Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergent XY* transition driven by symmetry fractionalization and anyon condensation

Anyons in a topologically ordered phase can carry fractional quantum numbers with respect to the symmetry group of the considered system, one example being the fractional charge of the quasiparticles and quasiholes in the fractional quantum Hall effect. When such symmetry-fractionalized anyons condense, the resulting phase must spontaneously break the symmetry and display a local order parameter. In this paper, we study the phase diagram and anyon condensation transitions of a \mathbb{Z}_2ℤ2 topological order perturbed by Ising interactions in the Toric Code. The interplay between the global (``onsite’’) Ising (\mathbb{Z}_2ℤ2) symmetry and the lattice space group symmetries results in a non-trivial symmetry fractionalization class for the anyons, and is shown to lead to two characteristically different confined, symmetry-broken phases. To understand the anyon condensation transitions, we use the recently introduced critical torus energy spectrum technique to identify a line of emergent 2+1D XY* transitions ending at a fine-tuned (Ising^22)* critical point. We provide numerical evidence for the occurrence of two symmetry breaking patterns predicted by the specific symmetry fractionalization class of the condensed anyons in the explored phase diagram. In combination with large-scale quantum Monte Carlo simulations we measure unusually large critical exponents \etaη for the scaling of the correlation function at the continuous anyon condensation transitions, and we further identify lines of (weakly) first order transitions in the phase diagram. As an important additional result, we discuss the phase diagram of a resulting 2+1D Ashkin-Teller model, where we demonstrate that torus spectroscopy is capable of identifying emergent XY/O(2) critical behaviour, thereby solving some longstanding open questions in the domain of the 3D Ashkin-Teller models. To establish the generality of our results, we propose a field theoretical description capturing the transition from a \mathbb{Z}_2ℤ2 topological order to either \mathbb{Z}_2ℤ2 symmetry broken phase, which is in excellent agreement with the numerical results.
Federated Learning with Partial Model Personalization

We consider two federated learning algorithms for training partially personalized models, where the shared and personal parameters are updated either simultaneously or alternately on the devices. Both algorithms have been proposed in the literature, but their convergence properties are not fully understood, especially for the alternating variant. We provide convergence analyses of both algorithms in the general nonconvex setting with partial participation and delineate the regime where one dominates the other. Our experiments on real-world image, text, and speech datasets demonstrate that (a) partial personalization can obtain most of the benefits of full model personalization with a small fraction of personal parameters, and, (b) the alternating update algorithm often outperforms the simultaneous update algorithm by a small but consistent margin.
Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition

While the Transformer architecture has become ubiquitous in the machine learning field, its adaptation to 3D shape recognition is non-trivial. Due to its quadratic computational complexity, the self-attention operator quickly becomes inefficient as the set of input points grows larger. Furthermore, we find that the attention mechanism struggles to find useful connections between individual points on a global scale. In order to alleviate these problems, we propose a two-stage Point Transformer-in-Transformer (Point-TnT) approach which combines local and global attention mechanisms, enabling both individual points and patches of points to attend to each other effectively. Experiments on shape classification show that such an approach provides more useful features for downstream tasks than the baseline Transformer, while also being more computationally efficient. In addition, we also extend our method to feature matching for scene reconstruction, showing that it can be used in conjunction with existing scene reconstruction pipelines.
Experimental Validation of DeeP-LCC for Dissipating Stop-and-Go Waves in Mixed Traffic

—We present results on the experimental validation of leading cruise control (LCC) for connected and autonomous vehicles (CAVs). In a mixed trafﬁc situation that is dominated by human-driven vehicles, LCC strategies are promising to smooth undesirable stop-and-go waves. Our experiments are carried out on a mini-scale trafﬁc platform. We ﬁrst reproduce stop-and-go trafﬁc waves in a miniature scale, and then show that these trafﬁc instabilities can be dissipated by one or a few CAVs that utilize Data-EnablEd Predicted Leading Cruise Con- trol ( DeeP-LCC ). Rather than identifying a parametric trafﬁc model, DeeP-LCC relies on a data-driven non-parametric behavior representation for trafﬁc prediction and CAV control. DeeP-LCC also incorporates input and output constraints to achieve collision-free guarantees for CAVs. We experimentally demonstrate that DeeP-LCC is able to dissipate trafﬁc waves caused by car-following behavior and signiﬁcantly improve both driving safety and travel efﬁciency. CAVs utilizing DeeP-LCC may bring additional societal beneﬁts by mitigating stop-and-go waves in practical trafﬁc.
No eleventh conditional Ingleton inequality

A rational probability distribution on four binary random variables $X, Y, Z, U$ is constructed which satisfies the conditional independence relations $[X \mathrel{\text{$\perp\mkern-10mu\perp$}} Y]$, $[X \mathrel{\text{$\perp\mkern-10mu\perp$}} Z \mid U]$, $[Y \mathrel{\text{$\perp\mkern-10mu\perp$}} U \mid Z]$ and $[Z \mathrel{\text{$\perp\mkern-10mu\perp$}} U \mid XY]$ but whose entropy vector violates the Ingleton inequality. This settles a recent question of Studen\'y (IEEE Trans. Inf. Theory vol. 67, no. 11) and shows that there are, up to symmetry, precisely ten inclusion-minimal sets of conditional independence assumptions on four discrete random variables which make the Ingleton inequality hold. The last case in the classification of which of these inequalities are essentially conditional is also settled.
Approximating the parallel transport of an induced connection

Efficient numerical methods to approximate the parallel transport operators of the induced connection on a sub-bundle of a vector bundle are presented. These methods are simpler than naive applications of a Runge–Kutta algorithm and have accuracy up to order 4. They have the desirable property of being insensitive to choices of trivialization of the sub-bundle. The methods were developed to solve a problem of computing skyrmions using the Atiyah–Manton–Sutcliffe and Atiyah–Drinfeld–Hitchin–Manin constructions, but are applicable to a broader range of problems in computational geometry.
The Complexity of Infinite-Horizon General-Sum Stochastic Games

We study the complexity of computing stationary Nash equilibrium (NE) in n-player infinite-horizon general-sum stochastic games. We focus on the problem of computing NE in such stochastic games when each player is restricted to choosing a stationary policy and rewards are discounted. First, we prove that computing such NE is in PPAD (in addition to clearly being PPAD-hard). Second, we consider turn-based specializations of such games where at each state there is at most a single player that can take actions and show that these (seemingly-simpler) games remain PPAD-hard. Third, we show that under further structural assumptions on the rewards computing NE in such turn-based games is possible in polynomial time. Towards achieving these results we establish structural facts about stochastic games of broader utility, including monotonicity of utilities under single-state single-action changes and reductions to settings where each player controls a single state.
Prediction of COVID-19 using chest X-ray images

COVID-19, also known as Novel Coronavirus Disease, is a highly contagious disease that first surfaced in China in late 2019. SARS-CoV-2 is a coronavirus that belongs to the vast family of coronaviruses that causes this disease. The sickness originally appeared in Wuhan, China in December 2019 and quickly spread to over 213 nations, becoming a global pandemic. Fever, dry cough, and tiredness are the most typical COVID-19 symptoms. Aches, pains, and difficulty breathing are some of the other symptoms that patients may face. The majority of these symptoms are indicators of respiratory infections and lung abnormalities, which radiologists can identify. Chest x-rays of COVID-19 patients seem similar, with patchy and hazy lungs rather than clear and healthy lungs. On x-rays, however, pneumonia and other chronic lung disorders can resemble COVID-19. Trained radiologists must be able to distinguish between COVID-19 and an illness that is less contagious. Our AI algorithm seeks to give doctors a quantitative estimate of the risk of deterioration. So that patients at high risk of deterioration can be triaged and treated efficiently. The method could be particularly useful in pandemic hotspots when screening upon admission is important for allocating limited resources like hospital beds.
Bespoke Instrumental Variables for Causal Inference

Many proposals for the identiﬁcation of causal eﬀects in the presence of unmeasured confounding require an instrumental variable or negative control that satisﬁes strong, untestable assumptions. In this paper, we will instead show how one can identify causal eﬀects for a point exposure by using a measured confounder as a ‘bespoke instrumental variable’. This strategy requires an external reference population that does not have access to the exposure, and a stability condition on the confounder-outcome association between reference and target populations. Building on recent identiﬁcation results of Richardson and Tchetgen Tchetgen (2021), we develop the semiparametric eﬃciency theory for a general bespoke instrumental variable model, and obtain a multiply robust locally eﬃcient estimator of the average treatment eﬀect in the treated.
FashionCLIP: Connecting Language and Images for Product Representations

The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learning problems, we argue that practitioners would greatly beneﬁt from more transferable representations of products. In this work, we build on recent developments in contrastive learning to train FashionCLIP , a CLIP -like model for the fashion industry. We showcase its capabilities for retrieval, classiﬁcation and grounding, and release our model and code to the community.
Revealing A Trans-Planckian World Solves The Cosmological Constant Problem

The Planck scale is usually believed to be an unpassable wall. Putting a cutoff there and thinking of it as a quantized spacetime entity shows that. However, this is exactly the cause of many problems in quantum gravity. The cosmological constant problem also comes down to the problem of how to describe a trans-Planckian world by a continuum theory that shall be renormalizable and background free. Here, we show that when quantizing gravity in a diffeomorphism-invariant method, in which background freedom arises asymptotically as an exact conformal symmetry, the zero-point energy vanishes identically. Thus, there is no problem with the cosmological constant, which is a physical constant, namely a renormalization group invariant. We also argue that new quanta based on the background freedom emerge and spacetime is discretized dynamically. Primordial fluctuations will originate from such quanta.
Bag-of-Words vs. Sequence vs. Graph vs. Hierarchy for Single- and Multi-Label Text Classification

Graph neural networks have triggered a resur-gence of graph-based text classiﬁcation methods, deﬁning today’s state of the art. We show that a simple multi-layer perceptron (MLP) using a Bag of Words (BoW) outperforms the recent graph-based models TextGCN and HeteGCN in an inductive text classiﬁcation setting and is comparable with HyperGAT in single-label classiﬁcation. We also run our own experiments on multi-label classiﬁcation, where the simple MLP outperforms the recent sequential-based gMLP and aMLP models. Moreover, we ﬁne-tune a sequence-based BERT and a lightweight DistilBERT model, which both outperform all models on both single-label and multi-label settings in most datasets. These results question the importance of synthetic graphs used in modern text classiﬁers. In terms of parameters, DistilBERT is still twice as large as our BoW-based wide MLP, while graph-based models like TextGCN require setting up an O ( N 2 ) graph, where N is the vocabulary plus corpus size.
AdvEst: Adversarial Perturbation Estimation to Classify and Detect Adversarial Attacks against Speaker Identification

Adversarial attacks pose a severe security threat to the state-of-the-art speaker identification systems, thereby making it vital to propose countermeasures against them. Building on our previous work that used representation learning to classify and detect adversarial attacks, we propose an improvement to it using AdvEst, a method to estimate adversarial perturbation. First, we empirically validate our claim that training representation learning network using adversarial perturbations as opposed to adversarial examples (consisting of a combination of clean signal and adversarial perturbation) is beneficial because it poten-tially eliminates nuisance information. At inference time, we use a time-domain denoiser to estimate the adversarial perturbations from adversarial examples. Using our improved representation learning approach to obtain attack embeddings ( signatures ), we evaluate their performance for three applications: known attack classification, attack verification, and unknown attack detection. We show that common attacks in the literature (Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), Carlini-Wagner (CW) with different L p threat models) can be classified with an accuracy of ∼ 96 %. We also detect unknown attacks with an equal error rate (EER) of ∼ 9%, an absolute improvement of ∼ 12% from our previous work.
GPSAF: A Generalized Probabilistic Surrogate-Assisted Framework for Constrained Single- and Multi-objective Optimization

Significant effort has been made to solve computationally expensive optimization problems in the past two decades, and various optimization methods incorporating surrogates into optimization have been proposed. Most research focuses on either exploiting the surrogate by defining a utility optimization problem or customizing an existing optimization method to use one or multiple approximation models. However, only a little attention has been paid to generic concepts applicable to different types of algorithms and optimization problems simultaneously. Thus this paper proposes a generalized probabilistic surrogate-assisted framework (GPSAF), applicable to a broad category of unconstrained and constrained, single- and multi-objective optimization algorithms. The idea is based on a surrogate assisting an existing optimization method. The assistance is based on two distinct phases, one facilitating exploration and another exploiting the surrogates. The exploration and exploitation of surrogates are automatically balanced by performing a probabilistic knockout tournament among different clusters of solutions. A study of multiple well-known population-based optimization algorithms is conducted with and without the proposed surrogate assistance on single- and multi-objective optimization problems with a maximum solution evaluation budget of 300 or less. The results indicate the effectiveness of applying GPSAF to an optimization algorithm and the competitiveness with other surrogate-assisted algorithms.
Binary Natal Kicks in the Galactic Center: X-Ray Binaries, Hypervelocity Stars, and Gravitational Waves

Theoretical and observational studies suggest that stellar binaries exist in large numbers in galactic nuclei like our own Galactic Center. Neutron stars (NSs), and debatedly, black holes and white dwarfs, receive natal kicks at birth. In this work, we study the effect of two successive natal kicks on a population of stellar binaries orbiting the massive black hole (MBH) in our Galactic Center. These natal kicks can significantly alter the binary orbit in a variety of ways, and also the orbit of the binary around the MBH. We found a variety of dynamical outcomes resulting from these kicks, including a steeper cusp of single NSs relative to the initial binary distribution. Furthermore, hypervelocity star and binary candidates, including hypervelocity X-ray binaries, are a common outcome of natal kicks. In addition, we show that the population of X-ray binaries in the Galactic Center can be used as a diagnostic for the BH natal kick distribution. Finally, we estimate the rate of gravitational wave events triggered by natal kicks, including binary mergers and EMRIs.
MHMS: Multimodal Hierarchical Multimedia Summarization

Multimedia summarization with multimodal output can play an essential role in real-world applications, i.e., automatically generating cover images and titles for news articles or providing introductions to online videos. In this work, we propose a multimodal hierarchical multimedia summarization (MHMS) framework by interacting visual and language domains to generate both video and textual summaries. Our MHMS method contains video and textual segmentation and summarization module, respectively. It formulates a cross-domain alignment objective with optimal transport distance which leverages cross-domain interaction to generate the representative keyframe and textual summary. We evaluated MHMS on three recent multimodal datasets and demonstrated the effectiveness of our method in producing high-quality multimodal summaries.
The Sillwood Technologies System for the VoiceMOS Challenge 2022

In this paper we describe our entry for the VoiceMOS Challenge 2022 for both the main and out-of-domain (OOD) track of the competition. Our system is based on finetuning pre-trained self-supervised waveform prediction models, while improving its generalisation ability through stochastic weight averaging. Further, we use influence functions to identity possible low-quality data within the training set to further increase our model's performance for the OOD track. Our system ranked 5th and joint 7th for the main track and OOD track, respectively.
HINNPerf: Hierarchical Interaction Neural Network for Performance Prediction of Configurable Systems

Modern software systems are usually highly configurable, providing users with customized functionality through various configuration options. Understanding how system performance varies with different option combinations is important to determine optimal configurations that meet specific requirements. Due to the complex interactions among multiple options and the high cost of performance measurement under a huge configuration space, it is challenging to study how different configurations influence the system performance. To address these challenges, we propose HINNPerf, a novel hierarchical interaction neural network for performance prediction of configurable systems. HINNPerf employs the embedding method and hierarchic network blocks to model the complicated interplay between configuration options, which improves the prediction accuracy of the method. In addition, we devise a hierarchical regularization strategy to enhance the model robustness. Empirical results on 10 real-world configurable systems show that our method statistically significantly outperforms state-of-the-art approaches by achieving average 22.67% improvement in prediction accuracy. In addition, combined with the Integrated Gradients method, the designed hierarchical architecture provides some insights about the interaction complexity and the significance of configuration options, which might help users and developers better understand how the configurable system works and efficiently identify significant options affecting the performance.
"Gettr-ing" Deep Insights from the Social Network Gettr

As yet another alternative social network, Gettr positions itself as the"marketplace of ideas"where users should expect the truth to emerge without any administrative censorship. We looked deep inside the platform by analyzing it's structure, a sample of 6.8 million posts, and the responses from a sample of 124 Gettr users we interviewed to see if this actually is the case. Administratively, Gettr makes a deliberate attempt to stifle any external evaluation of the platform as collecting data is marred with unpredictable and abrupt changes in their API. Content-wise, Gettr notably hosts pro-Trump content mixed with conspiracy theories and attacks on the perceived"left. "It's social network structure is asymmetric and centered around prominent right-thought leaders, which is characteristic for all alt-platforms. While right-leaning users joined Gettr as a result of a perceived freedom of speech infringement by the mainstream platforms, left-leaning users followed them in numbers as to"keep up with the misinformation. "We contextualize these findings by looking into the Gettr's user interface design to provide a comprehensive insight into the incentive structure for joining and competing for the truth on Gettr.
Ultraviolet Spectroscopy and TARDIS Models of the Broad-lined Type Ic Supernova 2014ad

Few published ultraviolet (UV) spectra exist for stripped-envelope supernovae and none to date for broad-lined Type Ic supernovae (SNe Ic-bl). These objects have extremely high ejecta velocities and are the only supernova type directly linked to gamma-ray bursts (GRBs). Here we present two epochs of HST/STIS spectra of the SN Ic-bl 2014ad, the first UV spectra for this class. We supplement this with 26 new epochs of ground-based optical spectra, augmenting a rich spectral time series. The UV spectra do not show strong features and are consistent with broadened versions of other SN Ic spectra observed in the UV. We measure Fe ii 5169 Å velocities and show that SN 2014ad has even higher ejecta velocities than most SNe Ic both with and without observed GRBs. We construct models of the SN 2014ad UV+optical spectra using tardis, a 1D Monte Carlo radiative-transfer spectral synthesis code. The models fit the data well at multiple epochs in the optical but underestimate the flux in the UV, likely due to simplifying assumptions. We find that high densities at high velocities are needed to reproduce the spectra, with ∼3 M ⊙ of material at v > 22,000 km s−1, assuming spherical symmetry. Our nebular line fits suggest a steep density profile at low velocities. Together, these results imply a higher total ejecta mass than estimated from previous light-curve analysis and expected from theory. This may be reconciled by a flattening of the density profile at low velocity and extra emission near the center of the ejecta.