Miracle-dz/UniFAR-FTUs huggingface dataset:
Foveated Thermal Computational Imaging in the Wild Using All-Silicon Meta-Optics

Foveated imaging provides a better tradeoff between situational awareness (field of view) and resolution and is critical in long-wavelength infrared regimes because of the size, weight, power, and cost of thermal sensors. We demonstrate computational foveated imaging by exploiting the ability of a meta-optical frontend to discriminate between different polarization states and a computational backend to reconstruct the captured image/video. The frontend is a three-element optic: the first element which we call the"foveal"element is a metalens that focuses s-polarized light at a distance of $f_1$ without affecting the p-polarized light; the second element which we call the"perifoveal"element is another metalens that focuses p-polarized light at a distance of $f_2$ without affecting the s-polarized light. The third element is a freely rotating polarizer that dynamically changes the mixing ratios between the two polarization states. Both the foveal element (focal length = 150mm; diameter = 75mm), and the perifoveal element (focal length = 25mm; diameter = 25mm) were fabricated as polarization-sensitive, all-silicon, meta surfaces resulting in a large-aperture, 1:6 foveal expansion, thermal imaging capability. A computational backend then utilizes a deep image prior to separate the resultant multiplexed image or video into a foveated image consisting of a high-resolution center and a lower-resolution large field of view context. We build a first-of-its-kind prototype system and demonstrate 12 frames per second real-time, thermal, foveated image, and video capture in the wild.
A Data Quality Assessment Framework for AI-enabled Wireless Communication

Using artificial intelligent (AI) to re-design and enhance the current wireless communication system is a promising pathway for the future sixth-generation (6G) wireless network. The performance of AI-enabled wireless communication depends heavily on the quality of wireless air-interface data. Although there are various approaches to data quality assessment (DQA) for different applications, none has been designed for wireless air-interface data. In this paper, we propose a DQA framework to measure the quality of wireless air-interface data from three aspects: similarity, diversity, and completeness. The similarity measures how close the considered datasets are in terms of their statistical distributions; the diversity measures how well-rounded a dataset is, while the completeness measures to what degree the considered dataset satisfies the required performance metrics in an application scenario. The proposed framework can be applied to various types of wireless air-interface data, such as channel state information (CSI), signal-to-interference-plus-noise ratio (SINR), reference signal received power (RSRP), etc. For simplicity, the validity of our proposed DQA framework is corroborated by applying it to CSI data and using similarity and diversity metrics to improve CSI compression and recovery in Massive MIMO systems.
Backreaction issue for the black hole in de Sitter spacetime

We consider quantum real massive scalar field in the de Sitter-Schwarzschild space-time backround. To have an analytic head way we study in detail the two-dimensional case, assuming that the situation in four dimensions will not be much different conceptually. It is assumed, that quantum field is in a thermal state i.e. described by the planckian distribution for the exact modes in the geometry under consideration. We calculate approximately the expectation value of stress-energy tensor near the cosmological and black hole horizons. It is shown that for a generic temperature backreaction from quantum fields on the geometry cannot be neglected. Thus, de Sitter-Schwarzschild space-time geometry inevitably is strongly modified by the quantum fluctuations of the matter fields.
ALP: Alleviating CPU-Memory Data Movement Overheads in Memory-Centric Systems

Partitioning applications between near-data processing (NDP) and host CPU cores causes inter-segment data movement overhead, which is caused by moving data generated by one segment (e.g., instructions, functions) and used in other consecutive segments. Prior works take two approaches to this problem. The first approach maps segments to NDP or host cores based on the properties of each segment, neglecting the inter-segment data movement overhead. The second approach partitions applications based on the overall memory bandwidth savings, and does not offload each segment to the best-fitting core if they incur high inter-segment data movement. We show that 1) mapping each segment to its best-fitting core ideally can provide substantial benefits, and 2) the inter-segment data movement reduces this benefit significantly. We introduce ALP, a new programmer-transparent technique to alleviate the inter-segment data movement overhead between host and memory in NDP systems. ALP proactively and accurately transfers the required data between the segments based on the key observation that the instructions that generate the inter-segment data stay the same across different executions of a program. ALP uses a compiler pass to identify these instructions and uses specialized hardware to transfer their produced data at runtime. We evaluate ALP across a wide range of workloads and demonstrate 54.3% and 45.4% average speedup over CPU-only and NDP-only executions, respectively.
A Taxonomy and Review of Lightweight Blockchain Solutions for Internet of Things Networks

Internet of things networks have spread to most digital applications in the past years. Examples of these networks include smart home networks, wireless sensor networks, Internet of Flying Things, and many others. One of the main difficulties that confront these networks is the security of their information and communications. A large number of solutions have been proposed to safeguard these networks from various types of cyberattacks. Among these solutions is the blockchain, which gained popularity in the last few years due to its strong security characteristics, such as immutability, cryptography, and distributed consensus. However, implementing the blockchain framework within the devices of these networks is very challenging, due to the limited resources of these devices and the resource-demanding requirements of the blockchain. For this reason, a large number of researchers proposed various types of lightweight blockchain solutions for resource-constrained networks. The “lightweight” aspect can be related to the blockchain architecture, device authentication, cryptography model, consensus algorithm, or storage method. In this paper, we present a taxonomy of the lightweight blockchain solutions that have been proposed in the literature and discuss the different methods that have been applied so far in each “lightweight” category. Our review highlights the missing points in existing systems and paves the way to building a complete lightweight blockchain solution for resource-constrained networks.
Efficiently Moving Instead of Reweighting Collider Events with Machine Learning

There are many cases in collider physics and elsewhere where a calibration dataset is used to predict the known physics and / or noise of a target region of phase space. This calibration dataset usually cannot be used out-of-the-box but must be tweaked, often with conditional importance weights, to be maximally realistic. Using resonant anomaly detection as an example, we compare a number of alternative approaches based on transporting events with normalizing flows instead of reweighting them. We find that the accuracy of the morphed calibration dataset depends on the degree to which the transport task is set up to carry out optimal transport, which motivates future research into this area.
Connectivity-constrained Interactive Panoptic Segmentation

—We address interactive panoptic annotation, where one segment all object and stuff regions in an image. We investigate two graph-based segmentation algorithms that both enforce connectivity of each region, with a notable class-aware Integer Linear Programming (ILP) formulation that ensures global optimum. Both algorithms can take RGB, or utilize the feature maps from any DCNN, whether trained on the target dataset or not, as input. We then propose an interactive, scribble-based annotation framework. We present competitive semantic and panoptic segmentation results on the PASCAL VOC 2012 and Cityscapes validation dataset given initial scribbles. We also demonstrate that our interactive approach can reach 90 . 6% mIoU on VOC with just 3 correction scribbles, and 62 . 8% PQ on Cityscapes with 13 minutes scribbles annotation plus one round ( 2 minutes) of correction.
Adsorption at Nanoconfined Solid-Water Interfaces.

Reactions at solid-water interfaces play a foundational role in water treatment systems, catalysis, and chemical separations, and in predicting chemical fate and transport in the environment. Over the last century, experimental measurements and computational models have made tremendous progress in capturing reactions at solid surfaces. The interfacial reactivity of a solid surface, however, can change dramatically and unexpectedly when it is confined to the nanoscale. Nanoconfinement can arise in different geometries such as pores/cages (3D confinement), channels (2D confinement), and slits (1D confinement). Therefore, measurements on unconfined surfaces, and molecular models parameterized based on these measurements, fail to capture chemical behaviors under nanoconfinement. This review evaluates recent experimental and theoretical advances, with a focus on adsorption at solid-water interfaces. We review how nanoconfinement alters the physicochemical properties of water, and how the structure and dynamics of nanoconfined water dictate energetics, pathways, and products of adsorption in nanopores. Finally, the implications of these findings and future research directions are discussed. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 74 is April 2023. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Rapid determination of LISA sensitivity to extreme mass ratio inspirals with machine learning

Gravitational wave observations of the inspiral of stellar-mass compact objects into massive black holes (MBHs), extreme mass ratio inspirals (EMRIs), enable precision measurements of parameters such as the MBH mass and spin. The Laser Interferometer Space Antenna is expected to detect sufficient EMRIs to probe the underlying source population, testing theories of the formation and evolution of MBHs and their environments. Population studies are subject to selection effects that vary across the EMRI parameter space, which bias inference results if unaccounted for. This bias can be corrected, but evaluating the detectability of many EMRI signals is computationally expensive. We mitigate this cost by (i) constructing a rapid and accurate neural network interpolator capable of predicting the signal-to-noise ratio of an EMRI from its parameters, and (ii) further accelerating detectability estimation with a neural network that learns the selection function, leveraging our first neural network for data generation. The resulting framework rapidly estimates the selection function, enabling a full treatment of EMRI detectability in population inference analyses. We apply our method to an astrophysically-motivated EMRI population model, demonstrating the potential selection biases and subsequently correcting for them. Accounting for selection effects, we predict that with 116 EMRI detections LISA will measure the MBH mass function slope to a precision of 8.8%, the CO mass function slope to a precision of 4.6%, the width of the MBH spin magnitude distribution to a precision of 10% and the event rate to a precision of 12% with EMRIs at redshifts below z = 6.
A Photometric Survey of Globular Cluster Systems in Brightest Cluster Galaxies

Hubble Space Telescope imaging for 26 giant early-type galaxies, all drawn from the MAST archive, is used to carry out photometry of their surrounding globular cluster (GC) systems. Most of these targets are Brightest Cluster Galaxies and their distances range from 24–210 Mpc. The catalogs of photometry, completed with DOLPHOT, are publicly available. The GC color indices are converted to [Fe/H] through a combination of 12 Gyr single stellar population models and direct spectroscopic calibration of the fiducial color index (F475W–F850LP). All of the resulting metallicity distribution functions (MDFs) can be accurately matched by bimodal-Gaussian functions. The GC systems in all of the galaxies also exhibit shallow metallicity gradients with projected galactocentric distance that average Z∼Rgc−0.3 . Several parameters of the MDFs including the means, dispersions, and blue/red fractions are summarized. Perhaps the most interesting new result is the trend of blue/red GC fraction with galaxy mass, which connects with predictions from recent simulations of GC formation within hierarchical assembly of large galaxies. The observed trend reveals two major transition stages: for low-mass galaxies, the metal-rich (red) GC fraction f(red) increases steadily with galaxy mass, until halo mass M h ≃ 3 × 1012 M ⊙. Above this point, more than half the metal-poor (blue) GCs come from accreted satellites and f(red) starts declining. But above a still higher transition point near M h ≃ 1014 M ⊙, the data hint that f(red) may start to increase again because the metal-rich GCs also become dominated by accreted systems.
Explanations Can Reduce Overreliance on AI Systems During Decision-Making

Prior work has identified a resilient phenomenon that threatens the performance of human-AI decision-making teams: overreliance, when people agree with an AI, even when it is incorrect. Surprisingly, overreliance does not reduce when the AI produces explanations for its predictions, compared to only providing predictions. Some have argued that overreliance results from cognitive biases or uncalibrated trust, attributing overreliance to an inevitability of human cognition. By contrast, our paper argues that people strategically choose whether or not to engage with an AI explanation, demonstrating empirically that there are scenarios where AI explanations reduce overreliance. To achieve this, we formalize this strategic choice in a cost-benefit framework, where the costs and benefits of engaging with the task are weighed against the costs and benefits of relying on the AI. We manipulate the costs and benefits in a maze task, where participants collaborate with a simulated AI to find the exit of a maze. Through 5 studies (N = 731), we find that costs such as task difficulty (Study 1), explanation difficulty (Study 2, 3), and benefits such as monetary compensation (Study 4) affect overreliance. Finally, Study 5 adapts the Cognitive Effort Discounting paradigm to quantify the utility of different explanations, providing further support for our framework. Our results suggest that some of the null effects found in literature could be due in part to the explanation not sufficiently reducing the costs of verifying the AI's prediction.
AWT - Clustering Meteorological Time Series Using an Aggregated Wavelet Tree

Both clustering and outlier detection play an important role for meteorological measurements. We present the AWT algorithm, a clustering algorithm for time series data that also performs implicit outlier detection during the clustering. AWT integrates ideas of several well-known K-Means clustering algorithms. It chooses the number of clusters automatically based on a user-defined threshold parameter, and it can be used for heterogeneous meteorological input data as well as for data sets that exceed the available memory size. We apply AWT to crowd sourced 2-m temperature data with an hourly resolution from the city of Vienna to detect outliers and to investigate if the final clusters show general similarities and similarities with urban land-use characteristics. It is shown that both the outlier detection and the implicit mapping to land-use characteristic is possible with AWT which opens new possible fields of application, specifically in the rapidly evolving field of urban climate and urban weather.
Scalable Measures of Magic Resource for Quantum Computers

Non-stabilizerness or magic resource characterizes the amount of non-Clifford operations needed to prepare quantum states. It is a crucial resource for quantum computing and a necessary condition for quantum advantage. However, quantifying magic resource beyond a few qubits has been a major challenge. Here, we introduce efficient measures of magic resource for pure quantum states with a sampling cost that is independent of the number of qubits. Our method uses Bell measurements over two copies of a state, which we implement in experiment together with a cost-free error mitigation scheme. We show the transition of classically simulable stabilizer states into intractable quantum states on the IonQ quantum computer. For applications, we efficiently distinguish stabilizer and non-stabilizer states with low measurement cost even in the presence of experimental noise. Further, we propose a variational quantum algorithm to maximize our measure via the shift-rule. Our algorithm can be free of barren plateaus even for highly expressible variational circuits. Finally, we experimentally demonstrate a Bell measurement protocol for the stabilizer R\'enyi entropy as well as the Wallach-Meyer entanglement measure. Our results pave the way to understand the non-classical power of quantum computers, quantum simulators and quantum many-body systems.
A Generic Approach for Reproducible Model Distillation

Model distillation has been a popular method for producing interpretable machine learning. It uses an interpretable"student"model to mimic the predictions made by the black box"teacher"model. However, when the student model is sensitive to the variability of the data sets used for training even when keeping the teacher fixed, the corresponded interpretation is not reliable. Existing strategies stabilize model distillation by checking whether a large enough corpus of pseudo-data is generated to reliably reproduce student models, but methods to do so have so far been developed for a specific student model. In this paper, we develop a generic approach for stable model distillation based on central limit theorem for the average loss. We start with a collection of candidate student models and search for candidates that reasonably agree with the teacher. Then we construct a multiple testing framework to select a corpus size such that the consistent student model would be selected under different pseudo samples. We demonstrate the application of our proposed approach on three commonly used intelligible models: decision trees, falling rule lists and symbolic regression. Finally, we conduct simulation experiments on Mammographic Mass and Breast Cancer datasets and illustrate the testing procedure throughout a theoretical analysis with Markov process. The code is publicly available at https://github.com/yunzhe-zhou/GenericDistillation.
ConvLab: Multi-Domain End-to-End Dialog System Platform

We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches, ranging from conventional pipeline systems to end-to-end neural models, in common environments. ConvLab offers a set of fully annotated datasets and associated pre-trained reference models. As a showcase, we extend the MultiWOZ dataset with user dialog act annotations to train all component models and demonstrate how ConvLab makes it easy and effortless to conduct complicated experiments in multi-domain end-to-end dialog settings.
Optimal regression designs with previous observations

SUMMARY A method is developed for constructing D-optimal designs in a linear regression context with k explanatory variables, when previous observations are available and a fixed number of new sets of values of the explanatory variables are to be chosen within the unit sphere. The effect of previous observations on the relationship between D-optimality and E-optimality is discussed. The relationship between designs constructed sequentially to be optimum for each new set of values and fully optimal designs is also discussed. Some brief comment is made on the relevance of the ideas to Bayesian design.
Secular Resonances in Mean Motion Commensurabilities: The 2/1 and 3/2 Cases

Abstract The dynamic behavior of asteroids orbiting near the mean motion commensurabilities 2/1 and 3/2 with Jupiter is strongly affected by secular perturbations due to the outer planets, which are not present in the usual models based on the restricted three-body problem. Moreover, many interesting and puzzling phenomena involve inclined asteroid orbits and cannot be studied by planar models. Therefore, we have investigated the location and the dynamic effects of the three main secular resonances v 5 , v 6 , and v 16 , inside the mean motion commensurabilities. This is done by means of a seminumerical method, which allows us to obtain a 3-D picture of the dynamics, including the case of asteroids at high inclinations. Our findings throw new light on the existence of the Hecuba gap and the Hilda group in the asteroid belt. In particular, we have found that: (i) The interaction between the v 5 and the v 6 resonances gives rise to large chaotic zones for eccentricities >0.25 and >0.45 in the 3/2 and 2/1 resonances, respectively, which may cause close encounters with Jupiter and ejection from the solar system—however, the Hilda asteroids avoid this chaotic zone due to a phase-related protection mechanism. (ii) At large eccentricities the resonance causing a libration of the argument of perihelion is also present and can force strong secular changes of the eccentricity and the inclination. (iii) Although the Hilda group is bounded below the v 16 resonance curve in the inclination vs. eccentricity plane, such a boundary is not present in the 2/1 resonance, where v 16 affects zero-inclination orbits at eccentricities of about 0.2. However, this resonance can substantially increase the inclination but not the eccentricity and does not affect nearly circular orbits; therefore its location is not sufficient to explain the formation of the Hecuba gap.
A fast-growing dengue virus mutant reveals a dual role of STING in response to infection

The four dengue viruses (DENVs) have evolved multiple mechanisms to ensure its survival. Among these mechanisms is the ability to regulate its replication rate, which may contribute to avoiding premature immune activation that limit infection dissemination: DENVs associated with dengue epidemics have shown slower replication rate than pre-epidemic strains. Correspondingly, wild-type DENVs replicate more slowly than their clinically attenuated derivatives. To understand how DENVs ‘make haste slowly’, we generated and screened for DENV2 mutants with accelerated replication that also induced high type-I interferon (IFN) expression in infected cells. We chanced upon a single NS2B-I114T amino acid substitution, in an otherwise highly conserved amino acid residue. Accelerated DENV2 replication damaged host DNA as mutant infection was dependent on host DNA damage repair factors, namely RAD21, EID3 and NEK5. DNA damage induced cGAS/STING signalling and activated early type-I IFN response that inhibited infection dissemination. Unexpectedly, STING activation also supported mutant DENV replication in infected cells through STING-induced autophagy. Our findings thus show that DENV NS2B has multi-faceted role in controlling DENV replication rate and immune evasion and suggest that the dual role of STING in supporting virus replication within infected cells but inhibiting infection dissemination could be particularly advantageous for live attenuated vaccine development.