Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing the dark count rate of a large-format MKID array.

We present an empirical measurement of the dark count rate seen in a large-format MKID array identical to those currently in use at observatories such as Subaru on Maunakea. This work provides compelling evidence for their utility in future experiments that require low-count rate, quiet environments such as dark matter direct detection. Across the bandpass from 0.946-1.534 eV (1310-808 nm) an average count rate of (1.847 ± 0.003) × 10-3 photons/pixel/s is measured. Breaking this bandpass into 5 equal-energy bins based on the resolving power of the detectors we find the average dark count rate seen in an MKID is (6.26 ± 0.04) × 10-4 photons/pixel/s from 0.946-1.063 eV and (2.73 ± 0.02) × 10-4 photons/pixel/s at 1.416-1.534eV. Using lower-noise readout electronics to read out a single MKID pixel we demonstrate that the events measured while the detector is not illuminated largely appear to be a combination of real photons, possible fluorescence caused by cosmic rays, and phonon events in the array substrate. We also find that using lower-noise readout electronics on a single MKID pixel we measure a dark count rate of (9.3 ± 0.9) × 10-4 photons/pixel/s over the same bandpass (0.946-1.534 eV) With the single-pixel readout we also characterize the events when the detectors are not illuminated and show that these responses in the MKID are distinct from photons from known light sources such as a laser, likely coming from cosmic ray excitations.
KENGIC: KEyword-driven and N-Gram Graph based Image Captioning

This paper presents a Keyword-driven and N-gram Graph based approach for Image Captioning (KENGIC). Most current state-of-the-art image caption generators are trained end-to-end on large scale paired image-caption datasets which are very laborious and expensive to collect. Such models are limited in terms of their explainability and their applicability across different domains. To address these limitations, a simple model based on N-Gram graphs which does not require any end-to-end training on paired image captions is proposed. Starting with a set of image keywords considered as nodes, the generator is designed to form a directed graph by connecting these nodes through overlapping n-grams as found in a given text corpus. The model then infers the caption by maximising the most probable n-gram sequences from the constructed graph. To analyse the use and choice of keywords in context of this approach, this study analysed the generation of image captions based on (a) keywords extracted from gold standard captions and (b) from automatically detected keywords. Both quantitative and qualitative analyses demonstrated the effectiveness of KENGIC. The performance achieved is very close to that of current state-of-the-art image caption generators that are trained in the unpaired setting. The analysis of this approach could also shed light on the generation process behind current top performing caption generators trained in the paired setting, and in addition, provide insights on the limitations of the current most widely used evaluation metrics in automatic image captioning
Engineering Arbitrary Hamiltonians in Phase Space.

We introduce a general method to engineer arbitrary Hamiltonians in the Floquet phase space of a periodically driven oscillator, based on the noncommutative Fourier transformation technique. We establish the relationship between an arbitrary target Floquet Hamiltonian in phase space and the periodic driving potential in real space. We obtain analytical expressions for the driving potentials in real space that can generate novel Hamiltonians in phase space, e.g., rotational lattices and sharp-boundary wells. Our protocol can be realized in a range of experimental platforms for nonclassical state generation and bosonic quantum computation.
AVeCQ: Anonymous Verifiable Crowdsourcing with Worker Qualities

In crowdsourcing systems, requesters publish tasks, and interested workers provide answers to get rewards. Worker anonymity motivates participation since it protects their privacy. Anonymity with unlinkability is an enhanced version of anonymity because it makes it impossible to ``link'' workers across the tasks they participate in. Another core feature of crowdsourcing systems is worker quality which expresses a worker's trustworthiness and quantifies their historical performance. Notably, worker quality depends on the participation history, revealing information about it, while unlinkability aims to disassociate the workers' identities from their past activity. In this work, we present AVeCQ, the first crowdsourcing system that reconciles these properties, achieving enhanced anonymity and verifiable worker quality updates. AVeCQ relies on a suite of cryptographic tools, such as zero-knowledge proofs, to (i) guarantee workers' privacy, (ii) prove the correctness of worker quality scores and task answers, and (iii) commensurate payments. AVeCQ is developed modularly, where the requesters and workers communicate over a platform that supports pseudonymity, information logging, and payments. In order to compare AVeCQ with the state-of-the-art, we prototype it over Ethereum. AVeCQ outperforms the state-of-the-art in three popular crowdsourcing tasks (image annotation, average review, and Gallup polls). For instance, for an Average Review task with $5$ choices and $128$ participating workers AVeCQ is 40\% faster (including overhead to compute and verify the necessary proofs and blockchain transaction processing time) with the task's requester consuming 87\% fewer gas units.
Reactmine: a statistical search algorithm for inferring chemical reactions from time series data

Inferring chemical reaction networks (CRN) from concentration time series is a challenge encouragedby the growing availability of quantitative temporal data at the cellular level. This motivates thedesign of algorithms to infer the preponderant reactions between the molecular species observed ina given biochemical process, and build CRN structure and kinetics models. Existing ODE-basedinference methods such as SINDy resort to least square regression combined with sparsity-enforcingpenalization, such as Lasso. However, we observe that these methods fail to learn sparse modelswhen the input time series are only available in wild type conditions, i.e. without the possibility toplay with combinations of zeroes in the initial conditions. We present a CRN inference algorithmwhich enforces sparsity by inferring reactions in a sequential fashion within a search tree of boundeddepth, ranking the inferred reaction candidates according to the variance of their kinetics on theirsupporting transitions, and re-optimizing the kinetic parameters of the CRN candidates on the wholetrace in a final pass. We show that Reactmine succeeds both on simulation data by retrievinghidden CRNs where SINDy fails, and on two real datasets, one of fluorescence videomicroscopyof cell cycle and circadian clock markers, the other one of biomedical measurements of systemiccircadian biomarkers possibly acting on clock gene expression in peripheral organs, by inferringpreponderant regulations in agreement with previous model-based analyses. The code is available athttps://gitlab.inria. fr/julmarti/crninf/ together with introductory notebooks.
Comet 108P/Ciffreo: The Blob

Short-period comet 108P/Ciffreo is known for its peculiar double morphology, in which the nucleus is accompanied by a comoving, detached, diffuse “blob. ” We report new observations of 108P/Ciffreo taken with the Hubble Space Telescope and the Nordic Optical Telescope and use them to determine the cause of this unusual morphology. The separation and the longevity of the blob across several orbits together rule out the possibility of a single, slow-moving secondary object near the primary nucleus. We use a model of coma particle dynamics under the action of solar gravity and radiation pressure to show that the blob is an artifact of the turnaround of particles ejected sunward and repelled by sunlight. Numerical experiments limit the range of directions which can reproduce the morphology and explain why the comoving blob appearance is rare.
SURVIVRS: Surround Video-Based Virtual Reality for Surgery Guidance

There is a strong demand for virtual reality (VR) to bring quality healthcare to underserved populations. This paper addresses this need with the design and prototype of SURVIVRS: Surround Video-Based Virtual Reality for Surgery Guidance. SURVIVRS allows a remote specialist to guide a local surgery team through a virtual reality (VR) telepresence interface. SURVIVRS is motivated by a need for medical expertise in remote and hard-to-reach areas, such as low-to-middle-income countries (LMICs). The remote surgeon interface allows the live observation of a procedure and combines 3D user interface annotation and communication tools on streams of the surgical site and the patient vitals monitor. SURVIVRS also supports debriefing and educational experiences by offering the ability for users to watch recorded surgeries from the point of view of the remote expert. The main contributions of this work are: the feasibility demonstration of the SURVIVRS system through a rigorous 3D user interface design process; the implementation of a prototype application that realizes the proposed design; and a usability evaluation of SURVIVRS showing that the tool was highly favored by users from the general population. The paper discusses the next steps in this line of research aimed at more equitable and diverse access to healthcare.
Towards Inferential Reproducibility of Machine Learning Research

Reliability of machine learning evaluation -- the consistency of observed evaluation scores across replicated model training runs -- is affected by several sources of nondeterminism which can be regarded as measurement noise. Current tendencies to remove noise in order to enforce reproducibility of research results neglect inherent nondeterminism at the implementation level and disregard crucial interaction effects between algorithmic noise factors and data properties. This limits the scope of conclusions that can be drawn from such experiments. Instead of removing noise, we propose to incorporate several sources of variance, including their interaction with data properties, into an analysis of significance and reliability of machine learning evaluation, with the aim to draw inferences beyond particular instances of trained models. We show how to use linear mixed effects models (LMEMs) to analyze performance evaluation scores, and to conduct statistical inference with a generalized likelihood ratio test (GLRT). This allows us to incorporate arbitrary sources of noise like meta-parameter variations into statistical significance testing, and to assess performance differences conditional on data properties. Furthermore, a variance component analysis (VCA) enables the analysis of the contribution of noise sources to overall variance and the computation of a reliability coefficient by the ratio of substantial to total variance.
Further Experimental Evidence of the Dead Matter Has Memory Conjecture in Capacitive Devices

This brief provides new sets of experimental results supporting Westerlund’s conjecture that Dead Matter Has Memory (Westerlund, 1991). Memory effects in the dynamic response of electric double-layer capacitors (EDLCs) that integrate its prior history of stimulation and state have been experimentally observed and reported in a few recent studies. The different excitation signals used to quantify such effects in these studies aimed at charging a device to the same voltage value and the exact same accumulated charge level but in different manners. Having reached the same unique voltage-charge point, it was observed that different yet repeatable discharge patterns occur, proving the existence of memory. The aim of this brief is to provide further experimental evidence of the inherent memory effect in EDLCs in response to time-varying stationary input excitations with different statistical properties. In particular, different sets of charging voltage waveforms composed of fixed dc values with superimposed uniformly-distributed random fluctuations of different amplitudes were created and used to charge the same EDLC device to a unique voltage-charge point. The duration of these signals was the same but with different values of variance around the mean value. We observed different time-charge responses depending on the extent of the noise level in these charging waveforms. This is interpreted and discussed in the context of inherent memory using fractional-order voltage-charge equations of non-ideal capacitors.
Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy

Federated learning (FL) is increasingly deployed among multiple clients to train a shared model over decentralized data. To address privacy concerns, FL systems need to safeguard the clients' data from disclosure during training and control data leakage through trained models when exposed to untrusted domains. Distributed differential privacy (DP) offers an appealing solution in this regard as it achieves a balanced tradeoff between privacy and utility without a trusted server. However, existing distributed DP mechanisms are impractical in the presence of client dropout, resulting in poor privacy guarantees or degraded training accuracy. In addition, these mechanisms suffer from severe efficiency issues. We present Dordis, a distributed differentially private FL framework that is highly efficient and resilient to client dropout. Specifically, we develop a novel `add-then-remove' scheme that enforces a required noise level precisely in each training round, even if some sampled clients drop out. This ensures that the privacy budget is utilized prudently, despite unpredictable client dynamics. To boost performance, Dordis operates as a distributed parallel architecture via encapsulating the communication and computation operations into stages. It automatically divides the global model aggregation into several chunk-aggregation tasks and pipelines them for optimal speedup. Large-scale deployment evaluations demonstrate that Dordis efficiently handles client dropout in various realistic FL scenarios, achieving the optimal privacy-utility tradeoff and accelerating training by up to 2.4$\times$ compared to existing solutions.
Nonlinear approaches to satisfiability problems

Interpretation and Partition Refinement for Model Checking D.R. Dams Faculty of Mathernaties and Computing Scienee, TUE, 1996-13 Topological Dualities in Bemantics M. M. Bonsangue Faculty of Mathernaties and Computer Scienee, VUA, 1996-14 Algorithms for Graphs of Small Treewidth B. L. E. de Fluiter Faculty of Mathernaties and Computer Scienee, UU, 1997-01 Process-algebraic Transformations in Context W. T. M. Kars Faculty of Computer Scienee, UT, 1997-02 A Generic Theory of Data Types P.F. Hoogendijk Faculty of Mathernaties and Computing Science, TUE, 1997-03 The Evolution of Type Theory in Logic and Mathernaties T.D. L. Laan Faeulty of Mathernaties and Computing Scienee, TUE, 1997-04 Preservation of Termination for Explicit Substitution C. J. Bloo Faeulty of Mathernaties and Computing Scienee, TUE, 1997-05 DiscreteTime Process Algebra J. J. Vereijken Faculty of Mathernaties and Computing Scienee, TUE, 1997-06 A Functional Approach to Syntax and Typing F. A. M. van den Beuken Faculty of Mathernaties and Informaties, KUN, 1997-07 Ins and Outs in Refusal Testing A.W. Heerink Faculty of Computer Science, UT, 1998-01 A Discrete-Event Simulator for Systems Engineering G. Naumoski and W. Alberts Faculty of Mechanica! Engineering, TUE, 1998-02 Scheduling with Communication for Multiprocessor Computation J. Verriet Faculty of Mathernaties and Computer Seienee, UU, 1998-03 An Asynchronous Low-Power 80C51 Microcontroller J. S.H. van Gageldonk Faculty of Mathernaties and Computing Scienee, TUE, 1998-04 In Terms of Nets: System Design with Petri Nets and Process Algebra A. A. Basten Faculty of Mathernaties and Oomputing Science, TUE, 1998-05 Inductive Datatypes with Laws and Subtyping -A Relational Model E. Voermans Faeulty of Mathernaties and Computing Science, TUE, 1999-01 Towards Probabilistic Unification-based Parsing H. ter Doest Faculty of Computer Scienee, UT, 1999-02 Algorithms for the Simulation of Surface Processes J.P.L. Segers Faeulty of Mathernaties and Computing Seienee, TUE, 1999-03 Recombinative Evolutionary Search C.H. M. van Kemenade LIAOS, faeulty of Mathernaties and Natura! Seienees, Leiden University, 1999-04 Learning Reliability: a Study on Indecisiveness in Sample Selection E.I. Barakova Faculty of Mathernaties and Natura! Sciences, RUG, 1999-05 Schedulere Optimization in RealTime Distributed Databases M.P. Bodlaender Faculty of Mathernaties and Oomputing Science, TUE, 1999-06 Message Sequence Chart: Syntax and Bemantics M.A. Reniers Faculty of Mathernaties and Cornputing Science, TUE, 1999-07 Nonlinear Approaches to Satisfiability Problems J . P. Warners Faculty of Mathernaties and Cornputing Science, TUE, 1999-08
Leveraging User-Triggered Supervision in Contextual Bandits

We study contextual bandit (CB) problems, where the user can sometimes respond with the best action in a given context. Such an interaction arises, for example, in text prediction or autocompletion settings, where a poor suggestion is simply ignored and the user enters the desired text instead. Crucially, this extra feedback is user-triggered on only a subset of the contexts. We develop a new framework to leverage such signals, while being robust to their biased nature. We also augment standard CB algorithms to leverage the signal, and show improved regret guarantees for the resulting algorithms under a variety of conditions on the helpfulness of and bias inherent in this feedback.
Multi-site Organ Segmentation with Federated Partial Supervision and Site Adaptation

Objective and Impact Statement: Accurate organ segmentation is critical for many clinical applications at different clinical sites, which may have their specific application requirements that concern different organs. Introduction: However, learning high-quality, site-specific organ segmentation models is challenging as it often needs on-site curation of a large number of annotated images. Security concerns further complicate the matter. Methods: The paper aims to tackle these challenges via a two-phase aggregation-then-adaptation approach. The first phase of federated aggregation learns a single multi-organ segmentation model by leveraging the strength of 'bigger data', which are formed by (i) aggregating together datasets from multiple sites that with different organ labels to provide partial supervision, and (ii) conducting partially supervised learning without data breach. The second phase of site adaptation is to transfer the federated multi-organ segmentation model to site-specific organ segmentation models, one model per site, in order to further improve the performance of each site's organ segmentation task. Furthermore, improved marginal loss and exclusion loss functions are used to avoid 'knowledge conflict' problem in a partially supervision mechanism. Results and Conclusion: Extensive experiments on five organ segmentation datasets demonstrate the effectiveness of our multi-site approach, significantly outperforming the site-per-se learned models and achieving the performance comparable to the centrally learned models.
Efficient post-processing of electromagnetic plane wave simulations to model arbitrary structured beams incident on axisymmetric structures

The study of an optical beam interacting with material structures is a fundamental of nanophotonics. Computational electromagnetic solvers facilitate the rapid calculation of the scattering from material structures with arbitrary geometry and complexity, but have limited efficiency when employing structured excitation fields. We have developed a post-processing method and package that can efficiently calculate the full three-dimensional electric and magnetic fields for any optical beam incident on a particle or structure with at least one axis of continuous rotational symmetry, called an axisymmetric body (such as a sphere, cylinder, cone, torus or surface). Provided an initial batch of plane wave simulations is computed, this open-source package combines data from computational electromagnetic solvers in a post-processing fashion using the angular spectrum representation to create arbitrarily structured beams, including vector vortex beams. Any and all possible incident beams can be generated from the initial batch of PWSs, without the need for further simulations. This allows for efficiently performing parameter sweeps such as changing the angle of illumination or translating the particle position relative to the beam, all in post-processing, with no need for additional time-consuming simulations. We demonstrate some applications by numerically calculating optical force and torque maps for a spherical plasmonic nanoparticle in a tightly focused Gaussian beam, a plasmonic nanocone in an azimuthally polarised beam and compute the fields of a non-paraxial Laguerre–Gaussian vortex beam reflecting on a multilayered surface. We believe this package, called BEAMS, is a valuable tool for rapidly quantifying electromagnetic systems that are beyond traditional analytical methods.
Weighted Edit Distance Computation: Strings, Trees, and Dyck

Given two strings of length n over alphabet Σ, and an upper bound k on their edit distance, the algorithm of Myers (Algorithmica’86) and Landau and Vishkin (JCSS’88) from almost forty years back computes the unweighted string edit distance in O(n+k2) time. To date, it remains the fastest algorithm for exact edit distance computation, and it is optimal under the Strong Exponential Hypothesis (Backurs and Indyk; STOC’15). Over the years, this result has inspired many developments, including fast approximation algorithms for string edit distance as well as similar Õ(n+poly(k))-time algorithms for generalizations to tree and Dyck edit distances. Surprisingly, all these results hold only for unweighted instances. While unweighted edit distance is theoretically fundamental, almost all real-world applications require weighted edit distance, where different weights are assigned to different edit operations (insertions, deletions, and substitutions), and the weights may vary with the characters being edited. Given a weight function w : Σ∪{ε} × Σ∪{ε} → ℝ≥ 0 (such that w(a,a) = 0 and w(a,b) ≥ 1 for all a, b∈ Σ∪{ε} with a ≠ b), the goal is to find an alignment that minimizes the total weight of edits. Except for the vanilla O(n2)-time dynamic-programming algorithm and its almost trivial O(nk)-time implementation (k being an upper bound on the sought total weight), none of the aforementioned developments on the unweighted edit distance applies to the weighted variant. In this paper, we propose the first O(n+poly(k))-time algorithm that computes the weighted string edit distance exactly, thus bridging a fundamental decades-old gap between our understanding of unweighted and weighted edit distance. We then generalize this result to the weighted tree and Dyck edit distances, bringing in several new techniques, which lead to a deterministic algorithm that improves upon the previous work even for unweighted tree edit distance. Given how fundamental weighted edit distance is, we believe our O(n+poly(k))-time algorithm will be instrumental for further significant developments in the area.