Miracle-dz/UniFAR-FTUs huggingface dataset:
Observation of the Antimatter Hypernucleus $^4_{\bar{\Lambda}}\overline{\hbox{H}}$

Matter-antimatter asymmetry is a research topic of fundamental interest, as it is the basis for the existence of the matter world, which survived annihilation with antimatter in the early Universe. High energy nuclear collisions create conditions similar to the Universe microseconds after the Big Bang, with comparable amounts of matter and antimatter. Much of the antimatter created escapes the rapidly expanding fireball without annihilation, making such collisions an effective experimental tool to create heavy antimatter nuclear objects and study their properties. In this paper, we report the first observation of the antimatter hypernucleus $^4_{\bar{\Lambda}}\overline{\hbox{H}}$, composed of an $\bar{\Lambda}$, an antiproton and two antineutrons. The discovery was made through its two-body decay after production in ultrarelativistic heavy ion collisions by the STAR experiment at the Relativistic Heavy Ion Collider. In total, 15.6 candidate $^4_{\bar{\Lambda}}\overline{\hbox{H}}$ antimatter hypernuclei are obtained with an estimated background count of 6.4. Lifetimes of the antihypernuclei $^3_{\bar{\Lambda}}\overline{\hbox{H}}$ and $^4_{\bar{\Lambda}}\overline{\hbox{H}}$ are measured and compared with lifetimes of their corresponding hypernuclei, testing the symmetry between matter and antimatter. Various production yield ratios among (anti)hypernuclei and (anti)nuclei are also measured and compared with theoretical model predictions, shedding light on their production mechanism.
Stranger Danger! Cross-Community Interactions with Fringe Users Increase the Growth of Fringe Communities on Reddit

Fringe communities promoting conspiracy theories and extremist ideologies have thrived on mainstream platforms, raising questions about the mechanisms driving their growth. Here, we hypothesize and study a possible mechanism: new members may be recruited through fringe-interactions: the exchange of comments between members and non-members of fringe communities. We apply text-based causal inference techniques to study the impact of fringe-interactions on the growth of three prominent fringe communities on Reddit: r/Incel, r/GenderCritical, and r/The_Donald. Our results indicate that fringe-interactions attract new members to fringe communities. Users who receive these interactions are up to 4.2 percentage points (pp) more likely to join fringe communities than similar, matched users who do not. This effect is influenced by 1) the characteristics of communities where the interaction happens (e.g., left vs. right-leaning communities) and 2) the language used in the interactions. Interactions using toxic language have a 5pp higher chance of attracting newcomers to fringe communities than non-toxic interactions. We find no effect when repeating this analysis by replacing fringe (r/Incel, r/GenderCritical, and r/The_Donald) with non-fringe communities (r/climatechange, r/NBA, r/leagueoflegends), suggesting this growth mechanism is specific to fringe communities. Overall, our findings suggest that curtailing fringe-interactions may reduce the growth of fringe communities on mainstream platforms.
Effect of physical and chemical pressure on the superconductivity of caged-type quasiskutterudite Lu5Rh6Sn18

Lu5Rh6Sn18 is one of the caged-type quasiskutterudite superconductors with superconducting transition temperature Tc = 4.12 K. Here, we investigate the effect of pressure on the superconductivity in Lu5Rh6Sn18 by combining high pressure electrical transport, synchrotron x-ray diffraction (XRD) and chemical doping. Application of high pressure can enhance both the metallicity and the superconducting transition temperature in Lu5Rh6Sn18. Tc is found to show a continuous increase reaching up to 5.50 K at 11.4 GPa. Our high pressure synchrotron XRD measurements demonstrate the stability of the pristine crystal structure up to 12.0 GPa. In contrast, Tc is suppressed after the substitution of La ions in Lu sites, inducing negative chemical pressure. Our study provides valuable insights into the improvement of superconductivity in caged compounds.
Scale and conformal invariance in rotating few-fermion systems

We show that rotating two-dimensional Fermi gases possess a nonrelativistic scale and conformal invariance at weak interactions, where the scale invariance of universal short-range interactions is not broken by quantum effects. We demonstrate the symmetry in the excitation spectrum of few-fermion ensembles in a harmonic trap obtained by exact diagonalization, which are constrained by the operator-state correspondence. The excitation spectrum is shown to split in a set of primary states, the energies of which correspond to scaling dimensions of conformal operators, and derived excited states that consist of breathing modes as well as two different center-of-mass excitations, which describe cyclotron and guiding-center excitations of the total particle cloud. Furthermore, the conformal symmetry is manifest in the many-body wave function, where it dictates the form of the hyperradial component, which we demonstrate using Monte Carlo sampling of few-body wave functions.
Impacts of stormwater runoff from roads in South East Queensland

This paper describes the design, execution and interpretation of results from novel rainfall simulation experiments conducted on suburban Brisbane roads in 2003. These experiments employed state-of-the-art rainfall simulation technology to deliver controlled 3-month, ten-minute rainfall events to three predefined road areas. The temporal evolution of nutrient, sediment, heavy metal and petroleum hydrocarbon loads from each road surface was also measured downstream of the simulations at one-minute intervals. These experiments provide the first quantitative assessment of the pollutant export characteristics of suburban Brisbane roads under representative and quantifiable rainfall conditions. In particular, the study showed road runoff to be characterised by: a pollutant concentration first flush lasting approximately 3 minutes; and an approximately constant mass loading rate for a set rainfall intensity. The data showed that suspended solids and nutrient loads from roads can contribute up to 30% of total (urban) catchment loads, whilst up to 40% of heavy metals and petroleum hydrocarbon catchment loads originate from roads. It also showed that the majority of pollutant loads from roads are exported from frequent small rainfall events, as compared to infrequent larger rainfall events. Thirty 'washoff' experiments are also described, where high-pressure washing was used to liberate and collect maximum likely pollutant loads from predefined areas of suburban and arterial roads, and motorways. These experiments are discussed in the context of providing an 'upper limit' for considering the pollutant export observed in the rainfall simulations. The results are used to provide a quantitative justification for the prioritisation of road runoff treatment strategies.
GRI: Graph-based Relative Isomorphism of Word Embedding Spaces

Automated construction of bilingual dictionaries using monolingual embedding spaces is a core challenge in machine translation. The end performance of these dictionaries relies upon the geometric similarity of individual spaces, i.e., their degree of isomorphism. Existing attempts aimed at controlling the relative isomorphism of different spaces fail to incorporate the impact of semantically related words in the training objective. To address this, we propose GRI that combines the distributional training objectives with attentive graph convolutions to unanimously consider the impact of semantically similar words required to define/compute the relative isomorphism of multiple spaces. Experimental evaluation shows that GRI outperforms the existing research by improving the average P@1 by a relative score of up to 63.6%. We release the codes for GRI at https://github.com/asif6827/GRI.
A contextuality witness inspired by optimal state discrimination

Many protocols and tasks in quantum information science rely inherently on the fundamental notion of contextuality to provide advantages over their classical counterparts, and contextuality represents one of the main differences between quantum and classical physics. In this work we present a witness for preparation contextuality inspired by optimal two-state discrimination. The main idea is based on finding the accessible averaged success and error probabilities in both classical and quantum models. We can then construct a noncontextuality inequality and associated witness which we find to be robust against depolarising noise and loss in the form of inconclusive events.
Clustering and physical properties of the star-forming galaxies and AGN: does assembly bias have a role in AGN activity?

We compare the spatial clustering and physical properties of the active galactic nuclei (AGN) and star-forming galaxies (SFG) at fixed stellar mass using a volume limited sample from the Sloan Digital Sky Survey (SDSS). The analysis of the two-point correlation function shows that the AGN are more strongly clustered than the SFG. The closer proximity to the $5^{th}$ nearest neighbour for the AGN than that for the SFG indicates that AGN prefer the denser regions. We compare the distributions of the $(u-r)$ colour, star formation rate (SFR), D$4000$ and concentration index of the AGN and SFG after matching their stellar mass distributions. The null hypothesis can be rejected at $>99.99\%$ confidence level in each case. The comparisons are also carried out at different densities. The differences persist at the same significance level in both the high and low density regions, implying that such differences do not originate from the variations in the density. Alternatively stated, the AGN activity can be triggered in both the high and low density regions. An analysis of the correlations between the different physical properties at fixed stellar mass reveals that the anticorrelations of SFR with morphology, colour and recent star formation history are $2-3$ times stronger for the AGN than for the SFG. It suggests that the presence of a bulge and the availability of gas are the two most crucial requirements for AGN activity. We propose a picture where the galaxies at fixed stellar mass may have widely different assembly histories, leading to significant variations in bulge properties and cold gas content. Whether a galaxy of a given stellar mass can acquire the suitable conditions for AGN activity remains uncertain due to a broad diversity of assembly history. We conclude that AGN are stochastic phenomena owing to an underlying role of the assembly bias.
InferDPT: Privacy-Preserving Inference for Black-box Large Language Model

Large language models (LLMs), like ChatGPT, have greatly simplified text generation tasks. However, they have also raised concerns about privacy risks such as data leakage and unauthorized data collection. Existing solutions for privacy-preserving inference face practical challenges related to computation time and communication costs. In this paper, we propose InferDPT, the first practical framework for the privacy-preserving Inference of black-box LLMs, implementing Differential Privacy in Text generation. InferDPT comprises two key modules: the"perturbation module"utilizes the exponential mechanism to generate a perturbed prompt, facilitating privacy-preserving inference with black-box LLMs, and the"extraction module", inspired by knowledge distillation and retrieval-augmented generation, extracts coherent and consistent text from the perturbed generation result, ensuring successful text generation completion. To address privacy concerns related to previous exponential mechanisms' susceptibility to embedding revision attacks, we introduce RANTEXT, a novel differential privacy mechanism integrated into the perturbation module of InferDPT, which introduces the concept of"RANdom adjacency"for TEXT perturbation within the prompt. Experimental results across three datasets demonstrate that the text generation quality of InferDPT is comparable to that of non-private GPT-4, and RANTEXT surpasses existing state-of-the-art mechanisms, namely, SANTEXT+ and CUSTEXT+ in the trade-off between privacy and utility. Even with an privacy parameter epsilon value of 6.0, RANTEXT achieves an average privacy protection rate exceeding 90% against embedding revision attacks, which is 0.58 times higher than that of SANTEXT+ and 3.35 times higher than that of CUSTEXT+.
Reconciling early dark energy with a Harrison-Zeldovich spectrum

Recent attempts to fully resolve the Hubble tension from early dark energy models seem to favor a primordial Harrison-Zeldovich universe with its scalar spectrum being extremely scale invariant. Restoring the Harrison-Zeldovich spectrum within the single-field inflationary paradigm appears to be infeasible, turning to the multifield approach from either curvaton or waterfall models. In this Letter, we successfully align with the Harrison-Zeldovich spectrum within a single-field chaotic inflation by a nonminimal derivative coupling, and the previously disfavored chaotic potential by Planck+BICEP/Keck data in the standard $\Lambda$-cold-dark-matter model now returns back to the scope of future polarization observations of the cosmic microwave background.
Canonical normalizing flows for manifold learning

Manifold learning flows are a class of generative modelling techniques that assume a low-dimensional manifold description of the data. The embedding of such a manifold into the high-dimensional space of the data is achieved via learnable invertible transformations. Therefore, once the manifold is properly aligned via a reconstruction loss, the probability density is tractable on the manifold and maximum likelihood can be used to optimize the network parameters. Naturally, the lower-dimensional representation of the data requires an injective-mapping. Recent approaches were able to enforce that the density aligns with the modelled manifold, while efficiently calculating the density volume-change term when embedding to the higher-dimensional space. However, unless the injective-mapping is analytically predefined, the learned manifold is not necessarily an efficient representation of the data. Namely, the latent dimensions of such models frequently learn an entangled intrinsic basis, with degenerate information being stored in each dimension. Alternatively, if a locally orthogonal and/or sparse basis is to be learned, here coined canonical intrinsic basis, it can serve in learning a more compact latent space representation. Toward this end, we propose a canonical manifold learning flow method, where a novel optimization objective enforces the transformation matrix to have few prominent and non-degenerate basis functions. We demonstrate that by minimizing the off-diagonal manifold metric elements $\ell_1$-norm, we can achieve such a basis, which is simultaneously sparse and/or orthogonal. Canonical manifold flow yields a more efficient use of the latent space, automatically generating fewer prominent and distinct dimensions to represent data, and a better approximation of target distributions than other manifold flow methods in most experiments we conducted, resulting in lower FID scores.
knn-seq: Efficient, Extensible kNN-MT Framework

k-nearest-neighbor machine translation (kNN-MT) boosts the translation quality of a pre-trained neural machine translation (NMT) model by utilizing translation examples during decoding. Translation examples are stored in a vector database, called a datastore, which contains one entry for each target token from the parallel data it is made from. Due to its size, it is computationally expensive both to construct and to retrieve examples from the datastore. In this paper, we present an efficient and extensible kNN-MT framework, knn-seq, for researchers and developers that is carefully designed to run efficiently, even with a billion-scale large datastore. knn-seq is developed as a plug-in on fairseq and easy to switch models and kNN indexes. Experimental results show that our implemented kNN-MT achieves a comparable gain to the original kNN-MT, and the billion-scale datastore construction took 2.21 hours in the WMT'19 German-to-English translation task. We publish our knn-seq as an MIT-licensed open-source project and the code is available on https://github.com/naist-nlp/knn-seq . The demo video is available on https://youtu.be/zTDzEOq80m0 .
SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models

While advanced machine learning (ML) models are deployed in numerous real-world applications, previous works demonstrate these models have security and privacy vulnerabilities. Various empirical research has been done in this field. However, most of the experiments are performed on target ML models trained by the security researchers themselves. Due to the high computational resource requirement for training advanced models with complex architectures, researchers generally choose to train a few target models using relatively simple architectures on typical experiment datasets. We argue that to understand ML models' vulnerabilities comprehensively, experiments should be performed on a large set of models trained with various purposes (not just the purpose of evaluating ML attacks and defenses). To this end, we propose using publicly available models with weights from the Internet (public models) for evaluating attacks and defenses on ML models. We establish a database, namely SecurityNet, containing 910 annotated image classification models. We then analyze the effectiveness of several representative attacks/defenses, including model stealing attacks, membership inference attacks, and backdoor detection on these public models. Our evaluation empirically shows the performance of these attacks/defenses can vary significantly on public models compared to self-trained models. We share SecurityNet with the research community. and advocate researchers to perform experiments on public models to better demonstrate their proposed methods' effectiveness in the future.
Edge of entanglement in non-ergodic states: a complexity parameter formulation

We analyze the subsystem size scaling of the entanglement entropy of a non-ergodic pure state that can be described by a multi-parametric Gaussian ensemble of complex matrices in a bipartite basis. Our analysis indicates, for a given set of global constraints, the existence of infinite number of universality classes of local complexity, characterized by the complexity parameter, for which the entanglement entropy reveals a universal scaling with subsystem size. A rescaling of the complexity parameter helps us to identify the critical regime for the entanglement entropy of a broad range of pure non-ergodic states.
Relative entropy in de Sitter spacetime is a Noether charge

We compute the relative entropy between the vacuum and a coherent state for a massive scalar field in de Sitter spacetime, using Tomita-Takesaki modular theory and the Araki-Uhlmann formula for the relative entropy. Embedding de Sitter spacetime as a hyperboloid in the ambient Minkowski space, we can restrict the Minkowski wedge and the corresponding modular operator to de Sitter, and we verify that this construction gives the correct modular flow. We check that the relative entropy is positive and jointly convex, relate it to the Noether charge of translations along the trajectories of the modular flow, and determine the local temperature as seen by an observer that moves along these trajectories.
NuQKD: A Modular Quantum Key Distribution Simulation Framework for Engineering Applications

An experimental Quantum Key Distribution (QKD) implementation requires advanced costly hardware, unavailable in most research environments, making protocol testing and performance evaluation complicated. Historically, this has been a major motivation for the development of QKD simulation frameworks, to allow researchers to obtain insight before proceeding into practical implementations. Several simulators have been introduced over the recent years. However, only four are publicly available, only one of which models equipment imperfections. Currently, no open-source simulator includes all following capabilities: channel attenuation modelling, equipment imperfections and effect on key rates, estimation of elapsed time during quantum channel processes, use of truly random binary sequences for qubits and measurement bases, shared-bit fraction customization. In this paper, we present NuQKD, an open-source modular, intuitive simulator, featuring all the above capabilities. NuQKD establishes communication between two computer terminals, accepts custom inputs (iterations, raw key size, interception rate etc.) and evaluates the sifted key length, Quantum Bit Error Rate (QBER), elapsed communication time and more). NuQKD capabilities include optical fiber and free-space simulation, modeling of equipment/channel imperfections, bitstrings from True Random Number Generator, modular design and automated evaluation of performance metrics. We expect NuQKD to enable convenient and accurate representation of actual experimental conditions.
Multiscale Motion-Aware and Spatial-Temporal-Channel Contextual Coding Network for Learned Video Compression

Recently, learned video compression has achieved exciting performance. Following the traditional hybrid prediction coding framework, most learned methods generally adopt the motion estimation motion compensation (MEMC) method to remove inter-frame redundancy. However, inaccurate motion vector (MV) usually lead to the distortion of reconstructed frame. In addition, most approaches ignore the spatial and channel redundancy. To solve above problems, we propose a motion-aware and spatial-temporal-channel contextual coding based video compression network (MASTC-VC), which learns the latent representation and uses variational autoencoders (VAEs) to capture the characteristics of intra-frame pixels and inter-frame motion. Specifically, we design a multiscale motion-aware module (MS-MAM) to estimate spatial-temporal-channel consistent motion vector by utilizing the multiscale motion prediction information in a coarse-to-fine way. On the top of it, we further propose a spatial-temporal-channel contextual module (STCCM), which explores the correlation of latent representation to reduce the bit consumption from spatial, temporal and channel aspects respectively. Comprehensive experiments show that our proposed MASTC-VC is surprior to previous state-of-the-art (SOTA) methods on three public benchmark datasets. More specifically, our method brings average 10.15\% BD-rate savings against H.265/HEVC (HM-16.20) in PSNR metric and average 23.93\% BD-rate savings against H.266/VVC (VTM-13.2) in MS-SSIM metric.