Miracle-dz/UniFAR-FTUs huggingface dataset:
Digitized Counterdiabatic Quantum Algorithm for Protein Folding

We propose a hybrid classical-quantum digitized-counterdiabatic algorithm to tackle the protein folding problem on a tetrahedral lattice. Digitized-counterdiabatic quantum computing is a paradigm developed to compress quantum algorithms via the digitization of the counterdiabatic acceleration of a given adiabatic quantum computation. Finding the lowest energy configuration of the amino acid sequence is an NP-hard optimization problem that plays a prominent role in chemistry, biology, and drug design. We outperform state-of-the-art quantum algorithms using problem-inspired and hardware-efficient variational quantum circuits. We apply our method to proteins with up to 9 amino acids, using up to 17 qubits on quantum hardware. Specifically, we benchmark our quantum algorithm with Quantinuum's trapped ions, Google's and IBM's superconducting circuits, obtaining high success probabilities with low-depth circuits as required in the NISQ era.
Quantum computation mediated by an ancilla of arbitrary dimension

Models of universal quantum computation where the required interactions between computational qubits are mediated by some ancillary system are highly relevant to experimental realisations of a quantum computer. We introduce two such models of quantum computation, the first of which employs a d-level (qudit) ancilla and the second of which is based on the coherent states of n spins. The computation is implemented with geometric phase gates, created via controlled displacement operators defined on a discrete lattice phase space, in the case of a qudit ancilla, and a Bloch sphere, in the case of a spin coherent state ancilla. Both of these models are shown to be different possible generalisations to finite dimensions of the field-mode quantum bus (qubus) computer. We then consider the computational complexity of the qubus architecture and show that although in certain circumstances the model can be used to reduce the number of operations required to implement quantum circuits, contrary to previous claims [New J. Phys. 13, 095007, 2011] this is not the case for the quantum Fourier transform (QFT). Furthermore, using the generalisation of the qubus model to a qudit bus, we show that this reduction in the circuit complexity depends on the dimensionality of the bus.
An effectivization of the law of large numbers for algorithmically random sequences and its absolute speed limit of convergence

The law of large numbers is one of the fundamental properties which algorithmically random inﬁnite sequences ought to satisfy. In this paper, we show that the law of large numbers can be eﬀectivized for an arbitrary Schnorr random inﬁnite sequence, with respect to an arbitrary computable Bernoulli measure. Moreover, we show that an absolute speed limit of convergence exists in this eﬀectivization, and it equals 2 in a certain sense. In the paper, we also provide the corresponding eﬀectivization of almost sure convergence in the strong law of large numbers, and its absolute speed limit of convergence, in the context of probability theory, with respect to a large class of probability spaces and i.i. d. random variables on them, which are not necessarily computable.
The limitations of comonotonic additive risk measures: a literature review

Risk measures satisfying the axiom of comonotonic additivity are extensively studied, arguably because of the plethora of results indicating interesting aspects of such risk measures. Recent research, however, has shown that this axiom is incompatible with central properties in specific contexts. In this paper, we present a literature review of these incompatibilities. In addition, we use the Choquet representation of comonotonic additive risk measures to show they cannot be surplus invariant.
A monotonicity theorem for subharmonic functions on manifolds

We provide a sharp monotonicity theorem about the distribution of subharmonic functions on manifolds, which can be regarded as a new, measure theoretic form of the uncertainty principle. As an illustration of the scope of this result, we deduce contractivity estimates for analytic functions on the Riemann sphere, the complex plane and the Poincar\'e disc, with a complete description of the extremal functions, hence providing a unified and illuminating perspective of a number of results and conjectures on this subject, in particular on the Wehrl entropy conjecture by Lieb and Solovej. In this connection, we completely prove that conjecture for SU(2), by showing that the corresponding extremals are only the coherent states. Also, we show that the above (global) estimates admit a local counterpart and in all cases we characterize also the extremal subsets, among those of fixed assigned measure.
Optimum Er concentration for in situ doped GaN visible and infrared luminescence

GaN thin films have been doped with varying Er concentrations (0.01–10 at. %) during molecular-beam-epitaxy growth. As expected, the visible and infrared (IR) emissions, from photoluminescence (PL) and electroluminescence (EL), are a strong function of Er concentration. We report on the determination of an optimum Er doping level for PL and EL intensity. Secondary ion mass spectroscopy and Rutherford backscattering measurements showed that the Er concentration in GaN increased exponentially with Er cell temperature. PL and EL intensity of green emission at 537 and 558 nm, due to Er 4f–4f inner shell transitions, exhibited a maximum at ∼1 at. % Er. IR PL intensity at 1.54 μm, due to another Er transition, revealed the same maximum for ∼1 at. % Er concentration. PL lifetime measurements at 537 nm showed that samples with Er concentration <1 at. % had a lifetime of ∼5 μs. For Er concentration ⩾1 at. %, the lifetime decreased rapidly to values below 1 μs. This concentration quenching is believed to be due to .
Insertion algorithms for Gelfand $S_n$-graphs

The two tableaux assigned by the Robinson--Schensted correspondence are equal if and only if the input permutation is an involution, so the RS algorithm restricts to a bijection between involutions in the symmetric group and standard tableaux. Beissinger found a concise way of formulating this restricted map, which involves adding an extra cell at the end of a row after a Schensted insertion process. We show that by changing this algorithm slightly to add cells at the end of columns rather than rows, one obtains a different bijection from involutions to standard tableaux. Both maps have an interesting connection to representation theory. Specifically, our insertion algorithms classify the molecules (and conjecturally the cells) in the pair of $W$-graphs associated to the unique equivalence class of perfect models for a generic symmetric group.
Anatomy of open-boundary bulk in multiband non-Hermitian systems

Although the non-Bloch band theory is a milestone in elaborating bulk energy bands of non-Hermitian systems under the open-boundary condition (OBC), vital issues related to multivalued functions of non-Hermitian energy bands remain unsolved. In this paper, we anatomize the bulk properties of one-dimensional multiband non-Hermitian systems under OBC. We put forward the energy-band branches (EBBs) to settle the multivalued functions of non-Hermitian energy bands, which become gapped or gapless corresponding to disconnected or connected EBBs in the complex energy plane, where the branch points and branch cuts play a crucial role. We clarify the precise significance of the non-Hermitian skin effect, which illustrates the asymptotic behavior of EBB eigenstates (bulk eigenstates) in the deep bulk and compensates previous non-Bloch band theory. We also obtain a general expression of open-boundary Green's functions based on such EBBs and generalized Brillouin zones, useful for studies on non-Hermitian dynamical evolution.
Learning to Detect Noisy Labels Using Model-Based Features

Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation. Many existing approaches are based on heuristics such as sample losses, which might not be flexible enough to achieve optimal solutions. Meta learning based methods address this issue by learning a data selection function, but can be hard to optimize. In light of these pros and cons, we propose Selection-Enhanced Noisy label Training (SENT) that does not rely on meta learning while having the flexibility of being data-driven. SENT transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features. Empirically, on a wide range of tasks including text classification and speech recognition, SENT improves performance over strong baselines under the settings of self-training and label corruption.
MixupE: Understanding and Improving Mixup from Directional Derivative Perspective

Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels. This technique is known to improve the generalization performance in many learning paradigms and applications. In this work, we first analyze Mixup and show that it implicitly regularizes infinitely many directional derivatives of all orders. Based on this new insight, we propose an improved version of Mixup, theoretically justified to deliver better generalization performance than the vanilla Mixup. To demonstrate the effectiveness of the proposed method, we conduct experiments across various domains such as images, tabular data, speech, and graphs. Our results show that the proposed method improves Mixup across multiple datasets using a variety of architectures, for instance, exhibiting an improvement over Mixup by 0.8% in ImageNet top-1 accuracy.
Rethinking Person Re-Identification via Semantic-based Pretraining

Pretraining is a dominant paradigm in computer vision. Generally, supervised ImageNet pretraining is commonly used to initialize the backbones of person re-identification (Re-ID) models. However, recent works show a surprising result that CNN-based pretraining on ImageNet has limited impacts on Re-ID system due to the large domain gap between ImageNet and person Re-ID data. To seek an alternative to traditional pretraining, here we investigate semantic-based pretraining as another method to utilize additional textual data against ImageNet pretraining. Specifically, we manually construct a diversified FineGPR-C caption dataset for the first time on person Re-ID events. Based on it, a pure semantic-based pretraining approach named VTBR is proposed to adopt dense captions to learn visual representations with fewer images. We train convolutional neural networks from scratch on the captions of FineGPR-C dataset, and then transfer them to downstream Re-ID tasks. Comprehensive experiments conducted on benchmark datasets show that our VTBR can achieve competitive performance compared with ImageNet pretraining—despite using up to 1.4× fewer images, revealing its potential in Re-ID pretraining. Our source code is also publicly available at https://github.com/JeremyXSC/VTBR.
Forcing with Urelements

I first isolate a hierarchy of axioms over ZFC that allows a proper class of urelements. The Collection Principle and Reflection Principle hold precisely when the urelements are arranged in a specific manner. I then turn to forcing with urelements. A new forcing machinery with urelements is proposed to address a problem with the existing approach regarding the property of fullness. Every new forcing relation is full just in case Collection holds in the ground model. Forcing with urelements can preserve, destroy, or recover various axioms within the hierarchy. Ground model definability fails when the ground model contains a proper class of urelements.
Chiral critical behavior of 3D lattice fermionic models with quartic interactions

We study the critical behavior of the three-dimensional (3D) Gross-Neveu (GN) model with $N_f$ Dirac fermionic flavors and quartic interactions, at the chiral ${\mathbb Z}_2$ transition in the massless ${\mathbb Z}_2$-symmetric limit. For this purpose, we consider a lattice GN model with staggered Kogut-Susskind fermions and a scalar field coupled to the scalar bilinear fermionic operator, which effectively realizes the attractive four-fermion interaction. We perform Monte Carlo (MC) simulations for $N_f=4,8,12,16$. By means of finite-size scaling analyses of the numerical data, we obtain estimates of the critical exponents that are compared with the large-$N_f$ predictions obtained using the continuum GN field theory. We observe a substantial agreement. This confirms that lattice GN models with staggered fermions provide a nonpertubative realization of the GN quantum field theory, even though the lattice interactions explicitly break the flavor ${\rm U}(N_f)\otimes {\rm U}(N_f)$ symmetry of the GN field theory, which is only recovered in the critical limit.
A linearly convergent Gauss-Newton subgradient method for ill-conditioned problems

We analyze a preconditioned subgradient method for optimizing composite functions $h \circ c$, where $h$ is a locally Lipschitz function and $c$ is a smooth nonlinear mapping. We prove that when $c$ satisfies a constant rank property and $h$ is semismooth and sharp on the image of $c$, the method converges linearly. In contrast to standard subgradient methods, its oracle complexity is invariant under reparameterizations of $c$.
Rigorous Negative Ion Binding Energies in Low-Energy Electron Elastic Collisions with Heavy Multi-Electron Atoms and Fullerene Molecules: Validation of Electron Affinities

Dramatically sharp resonances manifesting stable negative ion formation characterize Regge pole-calculated low-energy electron elastic total cross sections (TCSs) of heavy multi-electron systems. The novelty of the Regge pole analysis is in the extraction of rigorous and unambiguous negative ion binding energies (BEs), corresponding to the measured electron affinities (EAs) of the investigated multi-electron systems. The measured EAs have engendered the crucial question: is the EA of multi-electron atoms and fullerene molecules identified with the BE of the attached electron in the ground, metastable or excited state of the formed negative ion during a collision? Inconsistencies in the meaning of the measured EAs are elucidated and new EA values for Bk, Cf, Fm, and Lr are presented.
Network Characteristics of LEO Satellite Constellations: A Starlink-Based Measurement from End Users

Low Earth orbit Satellite Networks (LSNs) have been advocated as a key infrastructure for truly global coverage in the forthcoming 6G. This paper presents our initial measurement results and observations on the end-to-end network characteristics of Starlink, arguably the largest LSN constellation to date. Our findings confirm that LSNs are a promising solution towards ubiquitous Internet coverage over the Earth; yet, we also find that the users of Starlink experience much more dynamics in throughput and latency than terrestrial network users, and even frequent outages. Its user experiences are heavily affected by environmental factors such as terrain, solar storms, rain, clouds, and temperature, so is the power consumption. We further analyze Starlink’s current bent-pipe relay strategy and its limits, particularly for cross-ocean routes. We have also explored its mobility and portability potentials, and extended our experiments from urban cities to wild remote areas that are facing distinct practical and cultural challenges.
On the Lower Bound of Minimizing Polyak-Łojasiewicz functions

Polyak-Łojasiewicz (PL) (Polyak, 1963) condition is a weaker condition than the strong convexity but suffices to ensure a global convergence for the Gradient Descent algorithm. In this paper, we study the lower bound of algorithms using first-order oracles to find an approximate optimal solution. We show that any first-order algorithm requires at least Ω (cid:16) Lµ log 1 ε (cid:17) gradient costs to find an ε -approximate optimal solution for a general L -smooth function that has an µ -PL constant. This result demonstrates the optimality of the Gradient Descent algorithm to minimize smooth PL functions in the sense that there exists a “hard” PL function such that no first-order algorithm can be faster than Gradient Descent when ignoring a numerical constant. In contrast, it is well-known that the momentum technique, e.g. Nesterov (2003, chap. 2), can provably accelerate Gradient Descent to O (cid:16)(cid:113) L ˆ µ log 1 ε (cid:17) gradient costs for functions that are L -smooth and ˆ µ -strongly convex. Therefore, our result distinguishes the hardness of minimizing a smooth PL function and a smooth strongly convex function as the complexity of the former cannot be improved by any polynomial order in general.
Growth Kinetics and Properties of Thin Cobalt Films Electrodeposited on n-Si(100)

The cobalt thin films were directly grown on n-Si(100) substrates in a non-aqueous electrolyte of 0.1 M CoCl2 by using pulsed electrodeposition technique. In this paper, the growth, structural and magnetic properties of thin Co films have been investigated. Current transient experiments showed that Co nuclei in the early deposition stage forms through three dimensional (3D) instantaneous nucleation followed by diffusion-limited growth. From the atomic force microscopy (AFM) analysis, it has been found that the film surface consisted of cubic and distorted rectangular-shaped nanocrystallites with the average size of about 140 nm irrespective of the deposition voltage. X-ray diffraction (XRD) measurement indicated that Co was arranged with only a hexagonal close-packed (hcp) structure not involving face-centered cubic (fcc) Co or silicide phases. Vibrating sample magnetometer (VSM) measurements revealed that the easy magnetization axis of Co thin film is parallel to the film plane.
Multi-frequency multi-sampling fluorescence lifetime imaging using a high speed line-scan camera

We present a theoretical multi-frequency multi-sampling frequency-domain fluorescence lifetime imaging (FLI) technique, which determines fluorescence lifetime by measuring the phase delay and decrease of the modulation of the emission relative to the excitation. Such a system could be realized with high-speed line-scan cameras using more than 100000 frames per second. It is useful for the measurement of lifetimes in the microsecond range and can be, e.g., used to measure oxygen concentrations with Ruthenium complexes. Typically 100 samples are used, resulting in a temporal resolution of microsecond fluorescence lifetime measurement. By including frequency components with frequencies much higher than the Nyquist criterion, the measurement of fluorescence lifetimes much shorter than the sampling interval is possible. In order to optimize the SNR of the predict lifetimes, we investigate the photon economy of our technique. Various ranges of the exposure time relative to the range of modulation period and compositions of multiple frequencies have been studied. The presented approach is validated by numerical simulations by Monte Carlo method.
DeepCuts: Single-Shot Interpretability based Pruning for BERT

As language models have grown in parameters and layers, it has become much harder to train and infer with them on single GPUs. This is severely restricting the availability of large language models such as GPT-3, BERT-Large, and many others. A common technique to solve this problem is pruning the network architecture by removing transformer heads, fully-connected weights, and other modules. The main challenge is to discern the important parameters from the less important ones. Our goal is to find strong metrics for identifying such parameters. We thus propose two strategies: Cam-Cut based on the GradCAM interpretations, and Smooth-Cut based on the SmoothGrad, for calculating the importance scores. Through this work, we show that our scoring functions are able to assign more relevant task-based scores to the network parameters, and thus both our pruning approaches significantly outperform the standard weight and gradient-based strategies, especially at higher compression ratios in BERT-based models. We also analyze our pruning masks and find them to be significantly different from the ones obtained using standard metrics.
Improved numerical integration of thick shell finite elements

A quadratic thick shell element derived from a three-dimensional isoparametric element was first introduced by Ahmad and co-workers in 1968. This element was noted, however, to be relatively inefficient in representing bending deformations in thin shell or thin plate applications. The present paper outlines a selective integration scheme for evaluating the stiffness matrix of the element, in which each component of the strain energy is evaluated separately using a different Gaussian integration grid for each contribution. By this procedure, the excessive bending stiffness of the element, which results from the use of me quadratic interpolation functions, is avoided. The improved performance of this element, as compared with the original thick shell element, is demonstrated by analyses of a variety of thin and thick shell problems. 1
Dynamics of Social Networks: Multi-agent Information Fusion, Anticipatory Decision Making and Polling

This paper surveys mathematical models, structural results and algorithms in controlled sensing with social learning in social networks. Part 1, namely Bayesian Social Learning with Controlled Sensing addresses the following questions: How does risk averse behavior in social learning affect quickest change detection? How can information fusion be priced? How is the convergence rate of state estimation affected by social learning? The aim is to develop and extend structural results in stochastic control and Bayesian estimation to answer these questions. Such structural results yield fundamental bounds on the optimal performance, give insight into what parameters affect the optimal policies, and yield computationally efficient algorithms. Part 2, namely, Multi-agent Information Fusion with Behavioral Economics Constraints generalizes Part 1. The agents exhibit sophisticated decision making in a behavioral economics sense; namely the agents make anticipatory decisions (thus the decision strategies are time inconsistent and interpreted as subgame Bayesian Nash equilibria). Part 3, namely {\em Interactive Sensing in Large Networks}, addresses the following questions: How to track the degree distribution of an infinite random graph with dynamics (via a stochastic approximation on a Hilbert space)? How can the infected degree distribution of a Markov modulated power law network and its mean field dynamics be tracked via Bayesian filtering given incomplete information obtained by sampling the network? We also briefly discuss how the glass ceiling effect emerges in social networks. Part 4, namely \emph{Efficient Network Polling} deals with polling in large scale social networks. In such networks, only a fraction of nodes can be polled to determine their decisions. Which nodes should be polled to achieve a statistically accurate estimates?