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We present the results of a Keck and NOEMA spectroscopic survey of 507 galaxies, where we confirm the presence of two massive overdensities at $z = 3.090 - 3.110$ and $z = 3.133 - 3.155$ in the neighborhood of the GOODS-N, each with over a dozen spectroscopically confirmed members. We find that both of these have galaxy overdensities of NIR-detected galaxies of $\delta_{\rm gal, obs} = 6 - 9$ within corrected volumes of $(6 - 7) \times 10^3~{\rm cMpc}^3$. We estimate the properties of the $z = 0$ descendants of these overdensities using a spherical collapse model and find that both should virialize by $z \simeq 0.5 - 0.8$, with total masses of $M_{\rm tot} \simeq (6 - 7) \times 10^{14}~{\rm M}_\odot$. The same spherical collapse calculations, as well as a clustering-of-clusters statistical analysis, suggest a >80% likelihood that the two overdensities will collapse into a single cluster with $M_{\rm tot} = (1.0 - 1.5) \times 10^{15}~{\rm M}_\odot$ by $z \sim 0.1-0.4$. The $z = 3.14$ substructure contains a core of four bright dusty star-forming galaxies with $\Sigma {\rm SFR} = 2700 \pm 700~{\rm M}_\odot~{\rm yr}^{-1}$ in a volume of only 280 ${\rm cMpc}^3$., Comment: 18 pages, 8 figures. Accepted to ApJ

We present a 34-year timing solution of the redback pulsar system Terzan 5A (Ter5A). Ter5A, also known as B1744$-$24A or J1748$-$2446A, has a 11.56 ms pulse period, a $\sim$0.1 solar mass dwarf companion star, and an orbital period of 1.82 hours. Ter5A displays highly variable eclipses and orbital perturbations. Using new timing techniques, we have determined a phase-connected timing solution for this system over 34 years. This is the longest ever published for a redback pulsar. We find that the pulsar's spin variability is much larger than most globular cluster pulsars. In fact, of the nine redback pulsars with published or in preparation long-term timing solutions, Ter5A is by far the noisiest. We see no evidence of strong correlations between orbital and spin variability of the pulsar. We also find that long-term astrometric timing measurements are likely too contaminated by this variability to be usable, and therefore require careful short-term timing to determine reasonable positions. Finally, we measure an orbital period contraction of $-2.5(3) \times 10^{-13}$, which is likely dominated by the general relativistic orbital decay of the system. The effects of the orbital variability due to the redback nature of the pulsar are not needed to explain the observed orbital period derivative, but they are constrained to less than $\sim$30% of the observed value.

With plasma currents up to 8.7 MA, the SPARC tokamak runs the risk of forming multi-MA beams of relativistic "runaway" electrons (REs) which could damage plasma facing components if unmitigated. The infrared (IR) and visible imaging and visible spectroscopy systems in SPARC are designed with measurements of synchrotron emission from REs in mind. Synchrotron radiation is emitted by REs along their direction of motion, opposite the plasma current. Matched clockwise and counterclockwise wide views are proposed to detect synchrotron and background radiation, allowing observation of RE synchrotron emission in both plasma current configurations. Due to SPARC's high toroidal magnetic field strength, 12.2 T on axis, the synchrotron light spectrum is expected to peak in the visible-IR wavelength range. The synthetic diagnostic tool SOFT (Synchrotron Orbit-Following Toolkit) is used to model synchrotron images and spectra for three scenarios, with appropriate magnetic equilibria for each: REs generated during plasma current ramp-up, steady-state flattop (although unlikely, but serving as a reference), and disruptions. Required time resolutions, achievable spatial coverage, and appropriate spectral ranges for various RE energies are assessed.

We study the complexity of testing properties of quantum channels. First, we show that testing identity to any channel $\mathcal N: \mathbb C^{d_{\mathrm{in}} \times d_{\mathrm{in}}} \to \mathbb C^{d_{\mathrm{out}} \times d_{\mathrm{out}}}$ in diamond norm distance requires $\Omega(\sqrt{d_{\mathrm{in}}} / \varepsilon)$ queries, even in the strongest algorithmic model that admits ancillae, coherence, and adaptivity. This is due to the worst-case nature of the distance induced by the diamond norm. Motivated by this limitation and other theoretical and practical applications, we introduce an average-case analogue of the diamond norm, which we call the average-case imitation diamond (ACID) norm. In the weakest algorithmic model without ancillae, coherence, or adaptivity, we prove that testing identity to certain types of channels in ACID distance can be done with complexity independent of the dimensions of the channel, while for other types of channels the complexity depends on both the input and output dimensions. Building on previous work, we also show that identity to any fixed channel can be tested with $\tilde O(d_{\mathrm{in}} d_{\mathrm{out}}^{3/2} / \varepsilon^2)$ queries in ACID distance and $\tilde O(d_{\mathrm{in}}^2 d_{\mathrm{out}}^{3/2} / \varepsilon^2)$ queries in diamond distance in this model. Finally, we prove tight bounds on the complexity of channel tomography in ACID distance.

Machine Learning as a Service (MLaaS) is often provided as a pay-per-query, black-box system to clients. Such a black-box approach not only hinders open replication, validation, and interpretation of model results, but also makes it harder for white-hat researchers to identify vulnerabilities in the MLaaS systems. Model extraction is a promising technique to address these challenges by reverse-engineering black-box models. Since training data is typically unavailable for MLaaS models, this paper focuses on the realistic version of it: data-free model extraction. We propose a data-free model extraction approach, CaBaGe, to achieve higher model extraction accuracy with a small number of queries. Our innovations include (1) a novel experience replay for focusing on difficult training samples; (2) an ensemble of generators for steadily producing diverse synthetic data; and (3) a selective filtering process for querying the victim model with harder, more balanced samples. In addition, we create a more realistic setting, for the first time, where the attacker has no knowledge of the number of classes in the victim training data, and create a solution to learn the number of classes on the fly. Our evaluation shows that CaBaGe outperforms existing techniques on seven datasets -- MNIST, FMNIST, SVHN, CIFAR-10, CIFAR-100, ImageNet-subset, and Tiny ImageNet -- with an accuracy improvement of the extracted models by up to 43.13%. Furthermore, the number of queries required to extract a clone model matching the final accuracy of prior work is reduced by up to 75.7%.

For random-walk Metropolis (RWM) and parallel tempering (PT) algorithms, an asymptotic acceptance rate of around 0.234 is known to be optimal in the high-dimensional limit. However, its practical relevance is uncertain due to restrictive derivation conditions. We synthesise previous theoretical advances in extending the 0.234 acceptance rate to more general settings, and demonstrate its applicability with a comprehensive empirical simulation study on examples examining how acceptance rates affect Expected Squared Jumping Distance (ESJD). Our experiments show the optimality of the 0.234 acceptance rate for RWM is surprisingly robust even in lower dimensions across various proposal and multimodal target distributions which may or may not have an i.i.d. product density. Parallel tempering experiments also show that the idealized 0.234 spacing of inverse temperatures may be approximately optimal for low dimensions and non i.i.d. product target densities, and that constructing an inverse temperature ladder with spacings given by a swap acceptance of 0.234 is a viable strategy. However, we observe the applicability of the 0.234 acceptance rate heuristic diminishes for both RWM and PT algorithms below a certain dimension which differs based on the target density, and that inhomogeneously scaled components in the target density further reduces its applicability in lower dimensions., Comment: Submitted to Communications in Statistics - Simulation and Computation

In this bachelor's thesis, isotope effects for pedestal instabilities have been studied based on an ASDEX Upgrade H-Mode scenario. This was done using the JOREK code for extended MHD simulations, including the ion diamagnetic drift and the establishment of ExB flows. Simulations with single toroidal harmonics were performed for multiple times during the build-up of the pedestal, to assess the evolution of the linear stability of modes occurring near the edge. When changing the average ion masses from 2.0 to 2.5 and 3.0, the variations were small, and MHD's momentum equation was shown to be the major cause for them. As a second step, simulations with multiple toroidal harmonics were performed to simulate an Edge Localized Mode (ELM) crash, again comparing between the average ion masses of 2.0, 2.5, and 3.0. The resulting variations of heat and particle losses were compared to JET results. The experimental differences between ion masses could not be reproduced. Together with the fact that the simulations' pedestal for an average ion mass of 2.5 and 3.0 was not matched to the experiment, this indicates that other effects, such as small-scale turbulences, must be included - for example, by adapting the simulations' transport coefficients - to explain the experimental differences.

We review and provide new proofs of results used to compare the efficiency of estimates generated by reversible MCMC algorithms on a general state space. We provide a full proof of the formula for the asymptotic variance for real-valued functionals on $\phi$-irreducible reversible Markov chains, first introduced by Kipnis and Varadhan. Given two Markov kernels $P$ and $Q$ with stationary measure $\pi$, we say that the Markov kernel $P$ efficiency dominates the Markov kernel $Q$ if the asymptotic variance with respect to $P$ is at most the asymptotic variance with respect to $Q$ for every real-valued functional $f\in L^2(\pi)$. Assuming only a basic background in functional analysis, we prove that for two $\phi$-irreducible reversible Markov kernels $P$ and $Q$, $P$ efficiency dominates $Q$ if and only if the operator $Q-P$, where $P$ is the operator on $L^2(\pi)$ that maps $f\mapsto\int f(y)P(\cdot,dy)$ and similarly for $Q$, is positive on $L^2(\pi)$, i.e. $\langle f,(Q-P)f\rangle\geq0$ for every $f\in L^2(\pi)$. We use this result to show that reversible antithetic kernels are more efficient than i.i.d. sampling, and that efficiency dominance is a partial ordering on $\phi$-irreducible reversible Markov kernels. We also provide a proof based on that of Tierney that Peskun dominance is a sufficient condition for efficiency dominance for reversible kernels. Using these results, we show that Markov kernels formed by randomly selecting other "component" Markov kernels will always efficiency dominate another Markov kernel formed in this way, as long as the component kernels of the former efficiency dominate those of the latter. These results on the efficiency dominance of combining component kernels generalises the results on the efficiency dominance of combined chains introduced by Neal and Rosenthal from finite state spaces to general state spaces., Comment: 27 pages

Waveguide quantum electrodynamics, the study of atomic systems interacting with propagating electromagnetic fields, is a powerful platform for understanding the complex interplay between light and matter. Qubit control is an indispensable tool in this field, and most experiments have so far focused on narrowband electromagnetic waves that interact with qubits at specific frequencies. This interaction, however, changes significantly with fast, broadband pulses, as waveguide properties like dispersion affect the pulse evolution and its impact on the qubit. Here, we use dispersion to achieve spatial addressing of superconducting qubits separated by a sub-wavelength distance within a microwave waveguide. This novel approach relies on a self-focusing effect to create a position-dependent interaction between the pulse and the qubits. This experiment emphasizes the importance of dispersion in the design and analysis of quantum experiments, and offers new avenues for the rapid control of quantum states.

Experimental measurements of plasma and neutral profiles across the pedestal are used in conjunction with 2D edge modeling to examine pedestal stiffness in Alcator C-Mod H-mode plasmas. Experiments on Alcator C-Mod observed pedestal degradation and loss in confinement below a critical value of net power crossing the separatrix, $P_\mathrm{net} = P_\mathrm{net}^\mathrm{crit} \approx 2.3$ MW. New analysis of ionization and particle flux profiles reveal saturation of the pedestal electron density, $n_{e}^\mathrm{ped}$ despite continuous increases in ionization throughout the pedestal, inversely related to $P_\mathrm{net}$. A limit to the pedestal $\nabla n_{e}$ emerges as the particle flux, $\Gamma_{D}$ continues to grow, implying increases in the effective particle diffusivity, $D_\mathrm{eff}$. This is well-correlated with the separatrix collisionality, $\nu^{*}_\mathrm{sep}$ and a turbulence control parameter, $\alpha_{t}$, implying a possible transition in type of turbulence. The transition is well correlated with the experimentally observed value of $P_\mathrm{net}^\mathrm{crit}$. SOLPS-ITER modeling is performed for select discharges from the power scan, constrained with experimental electron and neutral densities, measured at the outer midplane. The modeling confirms general growth in $D_\mathrm{eff}$, consistent with experimental findings, and additionally suggests even larger growth in $\chi_{e}$ at the same $P_\mathrm{net}^\mathrm{crit}$.

This article develops general conditions for weak convergence of adaptive Markov chain Monte Carlo processes and is shown to imply a weak law of large numbers for bounded Lipschitz continuous functions. This allows an estimation theory for adaptive Markov chain Monte Carlo where previously developed theory in total variation may fail or be difficult to establish. Extensions of weak convergence to general Wasserstein distances are established along with a weak law of large numbers for possibly unbounded Lipschitz functions. Applications are applied to auto-regressive processes in various settings, unadjusted Langevin processes, and adaptive Metropolis-Hastings.

In this paper, we initiate the study of constant dimension subspace codes restricted to Schubert varieties, which we call Schubert subspace codes. These codes have a very natural geometric description, as objects that we call intersecting sets with respect to a fixed subspace. We provide a geometric construction of maximum size constant dimension subspace codes in some Schubert varieties with the largest possible value for the minimum subspace distance. Finally, we generalize the problem to different values of the minimum distance.

The Transiting Exoplanet Survey Satellite (TESS) has discovered hundreds of new worlds, with TESS planet candidates now outnumbering the total number of confirmed planets from $\textit{Kepler}$. Owing to differences in survey design, TESS continues to provide planets that are better suited for subsequent follow-up studies, including mass measurement through radial velocity (RV) observations, compared to Kepler targets. In this work, we present the TESS-Keck Survey's (TKS) Mass Catalog: a uniform analysis of all TKS RV survey data which has resulted in mass constraints for 126 planets and candidate signals. This includes 58 mass measurements that have reached $\geq5\sigma$ precision. We confirm or validate 32 new planets from the TESS mission either by significant mass measurement (15) or statistical validation (17), and we find no evidence of likely false positives among our entire sample. This work also serves as a data release for all previously unpublished TKS survey data, including 9,204 RV measurements and associated activity indicators over our three year survey. We took the opportunity to assess the performance of our survey, and found that we achieved many of our goals including measuring the mass of 38 small ($<4R_{\oplus}$) planets, nearly achieving the TESS mission's basic science requirement. In addition, we evaluated the performance of the Automated Planet Finder (APF) as survey support and observed meaningful constraints on system parameters due to its more uniform phase coverage. Finally, we compared our measured masses to those predicted by commonly used mass-radius relations and investigated evidence of systematic bias., Comment: 51 pages (22 of text), 24 figures

Large Language Models (LLM) have become a popular approach for implementing Retrieval Augmented Generation (RAG) systems, and a significant amount of effort has been spent on building good models and metrics. In spite of increased recognition of the need for rigorous evaluation of RAG systems, few tools exist that go beyond the creation of model output and automatic calculation. We present InspectorRAGet, an introspection platform for RAG evaluation. InspectorRAGet allows the user to analyze aggregate and instance-level performance of RAG systems, using both human and algorithmic metrics as well as annotator quality. InspectorRAGet is suitable for multiple use cases and is available publicly to the community. The demo video is available at https://youtu.be/MJhe8QIXcEc

The next generation of soft electronics will expand to the third dimension. This will require the integration of mechanically-compliant three-dimensional functional structures with stretchable materials. This study demonstrates omnidirectional direct ink writing (DIW) of Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) aerogels with tunable electrical and mechanical performance, which can be integrated with soft substrates. Several PEDOT:PSS hydrogels were formulated for DIW and freeze-dried directly on stretchable substrates to form integrated aerogels displaying high shape fidelity and minimal shrinkage. The effect of additives and processing in the PEDOT:PSS hydro and aerogels morphology, and the link with their electromechanical properties was elucidated. This technology demonstrated 3D-structured stretchable interconnects and planar thermoelectric generators (TEGs) for skin electronics, as well as vertically-printed high aspect ratio thermoelectric pillars with a high ZT value of 3.2 10^-3 and ultra-low thermal conductivity of 0.065 W/(m K). Despite their comparatively low ZT, the aerogel pillars outpowered their dense counterparts in realistic energy harvesting scenarios where contact resistances cannot be ignored, and produced up to 26 nW/cm2 (corresponding to a gravimetric power density of 0.76 mW/kg) for a difference of temperature of 15 K. This work suggests promising advancements in soft and energy-efficiency electronic systems relevant to soft robotics and wearable.

Retrieval Augmented Generation (RAG) has become a popular application for large language models. It is preferable that successful RAG systems provide accurate answers that are supported by being grounded in a passage without any hallucinations. While considerable work is required for building a full RAG pipeline, being able to benchmark performance is also necessary. We present ClapNQ, a benchmark Long-form Question Answering dataset for the full RAG pipeline. ClapNQ includes long answers with grounded gold passages from Natural Questions (NQ) and a corpus to perform either retrieval, generation, or the full RAG pipeline. The ClapNQ answers are concise, 3x smaller than the full passage, and cohesive, with multiple pieces of the passage that are not contiguous. RAG models must adapt to these properties to be successful at ClapNQ. We present baseline experiments and analysis for ClapNQ that highlight areas where there is still significant room for improvement in grounded RAG. CLAPNQ is publicly available at https://github.com/primeqa/clapnq, Comment: 25 pages

The negatively charged tin-vacancy center in diamond (SnV$^-$) is an emerging platform for building the next generation of long-distance quantum networks. This is due to the SnV$^-$'s favorable optical and spin properties including bright emission, insensitivity to electronic noise, and long spin coherence times at temperatures above 1 Kelvin. Here, we demonstrate measurement of a single SnV$^-$ electronic spin with a single-shot readout fidelity of $87.4\%$, which can be further improved to $98.5\%$ by conditioning on multiple readouts. We show this performance is compatible with rapid microwave spin control, demonstrating that the trade-off between optical readout and spin control inherent to group-IV centers in diamond can be overcome for the SnV$^-$. Finally, we use weak quantum measurement to study measurement induced dephasing; this illuminates the fundamental interplay between measurement and decoherence in quantum mechanics, and makes use of the qubit's spin coherence as a metrological tool. Taken together, these results overcome an important hurdle in the development of the SnV$^-$ based quantum technologies, and in the process, develop techniques and understanding broadly applicable to the study of solid-state quantum emitters.

The Transiting Exoplanet Survey Satellite (TESS) continues to dramatically increase the number of known transiting exoplanets, and is optimal for monitoring bright stars amenable to radial velocity (RV) and atmospheric follow-up observations. TOI-1386 is a solar-type (G5V) star that was detected via TESS photometry to exhibit transit signatures in three sectors with a period of 25.84 days. We conducted follow-up RV observations using Keck/HIRES as part of the TESS-Keck Survey (TKS), collecting 64 RV measurements of TOI-1386 with the HIRES spectrograph over 2.5 years. Our combined fit of the TOI-1386 photometry and RV data confirm the planetary nature of the detected TESS signal, and provide a mass and radius for planet b of $0.148\pm0.019$ $M_J$ and $0.540\pm0.017$ $R_J$, respectively, marking TOI-1386 b as a warm sub-Saturn planet. Our RV data further reveal an additional outer companion, TOI-1386 c, with an estimated orbital period of 227.6 days and a minimum mass of $0.309\pm0.038$ $M_J$. The dynamical modeling of the system shows that the measured system architecture is long-term stable, although there may be substantial eccentricity oscillations of the inner planet due to the dynamical influence of the outer planet., Comment: Accepted for publication in The Astronomical Journal. 15 pages, 7 figures

The free distance of a convolutional code is a reliable indicator of its performance. However its computation is not an easy task. In this paper, we present some algorithms to compute the free distance with good efficiency that work for convolutional codes of all rates and over any field. Furthermore we discuss why an algorithm which is claimed to be very efficient is incorrect.

D-Nikud, a novel approach to Hebrew diacritization that integrates the strengths of LSTM networks and BERT-based (transformer) pre-trained model. Inspired by the methodologies employed in Nakdimon, we integrate it with the TavBERT pre-trained model, our system incorporates advanced architectural choices and diverse training data. Our experiments showcase state-of-the-art results on several benchmark datasets, with a particular emphasis on modern texts and more specified diacritization like gender.

The field of healthcare has increasingly turned its focus towards Large Language Models (LLMs) due to their remarkable performance. However, their performance in actual clinical applications has been underexplored. Traditional evaluations based on question-answering tasks don't fully capture the nuanced contexts. This gap highlights the need for more in-depth and practical assessments of LLMs in real-world healthcare settings. Objective: We sought to evaluate the performance of LLMs in the complex clinical context of adult critical care medicine using systematic and comprehensible analytic methods, including clinician annotation and adjudication. Methods: We investigated the performance of three general LLMs in understanding and processing real-world clinical notes. Concepts from 150 clinical notes were identified by MetaMap and then labeled by 9 clinicians. Each LLM's proficiency was evaluated by identifying the temporality and negation of these concepts using different prompts for an in-depth analysis. Results: GPT-4 showed overall superior performance compared to other LLMs. In contrast, both GPT-3.5 and text-davinci-003 exhibit enhanced performance when the appropriate prompting strategies are employed. The GPT family models have demonstrated considerable efficiency, evidenced by their cost-effectiveness and time-saving capabilities. Conclusion: A comprehensive qualitative performance evaluation framework for LLMs is developed and operationalized. This framework goes beyond singular performance aspects. With expert annotations, this methodology not only validates LLMs' capabilities in processing complex medical data but also establishes a benchmark for future LLM evaluations across specialized domains.

When employing Direct Volume Rendering (DVR) for visualizing volumetric scalar fields, classification is generally performed on a piecewise constant or piecewise linear approximation of the field on a viewing ray. Smoothed Particle Hydrodynamics (SPH) data sets define volumetric scalar fields as the sum of individual particle contributions, at highly varying spatial resolution. We present an approach for approximating SPH scalar fields along viewing rays with piece-wise polynomial functions of higher order. This is done by approximating each particle contribution individually and then efficiently summing the results, thus generating a higher-order representation of the field with a resolution adapting to the data resolution in the volume.

Most low-density parity-check (LDPC) code constructions are considered over finite fields. In this work, we focus on regular LDPC codes over integer residue rings and analyze their performance with respect to the Lee metric. Their error-correction performance is studied over two channel models, in the Lee metric. The first channel model is a discrete memoryless channel, whereas in the second channel model an error vector is drawn uniformly at random from all vectors of a fixed Lee weight. It is known that the two channel laws coincide in the asymptotic regime, meaning that their marginal distributions match. For both channel models, we derive upper bounds on the block error probability in terms of a random coding union bound as well as sphere packing bounds that make use of the marginal distribution of the considered channels. We estimate the decoding error probability of regular LDPC code ensembles over the channels using the marginal distribution and determining the expected Lee weight distribution of a random LDPC code over a finite integer ring. By means of density evolution and finite-length simulations, we estimate the error-correction performance of selected LDPC code ensembles under belief propagation decoding and a low-complexity symbol message passing decoding algorithm and compare the performances. The analysis developed in this paper may serve to design regular LDPC codes over integer residue rings for storage and cryptographic application.

Offensive language such as hate, abuse, and profanity (HAP) occurs in various content on the web. While previous work has mostly dealt with sentence level annotations, there have been a few recent attempts to identify offensive spans as well. We build upon this work and introduce Muted, a system to identify multilingual HAP content by displaying offensive arguments and their targets using heat maps to indicate their intensity. Muted can leverage any transformer-based HAP-classification model and its attention mechanism out-of-the-box to identify toxic spans, without further fine-tuning. In addition, we use the spaCy library to identify the specific targets and arguments for the words predicted by the attention heatmaps. We present the model's performance on identifying offensive spans and their targets in existing datasets and present new annotations on German text. Finally, we demonstrate our proposed visualization tool on multilingual inputs.

In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 45 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and consensus recommendations resulting from the workshop. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations., Comment: Outcomes of a wwPDB/EMDB workshop on cryoEM data management, deposition and validation

Large language models (LLMs) have shown promise in safety-critical applications such as healthcare, yet the ability to quantify performance has lagged. An example of this challenge is in evaluating a summary of the patient's medical record. A resulting summary can enable the provider to get a high-level overview of the patient's health status quickly. Yet, a summary that omits important facts about the patient's record can produce a misleading picture. This can lead to negative consequences on medical decision-making. We propose MED-OMIT as a metric to explore this challenge. We focus on using provider-patient history conversations to generate a subjective (a summary of the patient's history) as a case study. We begin by discretizing facts from the dialogue and identifying which are omitted from the subjective. To determine which facts are clinically relevant, we measure the importance of each fact to a simulated differential diagnosis. We compare MED-OMIT's performance to that of clinical experts and find broad agreement We use MED-OMIT to evaluate LLM performance on subjective generation and find some LLMs (gpt-4 and llama-3.1-405b) work well with little effort, while others (e.g. Llama 2) perform worse., Comment: Accepted to ML4H 2024

We report on our effort to create a corpus dataset of different social context situations in an office setting for further disciplinary and interdisciplinary research in computer vision, psychology, and human-robot-interaction. For social robots to be able to behave appropriately, they need to be aware of the social context they act in. Consider, for example, a robot with the task to deliver a personal message to a person. If the person is arguing with an office mate at the time of message delivery, it might be more appropriate to delay playing the message as to respect the recipient's privacy and not to interfere with the current situation. This can only be done if the situation is classified correctly and in a second step if an appropriate behavior is chosen that fits the social situation. Our work aims to enable robots accomplishing the task of classifying social situations by creating a dataset composed of semantically annotated video scenes of office situations from television soap operas. The dataset can then serve as a basis for conducting research in both computer vision and human-robot interaction., Comment: In SCRITA 2023 Workshop Proceedings (arXiv:2311.05401) held in conjunction with 32nd IEEE International Conference on Robot & Human Interactive Communication, 28/08 - 31/08 2023, Busan (Korea)

Large language models (LLMs) have demonstrated their potential in social science research by emulating human perceptions and behaviors, a concept referred to as algorithmic fidelity. This study assesses the algorithmic fidelity and bias of LLMs by utilizing two nationally representative climate change surveys. The LLMs were conditioned on demographics and/or psychological covariates to simulate survey responses. The findings indicate that LLMs can effectively capture presidential voting behaviors but encounter challenges in accurately representing global warming perspectives when relevant covariates are not included. GPT-4 exhibits improved performance when conditioned on both demographics and covariates. However, disparities emerge in LLM estimations of the views of certain groups, with LLMs tending to underestimate worry about global warming among Black Americans. While highlighting the potential of LLMs to aid social science research, these results underscore the importance of meticulous conditioning, model selection, survey question format, and bias assessment when employing LLMs for survey simulation. Further investigation into prompt engineering and algorithm auditing is essential to harness the power of LLMs while addressing their inherent limitations., Comment: 34 pages, 6 figures, 1 table

We compare systems with single giant planets to systems with multiple giant planets using a catalog of planets from a high-precision radial velocity survey of FGKM stars. Our comparison focuses on orbital properties, planet masses, and host star properties. We use hierarchical methods to model the orbital eccentricity distributions of giant singles and giant multis, and find that the distributions are distinct. The multiple giant planets typically have moderate eccentricities and their eccentricity distribution extends to $e=0.47$ (90th percentile), while the single giant planets have a pile-up of nearly circular orbits and a long tail that extends to $e=0.77$. We determine that stellar hosts of multiple giants are distinctly more metal-rich than hosts of solitary giants, with respective mean metallicities $0.228\pm0.027$ vs. $0.129\pm0.019$ dex. We measure the distinct occurrence distributions of single and multiple giants with respect to orbital separation, and find that single gas giants have a $\sim$2.3$\sigma$ significant hot ($a<0.06$) Jupiter pile-up not seen among multi giant systems. We find that the median mass ($\msini$ ) of giants in multiples is nearly double that of single giants (1.71 $\mjup$ vs. 0.92 $\mjup$ ). We find that giant planets in the same system have correlated masses, analogous to the `peas in a pod' effect seen among less massive planets.

Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretability means. Why does the need for interpretability in MLMI arise? What goals does one actually seek to address when interpretability is needed? To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI. By reasoning about real-world tasks and goals common in both medical image analysis and its intersection with machine learning, we identify five core elements of interpretability: localization, visual recognizability, physical attribution, model transparency, and actionability. From this, we arrive at a framework for interpretability in MLMI, which serves as a step-by-step guide to approaching interpretability in this context. Overall, this paper formalizes interpretability needs in the context of medical imaging, and our applied perspective clarifies concrete MLMI-specific goals and considerations in order to guide method design and improve real-world usage. Our goal is to provide practical and didactic information for model designers and practitioners, inspire developers of models in the medical imaging field to reason more deeply about what interpretability is achieving, and suggest future directions of interpretability research., Comment: Published in IEEE Access

The European Spallation Source, currently under construction in Lund, Sweden, is a multidisciplinary international laboratory. Once completed to full specifications, it will operate the world's most powerful pulsed neutron source. Supported by a 3 million Euro Research and Innovation Action within the EU Horizon 2020 program, a design study (HighNESS) has been completed to develop a second neutron source located below the spallation target. Compared to the first source, designed for high cold and thermal brightness, the new source has been optimized to deliver higher intensity, and a shift to longer wavelengths in the spectral regions of cold (CN, 2--20\,\AA), very cold (VCN, 10--120\,\AA), and ultracold (UCN, ${>}\,{500}$\,\AA) neutrons. The second source comprises a large liquid deuterium moderator designed to produce CN and support secondary VCN and UCN sources. Various options have been explored in the proposed designs, aiming for world-leading performance in neutronics. These designs will enable the development of several new instrument concepts and facilitate the implementation of a high-sensitivity neutron-antineutron oscillation experiment (NNBAR). This document serves as the Conceptual Design Report for the HighNESS project, representing its final deliverable., Comment: 269 pages, 255 figures. Volume I of the final deliverable of the HighNESS Project (HORIZON 2020 grant agreement ID: 951782)

This paper focuses on the identification of different algorithm-based biases in robotic behaviour and their consequences in human-robot mixed groups. We propose to develop computational models to detect episodes of microaggression, discrimination, and social exclusion informed by a) observing human coping behaviours that are used to regain social inclusion and b) using system inherent information that reveal unequal treatment of human interactants. Based on this information we can start to develop regulatory mechanisms to promote fairness and social inclusion in HRI., Comment: 4 pages

This paper explores how and when to use common random number (CRN) simulation to evaluate Markov chain Monte Carlo (MCMC) convergence rates. We discuss how CRN simulation is closely related to theoretical convergence rate techniques such as one-shot coupling and coupling from the past. We present conditions under which the CRN technique generates an unbiased estimate of the squared $L^2-$Wasserstein distance between two random variables. We also discuss how this unbiasedness over a single iteration does not extend to unbiasedness over multiple iterations. We provide an upper bound on the Wasserstein distance of a Markov chain to its stationary distribution after $N$ steps in terms of averages over CRN simulations. Finally, we apply our result to a Bayesian regression Gibbs sampler.

JWST has ushered in an era of unprecedented ability to characterize exoplanetary atmospheres. While there are over 5,000 confirmed planets, more than 4,000 TESS planet candidates are still unconfirmed and many of the best planets for atmospheric characterization may remain to be identified. We present a sample of TESS planets and planet candidates that we identify as "best-in-class" for transmission and emission spectroscopy with JWST. These targets are sorted into bins across equilibrium temperature $T_{\mathrm{eq}}$ and planetary radius $R{_\mathrm{p}}$ and are ranked by transmission and emission spectroscopy metric (TSM and ESM, respectively) within each bin. In forming our target sample, we perform cuts for expected signal size and stellar brightness, to remove sub-optimal targets for JWST. Of the 194 targets in the resulting sample, 103 are unconfirmed TESS planet candidates, also known as TESS Objects of Interest (TOIs). We perform vetting and statistical validation analyses on these 103 targets to determine which are likely planets and which are likely false positives, incorporating ground-based follow-up from the TESS Follow-up Observation Program (TFOP) to aid the vetting and validation process. We statistically validate 23 TOIs, marginally validate 33 TOIs to varying levels of confidence, deem 29 TOIs likely false positives, and leave the dispositions for 4 TOIs as inconclusive. 14 of the 103 TOIs were confirmed independently over the course of our analysis. We provide our final best-in-class sample as a community resource for future JWST proposals and observations. We intend for this work to motivate formal confirmation and mass measurements of each validated planet and encourage more detailed analysis of individual targets by the community., Comment: Submitted to AJ. Machine-readable versions of Tables 2 and 3 are included. 40 pages, 7 figures, 3 tables

Adenosine-5'-triphosphate (ATP) plays a crucial role in many biocatalytic reactions and its regeneration can influence the performance of a related enzymatic reaction significantly. Here, we established an electrochemically coupled ATP regeneration by pyruvate oxidase and acetate kinase (ACK) for the phosphorylation of mevalonate catalyzed by mevalonate kinase. A yield of 84% for the product mevalonate phosphate was reached and a total turnover number for ADP of 68. These metrics are promising for the development of an economic feasible bioprocess and surpass many other enzymatic ATP regeneration systems. A comparison was made to polyphosphate kinases (PPKs), ACK, pyruvate kinase, and creatine kinase in terms of the phosphate donor properties and biocatalytic metrics of exemplary reactions. Furthermore, our system was expanded by a PPK that enables the phosphorylation of AMP, which can broaden the spectrum of applications even further., Comment: 18 pages, 3 figures, 3 tables

Automating the monitoring of industrial processes has the potential to enhance efficiency and optimize quality by promptly detecting abnormal events and thus facilitating timely interventions. Deep learning, with its capacity to discern non-trivial patterns within large datasets, plays a pivotal role in this process. Standard deep learning methods are suitable to solve a specific task given a specific type of data. During training, deep learning demands large volumes of labeled data. However, due to the dynamic nature of the industrial processes and environment, it is impractical to acquire large-scale labeled data for standard deep learning training for every slightly different case anew. Deep transfer learning offers a solution to this problem. By leveraging knowledge from related tasks and accounting for variations in data distributions, the transfer learning framework solves new tasks with little or even no additional labeled data. The approach bypasses the need to retrain a model from scratch for every new setup and dramatically reduces the labeled data requirement. This survey first provides an in-depth review of deep transfer learning, examining the problem settings of transfer learning and classifying the prevailing deep transfer learning methods. Moreover, we delve into applications of deep transfer learning in the context of a broad spectrum of time series anomaly detection tasks prevalent in primary industrial domains, e.g., manufacturing process monitoring, predictive maintenance, energy management, and infrastructure facility monitoring. We discuss the challenges and limitations of deep transfer learning in industrial contexts and conclude the survey with practical directions and actionable suggestions to address the need to leverage diverse time series data for anomaly detection in an increasingly dynamic production environment., Comment: 27 pages, 8 figures, 2 tables, published in IEEE Acess

With JWST's successful deployment and unexpectedly high fuel reserves, measuring the masses of sub-Neptunes transiting bright, nearby stars will soon become the bottleneck for characterizing the atmospheres of small exoplanets via transmission spectroscopy. Using a carefully curated target list and more than two years' worth of APF-Levy and Keck-HIRES Doppler monitoring, the TESS-Keck Survey is working toward alleviating this pressure. Here we present mass measurements for 11 transiting planets in eight systems that are particularly suited to atmospheric follow-up with JWST. We also report the discovery and confirmation of a temperate super-Jovian-mass planet on a moderately eccentric orbit. The sample of eight host stars, which includes one subgiant, spans early-K to late-F spectral types ($T_\mathrm{eff} =$ 5200--6200 K). We homogeneously derive planet parameters using a joint photometry and radial velocity modeling framework, discuss the planets' possible bulk compositions, and comment on their prospects for atmospheric characterization., Comment: Accepted for publication in The Astronomical Journal on 2023-Jun-22. 60 pages, 17 Tables, 28 Figures

The negatively charged tin-vacancy (SnV-) center in diamond is a promising solid-state qubit for applications in quantum networking due to its high quantum efficiency, strong zero phonon emission, and reduced sensitivity to electrical noise. The SnV- has a large spin-orbit coupling, which allows for long spin lifetimes at elevated temperatures, but unfortunately suppresses the magnetic dipole transitions desired for quantum control. Here, by use of a naturally strained center, we overcome this limitation and achieve high-fidelity microwave spin control. We demonstrate a pi-pulse fidelity of up to 99.51+/0.03%$ and a Hahn-echo coherence time of T2echo = 170.0+/-2.8 microseconds, both the highest yet reported for SnV- platform. This performance comes without compromise to optical stability, and is demonstrated at 1.7 Kelvin where ample cooling power is available to mitigate drive induced heating. These results pave the way for SnV- spins to be used as a building block for future quantum technologies., Comment: Final published version

We present a polynomial-time quantum algorithm making a single query (in superposition) to a classical oracle, such that for every state $|\psi\rangle$ there exists a choice of oracle that makes the algorithm construct an exponentially close approximation of $|\psi\rangle$. Previous algorithms for this problem either used a linear number of queries and polynomial time, or a constant number of queries and polynomially many ancillae but no nontrivial bound on the runtime. As corollaries we do the following: - We simplify the proof that statePSPACE $\subseteq$ stateQIP (a quantum state analogue of PSPACE $\subseteq$ IP) and show that a constant number of rounds of interaction suffices. - We show that QAC$\mathsf{_f^0}$ lower bounds for constructing explicit states would imply breakthrough circuit lower bounds for computing explicit boolean functions. - We prove that every $n$-qubit state can be constructed to within 0.01 error by an $O(2^n/n)$-size circuit over an appropriate finite gate set. More generally we give a size-error tradeoff which, by a counting argument, is optimal for any finite gate set., Comment: 40 pages, 2 figures

We present the stellar and planetary properties for 85 TESS Objects of Interest (TOIs) hosting 108 planet candidates which comprise the TESS-Keck Survey (TKS) sample. We combine photometry, high-resolution spectroscopy, and Gaia parallaxes to measure precise and accurate stellar properties. We then use these parameters as inputs to a lightcurve processing pipeline to recover planetary signals and homogeneously fit their transit properties. Among these transit fits, we detect significant transit-timing variations among at least three multi-planet systems (TOI-1136, TOI-1246, TOI-1339) and at least one single-planet system (TOI-1279). We also reduce the uncertainties on planet-to-star radius ratios $R_p/R_\star$ across our sample, from a median fractional uncertainty of 8.8$\%$ among the original TOI Catalog values to 3.0$\%$ among our updated results. With this improvement, we are able to recover the Radius Gap among small TKS planets and find that the topology of the Radius Gap among our sample is broadly consistent with that measured among Kepler planets. The stellar and planetary properties presented here will facilitate follow-up investigations of both individual TOIs and broader trends in planet properties, system dynamics, and the evolution of planetary systems., Comment: Accepted at The Astronomical Journal; 21 pages, 9 figures

We review criteria for comparing the efficiency of Markov chain Monte Carlo (MCMC) methods with respect to the asymptotic variance of estimates of expectations of functions of state, and show how such criteria can justify ways of combining improvements to MCMC methods. We say that a chain on a finite state space with transition matrix $P$ efficiency-dominates one with transition matrix $Q$ if for every function of state it has lower (or equal) asymptotic variance. We give elementary proofs of some previous results regarding efficiency dominance, leading to a self-contained demonstration that a reversible chain with transition matrix $P$ efficiency-dominates a reversible chain with transition matrix $Q$ if and only if none of the eigenvalues of $Q-P$ are negative. This allows us to conclude that modifying a reversible MCMC method to improve its efficiency will also improve the efficiency of a method that randomly chooses either this or some other reversible method, and to conclude that improving the efficiency of a reversible update for one component of state (as in Gibbs sampling) will improve the overall efficiency of a reversible method that combines this and other updates. It also explains how antithetic MCMC can be more efficient than i.i.d. sampling. We also establish conditions that can guarantee that a method is not efficiency-dominated by any other method., Comment: 24 pages

We analyze the dust properties of 57 870 $\mu$m selected dusty star-forming galaxies in the GOODS-S using new deep ALMA 1.2 mm, 2 mm, and 3 mm continuum imaging together with other far-infrared through millimeter data. We fit the spectral energy distributions (SEDs) with optically thin modified blackbodies to constrain the emissivity indices and effective dust temperatures, finding a median emissivity index of $\beta = 1.78^{+0.43}_{-0.25}$ and a median temperature of $T_d = 33.6^{+12.1}_{-5.4}$ K. We observe a negative correlation between $\beta$ and $T_d$. By testing several SED models, we determine that the derived emissivity indices can be influenced by opacity assumptions. Our temperature measurements are consistent with no evolution in dust temperature with redshift., Comment: Accepted for publication in ApJ. 21 pages, 12 figures, 2 tables

Class imbalance problems widely exist in the medical field and heavily deteriorates performance of clinical predictive models. Most techniques to alleviate the problem rebalance class proportions and they predominantly assume the rebalanced proportions should be a function of the original data and oblivious to the model one uses. This work challenges this prevailing assumption and proposes that links the optimal class proportions to the model complexity, thereby tuning the class proportions per model. Our experiments on the opioid overdose prediction problem highlight the performance gain of tuning class proportions. Rigorous regression analysis also confirms the advantages of the theoretical framework proposed and the statistically significant correlation between the hyperparameters controlling the model complexity and the optimal class proportions.

There exists a large literature of construction of convolutional codes with maximal or near maximal free distance. Much less is known about constructions of convolutional codes having optimal or near optimal column distances. In this paper, a new construction of convolutional codes over the binary field with optimal column distances is presented.

Maximum-distance separable (MDS) convolutional codes are characterized by the property that their free distance reaches the generalized Singleton bound. In this paper, new criteria to construct MDS convolutional codes are presented. Additionally, the obtained convolutional codes have optimal first (reverse) column distances and the criteria allow to relate the construction of MDS convolutional codes to the construction of reverse superregular Toeplitz matrices. Moreover, we present some construction examples for small code parameters over small finite fields.

Goal 1 of the National Academies of Science, Engineering and Mathematics Exoplanet Science Strategy is "to understand the formation and evolution of planetary systems as products of the process of star formation, and characterize and explain the diversity of planetary system architectures, planetary compositions, and planetary environments produced by these processes", with the finding that "Current knowledge of the demographics and characteristics of planets and their systems is substantially incomplete." One significant roadblock to our ongoing efforts to improve our demographics analyses is the lack of comprehensive meta-data accompanying published exoplanet surveys. The Exoplanet Program Analysis Group (ExoPAG) Science Interest Group 2: Exoplanet Demographics has prepared this document to provide guidance to survey architects, authors, referees and funding agencies as to the most valuable such data products for five different exoplanet detection techniques - transit, radial velocity, direct imaging, microlensing and astrometry. We find that making these additional data easily available would greatly enhance the community's ability to perform robust, reproducible demographics analyses, and make progress on achieving the most important goals identified by the exoplanet and wider astronomical community., Comment: 21 pages, final report after community feedback addressed

We design and test a low-loss interface between superconducting 3-dimensional microwave cavities and 2-dimensional circuits, where the coupling rate is highly tunable. This interface seamlessly integrates a loop antenna and a Josephson junction-based coupling element. We demonstrate that the loss added by connecting this interface to the cavity is 1.28 kHz, corresponding to an inverse quality factor of $1/(4.5 \times 10^6)$. Furthermore, we show that the cavity's external coupling rate to a 50 $\Omega$ transmission line can be tuned from negligibly small to over 3 orders of magnitude larger than its internal loss rate in a characteristic time of 3.2 ns. This switching speed does not impose additional limits on the coupling rate because it is much faster than the coupling rate. Moreover, the coupler can be controlled by low frequency signals to avoid interference with microwave signals near the cavity or qubit frequencies. Finally, the coupling element introduces a 0.04 Hz/photon self-Kerr nonlinearity to the cavity, remaining linear in high photon number operations.

We present egglog, a fixpoint reasoning system that unifies Datalog and equality saturation (EqSat). Like Datalog, it supports efficient incremental execution, cooperating analyses, and lattice-based reasoning. Like EqSat, it supports term rewriting, efficient congruence closure, and extraction of optimized terms. We identify two recent applications--a unification-based pointer analysis in Datalog and an EqSat-based floating-point term rewriter--that have been hampered by features missing from Datalog but found in EqSat or vice-versa. We evaluate egglog by reimplementing those projects in egglog. The resulting systems in egglog are faster, simpler, and fix bugs found in the original systems., Comment: PLDI 2023

Cell-free protein synthesis (CFPS) systems are an attractive to complement the usual cell-based synthesis of proteins, especially for screening approaches. The literature describes a wide variety of CFPS systems, but their performance is difficult to compare since the reaction components are often used at different concentrations. Therefore, we have developed a calculation tool based on amino acid balancing to evaluate the performance of CFPS by determining the fractional yield as the ratio between theoretically achievable and achieved protein molar concentration. This tool was applied to a series of experiments from our lab and to various systems described in the literature to identify systems that synthesize proteins very efficiently and those that still have potential for higher yields. The well-established Escherichia coli system showed a high efficiency in the utilization of amino acids, but interestingly, less-attention-paid systems, such as the one based on Streptomyces lividans, also demonstrated exceptional fractional yields of 100%, meaning complete amino acid conversion. The methods and tools described here can quickly identify when a system has reached its maximum or has other limitations. We believe that the approach described here will facilitate the evaluation and optimization of existing CFPS systems and provide the basis for the systematic development of new CFPS systems., Comment: 20 pages, 3 figures, 2 tables

High-quality non-degree workforce programs at community colleges, including both credit-bearing and non-credit programs, play a crucial role in equipping low-income and marginalized workers and learners with the skills and credentials they need to secure quality jobs and careers in a variety of occupations. Financing these programs can be a challenge for many colleges. As part of its New Models for Career Preparation project, The Center on Education and Labor at New America (CELNA), with support from Lumina Foundation, commissioned Non-Profit Finance Fund to undertake a study examining innovative financing approaches. This report presents the findings from the study. [This report is a partnership with the Nonprofit Finance Fund (NFF).]