Miracle-dz/UniFAR-FTUs huggingface dataset:
Pseudo standard entanglement structure cannot be distinguished from standard entanglement structure

An experimental verification of the maximally entangled state ensures that the constructed state is close to the maximally entangled state, but it does not guarantee that the state is exactly the same as the maximally entangled state. Further, the entanglement structure is not uniquely determined in general probabilistic theories even if we impose that the local subsystems are fully equal to quantum systems. Therefore, the existence of the maximally entangled state depends on whether the standard entanglement structure (SES) is valid. To examine this issue, we introduce pseudo SES as a structure of quantum composite system under natural assumptions based on the existence of projective measurements and the existence of approximations of all maximally entangled standard states. Surprisingly, there exist infinitely many pseudo SESs different from the SES. In our setting, any maximally entangled state can be arbitrarily approximated by an entangled state that belongs to our obtained pseudo standard entanglement structure. That is, experimental verification does not exclude the possibility of our obtained pseudo standard entanglement structure that is different from the standard entanglement structure. On the other hand, such pseudo structures never possess global unitary symmetry, i.e. global unitary symmetry is essential condition for the SES.
Constraining the Radio Properties of the $z$=6.44 QSO VIK J2318$-$3113

The recent detection of the quasi-stellar object (QSO) VIKING J231818.3$-$311346 (hereafter VIK J2318$-$3113) at redshift $z=6.44$ in the Rapid ASKAP Continuum Survey (RACS) uncovered its radio-loud nature, making it one of the most distant known to date in this class. By using data from several radio surveys of the Galaxy and Mass Assembly 23$^\mathrm{h}$ field and from dedicated follow-up, we were able to constrain the radio spectrum of VIK J2318$-$3113 in the observed range $\sim$0.1--10 GHz. At high frequencies (0.888--5.5 GHz in the observed frame) the QSO presents a steep spectrum ($\alpha_{\rm r}$=1.24, with $S_\nu\propto \nu^{-\alpha_{\rm r}}$), while at lower frequencies (0.4--0.888 GHz in the observed frame) it is nearly flat. The overall spectrum can be modelled by either a curved function with a rest-frame turnover around 5 GHz, or with a smoothly varying double power law that is flat below a rest-frame break frequency of about 20 GHz and which significantly steepens above it. Based on the model adopted, we estimated that the radio jets of VIK J2318$-$3113 must be a few hundred years old, in the case of a turnover, or less than few$\times$10$^4$ years, in the case of a break in the spectrum. Having multiple observations at two frequencies (888 MHz and 5.5 GHz), we further investigated the radio variability previously reported for this source. We found that the marginally significant flux density variations are consistent with the expectations from refractive interstellar scintillation, even though relativistic effects related to the orientation of the source may still play a non-negligible role. Further radio and X-ray observations are required to conclusively discern the nature of this variation.
Distributed Coded Modulation Schemes for Multiple Access Relay Channels

In this paper, we investigate network nest coded modulation schemes for multiple access relay channels. The performance of the distributed systems which are based on distributed convolutional codes with network coded modulation is presented. An analytical upper bound on bit error probability performance for the studied distributed systems with Maximum Likelihood Sequence Detection (MLSD) is derived. The constructured bounds for the investigated systems are shown to be asymptotically tight for increasing channel Signal-to-Noise Ratio (SNR) values
AraT5: Text-to-Text Transformers for Arabic Language Generation

Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format was recently proposed as a simple and effective transfer learning approach. Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-English tasks involving diverse data. To investigate this question, we apply mT5 on a language with a wide variety of dialects–Arabic. For evaluation, we introduce a novel benchmark for ARabic language GENeration (ARGEN), covering seven important tasks. For model comparison, we pre-train three powerful Arabic T5-style models and evaluate them on ARGEN. Although pre-trained with ~49 less data, our new models perform significantly better than mT5 on all ARGEN tasks (in 52 out of 59 test sets) and set several new SOTAs. Our models also establish new SOTA on the recently-proposed, large Arabic language understanding evaluation benchmark ARLUE (Abdul-Mageed et al., 2021). Our new models are publicly available. We also link to ARGEN datasets through our repository: https://github.com/UBC-NLP/araT5.
Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information

In linguistics, a sememe is defined as the minimum semantic unit of languages. Sememe knowledge bases (KBs), which are built by manually annotating words with sememes, have been successfully applied to various NLP tasks. However, existing sememe KBs only cover a few languages, which hinders the wide utilization of sememes. To address this issue, the task of sememe prediction for BabelNet synsets (SPBS) is presented, aiming to build a multilingual sememe KB based on BabelNet, a multilingual encyclopedia dictionary. By automatically predicting sememes for a BabelNet synset, the words in many languages in the synset would obtain sememe annotations simultaneously. However, previous SPBS methods have not taken full advantage of the abundant information in BabelNet. In this paper, we utilize the multilingual synonyms, multilingual glosses and images in BabelNet for SPBS. We design a multimodal information fusion model to encode and combine this information for sememe prediction. Experimental results show the substantial outperformance of our model over previous methods (about 10 MAP and F1 scores). All the code and data of this paper can be obtained at https://github.com/thunlp/MSGI.
Adversarial counterfactual augmentation: application in Alzheimer’s disease classification

Due to the limited availability of medical data, deep learning approaches for medical image analysis tend to generalise poorly to unseen data. Augmenting data during training with random transformations has been shown to help and became a ubiquitous technique for training neural networks. Here, we propose a novel adversarial counterfactual augmentation scheme that aims at finding the most effective synthesised images to improve downstream tasks, given a pre-trained generative model. Specifically, we construct an adversarial game where we update the input conditional factor of the generator and the downstream classifier with gradient backpropagation alternatively and iteratively. This can be viewed as finding the ‘weakness’ of the classifier and purposely forcing it to overcome its weakness via the generative model. To demonstrate the effectiveness of the proposed approach, we validate the method with the classification of Alzheimer’s Disease (AD) as a downstream task. The pre-trained generative model synthesises brain images using age as conditional factor. Extensive experiments and ablation studies have been performed to show that the proposed approach improves classification performance and has potential to alleviate spurious correlations and catastrophic forgetting. Code: https://github.com/xiat0616/adversarial_counterfactual_augmentation
Kondo cooling in quantum impurity systems

The Peltier effect is the reverse phenomenon of the Seebeck effect, and has been observed experimentally in nanoscale junctions. However, despite its promising applications in local cooling of nanoelectronic devices, the role of strong electron correlations on such a phenomenon is still unclear. Here, by analyzing the thermoelectric properties of quantum impurity systems out of equilibrium, we unveil the essential role of electron-electron interactions and quantum resonant states in Peltier cooling, leading to the prediction of the Kondo cooling phenomenon. The existence of such Kondo cooling is validated by a reverse heat current and a lowered local temperature in a model junction. The discovery of this unconventional Peltier cooling offers a new approach toward nano-refrigeration, and highlights the unique role of strong electron correlations in nonequilibrium quantum systems.
Distraction is All You Need for Fairness

Bias in training datasets must be managed for various groups in classification tasks to ensure parity or equal treatment. With the recent growth in artificial intelligence models and their expanding role in automated decision-making, ensuring that these models are not biased is vital. There is an abundance of evidence suggesting that these models could contain or even amplify the bias present in the data on which they are trained, inherent to their objective function and learning algorithms; Many researchers direct their attention to this issue in different directions, namely, changing data to be statistically independent, adversarial training for restricting the capabilities of a particular competitor who aims to maximize parity, etc. These methods result in information loss and do not provide a suitable balance between accuracy and fairness or do not ensure limiting the biases in training. To this end, we propose a powerful strategy for training deep learning models called the Distraction module, which can be theoretically proven effective in controlling bias from affecting the classification results. This method can be utilized with different data types (e.g., Tabular, images, graphs, etc.). We demonstrate the potency of the proposed method by testing it on UCI Adult and Heritage Health datasets (tabular), POKEC-Z, POKEC-N and NBA datasets (graph), and CelebA dataset (vision). Using state-of-the-art methods proposed in the fairness literature for each dataset, we exhibit our model is superior to these proposed methods in minimizing bias and maintaining accuracy.
Causal and self-dual morphisms in four complex dimensions

We deﬁne a class of maps between holomorphically embedded null curves which generalize conformal transformations, and can be deﬁned in any complex dimension. In four dimensions, we can also deﬁne a similar map between self-dual surfaces, which generalize ﬂat α -planes. These maps are respectively called causal and self-dual morphisms. It is shown that there exist an inﬁnite class of non-trivial examples for both types of maps in four dimensions.
Concentration Properties of Random Codes

This paper shows that, for discrete memoryless channels, the error exponent of a randomly generated code with independent codewords converges in probability to its expectation—the typical error exponent. For high rates, the result follows from the fact that the random-coding error exponent and the sphere-packing error exponent coincide. For low rates, instead, the convergence is based on the fact that the union bound accurately characterizes the error probability. The paper also zooms into the behavior at asymptotically low rates, and shows that the normalized error exponent converges in distribution to the standard Gaussian or a Gaussian-like distribution. We also state several results on the convergence of the error probability and error exponent for generic ensembles and channels.
OcclusionFusion: Occlusion-aware Motion Estimation for Real-time Dynamic 3D Reconstruction

RGBD-based real-time dynamic 3D reconstruction suffers from inaccurate inter-frame motion estimation as errors may accumulate with online tracking. This problem is even more severe for single-view-based systems due to strong occlusions. Based on these observations, we propose OcclusionFusion, a novel method to calculate occlusion-aware 3D motion to guide the reconstruction. In our technique, the motion of visible regions is first estimated and combined with temporal information to infer the motion of the occluded regions through an LSTM-involved graph neural network. Furthermore, our method computes the confidence of the estimated motion by modeling the network output with a probabilistic model, which alleviates untrust-worthy motions and enables robust tracking. Experimental results on public datasets and our own recorded data show that our technique outperforms existing single-view-based real-time methods by a large margin. With the reduction of the motion errors, the proposed technique can handle long and challenging motion sequences. Please check out the project page for sequence results: https://wenbinlin.github. io/OcclusionFusion.
Implicit field supervision for robust non-rigid shape matching

Establishing a correspondence between two non-rigidly deforming shapes is one of the most fundamental problems in visual computing. Existing methods often show weak resilience when presented with challenges innate to real-world data such as noise, outliers, self-occlusion etc. On the other hand, auto-decoders have demonstrated strong expressive power in learning geometrically meaningful latent embeddings. However, their use in \emph{shape analysis} has been limited. In this paper, we introduce an approach based on an auto-decoder framework, that learns a continuous shape-wise deformation field over a fixed template. By supervising the deformation field for points on-surface and regularising for points off-surface through a novel \emph{Signed Distance Regularisation} (SDR), we learn an alignment between the template and shape \emph{volumes}. Trained on clean water-tight meshes, \emph{without} any data-augmentation, we demonstrate compelling performance on compromised data and real-world scans.
Wehrl entropy, coherent states and quantum channels

We review Wehrl’s definition of a semiclassical entropy in terms of coherent states and give an introductory overview of Lieb’s conjecture, its proof (including earlier results), generalizations, and the role of covariant quantum channels in this context. These structures motivate an alternative definition of coherent states and have interesting physical applications and implications. Dedicated to Elliott Lieb on the occasion of his 90th birthday.
Reachability In Simple Neural Networks

We investigate the complexity of the reachability problem for (deep) neural networks: does it compute valid output given some valid input? It was recently claimed that the problem is NP-complete for general neural networks and specifications over the input/output dimension given by conjunctions of linear inequalities. We recapitulate the proof and repair some flaws in the original upper and lower bound proofs. Motivated by the general result, we show that NP-hardness already holds for restricted classes of simple specifications and neural networks. Allowing for a single hidden layer and an output dimension of one as well as neural networks with just one negative, zero and one positive weight or bias is sufficient to ensure NP-hardness. Additionally, we give a thorough discussion and outlook of possible extensions for this direction of research on neural network verification.
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning

A Temporal Knowledge Graph (TKG) is a sequence of KGs corresponding to different timestamps. TKG reasoning aims to predict potential facts in the future given the historical KG sequences. One key of this task is to mine and understand evolutional patterns of facts from these sequences. The evolutional patterns are complex in two aspects, length-diversity and time-variability. Existing models for TKG reasoning focus on modeling fact sequences of a fixed length, which cannot discover complex evolutional patterns that vary in length. Furthermore, these models are all trained offline, which cannot well adapt to the changes of evolutional patterns from then on. Thus, we propose a new model, called Complex Evolutional Network (CEN), which uses a length-aware Convolutional Neural Network (CNN) to handle evolutional patterns of different lengths via an easy-to-difficult curriculum learning strategy. Besides, we propose to learn the model under the online setting so that it can adapt to the changes of evolutional patterns over time. Extensive experiments demonstrate that CEN obtains substantial performance improvement under both the traditional offline and the proposed online settings.
Social Choice Around the Block: On the Computational Social Choice of Blockchain

One of the most innovative aspects of blockchain technology consists in the introduction of an incentive layer to regulate the behavior of distributed protocols. The designer of a blockchain system faces therefore issues that are akin to those relevant for the design of economic mechanisms, and faces them in a computational setting. From this perspective the present paper argues for the importance of computational social choice in blockchain research. It identifies a few challenges at the interface of the two fields that illustrate the strong potential for cross-fertilization between them.
Towards Building A Group-based Unsupervised Representation Disentanglement Framework

Disentangled representation learning is one of the major goals of deep learning, and is a key step for achieving explainable and generalizable models. A well-defined theoretical guarantee still lacks for the VAE-based unsupervised methods, which are a set of popular methods to achieve unsupervised disentanglement. The Group Theory based definition of representation disentanglement mathematically connects the data transformations to the representations using the formalism of group. In this paper, built on the group-based definition and inspired by the n-th dihedral group, we first propose a theoretical framework towards achieving unsupervised representation disentanglement. We then propose a model, based on existing VAE-based methods, to tackle the unsupervised learning problem of the framework. In the theoretical framework, we prove three sufficient conditions on model, group structure, and data respectively in an effort to achieve, in an unsupervised way, disentangled representation per group-based definition. With the first two of the conditions satisfied and a necessary condition derived for the third one, we offer additional constraints, from the perspective of the group-based definition, for the existing VAE-based models. Experimentally, we train 1800 models covering the most prominent VAE-based methods on five datasets to verify the effectiveness of our theoretical framework. Compared to the original VAE-based methods, these Groupified VAEs consistently achieve better mean performance with smaller variances.
Evolution and conservation genetics of an insular hemiparasitic plant lineage at the limit of survival: the case of Thesium sect. Kunkeliella in the Canary Islands

Abstract Premise The diversification of island flora has been widely studied. However, the role of environmental niches in insular radiation processes has been less investigated. We combined population genetic analyses with species distribution modelling to clarify the genetic relationships, diversification patterns, species niche requirements, and conservation of Thesium sect. Kunkeliella, a clade of rare hemiparasitic plants endemic to the Canaries. Methods We studied the three extant Thesium species and a new taxon from La Palma Island. We developed 12 microsatellites and performed population genetic analysis and studied the demographic history of the group. To evaluate the role of niche conservatism in the diversification of the group, we performed species distribution modelling (ESM) with four algorithms. Results All species presented moderate genetic diversity values for rare endemics. Thesium canariense (Gran Canaria) showed high differentiation, whereas T. subsucculentum, T. retamoides (Tenerife), and La Palma populations are closely related. The lineage may have undergone a recent diversification with colonization proceeding east to west, with T. canariense as sister to the others. We detected a climatic niche shift, as taxa showed different distributions across the temperature gradient. There is enough evidence to describe La Palma populations as a new species. Conclusions We characterized the evolutionary history of Thesium sect. Kunkeliella by integrating genetic and ecological assessments. Our results indicate that this clade has undergone a recent radiation process with niche differentiation among species. The results increase our knowledge about insular radiations and will inform the conservation management of the study species.
Adolescent asthma management self-efficacy and responsibility: impact on asthma control and quality-of-life

Abstract Objective The purpose of this study was to investigate the extent to which adolescent asthma management self-efficacy, outcome expectations, and asthma responsibility were associated with asthma control and quality-of-life. Adolescent self-efficacy and outcome expectations are important components of social cognitive theory, which guided this research. Methods English- and Spanish-speaking adolescents ages 11–17 with persistent asthma were recruited at four pediatric clinics. Adolescents were interviewed and parents completed questionnaires. Multiple linear regression was used to analyze the data. Results Three hundred and fifty-nine adolescents were recruited. Older adolescent age, male gender, and higher adolescent asthma management self-efficacy were significantly associated with higher adolescent responsibility; outcome expectations were not significantly associated with responsibility. Adolescent ratings of their own responsibility were higher than parent ratings of their child’s responsibility for almost all asthma management tasks. Adolescents with higher reported asthma management self-efficacy were significantly more likely to have better quality-of-life and controlled asthma. Adolescents with more positive outcome expectations were significantly more likely to have controlled asthma. Being Native American was associated with worse quality-of-life and asthma not being controlled. Being Black was associated with asthma not being controlled. Conclusions Parents and providers should work to improve adolescent self-efficacy in managing their asthma because it is associated with asthma responsibility, asthma control, and quality-of-life. Providers need to especially work with Native American and Black adolescents to improve quality-of-life and asthma control.