Miracle-dz/UniFAR-FTUs huggingface dataset:
Clifford Manipulations of Stabilizer States: A graphical rule book for Clifford unitaries and measurements on cluster states, and application to photonic quantum computing

Stabilizer states along with Clifford manipulations (unitary transformations and measurements) thereof -- despite being efficiently simulable on a classical computer -- are an important tool in quantum information processing, with applications to quantum computing, error correction and networking. Cluster states, defined on a graph, are a special class of stabilizer states that are central to measurement based quantum computing, all-photonic quantum repeaters, distributed quantum computing, and entanglement distribution in a network. All cluster states are local-Clifford equivalent to a stabilizer state. In this paper, we review the stabilizer framework, and extend it, by: incorporating general stabilizer measurements such as multi-qubit fusions, and providing an explicit procedure -- using Karnaugh maps from Boolean algebra -- for converting arbitrary stabilizer gates into tableau operations of the CHP formalism for efficient stabilizer manipulations. Using these tools, we develop a graphical rule-book and a MATLAB simulator with a graphical user interface for arbitrary stabilizer manipulations of cluster states, a user of which, e.g., for research in quantum networks, will not require any background in quantum information or the stabilizer framework. We extend our graphical rule-book to include dual-rail photonic-qubit cluster state manipulations with probabilistically-heralded linear-optical circuits for various rotated Bell measurements, i.e., fusions (including new `Type-I' fusions we propose, where only one of the two fused qubits is destructively measured), by incorporating graphical rules for their success and failure modes. Finally, we show how stabilizer descriptions of multi-qubit fusions can be mapped to linear optical circuits.
A Bayesian, multivariate calibration for Globigerinoides ruber Mg/Ca

The use of Mg/Ca in marine carbonates as a paleothermometer has been challenged by observations that implicate salinity as a contributing influence on Mg incorporation into biotic calcite and that dissolution at the sea‐floor alters the original Mg/Ca. Yet, these factors have not yet been incorporated into a single calibration model. We introduce a new Bayesian calibration for Globigerinoides ruber Mg/Ca based on 186 globally distributed core top samples, which explicitly takes into account the effect of temperature, salinity, and dissolution on this proxy. Our reported temperature, salinity, and dissolution (here expressed as deep‐water ΔCO32− ) sensitivities are (±2σ) 8.7±0.9%/°C, 3.9±1.2%/psu, and 3.3±1.3%/μmol. kg−1 below a critical threshold of 21 μmol/kg in good agreement with previous culturing and core‐top studies. We then perform a sensitivity experiment on a published record from the western tropical Pacific to investigate the bias introduced by these secondary influences on the interpretation of past temperature variability. This experiment highlights the potential for misinterpretations of past oceanographic changes when the secondary influences of salinity and dissolution are not accounted for. Multiproxy approaches could potentially help deconvolve the contributing influences but this awaits better characterization of the spatio‐temporal relationship between salinity and δ18Osw over millennial and orbital timescales.
Prompt Tuning for Zero-shot Compositional Learning

Open World Compositional Zero-Shot Learning (OW-CZSL) is known to be an extremely challenging task, which aims to recognize unseen compositions formed from seen attributes and objects without any prior assumption of the output space. In order to achieve this goal, a model has to be"smart"and"knowledgeable". To be smart, a model should be good at reasoning the interactions between attributes and objects from the seen compositions. While"knowledgeable"means the model owns"common sense"to the open world that can"foresee"some features of the unseen compositions. Most previous work focuses on the"smart"part, while few of them provided an effective solution to achieve the"knowledgeable"goal. In this paper, we proposed a framework named Multi-Modal Prompt Tuning (MMPT) to inherit the"knowledgeable"property from the large pre-trained vision-language model. Extensive experiments show that our proposed MMPT obtains new state-of-the-art results in OW-CZSL task. On the UT-Zappos dataset, MMPT pushes the AUC score to $29.8$, while the previous best score is $26.5$. On the more challenging MIT-States dataset, the AUC score of MMPT is 1.5 times better than the current state-of-the-art.
Regular sets of lines in rank 3 polar spaces

There are 6 families of finite polar spaces of rank $3$. The set of lines in a rank $3$ polar space form a rank $5$ association scheme. We determine the regular sets of minimal size in several of these polar spaces, and describe some examples. We also give a new family of Cameron--Liebler sets of generators in the polar spaces $O^+(10,q)$ when $q = 3^h$ using a regular set of lines in $O(7,q)$.
Hierarchical ML Codebook Design for Extreme MIMO Beam Management

Beam management is a strategy to unify beamforming and channel state information (CSI) acquisition with large antenna arrays in 5G. Codebooks serve multiple uses in beam management including beamforming reference signals, CSI reporting, and analog beam training. In this paper, we propose and evaluate a machine learning-refined codebook design process for extremely large multiple-input multiple-output (X-MIMO) systems. We propose a neural network and beam selection strategy to design the initial access and refinement codebooks using end-to-end learning from beamspace representations. The algorithm, called Extreme-Beam Management (X-BM), can significantly improve the performance of extremely large arrays as envisioned for 6G and capture realistic wireless and physical layer aspects. Our results show an 8dB improvement in initial access and overall effective spectral efficiency improvements compared to traditional codebook methods.
Working Backwards: Learning to Place by Picking

We present placing via picking (PvP), a method to autonomously collect real-world demonstrations for a family of placing tasks in which objects must be manipulated to specific contact-constrained locations. With PvP, we approach the collection of robotic object placement demonstrations by reversing the grasping process and exploiting the inherent symmetry of the pick and place problems. Specifically, we obtain placing demonstrations from a set of grasp sequences of objects initially located at their target placement locations. Our system can collect hundreds of demonstrations in contact-constrained environments without human intervention by combining two modules: tactile regrasping and compliant control for grasps. We train a policy directly from visual observations through behavioral cloning, using the autonomously-collected demonstrations. By doing so, the policy can generalize to object placement scenarios outside of the training environment without privileged information (e.g., placing a plate picked up from a table). We validate our approach in home robotic scenarios that include dishwasher loading and table setting. Our approach yields robotic placing policies that outperform policies trained with kinesthetic teaching, both in terms of performance and data efficiency, while requiring no human supervision.
Deep Learning Segmentation of Spiral Arms and Bars

We present the first deep learning model for segmenting galactic spiral arms and bars. In a blinded assessment by expert astronomers, our predicted spiral arm masks are preferred over both current automated methods (99% of evaluations) and our original volunteer labels (79% of evaluations). Experts rated our spiral arm masks as `mostly good' to `perfect' in 89% of evaluations. Bar lengths trivially derived from our predicted bar masks are in excellent agreement with a dedicated crowdsourcing project. The pixelwise precision of our masks, previously impossible at scale, will underpin new research into how spiral arms and bars evolve.
Cosmological constraints from the EFT power spectrum and tree-level bispectrum of 21cm intensity maps

We explore the information content of 21cm intensity maps in redshift space using the 1-loop Effective Field Theory power spectrum model and the bispectrum at tree level. The 21cm signal contains signatures of dark matter, dark energy and the growth of large-scale structure in the Universe. These signatures are typically analyzed via the 2-point correlation function or power spectrum. However, adding the information from the 3-point correlation function or bispectrum will be crucial to exploiting next-generation intensity mapping experiments. The bispectrum could offer a unique opportunity to break key parameter degeneracies that hinder the measurement of cosmological parameters and improve on the precision. We use a Fisher forecast analysis to estimate the constraining power of the HIRAX survey on cosmological parameters, dark energy and modified gravity.
A Review of Password-less User Authentication Schemes

Since the demise of the password was predicted in 2004, different attempts in industry and academia have been made to create an alternative for the use of passwords in authentication, without compromising on security and user experience. This review examines password-less authentication schemes that have been proposed since after the death knell was placed on passwords in 2004. We start with a brief discussion of the requirements of authentication systems and then identify various password-less authentication proposals to date. We then evaluate the truly password-less and practical schemes using a framework that examines authentication credentials based on their impact on user experience, overall security, and ease of deployment. The findings of this review observe a difficulty in balancing security with a user experience compared to that of passwords in new password-less schemes, providing the opportunity for new applied research to leverage existing knowledge and combine technologies and techniques in innovative ways that can address this imbalance.
Post-Newtonian Binary Dynamics in Effective Field Theory of Horndeski Gravity

General relativity has been very successful since its proposal more a century ago. However, various cosmological observations and theoretical consistency still motivate us to explore extended gravity theories. Horndeski gravity stands out as one attractive theory by introducing only one scalar field. Here we formulate the post-Newtonian effective field theory of Horndeski gravity and investigate the conservative dynamics of the inspiral compact binary systems. We calculate the leading effective Lagrangian for a compact binary and obtain the periastron advance per period. In particular, we apply our analytical calculation to two binary systems, PSR B 1534+12 and PSR J0737-3039, and constrain the relevant model parameters. The theoretical framework can also be extended to higher order systematically.
Higher Memory Effects in Numerical Simulations of Binary Black Hole Mergers

Gravitational memory effects are predictions of general relativity that are characterized by an observable effect that persists after the passage of gravitational waves. In recent years, they have garnered particular interest, both due to their connection to asymptotic symmetries and soft theorems and because their observation would serve as a unique test of the nonlinear nature of general relativity. Apart from the more commonly known displacement and spin memories, however, there are other memory effects predicted by Einstein's equations that are associated with more subleading terms in the asymptotic expansion of the Bondi-Sachs metric. In this paper, we write explicit expressions for these higher memory effects in terms of their charge and flux contributions. Further, by using a numerical relativity simulation of a binary black hole merger, we compute the magnitude and morphology of these terms and compare them to those of the displacement and spin memory. We find that, although these terms are interesting from a theoretical perspective, due to their small magnitude they will be particularly challenging to observe with current and future detectors.
Creative Agents: Empowering Agents with Imagination for Creative Tasks

We study building embodied agents for open-ended creative tasks. While existing methods build instruction-following agents that can perform diverse open-ended tasks, none of them demonstrates creativity -- the ability to give novel and diverse task solutions implicit in the language instructions. This limitation comes from their inability to convert abstract language instructions into concrete task goals in the environment and perform long-horizon planning for such complicated goals. Given the observation that humans perform creative tasks with the help of imagination, we propose a class of solutions for creative agents, where the controller is enhanced with an imaginator that generates detailed imaginations of task outcomes conditioned on language instructions. We introduce several approaches to implementing the components of creative agents. We implement the imaginator with either a large language model for textual imagination or a diffusion model for visual imagination. The controller can either be a behavior-cloning policy learned from data or a pre-trained foundation model generating executable codes in the environment. We benchmark creative tasks with the challenging open-world game Minecraft, where the agents are asked to create diverse buildings given free-form language instructions. In addition, we propose novel evaluation metrics for open-ended creative tasks utilizing GPT-4V, which holds many advantages over existing metrics. We perform a detailed experimental analysis of creative agents, showing that creative agents are the first AI agents accomplishing diverse building creation in the survival mode of Minecraft. Our benchmark and models are open-source for future research on creative agents (https://github.com/PKU-RL/Creative-Agents).
Supervised learning of spatial features with STDP and homeostasis using Spiking Neural Networks on SpiNNaker

Artificial Neural Networks (ANN) have gained large popularity thanks to their ability to learn using the well-known backpropagation algorithm. On the other hand, Spiking Neural Networks (SNNs), despite having wider abilities than ANNs, have always presented a challenge in the training phase. This paper shows a new method to perform supervised learning on SNNs, using Spike Timing Dependent Plasticity (STDP) and homeostasis, aiming at training the network to identify spatial patterns. The method is tested using the SpiNNaker digital architecture. A SNN is trained to recognise one or multiple patterns and performance metrics are extracted to measure the performance of the network. Some considerations are drawn from the results showing that, in the case of a single trained pattern, the network behaves as the ideal detector, with 100% accuracy in detecting the trained pattern. However, as the number of trained patterns on a single network increases, the accuracy of the identification is linked to the similarities between these patterns. This method of training an SNN to detect spatial patterns may be applied on pattern recognition in static images or traffic analysis in computer networks, where each network packet represents a spatial pattern. It will be stipulated that the homeostatic factor may enable the network to detect patterns with some degree of similarities, rather than only perfectly matching patterns.
Materials Expert-Artificial Intelligence for Materials Discovery

The advent of material databases provides an unprecedented opportunity to uncover predictive descriptors for emergent material properties from vast data space. However, common reliance on high-throughput ab initio data necessarily inherits limitations of such data: mismatch with experiments. On the other hand, experimental decisions are often guided by an expert's intuition honed from experiences that are rarely articulated. We propose using machine learning to"bottle"such operational intuition into quantifiable descriptors using expertly curated measurement-based data. We introduce"Materials Expert-Artificial Intelligence"(ME-AI) to encapsulate and articulate this human intuition. As a first step towards such a program, we focus on the topological semimetal (TSM) among square-net materials as the property inspired by the expert-identified descriptor based on structural information: the tolerance factor. We start by curating a dataset encompassing 12 primary features of 879 square-net materials, using experimental data whenever possible. We then use Dirichlet-based Gaussian process regression using a specialized kernel to reveal composite descriptors for square-net topological semimetals. The ME-AI learned descriptors independently reproduce expert intuition and expand upon it. Specifically, new descriptors point to hypervalency as a critical chemical feature predicting TSM within square-net compounds. Our success with a carefully defined problem points to the"machine bottling human insight"approach as promising for machine learning-aided material discovery.
Fur flutter in fluid flow fends off foulers

The fouling of submerged surfaces detrimentally alters stratum properties. Inorganic and organic foulers alike attach to and accumulate on surfaces when the complex interaction between numerous variables governing attachment and colonization is favourable. Unlike naturally evolved solutions, industrial methods of repellence carry adverse environmental impacts. Mammal fur demonstrates high resistance to fouling; however, our understanding of the intricacies of such performance remains limited. Here, we show that the passive trait of fur to dynamically respond to an external flow field dramatically improves its anti-fouling performance over that of fibres rigidly fixed at both ends. We have previously discovered a statistically significant correlation between a group of flow- and stratum-related properties, and the quantified anti-fouling performance of immobile filaments. In this work, we improve the correlation by considering an additional physical factor, the ability of hair to flex. Our work establishes a parametric framework for the design of passive anti-fouling filamentous structures and invites other disciplines to contribute to the investigation of the anti-fouling prowess of mammalian interfaces.
Uncovering the Holocene roots of contemporary disease-scapes: bringing archaeology into One Health

The accelerating pace of emerging zoonotic diseases in the twenty-first century has motivated cross-disciplinary collaboration on One Health approaches, combining microbiology, veterinary and environmental sciences, and epidemiology for outbreak prevention and mitigation. Such outbreaks are often caused by spillovers attributed to human activities that encroach on wildlife habitats and ecosystems, such as land use change, industrialized food production, urbanization and animal trade. While the origin of anthropogenic effects on animal ecology and biogeography can be traced to the Late Pleistocene, the archaeological record—a long-term archive of human–animal–environmental interactions—has largely been untapped in these One Health approaches, thus limiting our understanding of these dynamics over time. In this review, we examine how humans, as niche constructors, have facilitated new host species and ‘disease-scapes’ from the Late Pleistocene to the Anthropocene, by viewing zooarchaeological, bioarchaeological and palaeoecological data with a One Health perspective. We also highlight how new biomolecular tools and advances in the ‘-omics’ can be holistically coupled with archaeological and palaeoecological reconstructions in the service of studying zoonotic disease emergence and re-emergence.
Operation note on dengue vector control against Aedes albopictus in Chiba City, Japan, where an autochthonous dengue case was confirmed in September 2014.

: An autochthonous dengue patient was confirmed on September 8, 2014 in Chiba City, Japan and emergency control against potential dengue vector Aedes albopictus was implemented in the residential area where the case was found. We examined the distribution of human dwellings and areas with trees and bushes in the residential area by using an aerial photograph, selected an operation area within 100 m radius of the patient’s house, and carried out vector survey and insecticide spray in the operation area. The mean biting density of Ae. albopictus examined at six collection sites within the operation area was 2.8/person/8 min before insecticide applications. Etofenprox 7% and phenothrin 10% diluted 1/50 were applied to adult resting sites and larval habitats of Ae. albopictus at a rate of 500 mL/m 2 and 50 mL/m 2 with a power-driven sprayer and hand-pump sprayers, respectively. Additionally, a liquefied carbon dioxide formulation of phenothrin was applied at a rate of 1 g/m 2 to drains with a heavy cover. The mean biting density was reduced to 1.2/person/8 min on the same day after insecticide application, and weekly inspections of the operation area in the following 3 weeks found a total of four mosquitoes.
Gene expression patterns of Salpa thompsoni reveal remarkable differences in metabolism and reproduction near the Antarctic Polar Front

Salpa thompsoni is an important grazer in the Southern Ocean and most abundant in the Antarctic Polar Front (APF) region. During recent decades, their distribution expanded southwards. However, it is unclear whether salps can maintain their populations in the high Antarctic regions throughout the year owing to a poor understanding of their physiological responses to changing environmental conditions. We examined gene expression signatures of salps collected in two geographically close regions south of the APF that differed in water mass composition and productivity. The observed differences in the expression of genes related to reproductive, cellular and metabolic processes reflect variations in water temperature and food supply between the two regions studied here. Our study contributes to a better understanding of the physiological responses of S. thompsoni to changing environmental conditions, and how the species may adapt to a changing environment through potential geographical population shifts under future climate change scenarios.