Miracle-dz/UniFAR-FTUs huggingface dataset:
Black holes in asymptotically safe gravity and beyond

Asymptotically safe quantum gravity is an approach to quantum gravity that achieves formulates a standard quantum field theory for the metric. Therefore, even the deep quantum gravity regime, that is expected to determine the true structure of the core of black holes, is described by a spacetime metric. The essence of asymptotic safety lies in a new symmetry of the theory -- quantum scale symmetry -- which characterizes the short-distance regime of quantum gravity. It implies the absence of physical scales. Therefore, the Newton coupling, which corresponds to a scale, namely the Planck length, must vanish asymptotically in the short-distance regime. This implies a weakening of the gravitational interaction, from which a resolution of classical spacetime singularities can be expected. In practise, properties of black holes in asymptotically safe quantum gravity cannot yet be derived from first principles, but are constructed using a heuristic procedure known as Renormalization Group improvement. The resulting asymptotic-safety inspired black holes have been constructed both for vanishing and for nonvanishing spin parameter. They are characterized by (i) the absence of curvature singularities, (ii) a more compact event horizon and photon sphere, (iii) a second (inner) horizon even at vanishing spin and (iv) a cold remnant as a possible final product of the Hawking evaporation. Observations can start to constrain the quantum-gravity scale that can be treated as a free parameter in asymptotic-safety inspired black holes. For slowly-spinning black holes, constraints from the EHT and X-ray observations can only constrain quantum-gravity scales far above the Planck length. In the limit of near-critical spin, asymptotic-safety inspired black holes may ``light up"in a way the ngEHT may be sensitive to, even for a quantum-gravity scale equalling the Planck length.
Progress applying density of states for gravitational waves

Many models of composite dark matter feature a first-order confinement transition in the early Universe, which would produce a stochastic background of gravitational waves that will be searched for by future gravitationalwave observatories. We present work in progress using lattice field theory to predict the properties of such first-order transitions. Targeting SU(N) Yang– Mills theories, this work employs the Logarithmic Linear Relaxation (LLR) density of states algorithm to avoid super-critical slowing down at the transition.
Formation of Multiple Counter-propagating Clusters in the Attractive Hamiltonian Mean-field Model

Many-body long-range interacting systems can remain approximately in a quasi-stationary state far-from-thermodynamic equilibrium. These states are typically characterized by a pair of counter-propagating density clusters, or by a single non-homogeneous core-halo in the phase-space of the particles. By using particle simulations based on the Hamiltonian mean-ﬁeld model, we show that this model supports stationary states with multiple cluster or particle holes in phase-space density. We also propose a mechanism based on wave-wave and wave-particle interactions that lead to the formation of these clusters, and characterize these new quasi-stationary states in terms of the initial parameters of the simulations.
Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars

We study grammar induction with mildly context-sensitive grammars for unsupervised discontinuous parsing. Using the probabilistic linear context-free rewriting system (LCFRS) formalism, our approach fixes the rule structure in advance and focuses on parameter learning with maximum likelihood. To reduce the computational complexity of both parsing and parameter estimation, we restrict the grammar formalism to LCFRS-2 (i.e., binary LCFRS with fan-out two) and further discard rules that require O(l6) time to parse, reducing inference to O(l5). We find that using a large number of nonterminals is beneficial and thus make use of tensor decomposition-based rank-space dynamic programming with an embedding-based parameterization of rule probabilities to scale up the number of nonterminals. Experiments on German and Dutch show that our approach is able to induce linguistically meaningful trees with continuous and discontinuous structures.
Short-term Prediction of Household Electricity Consumption Using Customized LSTM and GRU Models

—With the evolution of power systems as it is becoming more intelligent and interactive system while increasing in flexibility with a larger penetration of renewable energy sources, demand prediction on a short-term resolution will inevitably become more and more crucial in designing and managing the future grid, especially when it comes to an individual household level. Projecting the demand for electricity for a single energy user, as opposed to the aggregated power consumption of residential load on a wide scale, is difficult because of a considerable number of volatile and uncertain factors. This paper proposes a customized GRU (Gated Recurrent Unit) and Long Short-Term Memory (LSTM) architecture to address this challenging problem. LSTM and GRU are comparatively newer and among the most well-adopted deep learning approaches. The electricity consumption datasets were obtained from individual household smart meters. The comparison shows that the LSTM model performs better for home-level forecasting than alternative prediction techniques-GRU in this case. To compare the NN-based models with contrast to the conventional statistical technique-based model, ARIMA based model was also developed and benchmarked with LSTM and GRU model outcomes in this study to show the performance of the proposed model on the collected time series data.
Attentiveness Map Estimation for Haptic Teleoperation of Mobile Robot Obstacle Avoidance and Approach

Haptic feedback can improve safety of teleoperated robots when situational awareness is limited or operators are inattentive. Standard potential field approaches increase haptic resistance as an obstacle is approached, which is desirable when the operator is unaware of the obstacle but undesirable when the movement is intentional, such as when the operator wishes to inspect or manipulate an object. This letter presents a novel haptic teleoperation framework that estimates the operator's attentiveness to obstacles and dampens haptic feedback for intentional movement. A biologically-inspired attention model is developed based on computational working memory theories to integrate visual saliency estimation with spatial mapping. The attentiveness map is generated in real-time, and our system renders lower haptic forces for obstacles that the operator is estimated to be aware of. Experimental results in simulation show that the proposed framework outperforms haptic teleoperation without attentiveness estimation in terms of task performance, robot safety, and user experience.
Nonlinear Volterra Equations in a Hilbert Space

The existence and asymptotic behavior of the solutions of the equation \[ u(t) + \int_0^t {a(t - s)g(u(s))ds} = f(t),\quad 0 < t < \infty , \] in a Hilbert space H is investigated. Here $a(t)$ is a prescribed real function on $[0,\infty [$ and g belongs to a certain class of nonlinear maximal monotone operators in $H \times H$.
Bounds on Mixed Codes with Finite Alphabets

Mixed codes, which are error-correcting codes in the Cartesian product of different-sized spaces, model degrading storage systems well. While such codes have previously been studied for their algebraic properties (e.g., existence of perfect codes) or in the case of unbounded alphabet sizes, we focus on the case of finite alphabets, and generalize the Gilbert-Varshamov, sphere-packing, Elias-Bassalygo, and first linear programming bounds to that setting. In the latter case, our proof is also the first for the non-symmetric mono-alphabetic q-ary case using Navon and Samorodnitsky’s Fourier-analytic approach.
Face Generation and Editing with StyleGAN: A Survey

Our goal with this survey is to provide an overview of the state of the art deep learning methods for face generation and editing using StyleGAN. The survey covers the evolution of StyleGAN, from PGGAN to StyleGAN3, and explores relevant topics such as suitable metrics for training, different latent representations, GAN inversion to latent spaces of StyleGAN, face image editing, cross-domain face stylization, face restoration, and even Deepfake applications. We aim to provide an entry point into the field for readers that have basic knowledge about the field of deep learning and are looking for an accessible introduction and overview. Awesome list: https://github.com/ndrwmlnk/awesome-face-generation-and-editing.
Apparent fine tunings for field theories with broken space-time symmetries

We exhibit a class of effective field theories that have hierarchically small Wilson coefficients for operators that are not protected by symmetries but are not finely tuned. These theories possess bounded target spaces and vacua that break space-time symmetries. We give a physical interpretation of these theories as generalized solids with open boundary conditions. We show that these theories realize unusual RG flows where higher dimensional (seemingly irrelevant) operators become relevant even at weak coupling. Finally, we present an example of a field theory whose vacuum energy relaxes to a hierarchically small value compared to the UV cut-off.
Occupancy Grid Based Reactive Planner

—This paper proposes a perception and path planning pipeline for autonomous racing in an unknown bounded course. The pipeline was initially created for the 2021 evGrandPrix autonomous divi-sion and was further improved for the 2022 event, both of which resulting in ﬁrst place ﬁnishes. Using a simple LiDAR-based perception pipeline feeding into an occupancy grid based expansion algorithm, we determine a goal point to drive. This pipeline successfully achieved reliable and consistent laps in addition with occupancy grid algorithm to know the ways around a cone-deﬁned track with an averaging speeds of 6.85 m/s over a distance 434.2 meters for a total lap time of 63.4 seconds.
The case for 4-bit precision: k-bit Inference Scaling Laws

Quantization methods reduce the number of bits required to represent each parameter in a model, trading accuracy for smaller memory footprints and inference latencies. However, the final model size depends on both the number of parameters of the original model and the rate of compression. For example, a 30B 8-bit model and a 60B 4-bit model have the same number of bits but may have very different zero-shot accuracies. In this work, we study this trade-off by developing inference scaling laws of zero-shot performance in Large Language Models (LLMs) to determine the bit-precision and model size that maximizes zero-shot performance. We run more than 35,000 experiments with 16-bit inputs and k-bit parameters to examine which zero-shot quantization methods improve scaling for 3 to 8-bit precision at scales of 19M to 176B parameters across the LLM families BLOOM, OPT, NeoX/Pythia, and GPT-2. We find that it is challenging to improve the bit-level scaling trade-off, with the only improvements being the use of a small block size -- splitting the parameters into small independently quantized blocks -- and the quantization data type being used (e.g., Int vs Float). Overall, our findings show that {4-bit} precision is almost universally optimal for total model bits and zero-shot accuracy.
Epidemic Outbreaks on Quenched Scale-Free Networks

We present a ﬁnite-size scaling theory of a contact process with permanent immunity on uncorrelated scale-free networks. We model an epidemic outbreak by an analogue of susceptible-infected-removed model where an infected individual attacks only one susceptible in a time unit, in a way we can expect a non-vanishing critical threshold at scale-free networks. As we already know, the susceptible-infected-removed model can be mapped in a bond percolation process, allowing us to compare the critical behavior of site and bond universality classes on networks. We used the external ﬁeld ﬁnite-scale theory, where the dependence on the ﬁnite size enters the external ﬁeld deﬁned as the initial number of infected individuals. We can impose the scale of the external ﬁeld as N − 1 . The system presents an epidemic-endemic phase transition where the critical behavior obeys the mean-ﬁeld universality class, as we show theoretically and by simulations.
Thermometry of Quantum Environments with Impurity Probes

We study quantum impurity models as a platform for quantum thermometry. A single quantum spin- 12 impurity is coupled to an explicit, structured, fermionic thermal environment. We critically assess the thermometric capabilities of the impurity as a probe, when its coupling to the environment is of Ising or Kondo exchange type. In the Ising case, we ﬁnd sensitivity equivalent to that of an idealized two-level system, with peak thermometric performance obtained at a temperature that scales linearly in the applied control ﬁeld, independent of the coupling strength and environment spectral features. By contrast, a richer thermometric response can be realized for Kondo impurities, since strong probe-environment entanglement can then develop. At low temperatures, we uncover a regime with a universal thermometric response that is independent of microscopic details, controlled only by the low-energy spectral features of the environment. The many-body entanglement that develops in this regime means that low-temperature thermometry with a weakly applied control ﬁeld is inherently less sensitive, while optimal sensitivity is recovered by suppressing the entanglement with stronger ﬁelds.
Estimating Cloth Elasticity Parameters Using Position-Based Simulation of Compliant Constrained Dynamics

Clothing plays a vital role in real life and hence, is also important for virtual realities and virtual applications, such as online retail, virtual try-on, and real-time digital avatar interactions. However, choosing the correct parameters to generate realistic clothing requires expert knowledge and is often an arduous manual process. To alleviate this issue, we develop a pipeline for automatically determining the static material parameters required to simulate clothing of a particular material based on easily captured real-world fabrics. We use differentiable simulation to find an optimal set of parameters that minimizes the difference between simulated cloth and deformed target cloth. Our novel well-suited loss function is optimized through non-linear least squares. We designed our objective function to capture material-specific behavior, resulting in similar values for different wrinkle configurations of the same material. While existing methods carefully design experiments to isolate stretch parameters from bending modes, we embrace that stretching fabrics causes wrinkling. We estimate bending first, given that membrane stiffness has little effect on bending. Furthermore, our pipeline decouples the capture method from the optimization by registering a template mesh to the scanned data. These choices simplify the capture system and allow for wrinkles in scanned fabrics. We use a differentiable extended position-based dynamics (XPBD) cloth simulator, which is capable of real-time simulation. We demonstrate our method on captured data of three different real-world fabrics and on three digital fabrics produced by a third-party simulator.
Predicting Citi Bike Demand Evolution Using Dynamic Graphs

Bike sharing systems often suffer from poor capacity management as a result of variable demand. These bike sharing systems would benefit from models to predict demand in order to moderate the number of bikes stored at each station. In this paper, we attempt to apply a graph neural network model to predict bike demand in the New York City, Citi Bike dataset.
Graph-based Semantical Extractive Text Analysis

—In the past few decades, there has been an explosion in the amount of available data produced from various sources with different topics. The availability of this enormous data necessitates us to adopt effective computational tools to explore the data. This leads to an intense growing interest in the research community to develop computational methods focused on processing this text data. A line of study focused on condensing the text so that we are able to get a higher level of understanding in a shorter time. The two important tasks to do this are keyword extraction and text summarization. In keyword extraction, we are interested in ﬁnding the key important words from a text. This makes us familiar with the general topic of a text. In text summarization, we are interested in producing a short-length text which includes important information about the document. The TextRank algorithm, an unsupervised learning method that is an extension of the PageRank (algorithm which is the base algorithm of Google search engine for searching pages and ranking them) has shown its efﬁcacy in large-scale text mining, especially for text summarization and keyword extraction. this algorithm can automatically extract the important parts of a text (keywords or sentences) and declare them as the result. However, this algorithm neglects the semantic similarity between the different parts. In this work, we improved the results of the TextRank algorithm by incorporating the semantic similarity between parts of the text. Aside from keyword extraction and text summarization, we develop a topic clustering algorithm based on our framework which can be used individually or as a part of generating the summary to overcome coverage problems.
Synaptic Dynamics Realize First-order Adaptive Learning and Weight Symmetry

Gradient-based first-order adaptive optimization methods such as the Adam optimizer are prevalent in training artificial networks, achieving the state-of-the-art results. This work attempts to answer the question whether it is viable for biological neural systems to adopt such optimization methods. To this end, we demonstrate a realization of the Adam optimizer using biologically-plausible mechanisms in synapses. The proposed learning rule has clear biological correspondence, runs continuously in time, and achieves performance to comparable Adam's. In addition, we present a new approach, inspired by the predisposition property of synapses observed in neuroscience, to circumvent the biological implausibility of the weight transport problem in backpropagation (BP). With only local information and no separate training phases, this method establishes and maintains weight symmetry in the forward and backward signaling paths, and is applicable to the proposed biologically plausible Adam learning rule. These mechanisms may shed light on the way in which biological synaptic dynamics facilitate learning.
SrTR: Self-reasoning Transformer with Visual-linguistic Knowledge for Scene Graph Generation

Objects in a scene are not always related. The execution efficiency of the one-stage scene graph generation approaches are quite high, which infer the effective relation between entity pairs using sparse proposal sets and a few queries. However, they only focus on the relation between subject and object in triplet set subject entity, predicate entity, object entity, ignoring the relation between subject and predicate or predicate and object, and the model lacks self-reasoning ability. In addition, linguistic modality has been neglected in the one-stage method. It is necessary to mine linguistic modality knowledge to improve model reasoning ability. To address the above-mentioned shortcomings, a Self-reasoning Transformer with Visual-linguistic Knowledge (SrTR) is proposed to add flexible self-reasoning ability to the model. An encoder-decoder architecture is adopted in SrTR, and a self-reasoning decoder is developed to complete three inferences of the triplet set, s+o-p, s+p-o and p+o-s. Inspired by the large-scale pre-training image-text foundation models, visual-linguistic prior knowledge is introduced and a visual-linguistic alignment strategy is designed to project visual representations into semantic spaces with prior knowledge to aid relational reasoning. Experiments on the Visual Genome dataset demonstrate the superiority and fast inference ability of the proposed method.
Magnus Exponential Integrators for Stiff Time-Varying Stochastic Systems

We introduce exponential numerical integration methods for stiff stochastic dynamical systems of the form $d\mathbf{z}_t = L(t)\mathbf{z}_tdt + \mathbf{f}(t)dt + Q(t)d\mathbf{W}_t$. We consider the setting of time-varying operators $L(t), Q(t)$ where they may not commute $L(t_1)L(t_2) \neq L(t_2)L(t_1)$, raising challenges for exponentiation. We develop stochastic numerical integration methods using Mangus expansions for preserving statistical structures and for maintaining fluctuation-dissipation balance for physical systems. For computing the contributions of the fluctuation terms, our methods provide alternative approaches without needing directly to evaluate stochastic integrals. We present results for our methods for a class of SDEs arising in particle simulations and for SPDEs for fluctuations of concentration fields in spatially-extended systems. For time-varying stochastic dynamical systems, our introduced discretization approaches provide general exponential numerical integrators for preserving statistical structures while handling stiffness.
Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQL

The task of text-to-SQL aims to convert a natural language question into its corresponding SQL query within the context of relational tables. Existing text-to-SQL parsers generate a"plausible"SQL query for an arbitrary user question, thereby failing to correctly handle problematic user questions. To formalize this problem, we conduct a preliminary study on the observed ambiguous and unanswerable cases in text-to-SQL and summarize them into 6 feature categories. Correspondingly, we identify the causes behind each category and propose requirements for handling ambiguous and unanswerable questions. Following this study, we propose a simple yet effective counterfactual example generation approach that automatically produces ambiguous and unanswerable text-to-SQL examples. Furthermore, we propose a weakly supervised DTE (Detecting-Then-Explaining) model for error detection, localization, and explanation. Experimental results show that our model achieves the best result on both real-world examples and generated examples compared with various baselines. We release our data and code at: \href{https://github.com/wbbeyourself/DTE}{https://github.com/wbbeyourself/DTE}.
Vector vortex beams sorting of 120 modes in visible spectrum

Abstract Polarization (P), angular index (l), and radius index (p) are three independent degrees of freedom (DoFs) of vector vortex beams, which have found extensive applications in various domains. While efficient sorting of a single DoF has been achieved successfully, simultaneous sorting of all these DoFs in a compact and efficient manner remains a challenge. In this study, we propose a beam sorter that simultaneously handles all the three DoFs using a diffractive deep neural network (D2NN), and demonstrate the robust sorting of 120 Laguerre–Gaussian (LG) modes experimentally in the visible spectrum. Our proposed beam sorter underscores the considerable potential of D2NN in optical field manipulation and promises to enhance the diverse applications of vector vortex beams.