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The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

Large Language Models' knowledge of how to perform cyber-security attacks, create bioweapons, and manipulate humans poses risks of misuse. Previous work has proposed methods to unlearn this knowledge. Historically, it has been unclear whether unlearning techniques are removing information from the model weights or just making it harder to access. To disentangle these two objectives, we propose an adversarial evaluation method to test for the removal of information from model weights: we give an attacker access to some facts that were supposed to be removed, and using those, the attacker tries to recover other facts from the same distribution that cannot be guessed from the accessible facts. We show that using fine-tuning on the accessible facts can recover 88% of the pre-unlearning accuracy when applied to current unlearning methods, revealing the limitations of these methods in removing information from the model weights.

Pedestrian GPS data are key to a better understanding of micro-mobility and micro-behaviour within a neighbourhood. These data can bring new insights into walkability and livability in the context of urban sustainability. However, pedestrian open data are scarce and often lack a context for their transformation into actionable knowledge in a neighbourhood. Citizen science and public involvement practices are powerful instruments for obtaining these data and take a community-centred placemaking approach. The study shares some 3000 GPS recordings corresponding to 19 unique trajectories made and recorded by groups of participants from three distinct communities in a relatively small neighbourhood. The groups explored the neighbourhood through a number of tasks and chose different places to stop and perform various social and festive activities. The study shares not only raw data but also processed records with specific filtering and processing to facilitate and accelerate data usage. Citizen science practices and the data-collection protocols involved are reported in order to offer a complete perspective of the research undertaken jointly with an assessment of how community-centred placemaking and operative mapping are incorporated into local urban transformation actions.

Fej\'er monotonicity is a well-established property commonly observed in sequences generated by optimization algorithms. In this paper, we introduce an extension of this property, called Fej\'er* monotonicity, which was initially proposed in [SIAM J. Optim., 34(3), 2535-2556 (2024)]. We discuss and build upon the concept by exploring its behavior within Hilbert spaces, presenting an illustrative example and insightful results regarding weak and strong convergence. We also compare Fej\'er* monotonicity with other weak notions of Fej\'er-like monotonicity, to better establish the role of Fej\'er* monotonicity in optimization algorithms.

Mechanical THz vibrations in nanocrystals have recently been harnessed for quantum sensing and thermal management. The free boundaries of nanocrystals introduce new surface wave solutions, analogous to the seismic waves on Earth, yet the implications of these surface waves on nanocrystals have remained largely unexplored. Here, we use atomistic molecular dynamics simulations and experimental neutron spectroscopy to elucidate these THz-scale features in nanodiamond. Our key insight is that thermally induced Rayleigh surface phonons, which have a low group velocity and an amplitude that decays exponentially away from the surface, are responsible for the previously observed but unexplained linear scaling of the low-energy vibrational density of states in nanocrystals. Large thermal atomic displacements, relative to the nanoparticle radius, induce perpetual surface quakes, even at ambient conditions. Normalised to the radius, the surface displacement ratio in diamond nanocrystals exceeds that of the largest recorded earthquakes by a factor of $10^{5\pm1}$. We explicate how these dramatic Rayleigh waves coexist with other distinctive features including confined lattice phonons, soft surface modes, the acoustic gap, Love waves, and Lamb modes, thereby offering a complete framework for the vibrational dynamics of nanocrystals., Comment: 14 pages

In the realm of video dialog response generation, the understanding of video content and the temporal nuances of conversation history are paramount. While a segment of current research leans heavily on large-scale pretrained visual-language models and often overlooks temporal dynamics, another delves deep into spatial-temporal relationships within videos but demands intricate object trajectory pre-extractions and sidelines dialog temporal dynamics. This paper introduces the Dual Temporal Grounding-enhanced Video Dialog model (DTGVD), strategically designed to merge the strengths of both dominant approaches. It emphasizes dual temporal relationships by predicting dialog turn-specific temporal regions, filtering video content accordingly, and grounding responses in both video and dialog contexts. One standout feature of DTGVD is its heightened attention to chronological interplay. By recognizing and acting upon the dependencies between different dialog turns, it captures more nuanced conversational dynamics. To further bolster the alignment between video and dialog temporal dynamics, we've implemented a list-wise contrastive learning strategy. Within this framework, accurately grounded turn-clip pairings are designated as positive samples, while less precise pairings are categorized as negative. This refined classification is then funneled into our holistic end-to-end response generation mechanism. Evaluations using AVSD@DSTC-7 and AVSD@DSTC-8 datasets underscore the superiority of our methodology.

The widespread adoption of the QWERTY keyboard layout, designed primarily for English, presents significant challenges for speakers of indigenous languages such as Quechua, particularly in the Puno region of Peru. This research examines the extent to which the QWERTY layout affects the writing and digital communication of Quechua speakers. Through an analysis of the Quechua languages unique alphabet and character frequency, combined with insights from local speakers, we identify the limitations imposed by the QWERTY system on the efficient digital transcription of Quechua. The study further proposes alternative keyboard layouts, including optimizations of QWERTY and DVORAK, designed to enhance typing efficiency and reduce the digital divide for Quechua speakers. Our findings underscore the need for localized technological solutions to preserve linguistic diversity while improving digital literacy for indigenous communities. The proposed modifications offer a pathway toward more inclusive digital tools that respect and accommodate linguistic diversity.

Electric guitar tone modeling typically focuses on the non-linear transformation from clean to amplifier-rendered audio. Traditional methods rely on one-to-one mappings, incorporating device parameters into neural models to replicate specific amplifiers. However, these methods are limited by the need for specific training data. In this paper, we adapt a model based on the previous work, which leverages a tone embedding encoder and a feature wise linear modulation (FiLM) condition method. In this work, we altered conditioning method using a hypernetwork-based gated convolutional network (GCN) to generate audio that blends clean input with the tone characteristics of reference audio. By extending the training data to cover a wider variety of amplifier tones, our model is able to capture a broader range of tones. Additionally, we developed a real-time plugin to demonstrate the system's practical application, allowing users to experience its performance interactively. Our results indicate that the proposed system achieves superior tone modeling versatility compared to traditional methods., Comment: demo of the ISMIR paper

We demonstrate an interferometric piezoresponse force microscopy (PFM) method for quantifying the full, three-dimensional response of piezoelectric material without requiring sample rotation. We show that this approach yields quantitative 3D measurement independent of the sample orientation beneath the tip. This approach simplifies existing, angle-resolved PFM techniques, since it only requires four measurements (or three with less redundancy and error checking) to map the full three dimensional, time independent strain field. It also does not require rotating the sample with respect to the cantilever. It benefits from the greatly reduced noise floor of our laser doppler vibrometer interferometric displacement sensor (IDS) of 50fm per rootHz or our quadrature phase differential interferometer (QPDI) noise floor of 5fm per rootHz and the intrinsic accuracy of interferometric measurements. One important result is that the in-plane piezo sensitivities were systematically smaller than the vertical by a factor of 2 to 3. A simple analysis of vertical and lateral contact stiffnesses, due to the difference in the Young (vertical) and Shear (lateral) sample moduli predicts a factor of 2.5, in good agreement with the measurements. Finally, we fit observed OBD measurements to a linear combination of interferometrically measured in-plane and vertical responses for four different ferroelectric samples. We found that the in-plane response accounted for 20% or more of the OBD response and surprisingly, in two out of the four samples, the in-plane contribution was in fact 2x larger than the vertical., Comment: 11 pages, 4 figures

The emergence of anomalous Hall effects (AHE) in antiferromagnets presents an intriguing phenomenon with potential spintronic device applications due to their ultrafast switching dynamics. Mn3NiN antiperovskite stands out as a promising candidate owing to its noncollinear antiferromagnetism. In this study, we report a significant AHE observed in epitaxial Mn3NiN thin films below the N\'eel temperature, comparable to those observed in typical ferromagnets. Through a combination of magnetometry, neutron diffraction and ab-initio data analyses, we propose that the substantial AHE can be attributed to the non-vanishing Berry curvature arising from the {\Gamma}4g-type spin structure, coupled with uncompensated magnetic domains featuring a weak canted moment. This coupling, due to robust antiferromagnetic domains, enables deterministic detection and switching of AHE via the application of a magnetic field.

State-of-the-art medical multi-modal large language models (med-MLLM), like LLaVA-Med or BioMedGPT, leverage instruction-following data in pre-training. However, those models primarily focus on scaling the model size and data volume to boost performance while mainly relying on the autoregressive learning objectives. Surprisingly, we reveal that such learning schemes might result in a weak alignment between vision and language modalities, making these models highly reliant on extensive pre-training datasets - a significant challenge in medical domains due to the expensive and time-consuming nature of curating high-quality instruction-following instances. We address this with LoGra-Med, a new multi-graph alignment algorithm that enforces triplet correlations across image modalities, conversation-based descriptions, and extended captions. This helps the model capture contextual meaning, handle linguistic variability, and build cross-modal associations between visuals and text. To scale our approach, we designed an efficient end-to-end learning scheme using black-box gradient estimation, enabling faster LLaMa 7B training. Our results show LoGra-Med matches LLAVA-Med performance on 600K image-text pairs for Medical VQA and significantly outperforms it when trained on 10% of the data. For example, on VQA-RAD, we exceed LLAVA-Med by 20.13% and nearly match the 100% pre-training score (72.52% vs. 72.64%). We also surpass SOTA methods like BiomedGPT on visual chatbots and RadFM on zero-shot image classification with VQA, highlighting the effectiveness of multi-graph alignment., Comment: First version, fixed typo

Disaggregated memory breaks the boundary of monolithic servers to enable memory provisioning on demand. Using network-attached memory to provide memory expansion for memory-intensive applications on compute nodes can improve the overall memory utilization on a cluster and reduce the total cost of ownership. However, current software solutions for leveraging network-attached memory must consume resources on the compute node for memory management tasks. Emerging off-path smartNICs provide general-purpose programmability at low-cost low-power cores. This work provides a general architecture design that enables network-attached memory and offloading tasks onto off-path programmable SmartNIC. We provide a prototype implementation called SODA on Nvidia BlueField DPU. SODA adapts communication paths and data transfer alternatives, pipelines data movement stages, and enables customizable data caching and prefetching optimizations. We evaluate SODA in five representative graph applications on real-world graphs. Our results show that SODA can achieve up to 7.9x speedup compared to node-local SSD and reduce network traffic by 42% compared to disaggregated memory without SmartNIC offloading at similar or better performance.

We present the first Chandra X-ray Observatory (CXO) catalog of "pulsar X-ray filaments," or "misaligned outflows." These are linear, synchrotron radiating features powered by ultra-relativistic electrons and positrons that escape from bow shock pulsars. The filaments are misaligned with the (large) pulsar velocity, distinguishing them from the pulsar wind nebula (PWN) trail which is also often visible in CXO ACIS images. Spectral fits and morphological properties are extracted for five secure filaments and three candidates using a uniform method. We present a search of archival CXO data for linear diffuse features; the known examples are recovered and a few additional weak candidates are identified. We also report on a snapshot CXO ACIS survey of pulsars with properties similar to the filament producers, finding no new filaments, but some diffuse emission including one PWN trail. Finally, we provide an updated model for the pulsar properties required to create filaments in light of these new observations., Comment: 12+3 pages, 5 figures, 4 tables. Accepted to ApJ

Spin-based semiconductor qubits hold promise for scalable quantum computing, yet they require reliable autonomous calibration procedures. This study presents an experimental demonstration of online single-dot charge autotuning using a convolutional neural network integrated into a closed-loop calibration system. The autotuning algorithm explores the gates' voltage space to localize charge transition lines, thereby isolating the one-electron regime without human intervention. In 20 experimental runs on a device cooled to 25mK, the method achieved a success rate of 95% in locating the target electron regime, highlighting the robustness of this method against noise and distribution shifts from the offline training set. Each tuning run lasted an average of 2 hours and 9 minutes, primarily due to the limited speed of the current measurement. This work validates the feasibility of machine learning-driven real-time charge autotuning for quantum dot devices, advancing the development toward the control of large qubit arrays., Comment: 6 pages (main) + 5 pages (supplementary)

Hundreds of exoplanets between 1-1.8 times the size of the Earth have been discovered on close in orbits. However, these planets show such a diversity in densities that some appear to be made entirely of iron, while others appear to host gaseous envelopes. To test this diversity in composition, we update the masses of 5 rocky exoplanets (HD 93963 A b, Kepler-10 b, Kepler-100 b, Kepler-407 b, and TOI-1444 b) and present the confirmation of a new planet (TOI-1011) using 187 high precision RVs from Gemini/MAROON-X and Keck/KPF. Our updated planet masses suggest compositions closer to that of the Earth than previous literature values for all planets in our sample. In particular, we report that two previously identified ``super-Mercuries'' (Kepler-100 b and HD 93963 A b) have lower masses that suggest less iron-rich compositions. We then compare the ratio of iron to rock-building species to the abundance ratios of those elements in their host stars. These updated planet compositions do not suggest a steep relationship between planet and host star compositions, contradictory to previous results, and suggest that planets and host stars have similar abundance ratios., Comment: Submitted to AJ 09/30/2024

Isolated, low-mass galaxies provide the opportunity to assess the impact of reionization on their star formation histories (SFHs) without the ambiguity of environmental processes associated with massive host galaxies. There are very few isolated, low-mass galaxies that are close enough to determine their SFHs from resolved star photometry reaching below the oldest main sequence turnoff. JWST has increased the volume for which this is possible, and here we report on JWST observations of the low-mass, isolated galaxy Leo P. From NIRCam imaging in F090W, F150W, and F277W, we derive a SFH which shows early star formation followed by a pause subsequent to the epoch of reionization which is then later followed by a re-ignition of star formation. This is very similar to the SFHs from previous studies of other dwarf galaxies in the ``transition zone'' between quenched very low-mass galaxies and the more massive galaxies which show no evidence of the impact of reionization on their SFHs; this pattern is rarely produced in simulations of SFHs. The lifetime SFH reveals that Leo P's stellar mass at the epoch of reionization was in the range that is normally associated with being totally quenched. The extended pause in star formation from z~5-1 has important implications for the contribution of low-mass galaxies to the UV photon budget at intermediate redshifts. We also demonstrate that, due to higher sensitivity and angular resolution, observing in two NIRCam short wavelength filters is superior to observing in a combination of a short and a long wavelength filter., Comment: 24 pages, 9 figures, 3 tables

We introduce a spatiotemporal self-exciting point process $(N_t(x))$, boundedly finite both over time $[0,\infty)$ and space $\mathscr X$, with excitation structure determined by a graphon $W$ on $\mathscr X^2$. This graphon Hawkes process generalizes both the multivariate Hawkes process and the Hawkes process on a countable network, and despite being infinite-dimensional, it is surprisingly tractable. After proving existence, uniqueness and stability results, we show, both in the annealed and in the quenched case, that for compact, Euclidean $\mathscr X\subset\mathbb R^m$, any graphon Hawkes process can be obtained as the suitable limit of $d$-dimensional Hawkes processes $\tilde N^d$, as $d\to\infty$. Furthermore, in the stable regime, we establish an FLLN and an FCLT for our infinite-dimensional process on compact $\mathscr X\subset\mathbb R^m$, while in the unstable regime we prove divergence of $N_T(\mathscr X)/T$, as $T\to\infty$. Finally, we exploit a cluster representation to derive fixed-point equations for the Laplace functional of $N$, for which we set up a recursive approximation procedure. We apply those results to show that, starting with multivariate Hawkes processes $\tilde N^d_t$ converging to stable graphon Hawkes processes, the limits $d\to\infty$ and $t\to\infty$ commute.

The BICEP3 and BICEP Array polarimeters are small-aperture refracting telescopes located at the South Pole designed to measure primordial gravitational wave signatures in the Cosmic Microwave Background (CMB) polarization, predicted by inflation. Constraining the inflationary signal requires not only excellent sensitivity, but also careful control of instrumental systematics. Both instruments use antenna-coupled orthogonally polarized detector pairs, and the polarized sky signal is reconstructed by taking the difference in each detector pair. As a result, the differential response between detectors within a pair becomes an important systematic effect we must control. Additionally, mapping the intensity and polarization response in regions away from the main beam can inform how sidelobe levels affect CMB measurements. Extensive calibration measurements are taken in situ every austral summer for control of instrumental systematics and instrument characterisation. In this work, we detail the set of beam calibration measurements that we conduct on the BICEP receivers, from deep measurements of main beam response to polarized beam response and sidelobe mapping. We discuss the impact of these measurements for instrumental systematics studies and design choices for future CMB receivers., Comment: 13 pages, 7 figures, 1 table, Proceedings paper SPIE 2024

Cascade multiplication is a common technique to enhance the sensitivity of photon detectors. In this study, we demonstrate novel cascade-amplified superconducting detectors utilizing arrays of Josephson junctions. The mutual coupling between junctions induces avalanche-like switching of multiple junctions upon photon absorption, leading to cascade amplification of the readout voltage. We present two prototypes featuring either low-Tc linear Nb/NbxSi1-x/Nb arrays, or high-Tc stacked intrinsic Josephson junctions. Both devices exhibit clear antenna effects in microwave directivities, indicating good impedance matching and absorption efficiency. The combination of high absorption efficiency and large cascade amplification has the potential to produce broadband THz sensors with sensitivity exceeding 10^13 V/W., Comment: 9 pages, 3 figures

The Bitcoin Lightning Network, launched in 2018, serves as a layer 2 scaling solution for Bitcoin. The Lightning Network allows users to establish channels between each other and subsequently exchange off-chain payments. Together, these channels form a network that facilitates payments between parties even if they do not have a channel in common. The Lightning Network has gained popularity over the past five years as it offers an attractive alternative to on-chain transactions by substantially reducing transaction costs and processing times. Nevertheless, due to the privacy-centric design of the Lightning Network, little is understood about its inner workings. In this work, we conduct a measurement study of the Lightning Network to shed light on the lifecycle of channels. By combining Lightning gossip messages with on-chain Bitcoin data, we investigate the lifecycle of a channel from its opening through its lifetime to its closing. In particular, our analysis offers unique insights into the utilization patterns of the Lightning Network. Even more so, through decoding the channel closing transactions, we obtain the first dataset of Lightning Network payments, observe the imbalance of channels during the closing, and investigate whether both parties are involved in the closing, or one closes the channel unilaterally. For instance, we find nearly 60% of cooperatively closed channels are resurrected, i.e., their outputs were used to fund another channel.

We model the one-dimensional `classical' vacuum by a system of annihilating Brownian motions on $\mathbb{R}$ with pairwise immigration. A pair of reflecting or absorbing walls placed in such a vacuum at separation $L$ experiences an attractive force which decays exponentially with $L$. This phenomenon can be regarded as a purely classical Casimir effect for a system of interacting Brownian motions., Comment: 9 pages, 2 figures

Large-scale structure (LSS) studies in cosmology map and analyse matter in the Universe on the largest scales. Understanding the LSS can provide observational support for the Cosmological Principle (CP) and the Standard Cosmological Model ($\Lambda$CDM). In recent years, many discoveries have been made of LSSs that are so large that they become difficult to understand within $\Lambda$CDM. Reasons for this are: they potentially challenge the CP, (i.e. the scale of homogeneity); and their formation and origin are not fully understood. In this article we review two recent LSS discoveries: the Giant Arc (GA, $\sim 1$ Gpc) and the Big Ring (BR, $\sim 400$ Mpc). Both structures are in the same cosmological neighbourhood -- at the same redshift $z \sim 0.8$ and with a separation on the sky of only $\sim 12^\circ$. Both structures exceed the often-cited scale of homogeneity (Yadav+ 2010), so individually and together, these two intriguing structures raise more questions for the validity of the CP and potentially hint at new physics beyond the Standard Model. The GA and BR were discovered using a novel method of mapping faint matter at intermediate redshifts, interpreted from the MgII absorption doublets seen in the spectra of background quasars., Comment: This article is part of a Royal Society discussion-meeting issue `Challenging the Standard Cosmological Model'. Accepted for publication in Philosophical Transactions A

Human-robot physical interaction (pHRI) is a rapidly evolving research field with significant implications for physical therapy, search and rescue, and telemedicine. However, a major challenge lies in accurately understanding human constraints and safety in human-robot physical experiments without an IRB and physical human experiments. Concerns regarding human studies include safety concerns, repeatability, and scalability of the number and diversity of participants. This paper examines whether a physical approximation can serve as a stand-in for human subjects to enhance robot autonomy for physical assistance. This paper introduces the SHULDRD (Shoulder Haptic Universal Limb Dynamic Repositioning Device), an economical and anatomically similar device designed for real-time testing and deployment of pHRI planning tasks onto robots in the real world. SHULDRD replicates human shoulder motion, providing crucial force feedback and safety data. The device's open-source CAD and software facilitate easy construction and use, ensuring broad accessibility for researchers. By providing a flexible platform able to emulate infinite human subjects, ensure repeatable trials, and provide quantitative metrics to assess the effectiveness of the robotic intervention, SHULDRD aims to improve the safety and efficacy of human-robot physical interactions., Comment: Submitted to ICRA '25

Simulators are crucial during the development of a chip, like the RISC-V accelerator designed in the European Processor Initiative project. In this paper, we showcase the limitations of the current simulation solutions in the project and propose using QEMU with RAVE, a plugin we implement and describe in this document. This methodology can rapidly simulate and analyze applications running on the v1.0 and v0.7.1 RISC-V V-extension. Our plugin reports the vector and scalar instructions alongside useful information such as the vector-length being used, the single-element-width, and the register usage, among other vectorization metrics. We provide an API used from the simulated Application to control the RAVE plugin and the capability to generate vectorization traces that can be analyzed using Paraver. Finally, we demonstrate the efficiency of our solution between different evaluated machines and against other simulation methods used in the European Processor Accelerator (EPAC) project.

Accurate field boundary delineation is a critical challenge in digital agriculture, impacting everything from crop monitoring to resource management. Existing methods often struggle with noise and fail to generalize across varied landscapes, particularly when dealing with cloud cover in optical remote sensing. In response, this study presents a new approach that leverages time series data from Sentinel-2 (S2) and Sentinel-1 (S1) imagery to improve performance under diverse cloud conditions, without the need for manual cloud filtering. We introduce a 3D Vision Transformer architecture specifically designed for satellite image time series, incorporating a memory-efficient attention mechanism. Two models are proposed: PTAViT3D, which handles either S2 or S1 data independently, and PTAViT3D-CA, which fuses both datasets to enhance accuracy. Both models are evaluated under sparse and dense cloud coverage by exploiting spatio-temporal correlations. Our results demonstrate that the models can effectively delineate field boundaries, even with partial (S2 or S2 and S1 data fusion) or dense cloud cover (S1), with the S1-based model providing performance comparable to S2 imagery in terms of spatial resolution. A key strength of this approach lies in its capacity to directly process cloud-contaminated imagery by leveraging spatio-temporal correlations in a memory-efficient manner. This methodology, used in the ePaddocks product to map Australia's national field boundaries, offers a robust, scalable solution adaptable to varying agricultural environments, delivering precision and reliability where existing methods falter. Our code is available at https://github.com/feevos/tfcl., Comment: under review

The realisation of an universal quantum computer will require the operation of thousands to millions of qubits. The possibility of using existing industrial semiconductor fabrication techniques and infrastructure for up-scaling and reproducibility makes silicon based spin qubits one of the most promising platforms to achieve this goal. The implementation of the up to now largest semiconductor based quantum processor was realized in a silicon/silicon-germanium heterostructure known for its low charge noise, long qubit coherence times and fast driving speeds, but the high structural complexity creates challenges for industrial implementations. Here we demonstrate quantum dots hosted in a natural Si/SiGe heterostructure fully fabricated by an industrial 300$\,$mm semiconductor wafer process line from heterostructure growth to Co micromagnet monolithic integration. We report charge noise values below 2$\,\mathrm{\mu eV/\sqrt{Hz}}$, spin relaxation times of over 1$\,$s and coherence times $T_2^*$ and $T_2^H$ of 1$\,\mathrm{\mu s}$ and 50$\,\mathrm{\mu s}$ respectively, for quantum wells grown using natural silicon. Further, we achieve Rabi frequencies up to 5$\,$MHz and single qubit gate fidelities above 99$\,\%$. In addition to scalability, the high reproducibility of the 300$\,$mm processes enables the deterministic study of qubit metric dependencies on process parameters, which is essential for optimising qubit quality.

In state-of-the-art optical lattice clocks, beyond-electric-dipole polarizability terms lead to a break-down of magic wavelength trapping. In this Letter, we report a novel approach to evaluate lattice light shifts, specifically addressing recent discrepancies in the atomic multipolarizability term between experimental techniques and theoretical calculations. We combine imaging and multi-ensemble techniques to evaluate lattice light shift atomic coefficients, leveraging comparisons in a dual-ensemble lattice clock to rapidly evaluate differential frequency shifts. Further, we demonstrate application of a running wave field to probe both the multipolarizability and hyperpolarizability coefficients, establishing a new technique for future lattice light shift evaluations., Comment: 17 pages, 6 figures

With the ever increasing beam power at particle accelerator-based facilities for nuclear and particle physics, radioactive isotope production, and nuclear engineering, targets that can withstand this power, and shielding of secondary particles are becoming increasingly important. Here we present Monte Carlo (MC) calculations using the well-established Geant4 software to optimise and predict the antineutrino yield of a $^8$Li Decay-At-Rest (DAR) source. The source relies on 600~kW of beam power from a continuous wave proton beam impinging on a beryllium target, where spallation neutrons capture on $^7$Li to produce the $^8$Li. We further present an in-depth treatment of the neutron shielding surrounding this target. We show that we can produce the high antineutrino flux needed for the discovery-level experiment IsoDAR, searching for ``sterile'' neutrinos (predicted new fundamental particles) and other beyond standard model physics, while maintaining a neutron flux in the detector that is below natural backgrounds. The methods presented in this paper are easily transferable to other high-power targets and their associated shielding.

We analyse in the Taylor-Couette system, a canonical flow that has been studied extensively for over a century, a parameter regime exhibiting dynamics that can be approximated by a simple discrete map. The map has exceptionally neat mathematical properties, allowing to prove its chaotic nature as well as the existence of infinitely many unstable periodic orbits. Remarkably, the fluid system and the discrete map share a common catalog of unstable periodic solutions with the tent map, a clear indication of topological conjugacy. A sufficient number of these solutions enables the construction of a conjugacy homeomorphism, which can be used to predict the probability density function of direct numerical simulations. These results rekindle Hopf's aspiration of elucidating turbulence through the study of recurrent patterns., Comment: 5 pages, 5 figures

Music recommender systems frequently utilize network-based models to capture relationships between music pieces, artists, and users. Although these relationships provide valuable insights for predictions, new music pieces or artists often face the cold-start problem due to insufficient initial information. To address this, one can extract content-based information directly from the music to enhance collaborative-filtering-based methods. While previous approaches have relied on hand-crafted audio features for this purpose, we explore the use of contrastively pretrained neural audio embedding models, which offer a richer and more nuanced representation of music. Our experiments demonstrate that neural embeddings, particularly those generated with the Contrastive Language-Audio Pretraining (CLAP) model, present a promising approach to enhancing music recommendation tasks within graph-based frameworks., Comment: Accepted at the 2nd Music Recommender Workshop (@RecSys)

Our work examines the efficacy of employing advanced machine learning methods to solve captchas from Google's reCAPTCHAv2 system. We evaluate the effectiveness of automated systems in solving captchas by utilizing advanced YOLO models for image segmentation and classification. Our main result is that we can solve 100% of the captchas, while previous work only solved 68-71%. Furthermore, our findings suggest that there is no significant difference in the number of challenges humans and bots must solve to pass the captchas in reCAPTCHAv2. This implies that current AI technologies can exploit advanced image-based captchas. We also look under the hood of reCAPTCHAv2, and find evidence that reCAPTCHAv2 is heavily based on cookie and browser history data when evaluating whether a user is human or not. The code is provided alongside this paper., Comment: 10 pages. Accepted at COMPSAC 2024

Mainstream zero-shot TTS production systems like Voicebox and Seed-TTS achieve human parity speech by leveraging Flow-matching and Diffusion models, respectively. Unfortunately, human-level audio synthesis leads to identity misuse and information security issues. Currently, many antispoofing models have been developed against deepfake audio. However, the efficacy of current state-of-the-art anti-spoofing models in countering audio synthesized by diffusion and flowmatching based TTS systems remains unknown. In this paper, we proposed the Diffusion and Flow-matching based Audio Deepfake (DFADD) dataset. The DFADD dataset collected the deepfake audio based on advanced diffusion and flowmatching TTS models. Additionally, we reveal that current anti-spoofing models lack sufficient robustness against highly human-like audio generated by diffusion and flow-matching TTS systems. The proposed DFADD dataset addresses this gap and provides a valuable resource for developing more resilient anti-spoofing models., Comment: Accepted by IEEE SLT 2024

This paper presents SYBILGAT, a novel approach to Sybil detection in social networks using Graph Attention Networks (GATs). Traditional methods for Sybil detection primarily leverage structural properties of networks; however, they tend to struggle with a large number of attack edges and are often unable to simultaneously utilize both known Sybil and honest nodes. Our proposed method addresses these limitations by dynamically assigning attention weights to different nodes during aggregations, enhancing detection performance. We conducted extensive experiments in various scenarios, including pretraining in sampled subgraphs, synthetic networks, and networks under targeted attacks. The results show that SYBILGAT significantly outperforms the state-of-the-art algorithms, particularly in scenarios with high attack complexity and when the number of attack edges increases. Our approach shows robust performance across different network models and sizes, even as the detection task becomes more challenging. We successfully applied the model to a real-world Twitter graph with more than 269k nodes and 6.8M edges. The flexibility and generalizability of SYBILGAT make it a promising tool to defend against Sybil attacks in online social networks with only structural information., Comment: 9 pages, 1 figure, 6 tables

Global expansion of the Event Horizon Telescope (EHT) will see the strategic addition of antennas at new geographical locations, transforming the sensitivity and imaging fidelity of the $\lambda \sim 1\,$mm EHT array. A possible South African EHT station would leverage a strong geographical advantage, local infrastructure, and radio astronomy expertise, and have strong synergies with the Africa Millimetre Telescope in Namibia. We assessed three South African candidate millimetre sites using climatological simulations and found at least two promising sites. These sites are comparable to some existing EHT stations during the typical April EHT observing window and outperform them during most of the year, especially the southern hemisphere winter. Interferometric simulations of Africa-enhanced EHT arrays under the simulated atmospheric conditions demonstrate the improved array performance. In typical weather, the number of reliable visibility detections increased considerably, especially at $(u, v)$-distances corresponding to the angular sizes of the Sagittarius A$^*$ and Messier 87$^*$ black hole shadow diameters ($\sim40\,\mathrm{\mu}$as to $50\,\mathrm{\mu}$as). The simulation results underscore the sizable, positive impact of a strategically placed South African EHT station on ngEHT objectives and the resulting black hole science., Comment: 6 pages, 3 figures, 1 table. Submitted to URSI RSL

We establish a geometric criterion for local microlocal holonomies to be globally regular on the moduli space of Lagrangian fillings. This local-to-global regularity result holds for arbitrary Legendrian links and it is a key input for the study of cluster structures on such moduli spaces. Specifically, we construct regular functions on derived moduli stacks of sheaves with Legendrian microsupport by studying the Hochschild homology of the associated dg-categories via relative Lagrangian skeleta. In this construction, a key geometric result is that local microlocal merodromies along positive relative cycles in Lagrangian fillings yield global Hochschild 0-cycles for these dg-categories., Comment: 35 pages, 2 figures

The Square Kilometre Array mid-frequency array will enable high-redshift detections of neutral hydrogen (HI) emission in galaxies, providing important constraints on the evolution of cold gas in galaxies over cosmic time. Strong gravitational lensing will push back the HI emission frontier towards cosmic noon ($z\sim2$), as has been done for all prominent spectral lines in the interstellar medium of galaxies. Chakraborty & Roy (2023, MNRAS, 519, 4074) report a $z=1.3$ HI emission detection towards the well-modelled, galaxy-scale gravitational lens, SDSS J0826+5630. We carry out HI source modelling of the system and find that their claimed HI magnification, $\mu_{\rm HI} = 29 \pm 6$, requires an HI disk radius of $\lesssim 1.5$ kpc, which implies an implausible mean HI surface mass density in excess of $\Sigma_{\rm HI} > 2000$ M$_\odot$ pc$^{-2}$. This is several orders of magnitude above the highest measured peak values $(\Sigma_{\rm HI} \sim 10 \, {\rm M}_\odot\,{\rm pc}^{-2})$, above which HI is converted into molecular hydrogen. Our re-analysis requires this to be the highest HI mass galaxy known (M$_{\rm HI}~\sim 10^{11}$M$_\odot$), as well as strongly lensed, the latter having a typical probability of order 1 in 10$^{3-4}$. We conclude that the claimed detection is spurious., Comment: 6 pages, 5 figures. Accepted in MNRAS Letters

We report the sky-projected spin-orbit angle $\lambda$ for HD 191939 b, the innermost planet in a 6 planet system, using Keck/KPF to detect the Rossiter-McLaughlin (RM) effect. Planet b is a sub-Neptune with radius 3.4 $\pm$ 0.8 R$_{\oplus}$ and mass 10.0 $\pm$ 0.7 M$_{\oplus}$ with an RM amplitude $<$1 ms$^{-1}$. We find the planet is consistent with a well-aligned orbit, measuring $\lambda= \, $ 3.7 $\pm$ 5.0 degrees. Additionally, we place new constraints on the mass and period of the distant super-Jupiter, planet f, finding it to be 2.88 $\pm$ 0.26 $M_J$ on a 2898 $\pm$ 152 day orbit. With these new orbital parameters, we perform a dynamical analysis of the system and constrain the mutual inclination of the non-transiting planet e to be smaller than 12 degrees relative to the plane shared by the inner three transiting planets. Additionally, the further planet f is inclined off this shared plane, the greater the amplitude of precession for the entire inner system, making it increasingly unlikely to measure an aligned orbit for planet b. Through this analysis, we show that this system's wide variety of planets are all well-aligned with the star and nearly co-planar, suggesting that the system formed dynamically cold and flat out of a well-aligned proto-planetary disk, similar to our own solar system., Comment: 11 pages, 4 figures, Accepted for publication in The Astronomical Journal

Methane (CH4) is a potent greenhouse gas (GHG) with a short atmospheric half-life (~8.4 years) and a high short-term impact on global warming, significantly higher than CO2 (Kleinberg, 2020; Balcombe et al., 2018). Traditional metrics such as the 100-year Global Warming Potential (GWP100) obscure methane's short-term, negative climatic effects, potentially leading to inadequate policy responses (Kleinberg, 2020). This letter examines the limitations of GWP100 in capturing methane's true climate impact, explores alternative metrics, and discusses the implications of underreporting methane emissions. We highlight the necessity of adopting a more immediate perspective on methane to accelerate climate emergency action, while noting the adverse effects of the rapid growth rate of methane emissions on reduction efforts. Additionally, we hope that in the immediate future, during COP29, policymakers will adopt actions that give appropriate attention to methane's short-term warming potential to dramatically reduce emissions and address the immediate climate crisis., Comment: 14 pages, 3 figures, to be submitted to AGU Letters

Modern CAPTCHAs rely heavily on vision tasks that are supposedly hard for computers but easy for humans. However, advances in image recognition models pose a significant threat to such CAPTCHAs. These models can easily be fooled by generating some well-hidden "random" noise and adding it to the image, or hiding objects in the image. However, these methods are model-specific and thus can not aid CAPTCHAs in fooling all models. We show in this work that by allowing for more significant changes to the images while preserving the semantic information and keeping it solvable by humans, we can fool many state-of-the-art models. Specifically, we demonstrate that by adding masks of various intensities the Accuracy @ 1 (Acc@1) drops by more than 50%-points for all models, and supposedly robust models such as vision transformers see an Acc@1 drop of 80%-points. These masks can therefore effectively fool modern image classifiers, thus showing that machines have not caught up with humans -- yet., Comment: Under review

This article proves the following theorem, first enunciated by Roger Penrose about 70 years ago: In $\mathbb{RP}^2$, if regular conics are assigned to seven of the vertices of a combinatorial cube such that conics connected by an edge are in double contact, and the chords of contact associated to a cube face have a common point, then there exists a unique eighth conic which completes the cube and satisfies the same conditions. The proof is based on the following analogue theorem: In $\mathbb{RP}^3$, if regular quadrics are assigned to seven of the vertices of a combinatorial cube such that quadrics connected by an edge are in ring contact, and the ring planes associated to a cube face have a common axis, then there exists a unique eighth quadric which completes the cube and satisfies the same conditions., Comment: 21 pages, 12 figures

Many blockchain networks aim to preserve the anonymity of validators in the peer-to-peer (P2P) network, ensuring that no adversary can link a validator's identifier to the IP address of a peer due to associated privacy and security concerns. This work demonstrates that the Ethereum P2P network does not offer this anonymity. We present a methodology that enables any node in the network to identify validators hosted on connected peers and empirically verify the feasibility of our proposed method. Using data collected from four nodes over three days, we locate more than 15% of Ethereum validators in the P2P network. The insights gained from our deanonymization technique provide valuable information on the distribution of validators across peers, their geographic locations, and hosting organizations. We further discuss the implications and risks associated with the lack of anonymity in the P2P network and propose methods to help validators protect their privacy. The Ethereum Foundation has awarded us a bug bounty, acknowledging the impact of our results.

A robust face recognition model must be trained using datasets that include a large number of subjects and numerous samples per subject under varying conditions (such as pose, expression, age, noise, and occlusion). Due to ethical and privacy concerns, large-scale real face datasets have been discontinued, such as MS1MV3, and synthetic face generators have been proposed, utilizing GANs and Diffusion Models, such as SYNFace, SFace, DigiFace-1M, IDiff-Face, DCFace, and GANDiffFace, aiming to supply this demand. Some of these methods can produce high-fidelity realistic faces, but with low intra-class variance, while others generate high-variance faces with low identity consistency. In this paper, we propose a Triple Condition Diffusion Model (TCDiff) to improve face style transfer from real to synthetic faces through 2D and 3D facial constraints, enhancing face identity consistency while keeping the necessary high intra-class variance. Face recognition experiments using 1k, 2k, and 5k classes of our new dataset for training outperform state-of-the-art synthetic datasets in real face benchmarks such as LFW, CFP-FP, AgeDB, and BUPT. Our source code is available at: https://github.com/BOVIFOCR/tcdiff., Comment: SIBGRAPI 2024

We propose Tyche, a family of protocols for performing practically (as well as asymptotically) efficient multiparty lotteries, resistant against aborts and majority coalitions. Our protocols are based on a commit-and-reveal approach, requiring only a collision-resistant hash function. All our protocols use a blockchain as a public bulletin board and for buy-in collection and payout settlement. Importantly though, they do not rely on it or any other third party for providing randomness. Also, participants are not required to post any collateral beyond their buy-in. Any honest participant can eventually settle the lottery, and dishonest behavior never reduces the winning probability of any honest participant. Further, we adapt all three protocols into anonymous lotteries, where (under certain conditions) the winner is unlinkable to any particular participant. We show that our protocols are secure, fair, and some preserve the participants' privacy. Finally, we evaluate the performance of our protocols, particularly in terms of transaction fees, by implementing them on the Sui blockchain. There we see that per user transaction fees are reasonably low and our protocols could potentially support millions of participants.

We consider the cokernel $G_n = \mathbf{Cok}(A_{k} \cdots A_2 A_1)$ of a product of independent $n \times n$ random integer matrices with iid entries from generic nondegenerate distributions, in the regime where both $n$ and $k$ are sent to $\infty$ simultaneously. In this regime we show that the cokernel statistics converge universally to the reflecting Poisson sea, an interacting particle system constructed in arXiv:2312.11702, at the level of $1$-point marginals. In particular, $\operatorname{corank}(A_{k} \cdots A_2 A_1 \pmod{p}) \sim \log_p k$, and its fluctuations are $O(1)$ and converge to a discrete random variable defined in arXiv:2310.12275. The main difference with previous works studying cokernels of random matrices is that $G_n$ does not converge to a random finite group; for instance, the $p$-rank of $G_n$ diverges. This means that the usual moment method for random groups does not apply. Instead, we proceed by proving a `rescaled moment method' theorem applicable to a general sequence of random groups of growing size. This result establishes that fluctuations of $p$-ranks and other statistics still converge to limit random variables, provided that certain rescaled moments $\mathbb{E}[\#\operatorname{Hom}(G_n,H)]/C(n,H)$ converge., Comment: 35 pages. First version, comments welcome!

The EXperiment for Cryogenic Large-Aperture Intensity Mapping (EXCLAIM) is a balloon-borne telescope designed to survey star formation over cosmological time scales using intensity mapping in the 420 - 540 GHz frequency range. EXCLAIM uses a fully cryogenic telescope coupled to six on-chip spectrometers featuring kinetic inductance detectors (KIDs) to achieve high sensitivity, allowing for fast integration in dark atmospheric windows. The telescope receiver is cooled to $\approx$ 1.7 K by immersion in a superfluid helium bath and enclosed in a superfluid-tight shell with a meta-material anti-reflection coated silicon window. In addition to the optics and the spectrometer package, the receiver contains the magnetic shielding, the cryogenic segment of the spectrometer readout, and the sub-Kelvin cooling system. A three-stage continuous adiabatic demagnetization refrigerator (CADR) keeps the detectors at 100 mK while a $^4$He sorption cooler provides a 900 mK thermal intercept for mechanical suspensions and coaxial cables. We present the design of the EXCLAIM receiver and report on the flight-like testing of major receiver components, including the superfluid-tight receiver window and the sub-Kelvin coolers., Comment: 13 pages, 7 figures, 1 table

This work examines the resilience properties of the Snowball and Avalanche protocols that underlie the popular Avalanche blockchain. We experimentally quantify the resilience of Snowball using a simulation implemented in Rust, where the adversary strategically rebalances the network to delay termination. We show that in a network of $n$ nodes of equal stake, the adversary is able to break liveness when controlling $\Omega(\sqrt{n})$ nodes. Specifically, for $n = 2000$, a simple adversary controlling $5.2\%$ of stake can successfully attack liveness. When the adversary is given additional information about the state of the network (without any communication or other advantages), the stake needed for a successful attack is as little as $2.8\%$. We show that the adversary can break safety in time exponentially dependent on their stake, and inversely linearly related to the size of the network, e.g. in 265 rounds in expectation when the adversary controls $25\%$ of a network of 3000. We conclude that Snowball and Avalanche are akin to Byzantine reliable broadcast protocols as opposed to consensus.

Magnetic resonance microimaging (MR mu I) is an outstanding technique for studying water transfers in millimetric bio-based materials in a non-destructive and non-invasive manner. However, depending on the composition of the material, monitoring and quantification of these transfers can be very complex, and hence reliable image processing and analysis tools are necessary. In this study, a combination of MR mu I and multivariate curve resolution-alternating least squares (MCR-ALS) is proposed to monitor the water ingress into a potato starch extruded blend containing 20% glycerol that was shown to have interesting properties for biomedical, textile, and food applications. In this work, the main purpose of MCR is to provide spectral signatures and distribution maps of the components involved in the water uptake process that occurs over time with various kinetics. This approach allowed the description of the system evolution at a global (image) and a local (pixel) level, hence, permitted the resolution of two waterfronts, at two different times into the blend that could not be resolved by any other mathematical processing method usually used in magnetic resonance imaging (MRI). The results were supplemented by scanning electron microscopy (SEM) observations in order to interpret these two waterfronts in a biological and physico-chemical point of view.

Increasingly, large language models (LLMs) are being used to automate workplace processes requiring a high degree of creativity. While much prior work has examined the creativity of LLMs, there has been little research on whether they can generate valid creativity assessments for humans despite the increasingly central role of creativity in modern economies. We develop a psychometrically inspired framework for creating test items (questions) for a classic free-response creativity test: the creative problem-solving (CPS) task. Our framework, the creative psychometric item generator (CPIG), uses a mixture of LLM-based item generators and evaluators to iteratively develop new prompts for writing CPS items, such that items from later iterations will elicit more creative responses from test takers. We find strong empirical evidence that CPIG generates valid and reliable items and that this effect is not attributable to known biases in the evaluation process. Our findings have implications for employing LLMs to automatically generate valid and reliable creativity tests for humans and AI., Comment: CREAI 2024

Autoregressive models are a class of generative model that probabilistically predict the next output of a sequence based on previous inputs. The autoregressive sequence is by definition one-dimensional (1D), which is natural for language tasks and hence an important component of modern architectures like recurrent neural networks (RNNs) and transformers. However, when language models are used to predict outputs on physical systems that are not intrinsically 1D, the question arises of which choice of autoregressive sequence -- if any -- is optimal. In this paper, we study the reconstruction of critical correlations in the two-dimensional (2D) Ising model, using RNNs and transformers trained on binary spin data obtained near the thermal phase transition. We compare the training performance for a number of different 1D autoregressive sequences imposed on finite-size 2D lattices. We find that paths with long 1D segments are more efficient at training the autoregressive models compared to space-filling curves that better preserve the 2D locality. Our results illustrate the potential importance in choosing the optimal autoregressive sequence ordering when training modern language models for tasks in physics., Comment: 12 pages, 4 figures

We study a magnitude-limited sample of 36 Broad-lined Type Ic Supernovae (SNe Ic-BL) from the Zwicky Transient Facility Bright Transient Survey (detected between March 2018 and August 2021), which is the largest systematic study of SNe Ic-BL done in literature thus far. We present the light curves (LCs) for each of the SNe, and analyze the shape of the LCs to derive empirical parameters, along with the explosion epochs for every event. The sample has an average absolute peak magnitude in the r band of $M_r^{max}$ = -18.51 $\pm$ 0.15 mag. Using spectra obtained around peak light, we compute expansion velocities from the Fe II 5169 Angstrom line for each event with high enough signal-to-noise ratio spectra, and find an average value of $v_{ph}$ = 16,100 $\pm$ 1,100 km $s^{-1}$. We also compute bolometric LCs, study the blackbody temperature and radii evolution over time, and derive the explosion properties of the SNe. The explosion properties of the sample have average values of $M_{Ni}$ = $0.37_{-0.06}^{+0.08}$ solar masses, $M_{ej}$ = $2.45_{-0.41}^{+0.47}$ solar masses, and $E_K$= $4.02_{-1.00}^{+1.37} \times 10^{51}$ erg. Thirteen events have radio observations from the Very Large Array, with 8 detections and 5 non-detections. We find that the populations that have radio detections and radio non-detections are indistinct from one another with respect to their optically-inferred explosion properties, and there are no statistically significant correlations present between the events' radio luminosities and optically-inferred explosion properties. This provides evidence that the explosion properties derived from optical data alone cannot give inferences about the radio properties of SNe Ic-BL, and likely their relativistic jet formation mechanisms., Comment: 52 pages, 34 Figures, 8 Tables; Accepted to ApJ