multimodal instructions including natural language and image, and perform useful navigation. To achieve this, we study a widely useful category of navigation tasks we call Multimodal Instruction Navigation with demonstration Tours (MINT), in which the environment prior is provided through a previously recorded demonstration video. Recent advances in Vision Language Models (VLMs) have shown a promising path in achieving this goal as it demonstrates capabilities in perceiving and reasoning about multimodal inputs. However, VLMs are typically trained to predict textual output and it is an open research question about how to best utilize them in navigation. To solve MINT, we present Mobility VLA, a hierarchical Vision-Language-Action (VLA) navigation policy that combines the environment understanding and common sense reasoning power of long-context VLMs and a robust low-level navigation policy based on topological graphs. The high-level policy consists of a long-context VLM that takes the demonstration tour video and the multimodal user instruction as input to find the goal frame in the tour video. Next, a low-level policy uses the goal frame and an offline constructed topological graph to generate robot actions at every timestep. We evaluated Mobility VLA in a 836m^2 real world environment and show that Mobility VLA has a high end-to-end success rates on previously unsolved multimodal instructions such as "Where should I return this?" while holding a plastic bin. A video demonstrating Mobility VLA can be found here: https://youtu.be/-Tof__Q8_5s

State-of-the art Text-To-Music (TTM) generative AI models are large and require desktop or server class compute, making them infeasible for deployment on mobile phones. This paper presents an analysis of trade-offs between model compression and generation performance of TTM models. We study compression through knowledge distillation and specific modifications that enable applicability over the various components of the TTM model (encoder, generative model and the decoder). Leveraging these methods we create TinyTTM (89.2M params) that achieves a FAD of 3.66 and KL of 1.32 on MusicBench dataset, better than MusicGen-Small (557.6M params) but not lower than MusicGen-small fine-tuned on MusicBench., Comment: Proceedings of INTERSPEECH 2024

QCD gives rise to a rich spectrum of excited baryon states. Understanding their internal structure is important for many areas of nuclear physics, such as nuclear forces, dense matter, and neutrino-nucleus interactions. Generalized parton distributions (GPDs) are an established tool for characterizing the QCD structure of the ground-state nucleon. They are used to create 3D tomographic images of the quark/gluon structure and quantify the mechanical properties such as the distribution of mass, angular momentum and forces in the system. Transition GPDs extend these concepts to $N \rightarrow N^\ast$ transitions and can be used to characterize the 3D structure and mechanical properties of baryon resonances. They can be probed in high-momentum-transfer exclusive electroproduction processes with resonance transitions $e + N \rightarrow e' + M + N^\ast$, such as deeply-virtual Compton scattering ($M = \gamma$) or meson production ($M = \pi, K$, $etc.$), and in related photon/hadron-induced processes. This White Paper describes a research program aiming to explore baryon resonance structure with transition GPDs. This includes the properties and interpretation of the transition GPDs, theoretical methods for structures and processes, first experimental results from JLab 12 GeV, future measurements with existing and planned facilities (JLab detector and energy upgrades, COMPASS/AMBER, EIC, EicC, J-PARC, LHC ultraperihperal collisions), and the theoretical and experimental developments needed to realize this program.

We investigate the neutrino elastic differential cross-section (NDCS) and corresponding mean free path for neutral current scattering in the dense matter of a neutron star. A wide range of observed neutron star (NS) masses is considered, including the presence of $\Lambda$, $\Xi^{-}$, and $\Xi^{0}$ hyperons in the heaviest stars. Their presence significantly decreases the total neutrino mean free path in the heavier stars., Comment: 14 pages, 7 figures, 1 table

Nonstandard interaction is expected to be a crucial hint to explain the experimental data of neutrino scattering off electrons. In this context, the nonstandard interaction vector and axial-vector couplings are needed to be extracted from recent experiments and a few of them are now available in the literature. With these coupling bounds, in this paper, I explore their impacts on the neutrinos interacting with the free electron gas in dense matter. To this end, I compute and predict the neutrino differential cross section and mean free path in dense matter for all those experimental bounds. Interesting behavior in the neutrino cross sections and mean free path is found for the different nonstandard interaction couplings from different experiment constraints. I also found that the neutrino cross-section and mean free path in the dense matter are very sensitive to values and signs of the nonstandard interaction couplings, leading to different prediction results to the Standard Model cross-section and mean free path., Comment: 14 pages, 8 figures, 1 table

In this paper, we study the nuclear medium modifications of the meson electromagnetic form factors in the framework of the Nambu--Jona-Lasinio (NJL) model with the help of the Schwinger proper-time regularization scheme to tame loop divergence and simulate the effect of QCD confinement. In our current approach, the meson structure and nuclear medium are constructed in the same NJL model at the quark level. We examine the free space and in-medium charge radii for the kaon and pion, in addition to the spacelike elastic electromagnetic form factors of the kaon and pion as well as their quark-sector form factors, which reflect their internal structure. By comparing to the experimental data, we found that the free space elastic electromagnetic form factors for the mesons are consistent with the data, while the in-medium elastic electromagnetic form factors of the mesons are found to decrease as the nuclear matter density increases, leading to increase of meson charge radius, which is consistent with the prediction of other theory calculations. We also predict the axial nucleon coupling constant $g_A$ in nuclear medium computed via the Goldberger-Treiman relation (GTR), which is crucial for the search for the neutrinoless double beta decay ($0\nu \beta \beta$)., Comment: 13 pages, 13 figures, 2 tables

We introduce the $k$-Plane Insertion into Plane drawing ($k$-PIP) problem: given a plane drawing of a planar graph $G$ and a set $F$ of edges, insert the edges in $F$ into the drawing such that the resulting drawing is $k$-plane. In this paper, we show that the problem is NP-complete for every $k\ge 1$, even when $G$ is biconnected and the set $F$ of edges forms a matching or a path. On the positive side, we present a linear-time algorithm for the case that $k=1$ and $G$ is a triangulation.

In this present investigation, we explore the elastic scattering of pions with nuclei ($\pi$-$A$), primarily influenced by the $\Delta$(1232) resonance, within the Eikonal-Glauber model. The medium effects are incorporated by considering nuclear-density ($\rho_A$) dependent masses of baryons and strong coupling constants. These dependencies are computed and parameterized up to $\mathcal{O}(\rho_A^2)$ based on the quark-meson coupling (QMC) model. The Wood-Saxon type density profile is utilized for the bound nucleons within finite nuclei. The element $\pi^+$-$N$ scattering cross section for the Glauber approach is determined using the conventional effective Lagrangian method. Subsequently, we analyze the total cross sections for elastic scattering with $^4$He and $^{12}$C targets. Our numerical results demonstrate a favorable agreement with JINR data for the $^4$He target, accurately reproducing the total cross-section. However, when considering the $^{12}$C target, deviations of approximately $\lesssim10\%$. We also consider the multiple-scattering effects inside the nucleus approximately, using the single-channel meson-baryon Bethe-Salpeter equation, resulting in the effective width broadening of the $\Delta$ resonance to reproduce the data better., Comment: 8 pages, 4 figures

Imagine you are a dog behind a fence $Q$ and a hiker is passing by at constant speed along the hiking path $P$. In order to fulfil your duties as a watchdog, you desire to bark as long as possible at the human. However, your barks can only be heard in a fixed radius $\rho$ and, as a dog, you have bounded speed $s$. Can you optimize your route along the fence $Q$ in order to maximize the barking time with radius $\rho$, assuming you can run backwards and forward at speed at most $s$? We define the barking distance from a polyline $P$ on $n$ vertices to a polyline $Q$ on $m$ vertices as the time that the hiker stays in your barking radius if you run optimally along $Q$. This asymmetric similarity measure between two curves can be used to detect outliers in $Q$ compared to $P$ that other established measures like the Fr\'echet distance and Dynamic Time Warping fail to capture at times. We consider this measure in three different settings. In the discrete setting, the traversals of $P$ and $Q$ are both discrete. For this case we show that the barking distance from $P$ to $Q$ can be computed in $O(nm\log s)$ time. In the semi-discrete setting, the traversal of $Q$ is continuous while the one of $P$ is again discrete. Here, we show how to compute the barking distance in time $O(nm\log (nm))$. Finally, in the continuous setting in which both traversals are continuous, we show that the problem can be solved in polynomial time. For all the settings we show that, assuming SETH, no truly subquadratic algorithm can exist.

A geometric $t$-spanner $\mathcal{G}$ on a set $S$ of $n$ point sites in a metric space $P$ is a subgraph of the complete graph on $S$ such that for every pair of sites $p,q$ the distance in $\mathcal{G}$ is a most $t$ times the distance $d(p,q)$ in $P$. We call a connection between two sites a \emph{link}. In some settings, such as when $P$ is a simple polygon with $m$ vertices and a link is a shortest path in $P$, links can consist of $\Theta (m)$ segments and thus have non-constant complexity. The spanner complexity is a measure of how compact a spanner is, which is equal to the sum of the complexities of all links in the spanner. In this paper, we study what happens if we are allowed to introduce $k$ Steiner points to reduce the spanner complexity. We study such Steiner spanners in simple polygons, polygonal domains, and edge-weighted trees. We show that Steiner points have only limited utility. For a spanner that uses $k$ Steiner points, we provide an $\Omega(mn^{1/(t+1)}/k^{1/(t+1)})$ lower bound on the worst-case complexity of any $(t-\varepsilon)$-spanner, for any constant $\varepsilon \in (0,1)$ and integer constant $t \geq 2$. Additionally, we show NP-hardness for the problem of deciding whether a set of sites in a polygonal domain admits a $3$-spanner with a given maximum complexity using $k$ Steiner points. On the positive side, for trees we show how to build a $2t$-spanner that uses $k$ Steiner points of complexity $O(mn^{1/t}/k^{1/t} + n \log (n/k))$, for any integer $t \geq 1$. We generalize this to forests, and use it to obtain a $2\sqrt{2}t$-spanner in a simple polygon with complexity $O(mn^{1/t}(\log k)^{1+1/t}/k^{1/t} + n\log^2 n)$. When a link can be any path between two sites, we show how to improve the spanning ratio to $(2k+\varepsilon)$, for any constant $\varepsilon \in (0,2k)$, and how to build a $6t$-spanner in a polygonal domain with the same complexity., Comment: 25 pages, 10 figures

Large language models (LLMs) have been shown to exhibit a wide range of capabilities, such as writing robot code from language commands -- enabling non-experts to direct robot behaviors, modify them based on feedback, or compose them to perform new tasks. However, these capabilities (driven by in-context learning) are limited to short-term interactions, where users' feedback remains relevant for only as long as it fits within the context size of the LLM, and can be forgotten over longer interactions. In this work, we investigate fine-tuning the robot code-writing LLMs, to remember their in-context interactions and improve their teachability i.e., how efficiently they adapt to human inputs (measured by average number of corrections before the user considers the task successful). Our key observation is that when human-robot interactions are viewed as a partially observable Markov decision process (in which human language inputs are observations, and robot code outputs are actions), then training an LLM to complete previous interactions is training a transition dynamics model -- that can be combined with classic robotics techniques such as model predictive control (MPC) to discover shorter paths to success. This gives rise to Language Model Predictive Control (LMPC), a framework that fine-tunes PaLM 2 to improve its teachability on 78 tasks across 5 robot embodiments -- improving non-expert teaching success rates of unseen tasks by 26.9% while reducing the average number of human corrections from 2.4 to 1.9. Experiments show that LMPC also produces strong meta-learners, improving the success rate of in-context learning new tasks on unseen robot embodiments and APIs by 31.5%. See videos, code, and demos at: https://robot-teaching.github.io/.

People employ expressive behaviors to effectively communicate and coordinate their actions with others, such as nodding to acknowledge a person glancing at them or saying "excuse me" to pass people in a busy corridor. We would like robots to also demonstrate expressive behaviors in human-robot interaction. Prior work proposes rule-based methods that struggle to scale to new communication modalities or social situations, while data-driven methods require specialized datasets for each social situation the robot is used in. We propose to leverage the rich social context available from large language models (LLMs) and their ability to generate motion based on instructions or user preferences, to generate expressive robot motion that is adaptable and composable, building upon each other. Our approach utilizes few-shot chain-of-thought prompting to translate human language instructions into parametrized control code using the robot's available and learned skills. Through user studies and simulation experiments, we demonstrate that our approach produces behaviors that users found to be competent and easy to understand. Supplementary material can be found at https://generative-expressive-motion.github.io/.

Automatic Speech Recognition (ASR) still face challenges when recognizing time-variant rare-phrases. Contextual biasing (CB) modules bias ASR model towards such contextually-relevant phrases. During training, a list of biasing phrases are selected from a large pool of phrases following a sampling strategy. In this work we firstly analyse different sampling strategies to provide insights into the training of CB for ASR with correlation plots between the bias embeddings among various training stages. Secondly, we introduce a neighbourhood attention (NA) that localizes self attention (SA) to the nearest neighbouring frames to further refine the CB output. The results show that this proposed approach provides on average a 25.84% relative WER improvement on LibriSpeech sets and rare-word evaluation compared to the baseline., Comment: Accepted for IEEE ASRU 2023

On-device Automatic Speech Recognition (ASR) models trained on speech data of a large population might underperform for individuals unseen during training. This is due to a domain shift between user data and the original training data, differed by user's speaking characteristics and environmental acoustic conditions. ASR personalisation is a solution that aims to exploit user data to improve model robustness. The majority of ASR personalisation methods assume labelled user data for supervision. Personalisation without any labelled data is challenging due to limited data size and poor quality of recorded audio samples. This work addresses unsupervised personalisation by developing a novel consistency based training method via pseudo-labelling. Our method achieves a relative Word Error Rate Reduction (WERR) of 17.3% on unlabelled training data and 8.1% on held-out data compared to a pre-trained model, and outperforms the current state-of-the art methods., Comment: Accepted for IEEE ASRU 2023

In this work, we study the properties of sampling sets on families of large graphs by leveraging the theory of graphons and graph limits. To this end, we extend to graphon signals the notion of removable and uniqueness sets, which was developed originally for the analysis of signals on graphs. We state the formal definition of a $\Lambda-$removable set and conditions under which a bandlimited graphon signal can be represented in a unique way when its samples are obtained from the complement of a given $\Lambda-$removable set in the graphon. By leveraging such results we show that graphon representations of graphs and graph signals can be used as a common framework to compare sampling sets between graphs with different numbers of nodes and edges, and different node labelings. Additionally, given a sequence of graphs that converges to a graphon, we show that the sequences of sampling sets whose graphon representation is identical in $[0,1]$ are convergent as well. We exploit the convergence results to provide an algorithm that obtains approximately close to optimal sampling sets. Performing a set of numerical experiments, we evaluate the quality of these sampling sets. Our results open the door for the efficient computation of optimal sampling sets in graphs of large size.

The sliding cubes model is a well-established theoretical framework that supports the analysis of reconfiguration algorithms for modular robots consisting of face-connected cubes. The best algorithm currently known for the reconfiguration problem, by Abel and Kominers [arXiv, 2011], uses O(n3) moves to transform any n-cube configuration into any other n-cube configuration. As is common in the literature, this algorithm reconfigures the input into an intermediate canonical shape. In this paper we present an in-place algorithm that reconfigures any n-cube configuration into a compact canonical shape using a number of moves proportional to the sum of coordinates of the input cubes. This result is asymptotically optimal. Furthermore, our algorithm directly extends to dimensions higher than three.

We say that a (multi)graph $G = (V,E)$ has geometric thickness $t$ if there exists a straight-line drawing $\varphi : V \rightarrow \mathbb{R}^2$ and a $t$-coloring of its edges where no two edges sharing a point in their relative interior have the same color. The \textsc{Geometric Thickness} problem asks whether a given multigraph has geometric thickness at most $t$. This problem was shown to be NP-hard for $t=2$ [Durocher, Gethner, and Mondal, CG 2016]. In this paper, we settle the computational complexity of \textsc{Geometric Thickness} by showing that it is $\exists \mathbb{R}$-complete already for thickness $30$. Moreover, our reduction shows that the problem is $\exists \mathbb{R}$-complete for $4392$-planar graphs, where a graph is $k$-planar if it admits a topological drawing with at most $k$ crossings per edge. In the course of our paper, we answer previous questions on geometric thickness and on other related problems, in particular that simultaneous graph embeddings of $31$ edge-disjoint graphs and pseudo-segment stretchability with chromatic number $30$ are $\exists \mathbb{R}$-complete., Comment: 19 pages, 9 figures

In the present paper, I have investigated a neutral current (NC) antineutrinos scattering with neutron star (NS) matter constituents at zero temperature. The modeling of the standard matters in NS is constructed in the framework of both extended relativistic mean-field (E-RMF) and nonrelativistic Korea-IBS-Daegu-SKKU energy density functional (KIDS-EDF) models. In the E-RMF model, I use a new parameter of G3(M), which was constrained by the recent PREX II experiment measurement of neutron distribution of $^{208}\rm{Pb}$, while the KIDS-EDF models are constrained by terrestrial experiments, gravitational-wave signals, and astrophysical observations. Using both realistic and well-constrained matter models, I then calculate the antineutrino differential cross-section (ADCS) and antineutrino mean free path (AMFP) of the antineutrinos-NS matter constituents interaction using a linear response theory. It is found that the AMFP for the KIDS0 and KIDSA models are smaller in comparison to the SLy4 model and the E-RMF model with the G3(M) parameter. The AMFP result of the Skyrme model with the SLy4 parameter set is found to have an almost similar prediction to that of the E-RMF model with the G3(M) parameter. Contributions of each nucleon to total AMFP are also presented for the G3(M) model., Comment: 14 pages, 18 figures, and 3 tables, accepted in the Astronomy Journal

The quantity and types of biodiversity data being collected have increased in recent years. If we are to model and monitor biodiversity effectively, we need to respect how different data sets were collected, and effectively integrate these data types together. The framework that has emerged to do this is based on a point process formulation, with individuals as points and their distribution as a realisation of a random field. We describe this formulation and how the process model for the actual distribution is linked to the data that is collected through observation models. The observation models describe the data collection process and its uncertainties and biases. We provide an example of using these methods to model species of Norwegian freshwater fish, which shows how integrated models can be adapted to the data we can collect. We summarise the modelling issues that arise and the approaches that could be taken to solve them.

We present a novel numerical method for simulating the generation and propagation of surface gravity waves by vertical seabed displacements. The cornerstone of our method is the computation of a time dependent conformal map which incorporates the time dependent geometry of the seabed and the wave profile along the free surface. This enables us to handle general geometric configurations of the seabed and the wave. As benchmark we reproduce the results of Hammack on tsunami generation and propagation. Our results show that Hammack's linear theory accurately predicts wave generation. However, as the velocity of the sea bed displacement increases, nonlinear effects become increasingly noticeable. Notably, when the seabed uplift occurs rapidly, the following nonlinear dynamics of the wave differ significantly from the linear dynamics usually associated with tsunami propagation.

In this paper we discuss the results recently published in~[1] about algebraic signal models (ASMs) based on non commutative algebras and their use in convolutional neural networks. Relying on the general tools from algebraic signal processing (ASP), we study the filtering and stability properties of non commutative convolutional filters. We show how non commutative filters can be stable to small perturbations on the space of operators. We also show that although the spectral components of the Fourier representation in a non commutative signal model are associated to spaces of dimension larger than one, there is a trade-off between stability and selectivity similar to that observed for commutative models. Our results have direct implications for group neural networks, multigraph neural networks and quaternion neural networks, among other non commutative architectures. We conclude by corroborating these results through numerical experiments.

Anticipating the motion of all humans in dynamic environments such as homes and offices is critical to enable safe and effective robot navigation. Such spaces remain challenging as humans do not follow strict rules of motion and there are often multiple occluded entry points such as corners and doors that create opportunities for sudden encounters. In this work, we present a Transformer based architecture to predict human future trajectories in human-centric environments from input features including human positions, head orientations, and 3D skeletal keypoints from onboard in-the-wild sensory information. The resulting model captures the inherent uncertainty for future human trajectory prediction and achieves state-of-the-art performance on common prediction benchmarks and a human tracking dataset captured from a mobile robot adapted for the prediction task. Furthermore, we identify new agents with limited historical data as a major contributor to error and demonstrate the complementary nature of 3D skeletal poses in reducing prediction error in such challenging scenarios., Comment: Project page: https://human-scene-transformer.github.io/

Weakly supervised methods have emerged as a powerful tool for model-agnostic anomaly detection at the Large Hadron Collider (LHC). While these methods have shown remarkable performance on specific signatures such as di-jet resonances, their application in a more model-agnostic manner requires dealing with a larger number of potentially noisy input features. In this paper, we show that using boosted decision trees as classifiers in weakly supervised anomaly detection gives superior performance compared to deep neural networks. Boosted decision trees are well known for their effectiveness in tabular data analysis. Our results show that they not only offer significantly faster training and evaluation times, but they are also robust to a large number of noisy input features. By using advanced gradient boosted decision trees in combination with ensembling techniques and an extended set of features, we significantly improve the performance of weakly supervised methods for anomaly detection at the LHC. This advance is a crucial step towards a more model-agnostic search for new physics., Comment: 11 pages, 9 figures

The simplified hypothesis that an election is polarized as an explanation of recent electoral outcomes worldwide is centered on perceptions of voting patterns rather than ideological data from the electorate. While the literature focuses on measuring polarization using ideological-like data from electoral studies-which are limited to economically advantageous countries and are representative mostly to national scales-we argue that, in fact, voting patterns can lead to mapping effective proxies of citizen divisions on election day. This paper perspectives two complementary concepts, Election Polarization (EP) and Election Competitiveness (EC), as a means to understand voting patterns on Election Day. We present an agnostic approach that relies solely on election data and validate it using synthetic and real-world election data across 13 countries in the Eurozone, North America, Latin America, and New Zealand. Overall, we find that we can label and distinguish expectations of polarized and competitive elections in these countries, and we report that EP positively correlates with a metric of political polarization in the U.S., unlocking opportunities for studies of polarization at the regional level and for lower/middle-income countries where electoral studies are available, but surveys are limited.

Smart devices serviced by large-scale AI models necessitates user data transfer to the cloud for inference. For speech applications, this means transferring private user information, e.g., speaker identity. Our paper proposes a privacy-enhancing framework that targets speaker identity anonymization while preserving speech recognition accuracy for our downstream task~-~Automatic Speech Recognition (ASR). The proposed framework attaches flexible gradient reversal based speaker adversarial layers to target layers within an ASR model, where speaker adversarial training anonymizes acoustic embeddings generated by the targeted layers to remove speaker identity. We propose on-device deployment by execution of initial layers of the ASR model, and transmitting anonymized embeddings to the cloud, where the rest of the model is executed while preserving privacy. Experimental results show that our method efficiently reduces speaker recognition relative accuracy by 33%, and improves ASR performance by achieving 6.2% relative Word Error Rate (WER) reduction., Comment: Proceedings of INTERSPEECH 2023

Keyword Spotting (KWS) models on embedded devices should adapt fast to new user-defined words without forgetting previous ones. Embedded devices have limited storage and computational resources, thus, they cannot save samples or update large models. We consider the setup of embedded online continual learning (EOCL), where KWS models with frozen backbone are trained to incrementally recognize new words from a non-repeated stream of samples, seen one at a time. To this end, we propose Temporal Aware Pooling (TAP) which constructs an enriched feature space computing high-order moments of speech features extracted by a pre-trained backbone. Our method, TAP-SLDA, updates a Gaussian model for each class on the enriched feature space to effectively use audio representations. In experimental analyses, TAP-SLDA outperforms competitors on several setups, backbones, and baselines, bringing a relative average gain of 11.3% on the GSC dataset., Comment: INTERSPEECH 2023

In this paper, the implications of the symmetry energy on the hadron and quark phase transitions in the compact star, including the properties of the possible configurations of the quark-hadron hybrid stars, are investigated in the frameworks of the energy-density functional (EDF) models and the flavor SU(2) Nambu--Jona-Lasinio (NJL) model with the help of the Schwinger's covariant proper-time regularization (PTR) scheme. In this {theoretical setup}, the equations of states (EoSs) of hadronic matter for various values of symmetry energies obtained from the EDF models are employed to describe the hadronic matter, and the {flavor} SU(2) NJL model with various repulsive-vector interaction strengths are used to describe the quark matter. We then observe the obtained EoS in the mass-radius properties of the hybrid star configurations for various vector interactions and nuclear symmetry energies by solving the Tolman-Oppenheimer-Volkoff equation. We obtain that the critical density at which the phase transition occurs varies over the density (3.6--6.7)$\rho_0$ depending on the symmetry energy and the strength of the vector coupling $G_v$. The maximum mass of the neutron star (NS) is susceptible to $G_v$. When there is no repulsive force, the NS maximum mass is only about $1.5M_\odot$, but it becomes larger than $2.0M_\odot$ when the vector coupling constant is about half of the {attractive} scalar coupling constant. Surprisingly, the presence of the quark matter does not affect the canonical mass of NS ($1.4M_\odot$), so observing the canonical mass of NSs can provide unique constraints to the EoS of hadronic matter at high densities., Comment: 20 pages, 5 figures, 1 table