Miracle-dz/UniFAR-FTUs huggingface dataset:
Cutting planes for signomial programming

Cutting planes are of crucial importance when solving nonconvex nonlinear programs to global optimality, for example using the spatial branch-and-bound algorithms. In this paper, we discuss the generation of cutting planes for signomial programming. Many global optimization algorithms lift signomial programs into an extended formulation such that these algorithms can construct relaxations of the signomial program by outer approximations of the lifted set encoding nonconvex signomial term sets, i.e., hypographs, or epigraphs of signomial terms. We show that any signomial term set can be transformed into the subset of the difference of two concave power functions, from which we derive two kinds of valid linear inequalities. Intersection cuts are constructed using signomial term-free sets which do not contain any point of the signomial term set in their interior. We show that these signomial term-free sets are maximal in the nonnegative orthant, and use them to derive intersection sets. We then convexify a concave power function in the reformulation of the signomial term set, resulting in a convex set containing the signomial term set. This convex outer approximation is constructed in an extended space, and we separate a class of valid linear inequalities by projection from this approximation. We implement the valid inequalities in a global optimization solver and test them on MINLPLib instances. Our results show that both types of valid inequalities provide comparable reductions in running time, number of search nodes, and duality gap.
Higher Lower Bound for Sparse Oblivious Subspace Embeddings

An oblivious subspace embedding (OSE), characterized by parameters m, n, d, ǫ, δ , is a random matrix Π ∈ R m × n such that for any d -dimensional subspace T ⊆ R n , Pr Π [ ∀ x ∈ T, (1 − ǫ ) k x k 2 ≤ k Π x k 2 ≤ (1 + ǫ ) k x k 2 ] ≥ 1 − δ . When an OSE has 1 / (9 ǫ ) nonzero entries in each column, we show it must hold that m = Ω( d 2 /ǫ 1 − O ( δ ) ), which is the ﬁrst lower bound with multiplicative factors of d 2 and 1 /ǫ , improving on the previous Ω( ǫ O ( δ ) d 2 ) lower bound due to Li and Liu (PODS 2022).
In situ amplification of spin echoes within a kinetic inductance parametric amplifier

The use of superconducting microresonators together with quantum-limited Josephson parametric amplifiers has enhanced the sensitivity of pulsed electron spin resonance (ESR) measurements by more than four orders of magnitude. So far, the microwave resonators and amplifiers have been designed as separate components due to the incompatibility of Josephson junction–based devices with magnetic fields. This has produced complex spectrometers and raised technical barriers toward adoption of the technique. Here, we circumvent this issue by coupling an ensemble of spins directly to a weakly nonlinear and magnetic field–resilient superconducting microwave resonator. We perform pulsed ESR measurements with a 1-pL mode volume containing 6 × 107 spins and amplify the resulting signals within the device. When considering only those spins that contribute to the detected signals, we find a sensitivity of 2.8×103spins/Hz for a Hahn echo sequence at a temperature of 400 mK. In situ amplification is demonstrated at fields up to 254 mT, highlighting the technique’s potential for application under conventional ESR operating conditions.
Unsafe's Betrayal: Abusing Unsafe Rust in Binary Reverse Engineering via Machine Learning

Memory-safety bugs introduce critical software-security issues. Rust provides memory-safe mechanisms to avoid memory-safety bugs in programming, while still allowing unsafe escape hatches via unsafe code. However, the unsafe code that enhances the usability of Rust provides clear spots for finding memory-safety bugs in Rust source code. In this paper, we claim that these unsafe spots can still be identifiable in Rust binary code via machine learning and be leveraged for finding memory-safety bugs. To support our claim, we propose the tool textttrustspot, that enables reverse engineering to learn an unsafe classifier that proposes a list of functions in Rust binaries for downstream analysis. We empirically show that the function proposals by textttrustspot can recall $92.92\%$ of memory-safety bugs, while it covers only $16.79\%$ of the entire binary code. As an application, we demonstrate that the function proposals are used in targeted fuzzing on Rust packages, which contribute to reducing the fuzzing time compared to non-targeted fuzzing.
SceneTrilogy: On Human Scene-Sketch and its Complementarity with Photo and Text

In this paper, we extend scene understanding to include that of human sketch. The result is a complete trilogy of scene representation from three diverse and complementary modalities – sketch, photo, and text. Instead of learning a rigid three-way embedding and be done with it, wefocus on learning a flexible joint embedding that fully supports the “optionality” that this complementarity brings. Our embedding supports optionality on two axes: (i) optionality across modalities – use any combination of modalities as query for downstream tasks like retrieval, (ii) optionality across tasks – simultaneously utilising the embedding for either discriminative (e.g., retrieval) or generative tasks (e.g., captioning). This provides flexibility to end-users by exploiting the best of each modality, therefore serving the very purpose behind our proposal of a trilogy in the first place. First, a combination of information-bottleneck and conditional invertible neural networks disentangle the modality-specific component from modality-agnostic in sketch, photo, and text. Second, the modality-agnostic instances from sketch, photo, and text are synergised using a modified cross-attention. Once learned, we show our embedding can accommodate a multi-facet of scene-related tasks, including those enabled for the first time by the inclusion of sketch, all without any task-specific modifications. Project Page: https://pinakinathc.github. io/scenetrilogy
Effects of Visual Priming on Rating Scale Usage

Rating scales are much used in survey research. Often, it is assumed that the scores obtained through rating scales can be compared within and between respondents when studies are in one country. In addition, it is assumed that they can be treated as a numerical scale. In this paper, we study the anchoring effect of a visual stimulus on rating scale usage. To do so, we set up a randomized experiment where the experimental group was primed by asking to rate the filling of a cylinder that was presented visually. For a five point rating scale, we find the effect that primed respondents use Category 1 less and Categories 3 and 4 more, and no effect on Categories 2 and 5.
SN 1991bg - A type Ia supernova with a difference

We present 13 spectra and 31 photometric observations covering the first 150 days of SN 1991bg in NGC 4374 (M 84). Although SN 1991bg was a type Ia supernova displaying the characteristic Si II absorption at 6150 A near maximum and the Fe emission lines at late phases, it varied from the well-defined norm for SNe Ia in several important respects. The peculiarities include faster declines in the B and V light curves after maximum, a distinct color evolution, a very red B−V color near maximum, relatively faint peak luminosity, a distinct spectral evolution, and a short peak phase
Virasoro generators in the Fibonacci model tensor network: Tackling finite-size effects

In this paper, we extend the method implementing Virasoro operators in a tensor network we proposed in arXiv:2205.04500 and test it on the Fibonacci model, which is known to suffer from far more finite size effects. To pick up the"seed"state that would flow to the stress tensor in the thermodynamic limit, we make use of the topological idempotent that projects the transfer matrix to the trivial sector. Combined with an optimization method, the seed state can be identified. We demonstrate that the descendant states in the Fibonacci model can be correctly generated with this approximate stress tensor, giving further evidence that the method applies more generally.
Model-Based and Model-Free Point Prediction Algorithms for Locally Stationary Random Fields

The Model-Free Prediction Principle has been successfully applied to general regression problems, as well as problems involving stationary and locally stationary time series. In this paper, we demonstrate how Model-Free Prediction can be applied to handle random fields that are only locally stationary such as pixel values over an image or satellite data observed on an ocean surface, i.e., they can be assumed to be stationary only across a limited part over their entire region of definition. We construct novel one-step-ahead Model-Based and Model-Free point predictors and compare their performance using synthetic data as well as images from the CIFAR-10 dataset. In the latter case, we demonstrate that our best Model-Free point prediction results outperform those obtained using Model-Based prediction.
Overlapping oriented imbalanced ensemble learning method based on projective clustering and stagewise hybrid sampling

The challenge of imbalanced learning lies not only in class imbalance problem, but also in the class overlapping problem which is complex. However, most of the existing algorithms mainly focus on the former. The limitation prevents the existing methods from breaking through. To address this limitation, this paper proposes an ensemble learning algorithm based on dual clustering and stage-wise hybrid sampling (DCSHS). The DCSHS has three parts. Firstly, we design a projection clustering combination framework (PCC) guided by Davies-Bouldin clustering effectiveness index (DBI), which is used to obtain high-quality clusters and combine them to obtain a set of cross-complete subsets (CCS) with balanced class and low overlapping. Secondly, according to the characteristics of subset classes, a stage-wise hybrid sampling algorithm is designed to realize the de-overlapping and balancing of subsets. Finally, a projective clustering transfer mapping mechanism (CTM) is constructed for all processed subsets by means of transfer learning, thereby reducing class overlapping and explore structure information of samples. The major advantage of our algorithm is that it can exploit the intersectionality of the CCS to realize the soft elimination of overlapping majority samples, and learn as much information of overlapping samples as possible, thereby enhancing the class overlapping while class balancing. In the experimental section, more than 30 public datasets and over ten representative algorithms are chosen for verification. The experimental results show that the DCSHS is significantly best in terms of various evaluation criteria.
Lessons from the Mathematics of Two-Dimensional Euclidean Quantum Gravity

The search for a mathematical foundation for the path integral of Euclidean quantum gravity calls for the construction of random geometry on the spacetime manifold. Following developments in physics on the two-dimensional theory, random geometry on the 2-sphere has in recent years received much attention in the mathematical literature, which has led to a fully rigorous implementation of the path integral formulation of two-dimensional Euclidean quantum gravity. In this chapter we review several important mathematical developments that may serve as guiding principles for approaching Euclidean quantum gravity in dimensions higher than two. Our starting point is the discrete geometry encoded by random planar maps, which realizes a lattice discretization of the path integral. We recap the enumeration of planar maps via their generating functions and show how bijections with trees explain the surprising simplicity of some of these. Then we explain how to handle infinite planar maps and to analyze their exploration via the peeling process. The aforementioned trees provide the basis for the construction of the universal continuum limit of the random discrete geometries, known as the Brownian sphere, which represents the random geometry underlying two-dimensional Euclidean quantum gravity in the absence of matter.
Data-driven Innovation: Understanding the Direction for Future Research

In the contemporary age of information, organisations have realised the importance of data to innovate and thereby attain a competitive advantage. As a result, firms are more focused on understanding the potential to achieve data-driven innovation (DDI). Researchers too have focused on examining this novel phenomenon in a broader scope. In this study, we conducted a systematic and comprehensive review of the literature to understand the DDI phenomenon. The findings of this study benefit scholars in determining the gaps in the current body of knowledge as well as for practitioners to improve their data strategy to enhance and develop innovation capabilities.
Interdisciplinary Discovery of Nanomaterials Based on Convolutional Neural Networks

The material science literature contains up-to-date and comprehensive scientiﬁc knowledge of materials. However, their content is unstructured and diverse, result-ing in a signiﬁcant gap in providing sufﬁcient information for material design and synthesis. To this end, we used natural language processing (NLP) and computer vision (CV) techniques based on convolutional neural networks (CNN) to discover valuable experimental-based information about nanomaterials and synthesis methods in energy-material-related publications. Our ﬁrst system, TextMaster, extracts opinions from texts and classiﬁes them into challenges and opportunities, achieving 94% and 92% accuracy, respectively. Our second system, GraphMaster, realizes data extraction of tables and ﬁgures from publications with 98.3% classiﬁcation accuracy and 4.3% data extraction mean square error. Our results show that these systems could assess the suitability of materials for a certain application by evalu-ation of synthesis insights and case analysis with detailed references. This work offers a fresh perspective on mining knowledge from scientiﬁc literature, providing a wide swatch to accelerate nanomaterial research through CNN.
GAS-Net: Generative Artistic Style Neural Networks for Fonts

Generating new fonts is a time-consuming and labor-intensive, especially in a language with a huge amount of characters like Chinese. Various deep learning models have demonstrated the ability to efficiently generate new fonts with a few reference characters of that style. This project aims to develop a few-shot cross-lingual font generator based on AGIS-Net and improve the performance metrics mentioned in Section 3. Our approaches include redesigning the encoder and the loss function. We will validate our method on multiple languages and datasets mentioned in Section 4.
Spectro-spatial evolution of the CMB III: transfer functions, power spectra and Fisher forecasts

In this paper, we provide the first computations for the distortion transfer functions of the cosmic microwave background (CMB) in the perturbed Universe, following up on paper I and II in this series. We illustrate the physical effects inherent to the solutions, discussing and demonstrating various limiting cases for the perturbed photon spectrum. We clarify the relationship between distortion transfer functions and the photon spectrum itself, providing the machinery that can then compute constrainable CMB signal power spectra including spectral distortions for single energy injection and decaying particle scenarios. Our results show that the μ× T and y× T power spectra reach levels that can be constrained with current and future CMB experiments without violating existing constraints from COBE/FIRAS. The amplitude of the cross-correlation signal directly depends on the average distortion level, therefore establishing a novel fundamental link between the state of the primordial plasma from redshift 103 . z . 3 × 106 and the frequency-dependent CMB sky. This provides a new method to constrain average early energy release using CMB imagers. As an example we derive constraints on single energy release and decaying particle scenarios. This shows that LiteBIRD may be able to improve the energy release limits of COBE/FIRAS by up to a factor of ' 2.5, while PICO could tighten the constraints by more than one order of magnitude. The signals considered here could furthermore provide a significant challenge to reaching cosmic variance-limited constraints on primordial non-Gaussianity from distortion anisotropy studies. Our work further highlights the immense potential for a synergistic spectroscopic approach to future CMB measurements and analyses. ar X iv :2 21 2. 02 81 7v 1 [ as tr oph . C O ] 6 D ec 2 02 2
Multi-Task Edge Prediction in Temporally-Dynamic Video Graphs

Graph neural networks have shown to learn effective node representations, enabling node-, link-, and graph-level inference. Conventional graph networks assume static relations between nodes, while relations between entities in a video often evolve over time, with nodes entering and exiting dynamically. In such temporally-dynamic graphs, a core problem is inferring the future state of spatio-temporal edges, which can constitute multiple types of relations. To address this problem, we propose MTD-GNN, a graph network for predicting temporally-dynamic edges for multiple types of relations. We propose a factorized spatio-temporal graph attention layer to learn dynamic node representations and present a multi-task edge prediction loss that models multiple relations simultaneously. The proposed architecture operates on top of scene graphs that we obtain from videos through object detection and spatio-temporal linking. Experimental evaluations on ActionGenome and CLEVRER show that modeling multiple relations in our temporally-dynamic graph network can be mutually beneficial, outperforming existing static and spatio-temporal graph neural networks, as well as state-of-the-art predicate classification methods.
Privacy-Preserving Visual Localization with Event Cameras

We present a robust, privacy-preserving visual localization algorithm using event cameras. While event cameras can potentially make robust localization due to high dynamic range and small motion blur, the sensors exhibit large domain gaps making it difﬁcult to directly apply conventional image-based localization algorithms. To mitigate the gap, we propose applying event-to-image conversion prior to localization which leads to stable localization. In the privacy perspective, event cameras capture only a fraction of visual information compared to normal cameras, and thus can naturally hide sensitive visual details. To further en-hance the privacy protection in our event-based pipeline, we introduce privacy protection at two levels, namely sensor and network level. Sensor level protection aims at hiding facial details with lightweight ﬁltering while network level protection targets hiding the entire user’s view in private scene applications using a novel neural network inference pipeline. Both levels of protection involve light-weight computation and incur only a small performance loss. We thus project our method to serve as a building block for practical location-based services using event cameras. The code and dataset will be made public through the following link:
Branch‐cut cosmology and the Bekenstein Criterion

In this contribution we address the implications of the Bekenstein Criterion in the branch-cut cosmology. The impossibility of packaging energy and entropy according to the Bekenstein Criterion in a finite size makes the transition phase of the branch-cut cosmology very peculiar, imposing a topological leap between the contraction and expansion phases of the primordial universe or a transition region similar to a wormhole, with space-time shaping itself topologically in the format of a helix-format around a branch point. Singularity means that there is no way for space-time to begin smoothly. The branch-cut cosmology alternatively proposes a non-temporal beginning at all, a pure space configuration, through a Wick rotation which replaces the imaginary time component by the temperature, the cosmological time.
Scale-Invariant Specifications for Human-Swarm Systems

We present a method for controlling a swarm using its spectral decomposition—that is, by describing the set of trajectories of a swarm in terms of a spatial distribution throughout the operational domain—guaranteeing scale invariance with respect to the number of agents both for computation and for the operator tasked with controlling the swarm. We use ergodic control, decentralized across the network, for implementation. In the DARPA OFFSET program field setting, we test this interface design for the operator using the STOMP interface—the same interface used by Raytheon BBN throughout the duration of the OFFSET program. In these tests, we demonstrate that our approach is scale-invariant—the user specification does not depend on the number of agents; it is persistent—the specification remains active until the user specifies a new command; and it is real-time—the user can interact with and interrupt the swarm at any time. Moreover, we show that the spectral/ergodic specification of swarm behavior degrades gracefully as the number of agents goes down, enabling the operator to maintain the same approach as agents become disabled or are added to the network. We demonstrate the scale invariance and dynamic response of our system in a field-relevant simulator on a variety of tactical scenarios with up to 50 agents. We also demonstrate the dynamic response of our system in the field with a smaller team of agents. Lastly, we make the code for our system available.
Independence preserving property of Kummer laws

We prove that if $X,Y$ are positive, independent, non-Dirac random variables and if for $\alpha,\beta\ge 0$, $\alpha\neq \beta$, $$ \psi_{\alpha,\beta}(x,y)=\left(y\,\tfrac{1+\beta(x+y)}{1+\alpha x+\beta y},\;x\,\tfrac{1+\alpha(x+y)}{1+\alpha x+\beta y}\right), $$ then the random variables $U$ and $V$ defined by $(U,V)=\psi_{\alpha,\beta}(X,Y)$ are independent if and only if $X$ and $Y$ follow Kummer distributions with suitably related parameters. In other words, any invariant measure for a lattice recursion model governed by $\psi_{\alpha,\beta}$ in the scheme introduced by Croydon and Sasada in \cite{CS2020} is necessarily a product measure with Kummer marginals. The result extends earlier characterizations of Kummer and gamma laws by independence of $$ U=\tfrac{Y}{1+X}\quad\mbox{and}\quad V= X\left(1+\tfrac{Y}{1+X}\right), $$ which corresponds to the case of $\psi_{1,0}$. We also show that this independence property of Kummer laws covers, as limiting cases, several independence models known in the literature: the Lukacs, the Kummer-Gamma, the Matsumoto-Yor and the discrete Korteweg de Vries models.
A K-variate Time Series Is Worth K Words: Evolution of the Vanilla Transformer Architecture for Long-term Multivariate Time Series Forecasting

Multivariate time series forecasting (MTSF) is a fundamental problem in numerous real-world applications. Recently, Transformer has become the de facto solution for MTSF, especially for the long-term cases. However, except for the one forward operation, the basic configurations in existing MTSF Transformer architectures were barely carefully verified. In this study, we point out that the current tokenization strategy in MTSF Transformer architectures ignores the token uniformity inductive bias of Transformers. Therefore, the vanilla MTSF transformer struggles to capture details in time series and presents inferior performance. Based on this observation, we make a series of evolution on the basic architecture of the vanilla MTSF transformer. We vary the flawed tokenization strategy, along with the decoder structure and embeddings. Surprisingly, the evolved simple transformer architecture is highly effective, which successfully avoids the over-smoothing phenomena in the vanilla MTSF transformer, achieves a more detailed and accurate prediction, and even substantially outperforms the state-of-the-art Transformers that are well-designed for MTSF.