Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Back-Translation with Uncertainty-based Confidence Estimation

While back-translation is simple and effective in exploiting abundant monolingual corpora to improve low-resource neural machine translation (NMT), the synthetic bilingual corpora generated by NMT models trained on limited authentic bilingual data are inevitably noisy. In this work, we propose to quantify the confidence of NMT model predictions based on model uncertainty. With word- and sentence-level confidence measures based on uncertainty, it is possible for back-translation to better cope with noise in synthetic bilingual corpora. Experiments on Chinese-English and English-German translation tasks show that uncertainty-based confidence estimation significantly improves the performance of back-translation.
Quantum preprocessing for information-theoretic security in two-party computation

In classical two-party computation, a trusted initializer who prepares certain initial correlations, known as one-time tables, can help make the inputs of both parties information-theoretically secure. We propose some bipartite quantum protocols with possible aborts for approximately generating such bipartite classical correlations with varying degrees of privacy, without introducing a third party. Under some weak requirements for the parties, the security level is nontrivial for use in bipartite computation. We show that the security is sometimes dependent on the noise level, but we propose a method for dealing with noise. The security is "forced security", which implies that the probability that some useful one-time tables are generated can approach $1$ in the noiseless case under quite weak assumptions about the parties, although the protocols allow aborts. We show how to use the generated one-time tables to achieve nontrivial information-theoretic security in generic two-party classical or quantum computation tasks, including (interactive) quantum homomorphic encryption. Our methods provide check-based implementations of some no-signaling correlations, including the PR-box type, with the help of communication which carry no information about the inputs in the generated correlations.
A Landauer Formula for Bioelectronic Applications

Recent electronic transport experiments using metallic contacts attached to proteins identified some “stylized facts”, which contradict conventional wisdom that increasing either the spatial distance between the electrodes or the temperature suppresses conductance exponentially. These include nearly temperature-independent conductance over the protein in the 30 to 300 K range, distance-independent conductance within a single protein in the 1 to 10 nm range and an anomalously large conductance in the 0.1 to 10 nS range. In this paper, we develop a generalization of the low temperature Landauer formula, which can account for the joint effects of tunneling and decoherence and can explain these new experimental findings. We use novel approximations, which greatly simplify the mathematical treatment and allow us to calculate the conductance in terms of a handful macroscopic parameters, instead of the myriads of microscopic parameters describing the details of an atomic level quantum chemical computation. The new approach makes it possible to get predictions for the outcomes of new experiments without relying solely on high performance computing and can distinguish important and unimportant details of the protein structures from the point of view of transport properties.
Form factors and decay of B̄s0 → J/ψϕ from QCD sum rule

We calculate [Formula: see text] translation form factors [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] based on QCD sum rule and study the nonleptonic two-body decay of [Formula: see text] with the form factors obtained. We calculate the time-integrated branching ratio of [Formula: see text] decay. The results for both the total branching ratio and the cases for the final vectors in longitudinal and transverse polarizations are consistent with experimental data.
Flexible Auto-Weighted Local-Coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering

Concept Factorization (CF) and its variants may produce inaccurate representation and clustering results due to the sensitivity to noise, hard constraint on the reconstruction error, and pre-obtained approximate similarities. To improve the representation ability, a novel unsupervised Robust Flexible Auto-weighted Local-coordinate Concept Factorization (RFA-LCF) framework is proposed for clustering high-dimensional data. Specifically, RFA-LCF integrates the robust flexible CF by clean data space recovery, robust sparse local-coordinate coding, and adaptive weighting into a unified model. RFA-LCF improves the representations by enhancing the robustness of CF to noise and errors, providing a flexible constraint on the reconstruction error and optimizing the locality jointly. For robust learning, RFA-LCF clearly learns a sparse projection to recover the underlying clean data space, and then the flexible CF is performed in the projected feature space. RFA-LCF also uses a L2,1-norm based flexible residue to encode the mismatch between the recovered data and its reconstruction, and uses the robust sparse local-coordinate coding to represent data using a few nearby basis concepts. For auto-weighting, RFA-LCF jointly preserves the manifold structures in the basis concept space and new coordinate space in an adaptive manner by minimizing the reconstruction errors on clean data, anchor points and coordinates. By updating the local-coordinate preserving data, basis concepts and new coordinates alternately, the representation abilities can be potentially improved. Extensive results on public databases show that RFA-LCF delivers the state-of-the-art clustering results compared with other related methods.
A Privacy-Preserving Longevity Study of Tor's Hidden Services

Tor and hidden services have emerged as a practical solution to protect user privacy against tracking and censorship. At the same time, little is known about the lifetime and nature of hidden services. Data collection and study of Tor hidden services is challenging due to its nature of providing privacy. Studying the lifetime of hidden services provides several benefits. For example, it allows investigation of the maliciousness of domains based on their lifetime. Short-lived hidden services are more likely not to be legitimate domains, e.g., used by ransomware, as compared to long-lived domains. In this work, we investigate the lifetime of hidden services by collecting data from a small (2%) subset of all Tor HSDir relays in a privacy-preserving manner. Based on the data collected, we devise protocols and extrapolation techniques to infer the lifetime of hidden services. Moreover we show that, due to Tor's specifics, our small subset of HSDir relays is sufficient to extrapolate lifetime with high accuracy, while respecting Tor user and service privacy and following Tor's research safety guidelines. Our results indicate that a large majority of the hidden services have a very short lifetime. In particular, 50% of all current Tor hidden services have an estimate lifetime of only 10 days or less, and 80% have a lifetime of less than a month.
Diversity Breeds Innovation With Discounted Impact and Recognition

Prior work poses a diversity paradox for science. Diversity breeds scientific innovation, and yet, diverse individuals have less successful scientific careers. But if diversity is good for innovation, why is science not rewarding diversity? We answer this question by utilizing a near-population of ~1.03 million US doctoral recipients from 1980-2015 and their careers into publishing and faculty roles. The article uses text analysis and machine learning techniques to answer a series of questions: How can we detect scientific innovation? Does diversity breed innovation? And are the innovations of diverse individuals adopted and rewarded? Our analyses show that underrepresented groups produce higher rates of scientific novelty. However, their novel contributions are discounted: e.g., innovations by gender minorities are taken up by other scholars at lower rates than innovations by gender majorities, and innovations by gender and racial minorities result in fewer academic positions. This suggests an unfair system in which diverse individuals innovate, but their innovations are disproportionately ignored and fail to convert into career success at the same rate as majority groups. In sum, there may be an unwarranted reproduction of stratification in academic careers that discounts diversity’s role in innovation and partly explains the underrepresentation of some groups in academia. Introduction Innovation is a key indicator of scientific progress. Innovation propels science into uncharted territories and expands humanity’s understanding of the natural and social world. Innovation is also believed to be predictive of successful scientific careers: innovators are science’s trailblazers and discoverers, so producing innovative science leads to successful academic careers (1). At a
Set Flow: A Permutation Invariant Normalizing Flow

We present a generative model that is defined on finite sets of exchangeable, potentially high dimensional, data. As the architecture is an extension of RealNVPs, it inherits all its favorable properties, such as being invertible and allowing for exact log-likelihood evaluation. We show that this architecture is able to learn finite non-i.i. d. set data distributions, learn statistical dependencies between entities of the set and is able to train and sample with variable set sizes in a computationally efficient manner. Experiments on 3D point clouds show state-of-the art likelihoods.
Imaginary-time matrix product state impurity solver in a real material calculation: Spin-orbit coupling in 
Sr2RuO4

Using an imaginary-time matrix-product state (MPS) based quantum impurity solver we perform a realistic dynamical mean-field theory (DMFT) calculation combined with density functional theory (DFT) for Sr2RuO4. We take the full Hubbard-Kanamori interactions and spin-orbit coupling (SOC) into account. The MPS impurity solver works at essentially zero temperature in the presence of SOC, a regime of parameters currently inaccessible to continuous-time quantum Monte Carlo methods, due to a severe sign problem. We show that earlier results obtained at high temperature, namely, that the diagonal self-energies are nearly unaffected by SOC and that interactions lead to an effective enhancement of the SOC, hold even at low temperature. We observe that realism makes the numerical solution of the impurity model with MPS much more demanding in comparison to earlier works on Bethe lattice models, requiring several algorithmic improvements.
MEBF: a fast and efficient Boolean matrix factorization method

Boolean matrix has been used to represent digital information in many fields, including bank transaction, crime records, natural language processing, protein-protein interaction, etc. Boolean matrix factorization (BMF) aims to find an approximation of a binary matrix as the Boolean product of two low rank Boolean matrices, which could generate vast amount of information for the patterns of relationships between the features and samples. Inspired by binary matrix permutation theories and geometric segmentation, we developed a fast and efficient BMF approach called MEBF (Median Expansion for Boolean Factorization). Overall, MEBF adopted a heuristic approach to locate binary patterns presented as submatrices that are dense in 1’s. At each iteration, MEBF permutates the rows and columns such that the permutated matrix is approximately Upper Triangular-Like (UTL) with so-called Simultaneous Consecutive-ones Property (SC1P). The largest submatrix dense in 1 would lies on the upper triangular area of the permutated matrix, and its location was determined based on a geometric segmentation of a triangular. We compared MEBF with other state of the art approaches on data scenarios with different sparsity and noise levels. MEBF demonstrated superior performances in lower reconstruction error, and higher computational efficiency, as well as more accurate sparse patterns than popular methods such as ASSO, PANDA and MP. We demonstrated the application of MEBF on both binary and non-binary data sets, and revealed its further potential in knowledge retrieving and data denoising.
A Radio-to-millimeter Census of Star-forming Galaxies in Protocluster 4C 23.56 at z = 2.5: Global and Local Gas Kinematics

We present a study of the gas kinematics of star-forming galaxies associated with protocluster 4C 23.56 at z = 2.49 using 0. ″4 resolution CO (4–3) data taken with ALMA. Eleven Hα emitters (HAEs) are detected in CO (4–3), including six HAEs that were previously detected in CO (3–2) at a coarser angular resolution. The detections in both CO lines are broadly consistent in the line widths and the redshifts, confirming both detections. With an increase in the number of spectroscopic redshifts, we confirm that the protocluster is composed of two merging groups with a total halo mass of log (Mcl/M⊙) = 13.4–13.6, suggesting that the protocluster would evolve into a Virgo-like cluster (>1014 M⊙). We compare the CO line widths and the CO luminosities with other (proto)clusters (ngal = 91) and general field (ngal = 80) galaxies from other studies. The 4C 23.56 protocluster galaxies have CO line widths and luminosities comparable to other protocluster galaxies on average. On the other hand, the CO line widths are on average broader by ≈50% compared to field galaxies, while the median CO luminosities are similar. The broader line widths can be attributed to both effects of unresolved gas-rich mergers and/or compact gas distribution, which is supported by our limited but decent angular resolution observations and the size estimate of three galaxies. Based on these results, we argue that gas-rich mergers may play a role in the retention of the specific angular momentum to a value similar to that of field populations during cluster assembly, though we need to verify this with a larger number of samples.
Identification, Doubly Robust Estimation, and Semiparametric Efficiency Theory of Nonignorable Missing Data With a Shadow Variable

We consider identification and estimation with an outcome missing not at random (MNAR). We study an identification strategy based on a so-called shadow variable. A shadow variable is assumed to be correlated with the outcome, but independent of the missingness process conditional on the outcome and fully observed covariates. We describe a general condition for nonparametric identification of the full data law under MNAR using a valid shadow variable. Our condition is satisfied by many commonly-used models; moreover, it is imposed on the complete cases, and therefore has testable implications with observed data only. We describe semiparametric estimation methods and evaluate their performance on both simulation data and a real data example. We characterize the semiparametric efficiency bound for the class of regular and asymptotically linear estimators, and derive a closed form for the efficient influence function.
ACFM: A Dynamic Spatial-Temporal Network for Traffic Prediction

As a crucial component in intelligent transportation systems, crowd flow prediction has recently attracted widespread research interest in the field of artificial intelligence (AI) with the increasing availability of large-scale traffic mobility data. Its key challenge lies in how to integrate diverse factors (such as temporal laws and spatial dependencies) to infer the evolution trend of crowd flow. To address this problem, we propose a unified neural network called Attentive Crowd Flow Machine (ACFM), which can effectively learn the spatial-temporal feature representations of crowd flow with an attention mechanism. In particular, our ACFM is composed of two progressive ConvLSTM units connected with a convolutional layer. Specifically, the first LSTM unit takes normal crowd flow features as input and generates a hidden state at each time-step, which is further fed into the connected convolutional layer for spatial attention map inference. The second LSTM unit aims at learning the dynamic spatial-temporal representations from the attentionally weighted crowd flow features. Further, we develop two deep frameworks based on ACFM to predict citywide short-term/long-term crowd flow by adaptively incorporating the sequential and periodic data as well as other external influences. Extensive experiments on two standard benchmarks well demonstrate the superiority of the proposed method for crowd flow prediction. Moreover, to verify the generalization of our method, we also apply the customized framework to forecast the passenger pickup/dropoff demands and show its superior performance in this traffic prediction task.
Fundamental Tradeoffs in Uplink Grant-Free Multiple Access with Protected CSI

In the envisioned 5G, uplink grant-free multiple access will become the enabler of ultra-reliable low-latency communications (URLLC) services. By removing the forward scheduling request (SR) and backward scheduling grant (SG), pilot-based channel estimation and data transmission are launched in one-shot communications with the aim of maintaining the reliability of $99.999\% $ or more and latency of 1ms or less under 5G new radio (NR) numerologies. The problem is that channel estimation can easily suffer from pilot aware attack which significantly reduces the system reliability. To solve this, we proposed to apply the hierarchical 2-D feature coding (H2DF) coding on time-frequency-code domain to safeguard channel state information (CSI), which informs a fundamental rethinking of reliability, latency and accessibility. Considering uplink large-scale single-input multiple-output (SIMO) reception of short packets, we characterize the analytical closed-form expression of reliability and define the accessibility of system. We find two fundamental tradeoffs: reliability-latency and reliability-accessibility. With the the help of the two fundamental trade-offs, we demonstrate how CSI protection could be integrated into uplink grant-free multiple access to strengthen URLLC services comprehensively.
Suzaku observation of diffuse X-ray emission from a southwest region of the Carina Nebula

A southwest region of the Carina Nebula was observed with the Suzaku observatory for $47\:$ks in 2010 December. This region shows distinctively soft X-ray emission in the Chandra campaign observations. Suzaku clearly detects the diffuse emission above known foreground and background components between 0.4–$5\:$keV at the surface brightness of $3.3\times 10^{-14}\:$erg$\:$s$^{-1}\:$arcmin$^{-2}$. The spectrum requires two plasma emission components with $kT \sim 0.2$ and $0.5\:$keV, which suffer interstellar absorption of $N_{\,\rm H} \sim 1.9 \times 10^{21}\:$cm$^{-2}$. Multiple absorption models assuming two-temperature plasmas at ionization equilibrium or non-equilibrium are tested but there is no significant difference in terms of $\chi ^{2}/$d.o. f. These plasma temperatures are similar to those of the central and eastern parts of the Carina Nebula measured in earlier Suzaku observations, but the surface brightness of the hot component is significantly lower than those of the other regions. This means that these two plasma components are physically separated and have different origins. The elemental abundances of O, Ne, and Mg with respect to Fe favor that the diffuse plasma originates from core-collapsed supernovae or massive stellar winds.
Dense gas formation and destruction in a simulated Perseus-like galaxy cluster with spin-driven black hole feedback

Context. Extended filamentary Hα emission nebulae are a striking feature of nearby galaxy clusters but the formation mechanism of the filaments, and the processes which shape their morphology remain unclear. Aims. We conduct an investigation into the formation, evolution and destruction of dense gas in the centre of a simulated, Perseus-like, cluster under the influence of a spin-driven jet. The jet is powered by the supermassive black hole (SMBH) located in the cluster’s brightest cluster galaxy. We particularly study the role played by condensation of dense gas from the diffuse intracluster medium, and the impact of direct uplifting of existing dense gas by the jets, in determining the spatial distribution and kinematics of the dense gas. Methods. We present a hydrodynamical simulation of an idealised Perseus-like cluster using the adaptive mesh refinement code RAMSES. Our simulation includes a SMBH that self-consistently tracks its spin evolution via its local accretion, and in turn drives a large-scale jet whose direction is based on the black hole’s spin evolution. The simulation also includes a live dark matter (DM) halo, a SMBH free to move in the DM potential, star formation and stellar feedback. Results. We show that the formation and destruction of dense gas is closely linked to the SMBH’s feedback cycle, and that its morphology is highly variable throughout the simulation. While extended filamentary structures readily condense from the hot intra-cluster medium, they are easily shattered into an overly clumpy distribution of gas during their interaction with the jet driven outflows. Condensation occurs predominantly onto infalling gas located 5−15 kpc from the centre during quiescent phases of the central AGN, when the local ratio of the cooling time to free fall time falls below 20, i.e. when tcool/tff < 20. Conclusions. We find evidence for both condensation and uplifting of dense gas, but caution that purely hydrodynamical simulations struggle to effectively regulate the cluster cooling cycle and produce overly clumpy distributions of dense gas morphologies, compared to observation.
Better Rewards Yield Better Summaries: Learning to Summarise Without References

Reinforcement Learning (RL)based document summarisation systems yield state-of-the-art performance in terms of ROUGE scores, because they directly use ROUGE as the rewards during training. However, summaries with high ROUGE scores often receive low human judgement. To find a better reward function that can guide RL to generate human-appealing summaries, we learn a reward function from human ratings on 2,500 summaries. Our reward function only takes the document and system summary as input. Hence, once trained, it can be used to train RL based summarisation systems without using any reference summaries. We show that our learned rewards have significantly higher correlation with human ratings than previous approaches. Human evaluation experiments show that, compared to the state-of-the-art supervised-learning systems and ROUGE-as-rewards RL summarisation systems, the RL systems using our learned rewards during training generate summaries with higher human ratings. The learned reward function and our source code are available at https://github.com/yg211/summary-reward-no-reference.