Miracle-dz/UniFAR-FTUs huggingface dataset:
Fillable contact structures from positive surgery

We consider the question of when the operation of contact surgery with positive surgery coefficient, along a knot $K$ in a contact 3-manifold $Y$, gives rise to a weakly fillable contact structure. We show that this happens if and only if $Y$ itself is weakly fillable, and $K$ is isotopic to the boundary of a properly embedded symplectic disk inside a filling of $Y$. Moreover, if $Y'$ is a contact manifold arising from positive contact surgery along $K$, then any filling of $Y'$ is symplectomorphic to the complement of a suitable neighborhood of such a disk in a filling of $Y$. Using this result we deduce several necessary conditions for a knot in the standard 3-sphere to admit a fillable positive surgery, such as quasipositivity and equality between the slice genus and the 4-dimensional clasp number, and we give a characterization of such knots in terms of a quasipositive braid expression. We show that knots arising as the closure of a positive braid always admit a fillable positive surgery, as do knots that have lens space surgeries, and suitable satellites of such knots. In fact the majority of quasipositive knots with up to 10 crossings admit a fillable positive surgery. On the other hand, in general (strong) quasipositivity, positivity, or Lagrangian fillability need not imply a knot admits a fillable positive contact surgery.
Truveta Mapper: A Zero-shot Ontology Alignment Framework

In this paper, a new perspective is suggested for unsupervised Ontology Matching (OM) or Ontology Alignment (OA) by treating it as a translation task. Ontologies are represented as graphs, and the translation is performed from a node in the source ontology graph to a path in the target ontology graph. The proposed framework, Truveta Mapper (TM), leverages a multi-task sequence-to-sequence transformer model to perform alignment across multiple ontologies in a zero-shot, unified and end-to-end manner. Multi-tasking enables the model to implicitly learn the relationship between different ontologies via transfer-learning without requiring any explicit cross-ontology manually labeled data. This also enables the formulated framework to outperform existing solutions for both runtime latency and alignment quality. The model is pre-trained and fine-tuned only on publicly available text corpus and inner-ontologies data. The proposed solution outperforms state-of-the-art approaches, Edit-Similarity, LogMap, AML, BERTMap, and the recently presented new OM frameworks in Ontology Alignment Evaluation Initiative (OAEI22), offers log-linear complexity, and overall makes the OM task efficient and more straightforward without much post-processing involving mapping extension or mapping repair. We are open sourcing our solution.
Numerical simulations for a hybrid model of kinetic ions and mass-less fluid electrons in canonical formulations

We study the structure-preserving discretizations of a hybrid model with kinetic ions and mass-less electrons. Diﬀerent from most existing works in the literature, we conduct the discretizations based on two equivalent formulations with vector potentials in diﬀerent gauges, and the distribution functions depend on canonical momentum (not velocity). Particle-in-cell methods are used for the distribution functions, and vector potentials are discretized by ﬁnite element methods in the framework of ﬁnite element exterior calculus. Splitting methods are used for time discretizations. For the ﬁrst formulation, ﬁlters are used to reduce the noises from particles and are shown to improve the numerical results signiﬁcantly. The schemes of the second formulation show good stability and accuracy because of the use of symplectic methods for canonical Hamiltonian systems. Magnetic ﬁelds obtained from the vector potentials are divergence-free naturally. Some numerical experiments are conducted to validate and compare the two discretizations.
Can Very Large Pretrained Language Models Learn Storytelling With A Few Examples?

While pre-trained language models can generate individually ﬂuent sentences for automatic story generation, they struggle to generate stories that are coherent, sensible and interesting. Current state-of-the-art (SOTA) story generation models explore using higher-level features such as plots or commonsense knowledge to improve the quality of generated stories. Prompt-based learning using very large pre-trained language models (VLPLMs) such as GPT3 has demonstrated impressive performance even across various NLP tasks. In this paper, we present an extensive study using automatic and human evaluation to compare the story generation capability of VLPLMs to those SOTA models in three different datasets where stories differ in style, register and length. Our results show that VLPLMs generate much higher quality stories than other story generation models, and to a certain extent rival human authors, although preliminary investigation also reveals that they tend to “plagiarise” real stories in scenarios that involve world knowledge.
Slow and fast collective neutrino oscillations: Invariants and reciprocity

The flavor evolution of a neutrino gas can show ''slow'' or ''fast'' collective motion. In terms of the usual Bloch vectors to describe the mean-field density matrices of a homogeneous neutrino gas, the slow two-flavor equations of motion (EOMs) are $\dot{\mathbf{P}}_\omega=(\omega\mathbf{B}+\mu\mathbf{P})\times\mathbf{P}_\omega$, where $\omega=\Delta m^2/2E$, $\mu=\sqrt{2} G_{\mathrm{F}} (n_\nu+n_{\bar\nu})$, $\mathbf{B}$ is a unit vector in the mass direction in flavor space, and $\mathbf{P}=\int d\omega\,\mathbf{P}_\omega$. For an axisymmetric angle distribution, the fast EOMs are $\dot{\mathbf{D}}_v=\mu(\mathbf{D}_0-v\mathbf{D}_1)\times\mathbf{D}_v$, where $\mathbf{D}_v$ is the Bloch vector for lepton number, $v=\cos\theta$ is the velocity along the symmetry axis, $\mathbf{D}_0=\int dv\,\mathbf{D}_v$, and $\mathbf{D}_1=\int dv\,v\mathbf{D}_v$. We discuss similarities and differences between these generic cases. Both systems can have pendulum-like instabilities (soliton solutions), both have similar Gaudin invariants, and both are integrable in the classical and quantum case. Describing fast oscillations in a frame comoving with $\mathbf{D}_1$ (which itself may execute pendulum-like motions) leads to transformed EOMs that are equivalent to an abstract slow system. These conclusions carry over to three flavors.
Theoretical properties of the ASMOD algorithm for empirical modelling

Empirical modelling algorithms build mathematical models of systems based on observed data. In this paper we present an analysis of the ASMOD algorithm. ASMOD uses B-splines to represent general nonlinear models of several variables. The internal structure of the model is, through an incremental refinement procedure, automatically adapted to the dependencies observed in the data. We derive, using risk minimization theory, uniform upper and lower bounds on the expected value of the criterion function used for model estimation. The bounds are given in terms of the empirical value of the criterion function computed on a finite number of data points. We also analyse the asymptotic convergence properties of the algorithm, and under natural conditions we show convergence to a model which minimizes the expected value of the estimation criterion.
On The Convergence Of Policy Iteration-Based Reinforcement Learning With Monte Carlo Policy Evaluation

A common technique in reinforcement learning is to evaluate the value function from Monte Carlo simulations of a given policy, and use the estimated value function to obtain a new policy which is greedy with respect to the estimated value function. A well-known longstanding open problem in this context is to prove the convergence of such a scheme when the value function of a policy is estimated from data collected from a single sample path obtained from implementing the policy (see page 99 of [Sutton and Barto, 2018], page 8 of [Tsitsiklis, 2002]). We present a solution to the open problem by showing that a first-visit version of such a policy iteration scheme indeed converges to the optimal policy provided that the policy improvement step uses lookahead [Silver et al., 2016, Mnih et al., 2016, Silver et al., 2017b] rather than a simple greedy policy improvement. We provide results both for the original open problem in the tabular setting and also present extensions to the function approximation setting, where we show that the policy resulting from the algorithm performs close to the optimal policy within a function approximation error.
A Stability Analysis of Fine-Tuning a Pre-Trained Model

Fine-tuning a pre-trained model (such as BERT, ALBERT, RoBERTa, T5, GPT, etc.) has proven to be one of the most promising paradigms in recent NLP research. However, numerous recent works indicate that fine-tuning suffers from the instability problem, i.e., tuning the same model under the same setting results in significantly different performance. Many recent works have proposed different methods to solve this problem, but there is no theoretical understanding of why and how these methods work. In this paper, we propose a novel theoretical stability analysis of fine-tuning that focuses on two commonly used settings, namely, full fine-tuning and head tuning. We define the stability under each setting and prove the corresponding stability bounds. The theoretical bounds explain why and how several existing methods can stabilize the fine-tuning procedure. In addition to being able to explain most of the observed empirical discoveries, our proposed theoretical analysis framework can also help in the design of effective and provable methods. Based on our theory, we propose three novel strategies to stabilize the fine-tuning procedure, namely, Maximal Margin Regularizer (MMR), Multi-Head Loss (MHLoss), and Self Unsupervised Re-Training (SURT). We extensively evaluate our proposed approaches on 11 widely used real-world benchmark datasets, as well as hundreds of synthetic classification datasets. The experiment results show that our proposed methods significantly stabilize the fine-tuning procedure and also corroborate our theoretical analysis.
Ferroelectric stripe domains in PbTiO3 thin films: Depolarization field and domain randomness

Observation of stripe domains in PbTiO3 thin films using standard x-ray diffraction analysis at room temperature is discussed. High-quality c-axis oriented thin films of varying thickness, from 6 to 210 unit cells, were grown on buffered NH4–HF etched SrTiO3(001) and Nb:SrTiO3(001) substrates using off-axis radio frequency magnetron sputtering. High-resolution linear Qx scans reveal a superstructure around the specular Bragg peaks, consistent with the presence of ferroelectric stripe domains. For thin samples, the stripe width is found to be proportional to the square root of the film thickness, with random in-plane orientation of domains. For films with a thickness of more than ∼100 unit cells, both monodomain samples and stripe domains were observed. We present evidence for the presence of a threshold depolarization field, above which there is a monotonically decreasing relationship between the domain width and the depolarization field. Furthermore, simulations show that random variations in size of the.
UNO: Unlimited Sampling Meets One-Bit Quantization

Recent results in one-bit sampling provide a framework for a relatively low-cost, low-power sampling, at a high rate by employing time-varying sampling threshold sequences. Another recent development in sampling theory is unlimited sampling, which is a high-resolution technique that relies on modulo ADCs to yield an unlimited dynamic range. In this paper, we leverage the appealing attributes of the two aforementioned techniques to propose a novel unlimited one-bit (UNO) sampling approach. In this framework, the information on the distance between the input signal value and the threshold is stored and utilized to accurately reconstruct the one-bit sampled signal. We then utilize this information to accurately reconstruct the signal from its one-bit samples via the randomized Kaczmarz algorithm (RKA). In the presence of noise, we employ the recent plug-and-play (PnP) priors technique with alternating direction method of multipliers (ADMM) to exploit integration of state-of-the-art regularizers in the reconstruction process. Numerical experiments with RKA and PnP-ADMM-based reconstruction illustrate the effectiveness of our proposed UNO, including its superior performance compared to the one-bit $\Sigma\Delta$ sampling.
Carbon Kagome nanotubes—quasi-one-dimensional nanostructures with flat bands

In recent years, a number of bulk materials and heterostructures have been explored due their connections with exotic materials phenomena emanating from flat band physics and strong electronic correlation. The possibility of realizing such fascinating material properties in simple realistic nanostructures is particularly exciting, especially as the investigation of exotic states of electronic matter in wire-like geometries is relatively unexplored in the literature. Motivated by these considerations, we introduce in this work carbon Kagome nanotubes (CKNTs)—a new allotrope of carbon formed by rolling up Kagome graphene, and investigate this material using specialized first principles calculations. We identify two principal varieties of CKNTs—armchair and zigzag, and find both varieties to be stable at room temperature, based on ab initio molecular dynamics simulations. CKNTs are metallic and feature dispersionless states (i.e., flat bands) near the Fermi level throughout their Brillouin zone, along with an associated singular peak in the electronic density of states. We calculate the mechanical and electronic response of CKNTs to torsional and axial strains, and show that CKNTs appear to be more mechanically compliant than conventional carbon nanotubes (CNTs). Additionally, we find that the electronic properties of CKNTs undergo significant electronic transitions—with emergent partial flat bands and tilted Dirac points—when twisted. We develop a relatively simple tight-binding model that can explain many of these electronic features. We also discuss possible routes for the synthesis of CKNTs. Overall, CKNTs appear to be unique and striking examples of realistic elemental quasi-one-dimensional materials that may display fascinating material properties due to strong electronic correlation. Distorted CKNTs may provide an interesting nanomaterial platform where flat band physics and chirality induced anomalous transport effects may be studied together.
PowerQuant: Automorphism Search for Non-Uniform Quantization

Deep neural networks (DNNs) are nowadays ubiquitous in many domains such as computer vision. However, due to their high latency, the deployment of DNNs hinges on the development of compression techniques such as quantization which consists in lowering the number of bits used to encode the weights and activations. Growing concerns for privacy and security have motivated the development of data-free techniques, at the expanse of accuracy. In this paper, we identity the uniformity of the quantization operator as a limitation of existing approaches, and propose a data-free non-uniform method. More specifically, we argue that to be readily usable without dedicated hardware and implementation, non-uniform quantization shall not change the nature of the mathematical operations performed by the DNN. This leads to search among the continuous automorphisms of $(\mathbb{R}_+^*,\times)$, which boils down to the power functions defined by their exponent. To find this parameter, we propose to optimize the reconstruction error of each layer: in particular, we show that this procedure is locally convex and admits a unique solution. At inference time, we show that our approach, dubbed PowerQuant, only require simple modifications in the quantized DNN activation functions. As such, with only negligible overhead, it significantly outperforms existing methods in a variety of configurations.
Security of Electrical, Optical and Wireless On-Chip Interconnects: A Survey

The advancement of manufacturing technologies has enabled the integration of more intellectual property (IP) cores on the same system-on-chip (SoC). Scalable and high throughput on-chip communication architecture has become a vital component in today’s SoCs. Diverse technologies such as electrical, wireless, optical, and hybrid are available for on-chip communication with different architectures supporting them. On-chip communication sub-system is shared across all the IPs and continuously used throughout the lifetime of the SoC. Therefore, the security of the on-chip communication is crucial because exploiting any vulnerability would be a goldmine for an attacker. In this survey, we provide a comprehensive review of threat models, attacks and countermeasures over diverse on-chip communication technologies as well as sophisticated architectures.
Improving Performance of Object Detection using the Mechanisms of Visual Recognition in Humans

Object recognition systems are usually trained and evaluated on high resolution images. However, in real world applications, it is common that the images have low resolutions or have small sizes. In this study, we first track the performance of the state-of-the-art deep object recognition network, Faster- RCNN, as a function of image resolution. The results reveals negative effects of low resolution images on recognition performance. They also show that different spatial frequencies convey different information about the objects in recognition process. It means multi-resolution recognition system can provides better insight into optimal selection of features that results in better recognition of objects. This is similar to the mechanisms of the human visual systems that are able to implement multi-scale representation of a visual scene simultaneously. Then, we propose a multi-resolution object recognition framework rather than a single-resolution network. The proposed framework is evaluated on the PASCAL VOC2007 database. The experimental results show the performance of our adapted multi-resolution Faster-RCNN framework outperforms the single-resolution Faster-RCNN on input images with various resolutions with an increase in the mean Average Precision (mAP) of 9.14% across all resolutions and 1.2% on the full-spectrum images. Furthermore, the proposed model yields robustness of the performance over a wide range of spatial frequencies.
An investigation of the structure near the L2,3 edges of argon, the M4,5 edges of krypton and the N4,5 edges of xenon, using electron impact with high resolution

The authors have investigated structure near the L2,3 edges of argon, the M4,5 edges of krypton and the N4,5 edges of xenon, using electron impact at an incident energy of 1.5 keV and with an energy resolution of typically 70 meV. The structure observed corresponds to the promotion, via an optically allowed or optically forbidden transition, of an electron from an inner shell of the atom to an unoccupied Rydberg orbital. The energies of these excited states have been obtained, enabling the accurate determination of the Rydberg series limits. The effective quantum numbers of the excited states have been found to be closely related to those of the 'equivalent-core' atoms K I, Rb I and Cs I.
Extremes of regularly varying stochastic volatility fields

We consider a stationary stochastic volatility ﬁeld Y v Z v with v ∈ Z d , where Z is regularly varying and Y has lighter tails and is independent of Z . We make—relative to existing literature—very general assumptions on the dependence structure of both ﬁelds. In particular this allows Y to be non-ergodic, in contrast to the typical assumption that it is i.i. d. , and Z to be given by an inﬁnite moving average. Considering the stochastic volatility ﬁeld on a (rather general) sequence of increasing index sets, we show the existence and form of a Y -dependent extremal functional generalizing the classical extremal index. More precisely, conditioned on the ﬁeld Y , the extremal functional shows exactly how the extremal clustering of the (conditional) stochastic volatility ﬁeld is given in terms of the extremal clustering of the regularly varying ﬁeld Z and the realization of Y . Secondly, we construct two different cluster counting processes on a ﬁxed, full-dimensional set with boundary of Lebesgue measure zero: By means of a coordinate-dependent upscaling of subsets, we systematically count the number of relevant clusters with an extreme observation. We show that both cluster processes converge to a Poisson point process with intensity given in terms of the extremal functional.
Characterization of supercuspidal representations and very regular elements

We prove that regular supercuspidal representations of $p$-adic groups are uniquely determined by their character values on very regular elements -- a special class of regular semisimple elements on which character formulae are very simple -- provided that this locus is sufficiently large. As a consequence, we resolve a question of Kaletha by giving a description of Kaletha's $L$-packets of regular supercuspidal representations which mirrors Langlands' construction for real groups following Harish-Chandra's characterization theorem for discrete series representations. Our techniques additionally characterize supercuspidal representations in general, giving $p$-adic analogues of results of Lusztig on reductive groups over finite fields. In particular, we establish an easy, non-cohomological characterization of unipotent supercuspidal representations when the residue field of the base field is sufficiently large.
New results from HAYSTAC’s phase II operation with a squeezed state receiver

A search for dark matter axions with masses $>10 \mu eV/c^{2}$ has been performed using the HAYSTAC experiment's squeezed state receiver to achieve sub-quantum limited noise. This report includes details of the design and operation of the experiment previously used to search for axions in the mass ranges $16.96-17.12$ and $17.14-17.28 \mu eV/c^{2}$($4.100-4.140$GHz) and $4.145-4.178$GHz) as well as upgrades to facilitate an extended search at higher masses. These upgrades include improvements to the data acquisition routine which have reduced the effective dead time by a factor of 5, allowing for the new region to be scanned $\sim$1.6 times faster with comparable sensitivity. No statistically significant evidence of an axion signal is found in the range $18.44-18.71\mu eV/c^{2}$($4.459-4.523$GHz), leading to an aggregate upper limit exclusion at the $90\%$ level on the axion-photon coupling of $2.06\times g_{\gamma}^{KSVZ}$.
Nondegenerate germs of holomorphic foliations with prescribed holonomy

We are interested in characterizing the holonomy maps associated to integral curves of non-degenerate singularities of holomorphic vector ﬁelds. Such a description is well-known in dimension 2 where is a key ingredient in the study of reduced singularities. The most intricate case in the 2 dimensional setting corresponds to (Siegel) saddle singularities. This work treats the analogous problem for saddles in higher dimension. We show that any germ of holomorphic biholomorphism, in any dimension, can be obtained as the holonomy map associated to an integral curve of a saddle singularity. A natural question is whether we can prescribe the linear part of the saddle germ of vector ﬁeld provided the holonomy map. The answer to this question is known to be positive in dimension 2. We see that this is not the case in higher dimension. In spite of this, we provide a positive result under a natural condition for the holonomy map.