Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Cryptography in Algorithmica

We construct a classical oracle relative to which P = NP yet single-copy secure pseudorandom quantum states exist. In the language of Impagliazzo’s five worlds, this is a construction of pseudorandom states in ”Algorithmica,” and hence shows that in a black-box setting, quantum cryptography based on pseudorandom states is possible even if one-way functions do not exist. As a consequence, we demonstrate that there exists a property of a cryptographic hash function that simultaneously (1) suffices to construct pseudorandom states, (2) holds for a random oracle, and (3) is independent of P vs. NP in the black-box setting. We also introduce a conjecture that would generalize our results to multi-copy secure pseudorandom states. We build on the recent construction by Aaronson, Ingram, and Kretschmer (CCC 2022) of an oracle relative to which P = NP but BQP ≠ QCMA, based on hardness of the OR ∘ Forrelation problem. Our proof also introduces a new discretely-defined variant of the Forrelation distribution, for which we prove pseudorandomness against AC0 circuits. This variant may be of independent interest.
UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph

Multi-hop Question Answering over Knowledge Graph~(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question on a large-scale Knowledge Graph (KG). To cope with the vast search space, existing work usually adopts a two-stage approach: it first retrieves a relatively small subgraph related to the question and then performs the reasoning on the subgraph to find the answer entities accurately. Although these two stages are highly related, previous work employs very different technical solutions for developing the retrieval and reasoning models, neglecting their relatedness in task essence. In this paper, we propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning. For model architecture, UniKGQA consists of a semantic matching module based on a pre-trained language model~(PLM) for question-relation semantic matching, and a matching information propagation module to propagate the matching information along the directed edges on KGs. For parameter learning, we design a shared pre-training task based on question-relation matching for both retrieval and reasoning models, and then propose retrieval- and reasoning-oriented fine-tuning strategies. Compared with previous studies, our approach is more unified, tightly relating the retrieval and reasoning stages. Extensive experiments on three benchmark datasets have demonstrated the effectiveness of our method on the multi-hop KGQA task. Our codes and data are publicly available at~\url{https://github.com/RUCAIBox/UniKGQA}.
A machine-learning based closed orbit feedback for the SSRF storage ring

In order to improve the stability of synchrotron radiation, we developed a new method of machine learning-based closed orbit feedback, and piloted it in the storage ring of the Shanghai Synchrotron Radiation Facility (SSRF). In our experiments, not only can the machine learning-based closed orbit feedback carry out horizontal, vertical and RF frequency feedback simultaneously, but it also has better convergence and convergence speed than the traditional Slow Orbit Feed Back (SOFB) system. What's more, the residual values of the correctors’ currents variations after correction can be almost ignored. This machine learning-based new method is expected to establish a new closed orbit feedback system and improve the orbit stability of the storage ring in daily operation.
Vizaj - An interactive javascript tool for visualizing spatial networks

In many fields of science and technology we are confronted with complex networks. Making sense of these networks often require the ability to visualize and explore their intermingled structure consisting of nodes and links. To facilitate the identification of significant connectivity patterns, many methods have been developed based on the rearrangement of the nodes so as to avoid link criss-cross. However, real networks are often embedded in a geometrical space and the nodes code for an intrinsic physical feature of the system that one might want to preserve. For these spatial networks, it is therefore crucial to find alternative strategies operating on the links and not on the nodes. Here, we introduce Vizaj a javascript web application to visualize spatial networks based on optimized geometrical criteria that reshape the link profiles. While optimized for 3D networks, Vizaj can also be used for 2D networks and offers the possibility to interactively customize the visualization via several controlling parameters, including network filtering and the effect of internode distance on the link trajectories. Vizaj is further equipped with additional options allowing to improve the final aesthetics, such as the color/size of both nodes and links, zooming/rotating/translating, and superimposing external objects. Vizaj is an open-source software which can be freely downloaded and updated via a github repository. Here, we provide a detailed description of its main features and algorithms together with a guide on how to use it. Finally, we validate its potential on several synthetic and real spatial networks from infrastructural to biological systems. We hope that Vizaj will help scientists and practitioners to make sense of complex networks and provide aesthetic while informative visualizations.
The Bumpy Road of Taking Automated Debugging to Industry

Debugging is arguably among the most difficult and extremely time consuming tasks of the software development life cycle. Therefore, it comes as no surprise that researchers have invested a considerable amount of effort in developing automated techniques and tools to support developers excel in these tasks. Despite the significant advances, including demonstrations of usefulness, efficacy, and efficiency, these techniques are yet to find their way into industrial adoption. In this paper, we reflect upon the commercialization efforts of a particular automated debugging technique and lay down potential reasons for lack of success stories as well as ideas to move forward.
Observables for cyclic causal set cosmologies

In causal set theory, cycles of cosmic expansion and collapse are modelled by causal sets with ‘breaks’ and ‘posts’ and a special role is played by cyclic dynamics in which the universe goes through perpetual cycles. We identify and characterise two algebras of observables for cyclic dynamics in which the causal set universe has infinitely many breaks. The first algebra is constructed from the cylinder sets associated with finite causal sets that have a single maximal element and offers a new framework for defining cyclic dynamics as random walks on a novel tree. The second algebra is generated by a collection of stem-sets and offers a physical interpretation of the observables in these models as statements about unlabelled stems with a single maximal element. There are analogous theorems for cyclic dynamics in which the causal set universe has infinitely many posts.
Improved Representation Learning Through Tensorized Autoencoders

The central question in representation learning is what constitutes a good or meaningful representation. In this work we argue that if we consider data with inherent cluster structures, where clusters can be characterized through different means and covariances, those data structures should be represented in the embedding as well. While Autoencoders (AE) are widely used in practice for unsupervised representation learning, they do not fulfil the above condition on the embedding as they obtain a single representation of the data. To overcome this we propose a meta-algorithm that can be used to extend an arbitrary AE architecture to a tensorized version (TAE) that allows for learning cluster-specific embeddings while simultaneously learning the cluster assignment. For the linear setting we prove that TAE can recover the principle components of the different clusters in contrast to principle component of the entire data recovered by a standard AE. We validated this on planted models and for general, non-linear and convolutional AEs we empirically illustrate that tensorizing the AE is beneficial in clustering and de-noising tasks.
Navigating causal deep learning

Causal deep learning (CDL) is a new and important research area in the larger field of machine learning. With CDL, researchers aim to structure and encode causal knowledge in the extremely flexible representation space of deep learning models. Doing so will lead to more informed, robust, and general predictions and inference -- which is important! However, CDL is still in its infancy. For example, it is not clear how we ought to compare different methods as they are so different in their output, the way they encode causal knowledge, or even how they represent this knowledge. This is a living paper that categorises methods in causal deep learning beyond Pearl's ladder of causation. We refine the rungs in Pearl's ladder, while also adding a separate dimension that categorises the parametric assumptions of both input and representation, arriving at the map of causal deep learning. Our map covers machine learning disciplines such as supervised learning, reinforcement learning, generative modelling and beyond. Our paradigm is a tool which helps researchers to: find benchmarks, compare methods, and most importantly: identify research gaps. With this work we aim to structure the avalanche of papers being published on causal deep learning. While papers on the topic are being published daily, our map remains fixed. We open-source our map for others to use as they see fit: perhaps to offer guidance in a related works section, or to better highlight the contribution of their paper.
AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data

In tunnel boring machine (TBM) underground projects, an accurate description of the rock-soil types distributed in the tunnel can decrease the construction risk ({\it e.g.} surface settlement and landslide) and improve the efficiency of construction. In this paper, we propose an active learning framework, called AL-iGAN, for tunnel geological reconstruction based on TBM operational data. This framework contains two main parts: one is the usage of active learning techniques for recommending new drilling locations to label the TBM operational data and then to form new training samples; and the other is an incremental generative adversarial network for geological reconstruction (iGAN-GR), whose weights can be incrementally updated to improve the reconstruction performance by using the new samples. The numerical experiment validate the effectiveness of the proposed framework as well.
Augment like there's no tomorrow: Consistently performing neural networks for medical imaging

Deep neural networks have achieved impressive performance in a wide variety of medical imaging tasks. However, these models often fail on data not used during training, such as data originating from a different medical centre. How to recognize models suffering from this fragility, and how to design robust models are the main obstacles to clinical adoption. Here, we present general methods to identify causes for model generalisation failures and how to circumvent them. First, we use $\textit{distribution-shifted datasets}$ to show that models trained with current state-of-the-art methods are highly fragile to variability encountered in clinical practice, and then develop a $\textit{strong augmentation}$ strategy to address this fragility. Distribution-shifted datasets allow us to discover this fragility, which can otherwise remain undetected after validation against multiple external datasets. Strong augmentation allows us to train robust models achieving consistent performance under shifts from the training data distribution. Importantly, we demonstrate that strong augmentation yields biomedical imaging models which retain high performance when applied to real-world clinical data. Our results pave the way for the development and evaluation of reliable and robust neural networks in clinical practice.
Evolution is driven by natural autoencoding: reframing species, interaction codes, cooperation and sexual reproduction

The continuity of life and its evolution, we proposed, emerge from an interactive group process manifested in networks of interaction. We term this process survival of the fitted. Here, we reason that survival of the fitted results from a natural computational process we term natural autoencoding. Natural autoencoding works by retaining repeating biological interactions while non-repeatable interactions disappear. (i) We define a species by its species interaction code, which consists of a compact description of the repeating interactions of species organisms with their external and internal environments. Species interaction codes are descriptions recorded in the biological infrastructure that enables repeating interactions. Encoding and decoding are interwoven. (ii) Evolution proceeds by natural autoencoding of sustained changes in species interaction codes. DNA is only one element in natural autoencoding. (iii) Natural autoencoding accounts for the paradox of genome randomization in sexual reproduction—recombined genomes are analogous to the diversified inputs required for artificial autoencoding. The increase in entropy generated by genome randomization compensates for the decrease in entropy generated by organized life. (iv) Natural autoencoding and artificial autoencoding algorithms manifest defined similarities and differences. Recognition of the importance of fittedness could well serve the future of a humanly livable biosphere.
Generative Toolkit for Scientiﬁc Discovery

With the growing availability of data within various scientiﬁc domains, generative models hold enormous potential to accelerate scientiﬁc discovery. They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly. We present the Generative Toolkit for Scientiﬁc Discovery (GT4SD). This extensible open-source library enables scientists, developers, and researchers to train and use state-of-the-art generative models to accelerate scientiﬁc discovery focused on material design.
OSSE Upper Limits to Pulsar Gamma-Ray Emission

Abstract : We present upper limits from CGRO Oriented Scintillation Spectrometer Experiment (OSSE) observations of pulsars and a summary of pulsar observations made with the Compton Gamma-Ray Observatory (CGRO). We also report an upper limit to phase-averaged emission for the globular cluster 47 Tuc which is thought to contain many millisecond pulsars. The 2sigma upper limit is 3 x 10(exp -5) photons sq cm s(exp-1) keV(exp-1) in the 50-150 keV energy range. The best predictor of a pulsar having a detectable gamma-ray emission appears to be the X-ray luminosities observed by the Rontgen Satellite (ROSAT) (~0.1-2.4 keBV) or the EINSTEIN Satellite (~0.1-4.0 keV). We compare the gamma-ray data to models related to the inferred magnetic field, the spin down rate, an/or the frequency. Apparent correlations indicated by the pulsars detected by OSSE and the CGRO Energetic Gamma Ray Experiment Telescope (EGRET) have counter-examples in all cases.
A Theoretical Study of the Photodissociation Mechanism of Cyanoacetylene in Its Lowest Singlet and Triplet Excited States

Cyanoacetylene (H5-C4 ≡ C3-C2 ≡ N1) is a minor constituent of the atmosphere of Titan, and its photochemistry plays an important role in the formation of the haze surrounding the satellite. In this paper, the complete active space self-consistent field (CASSCF) and multiconfigurational second-order perturbation (CASPT2) approaches are employed to investigate the photochemical processes for cyanoacetylene in its first singlet and triplet excited states with the cc-pVTZ basis set. Fissions of the C4-H5 and C2-C3 bonds in S1 yield H(2S) + CCCN(A2Π) and HCC(A2Π) + CN(X2Σ+), respectively. In T1, the corresponding dissociation products are H(2S) + CCCN(X2Σ+) and HCC(X2Σ) + CN(X2Σ+). At the CASPT2(14,13)//CASSCF(14,13) + ZPE level, the barriers for the adiabatic dissociation of the C4-H5 and C2-C3 bonds are 6.11 and 6.94 eV in S1 and 5.71 and 6.39 eV in T1, respectively, taking the energy of S0 minimum as reference. Based on the calculated potential energy surfaces, the existence of a metastable excited molecule is anticipated upon 260-230 nm photoexcitation, which provides a probable approach for cyanoacetylene to polymerize. The internal conversion (IC) process through vibronic interaction followed by C4-H5 fission in the ground state is found to account for the observed diffuse character in the UV absorption spectrum below 240 nm.
Unbiased Estimator for a Covariance Matrix Under Two-Step Monotone Incomplete Sample

In this article, we consider an inference for a covariance matrix under two-step monotone incomplete sample. The maximum likelihood estimator of the mean vector is unbiased but that of the covariance matrix is biased. We derive an unbiased estimator for the covariance matrix using some fundamental properties of the Wishart matrix. The properties of the estimators are investigated and the accuracies are checked by a numerical simulation.
Atomic-resolution annular dark-field STEM image calculations

A method based on the three-dimensional Bloch wave description has been developed for the simulation of atomic-resolution annular dark-field (ADF) scanning transmission electron microscopy (STEM) images, which includes the coherent Bragg reflection and the incoherent thermal diffuse scattering (TDS). The contribution of TDS is estimated using two kinds of optical potentials. The validity and accuracy of the method are demonstrated by comparisons in focus dependence between experimental and calculated high-angle (HA) ADF STEM images and in thickness dependence between experimental and calculated ADF STEM intensities. The method reduces the computing time for a HAADF STEM image calculation to about one-tenth of that required for the usual three-dimensional Bloch wave method.
Survival models for censored point processes

In studies of recurrent events, there can be a lot of information about a cohort over a period of time, but it may not be possible to extract as much information from the data as would be liked. This thesis considers data on individuals experiencing recurrent events, before and after they are randomised to treatment. The prerandomisation outcome is a period count, while the post-randomisation outcome is a survival time. Standard survival analysis may treat the pre-randomisation period count as a covariate, but it is proposed that point process models will give a more precise estimate of the treatment effect. A joint model is presented, based on a Poisson process with individual frailty. The pre-randomisation seizure counts are distributed as Poisson variables with rate depending on explanatory variables as well as a random frailty. The model for the post-randomisation survival times is the exponential distribution with the same individual seizure rate, modified by a multiplicative treatment effect. A conjugate mixing distribution (frailty) is used, and alternative mixing distributions are also discussed. The model is motivated by and illustrated on individual patient data from five randomised trials of two treatments for epilepsy. The data are presented, and the standard analyses are contrasted with the results of the joint model. This thesis also considers the relative efficiency of the joint model compared to other survival models. Finally, some extensions to the model are considered, including a more general non-conjugate mixing distribution, and alternative ways of including explanatory variables in the joint model.
Robust stabilization for uncertain discrete singular systems with state delay

The robust stabilization problem for uncertain discrete singular time‐delay systems is addressed in this paper. In terms of strict linear matrix inequality and a finite sum inequality, a delay‐dependent criterion for the nominal systems to be admissible is obtained. Based on the criterion, a state feedback controller, which guarantees that, for all admissible uncertainties, the resulting closed‐loop system is regular, causal and stable, is constructed. An explicit expression for the desired controller is also given. The obtained results include both delay‐independent and delay‐dependent cases. Some numerical examples are introduced to show the effectiveness of the given results. Copyright © 2008 John Wiley & Sons, Ltd.
Sweet's mechanism for merging magnetic fields in conducting fluids

Sweet's mechanism for the merging of two oppositely directed magnetic fields in a highly conducting fluid is investigated in a semi-quantitative manner. It is shown that two oppositely directed sunspot fields with scales of 104 km could be merged by Sweet's mechanism, if shoved firmly together, in about two weeks; their normal interdiffusion time would be of the order of 600 years. It is suggested that Sweet's mechanism may be of considerable astrophysical importance: It gives a means of altering quickly the configuration of magnetic fields in ionized gases, allowing a stable field to go over into an unstable configuration, subsequently converting much of the magnetic energy into kinetic energy of the fluid.
Influence of Lignin Concentration on Polyacrylonitrile Nanofibers for Lead Removal

Water contamination by heavy metals is one of the most serious environmental problems and harms human life. Numerous nanotechnologies have been utilized to overcome this problem so far. Herein, we introduce lignin/polyacrylonitrile (PAN) composite nanofibers prepared via electrospinning for the removal of lead from aqueous solution. The effects of blend ratios between lignin and PAN concentration (LP) were investigated. The performance of adsorption process depends on the following parameters including contact time of adsorbent and adsorbate (equilibrium times: after 16 h. for 10 mg/L of lead concentration), types of nanofibers (LP55 at 1 g/L), and the percentage of lead removal was 72.5 % within 24 hours by LP55 nanofibers. The highest correlation coefficients were performed for the pseudo-second order kinetic model both LP55 and PAN nanofibers. This study demonstrates that the potential of the biomass-derived material with nanotechnology for environmental remediation.
Reconstruction of Si(001) and adsorption of Si adatoms and ad-dimers on the surface: Many-body potential calculations

A recently developed potential function for covalent materials [Phys. Status Solidi B 212, 9 (1999)] is used to simulate the reconstruction of the Si(001) surface, the surface adsorption, and diffusion of Si on the surface. For the simulation of reconstruction of Si(001) surface, our numerical results show that an empirical potential can correctly predict buckling-dimerized asymmetrical $(001)\ensuremath{-}(2\ifmmode\times\else\texttimes\fi{}1),$ $p\ensuremath{-}(2\ifmmode\times\else\texttimes\fi{}2),$ and $c\ensuremath{-}(4\ifmmode\times\else\texttimes\fi{}2)$ configurations and the relative stability between them in energetics. For the calculations of surface adsorption, we consider the formation energies and diffusion activation energies of several possible binding sites. The predicted stable and metastable configurations and diffusion paths of Si adatom and Si ad-dimers on the $\mathrm{Si}(001)\ensuremath{-}(2\ifmmode\times\else\texttimes\fi{}1)$ surface are in agreement with those from first-principles calculations and experiments.