Miracle-dz/UniFAR-FTUs huggingface dataset:
Bit-Balance: Model-Hardware Codesign for Accelerating NNs by Exploiting Bit-Level Sparsity

Bit-serial architectures can handle Neural Networks (NNs) with different weight precision, achieving higher resource efficiency compared with bit-parallel architectures. Besides, the weights contain abundant zero bits owing to the fault tolerance of NNs, indicating that bit sparsity of NNs can be further exploited for performance improvement. However, the irregular proportion of zero bits in each weight causes imbalanced workloads in the Processing Element (PE) array, which degrades performance or induces overhead for sparse processing. Thus, this article proposed a channel-wise bit-sparsity quantization method that keeps the non-zero bit number of each weight in each channel from exceeding a certain threshold and clusters the channels with the same threshold to balance the workloads in PE array with little accuracy loss. Then, we co-designed a sparse bit-serial architecture, called Bit-balance, to improve overall performance, supporting weight-bit sparsity and adaptive bitwidth computation. The whole design was implemented with 65 nm technology at 1 GHz and performs at 447-, 37-, 59-, 240-, and 19-frame/s for AlexNet, VGG-16, ResNet-50, GoogleNet, and Yolo-v3 respectively. Compared with sparse bit-serial accelerator, Bitlet, Bit-balance achieves 1.6<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times}$</tex-math><alternatives><mml:math><mml:mo mathvariant="bold">×</mml:mo></mml:math><inline-graphic xlink:href="chen-ieq1-3324477.gif"/></alternatives></inline-formula> 2.1<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times}$</tex-math><alternatives><mml:math><mml:mo mathvariant="bold">×</mml:mo></mml:math><inline-graphic xlink:href="chen-ieq2-3324477.gif"/></alternatives></inline-formula> energy efficiency (frame/J) and 2.3<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times}$</tex-math><alternatives><mml:math><mml:mo mathvariant="bold">×</mml:mo></mml:math><inline-graphic xlink:href="chen-ieq3-3324477.gif"/></alternatives></inline-formula> 3.6<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times}$</tex-math><alternatives><mml:math><mml:mo mathvariant="bold">×</mml:mo></mml:math><inline-graphic xlink:href="chen-ieq4-3324477.gif"/></alternatives></inline-formula> resource efficiency (frame/mm<inline-formula><tex-math notation="LaTeX">${}^{\mathbf{2}}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mrow><mml:mrow><mml:mn mathvariant="bold">2</mml:mn></mml:mrow></mml:mrow></mml:msup></mml:math><inline-graphic xlink:href="chen-ieq5-3324477.gif"/></alternatives></inline-formula>).
Cutting multi-control quantum gates with ZX calculus

Circuit cutting, the decomposition of a quantum circuit into independent partitions, has become a promising avenue towards experiments with larger quantum circuits in the noisy-intermediate scale quantum (NISQ) era. While previous work focused on cutting qubit wires or two-qubit gates, in this work we introduce a method for cutting multi-controlled Z gates. We construct a decomposition and prove the upper bound O(62K) on the associated sampling overhead, where K is the number of cuts in the circuit. This bound is independent of the number of control qubits but can be further reduced to O(4.52K) for the special case of CCZ gates. Furthermore, we evaluate our proposal on IBM hardware and experimentally show noise resilience due to the strong reduction of CNOT gates in the cut circuits.
Real Estate Property Valuation using Self-Supervised Vision Transformers

The use of Artificial Intelligence (AI) in the real estate market has been growing in recent years. In this paper, we propose a new method for property valuation that utilizes self-supervised vision transformers, a recent breakthrough in computer vision and deep learning. Our proposed algorithm uses a combination of machine learning, computer vision and hedonic pricing models trained on real estate data to estimate the value of a given property. We collected and pre-processed a data set of real estate properties in the city of Boulder, Colorado and used it to train, validate and test our algorithm. Our data set consisted of qualitative images (including house interiors, exteriors, and street views) as well as quantitative features such as the number of bedrooms, bathrooms, square footage, lot square footage, property age, crime rates, and proximity to amenities. We evaluated the performance of our model using metrics such as Root Mean Squared Error (RMSE). Our findings indicate that these techniques are able to accurately predict the value of properties, with a low RMSE. The proposed algorithm outperforms traditional appraisal methods that do not leverage property images and has the potential to be used in real-world applications.
Gyri vs. Sulci: Disentangling Brain Core-Periphery Functional Networks via Twin-Transformer

The human cerebral cortex is highly convoluted into convex gyri and concave sulci. It has been demonstrated that gyri and sulci are significantly different in their anatomy, connectivity, and function, besides exhibiting opposite shape patterns, long-distance axonal fibers connected to gyri are much denser than those connected to sulci, and neural signals on gyri are more complex in low-frequency while sulci are more complex in high-frequency. Although accumulating evidence shows significant differences between gyri and sulci, their primary roles in brain function have not been elucidated yet. To solve this fundamental problem, we design a novel Twin-Transformer framework to unveil the unique functional roles of gyri and sulci as well as their relationship in the whole brain function. Our Twin-Transformer framework adopts two structure-identical (twin) Transformers to disentangle spatial-temporal patterns of gyri and sulci, one focuses on the information of gyri and the other is on sulci. The Gyro-Sulcal interactions, along with the tremendous but widely existing variability across subjects, are characterized in the loss design. We validated our Twin-Transformer on the HCP task-fMRI dataset, for the first time, to elucidate the different roles of gyri and sulci in brain function. Our results suggest that gyri and sulci could work together in a core-periphery network manner, that is, gyri could serve as core networks for information gathering and distributing, while sulci could serve as periphery networks for specific local information processing. These findings have shed new light on our fundamental understanding of the brain's basic structural and functional mechanisms.
Brillouin optical-fiber frequency-domain analysis for distributed temperature and strain measurements

Brillouin optical-fiber frequency-domain analysis is a new sensing technique for the distributed measurement of temperature and strain. Extensive theoretical investigations and experimental results of distributed temperature and strain measurements demonstrate the feasibility of this new concept. In an experimental demonstration, a spatial resolution of 3 m was achieved for a 1 km-long single-mode fiber.
Variable selection via the weighted group lasso for factor analysis models

We consider the problem of selecting variables in factor analysis models. The $L_1$ regularization procedure is introduced to perform an automatic variable selection. In the factor analysis model, each variable is controlled by multiple factors when there are more than one underlying factor. We treat parameters corresponding to the multiple factors as grouped parameters, and then apply the group lasso. Furthermore, the weight of the group lasso penalty is modified to obtain appropriate estimates and improve the performance of variable selection. Crucial issues in this modeling procedure include the selection of the number of factors and a regularization parameter. Choosing these parameters can be viewed as a model selection and evaluation problem. We derive a model selection criterion for evaluating the factor analysis model via the weighted group lasso. Monte Carlo simulations are conducted to investigate the effectiveness of the proposed procedure. A real data example is also given to illustrate our procedure. The Canadian Journal of Statistics 40: 345–361; 2012 © 2012 Statistical Society of Canada
Sublinear Approximation Schemes for Scheduling Precedence Graphs of Bounded Depth

We study the classical scheduling problem on parallel machines %with precedence constraints where the precedence graph has the bounded depth $h$. Our goal is to minimize the maximum completion time. We focus on developing approximation algorithms that use only sublinear space or sublinear time. We develop the first one-pass streaming approximation schemes using sublinear space when all jobs' processing times differ no more than a constant factor $c$ and the number of machines $m$ is at most $\tfrac {2n \epsilon}{3 h c }$. This is so far the best approximation we can have in terms of $m$, since no polynomial time approximation better than $\tfrac{4}{3}$ exists when $m = \tfrac{n}{3}$ unless P=NP. %the problem cannot be approximated within a factor of $\tfrac{4}{3}$ when $m = \tfrac{n}{3}$ even if all jobs have equal processing time. The algorithms are then extended to the more general problem where the largest $\alpha n$ jobs have no more than $c$ factor difference. % for some constant $0<\alpha \le 1$. We also develop the first sublinear time algorithms for both problems. For the more general problem, when $ m \le \tfrac { \alpha n \epsilon}{20 c^2 \cdot h } $, our algorithm is a randomized $(1+\epsilon)$-approximation scheme that runs in sublinear time. This work not only provides an algorithmic solution to the studied problem under big data % and cloud computing environment, but also gives a methodological framework for designing sublinear approximation algorithms for other scheduling problems.
Quickest Change Detection for Unnormalized Statistical Models

Classical quickest change detection algorithms require modeling pre-change and post-change distributions. Such an approach may not be feasible for various machine learning models because of the complexity of computing the explicit distributions. Additionally, these methods may suffer from a lack of robustness to model mismatch and noise. This paper develops a new variant of the classical Cumulative Sum (CUSUM) algorithm for the quickest change detection. This variant is based on Fisher divergence and the Hyvärinen score and is called the Hyvärinen score-based CUSUM (SCUSUM) algorithm. The SCUSUM algorithm allows the applications of change detection for unnormalized statistical models, i.e., models for which the probability density function contains an unknown normalization constant. The asymptotic optimality of the proposed algorithm is investigated by deriving expressions for average detection delay and the mean running time to a false alarm. Numerical results are provided to demonstrate the performance of the proposed algorithm.
Convergence of a quantum lattice Boltzmann scheme to the nonlinear Dirac equation for Gross-Neveu model in 1+1 dimensions

This paper studies the quantum lattice Boltzmann scheme for the nonlinear Dirac equations for Gross-Neveu model in $1+1$ dimensions. The initial data for the scheme are assumed to be convergent in $L^2$. Then for any $T\ge 0$ the corresponding solutions for the quantum lattice Boltzmann scheme are shown to be convergent in $C([0,T];L^2(R^1))$ to the strong solution to the nonlinear Dirac equations as the mesh sizes converge to zero. In the proof, at first a Glimm type functional is introduced to establish the stability estimates for the difference between two solutions for the corresponding quantum lattice Boltzmann scheme, which leads to the compactness of the set of the solutions for the quantum lattice Boltzmann scheme. Finally, the limit of any convergent subsequence of the solutions for the quantum lattice Boltzmann scheme is shown to coincide with the strong solution to a Cauchy problem for the nonlinear Dirac equations.
Temperature and strain-rate dependent fracture strength of graphynes

Graphyne is an allotrope of graphene. The mechanical properties of graphynes (α-, β-, γ- and 6,6,12-graphynes) under uniaxial tension deformation at different temperatures and strain rates are studied using molecular dynamics simulations. It is found that graphynes are more sensitive to temperature changes than graphene in terms of fracture strength and Young's modulus. The temperature sensitivity of the different graphynes is proportionally related to the percentage of acetylenic linkages in their structures, with the α-graphyne (having 100% of acetylenic linkages) being most sensitive to temperature. For the same graphyne, temperature exerts a more pronounced effect on the Young's modulus than fracture strength, which is different from that of graphene. The mechanical properties of graphynes are also sensitive to strain rate, in particular at higher temperatures.
Angular dependence of the Wigner time delay upon strong-field ionization from an aligned 
p
 orbital

We present experimental data on the strong-field ionization of the argon dimer in a co-rotating two-color (CoRTC) laser field. We observe a sub-cycle interference pattern in the photoelectron momentum distribution and infer the Wigner time delay using holographic angular streaking of electrons (HASE). We find that the Wigner time delay varies by more than 400 attoseconds as a function of the electron emission direction with respect to the molecular axis. The measured time delay is found to be independent of the parity of the dimer-cation and is in good agreement with our theoretical model based on the ionization of an aligned atomic p-orbital.
Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection

Prompt tuning with large-scale pretrained vision-language models empowers open-vocabulary predictions trained on limited base categories, e.g., object classification and detection. In this paper, we propose compositional prompt tuning with motion cues: an extended prompt tuning paradigm for compositional predictions of video data. In particular, we present Relation Prompt (RePro) for Open-vocabulary Video Visual Relation Detection (Open-VidVRD), where conventional prompt tuning is easily biased to certain subject-object combinations and motion patterns. To this end, RePro addresses the two technical challenges of Open-VidVRD: 1) the prompt tokens should respect the two different semantic roles of subject and object, and 2) the tuning should account for the diverse spatio-temporal motion patterns of the subject-object compositions. Without bells and whistles, our RePro achieves a new state-of-the-art performance on two VidVRD benchmarks of not only the base training object and predicate categories, but also the unseen ones. Extensive ablations also demonstrate the effectiveness of the proposed compositional and multi-mode design of prompts. Code is available at https://github.com/Dawn-LX/OpenVoc-VidVRD.
QLABGrad: A Hyperparameter-Free and Convergence-Guaranteed Scheme for Deep Learning

The learning rate is a critical hyperparameter for deep learning tasks since it determines the extent to which the model parameters are adjusted during the learning course. However, the choice of learning rates typically depends on empirical judgment, which may not result in satisfactory outcomes without intensive try-and-error experiments. In this study, we propose a novel learning rate adaptation scheme called QLABGrad. Without any user-specified hyperparameter, QLABGrad automatically determines the learning rate by optimizing the quadratic loss approximation-based (QLAB) function for a given gradient descent direction, where only one extra forward propagation is required. We theoretically prove the convergence of QLABGrad under the smooth Lipschitz condition on the loss function. Experiment results on multiple architectures, including MLP, CNN, and ResNet, on MNIST, CIFAR10, and ImageNet datasets, demonstrate that QLABGrad outperforms widely adopted schemes for deep learning.
Towards Large Certified Radius in Randomized Smoothing Using Quasiconcave Optimization

Randomized smoothing is currently the state-of-the-art method that provides certified robustness for deep neural networks. However, due to its excessively conservative nature, this method of incomplete verification often cannot achieve an adequate certified radius on real-world datasets. One way to obtain a larger certified radius is to use an input-specific algorithm instead of using a fixed Gaussian filter for all data points. Several methods based on this idea have been proposed, but they either suffer from high computational costs or gain marginal improvement in certified radius. In this work, we show that by exploiting the quasiconvex problem structure, we can find the optimal certified radii for most data points with slight computational overhead. This observation leads to an efficient and effective input-specific randomized smoothing algorithm. We conduct extensive experiments and empirical analysis on CIFAR-10 and ImageNet. The results show that the proposed method significantly enhances the certified radii with low computational overhead.
Extrinsic Calibration of 2D mm-Wavelength Radar Pairs Using Ego-Velocity Estimates

—Correct radar data fusion depends on knowledge of the spatial transform between sensor pairs. Current methods for determining this transform operate by aligning identiﬁable features in different radar scans, or by relying on measurements from another, more accurate sensor (e.g., a lidar unit). Feature- based alignment requires the sensors to have overlapping ﬁelds of view or necessitates the construction of an environment map. Several existing methods require bespoke retroreﬂective radar targets. These requirements limit both where and how calibration can be performed. In this paper, we take a different approach: instead of attempting to track targets or features, which can be difﬁcult in noisy radar data, we instead rely on ego-velocity estimates from each radar to perform calibration. Our method enables calibration of a subset of the transform parameters, including the yaw and axis of translation between the radar pair, without the need for a shared ﬁeld of view or for specialized structures in the environment. In general, the yaw and axis of translation are the most important parameters for data fusion, the most likely to vary over time, and the most difﬁcult to calibrate manually. We formulate calibration as a batch optimization problem, prove that the radar-radar system is identiﬁable, and specify the platform excitation requirements. Through simulations studies and real-world experiments, we establish that our method is more reliable and accurate at estimating the yaw and translation axis than state-of-the-art methods. Finally, we show that the full rigid-body transform can be recovered if relatively coarse information about the rotation rate is available.
Are UD Treebanks Getting More Consistent? A Report Card for English UD

Recent efforts to consolidate guidelines and treebanks in the Universal Dependencies project raise the expectation that joint training and dataset comparison is increasingly possible for high-resource languages such as English, which have multiple corpora. Focusing on the two largest UD English treebanks, we examine progress in data consolidation and answer several questions: Are UD English treebanks becoming more internally consistent? Are they becoming more like each other and to what extent? Is joint training a good idea, and if so, since which UD version? Our results indicate that while consolidation has made progress, joint models may still suffer from inconsistencies, which hamper their ability to leverage a larger pool of training data.
Selective Uncertainty Propagation in Offline RL

We consider the finite-horizon offline reinforcement learning (RL) setting, and are motivated by the challenge of learning the policy at any step h in dynamic programming (DP) algorithms. To learn this, it is sufficient to evaluate the treatment effect of deviating from the behavioral policy at step h after having optimized the policy for all future steps. Since the policy at any step can affect next-state distributions, the related distributional shift challenges can make this problem far more statistically hard than estimating such treatment effects in the stochastic contextual bandit setting. However, the hardness of many real-world RL instances lies between the two regimes. We develop a flexible and general method called selective uncertainty propagation for confidence interval construction that adapts to the hardness of the associated distribution shift challenges. We show benefits of our approach on toy environments and demonstrate the benefits of these techniques for offline policy learning.
A combinatorial proof of a partition perimeter inequality

The partition perimeter is a statistic defined to be one less than the sum of the number of parts and the largest part. Recently, Amdeberhan, Andrews, and Ballantine proved the following analog of Glaisher's theorem: for all $m \geq 2$ and $n \geq 1$, there are at least as many partitions with perimeter $n$ and parts $\not \equiv 0 \pmod{m}$ as partitions with perimeter $n$ and parts repeating fewer than $m$ times. In this work, we provide a combinatorial proof of their theorem by relating the combinatorics of the partition perimeter to that of compositions. Using this technique, we also show that a composition theorem of Huang implies a refinement of another perimeter theorem of Fu and Tang.
Observational connection of non-thermal X-ray emission from pulsars with their timing properties and thermal emission

The origin and radiation mechanisms of high energy emissions from pulsars have remained mysterious since their discovery. Here we report, based on a sample of 68 pulsars, observational connection of non-thermal X-ray emissions from pulsars with their timing properties and thermal emissions, which may provide some constraints on theoretical modeling. Besides strong correlations with the spin-down power $\dot{E}$ and the magnetic field strength at the light cylinder Blc, the non-thermal X-ray luminosity in 0.5 – 8 keV, Lp, represented by the power-law component in the spectral model, is found to be strongly correlated with the highest possible electric field strength in the polar gap, Epc, of the pulsar. The spectral power index Γp of that power-law component is also found, for the first time in the literature, to strongly correlate with $\dot{E}$, Blc and Epc, thanks to the large sample. In addition, we found that Lp can be well described by Lp∝T5.96 ± 0.64R2. 24 ± 0.18, where T and R are the surface temperature and the emitting-region radius of the surface thermal emission, represented by the black-body component in the spectral model. Γp, on the other hand, can be well described only when timing variables are included, and the relation is $\Gamma _{\rm p}= \log (T^{-5.8\pm 1.93}R^{-2.29\pm 0.85}P^{-1.19\pm 0.88}\dot{P}^{0.94\pm 0.44})$ plus a constant. These relations strongly suggest the existence of connections between surface thermal emission and electron-positron pair production in pulsar magnetospheres.
An algorithm to calculate transient distributions of cumulative rate and impulse based reward

Markov reward models have been used to solve a wide variety of problems. In these models, reward rates are associated to the states of a continuous time Markov chain, and impulse rewards are associated to transitions of the chain. Rate based rewards are gained per unit time in the associated state, while impulse rewards are gained instantaneously each time certain transitions occur. We develop an efficient algorithm to calculate the distribution of the total accumulated reward over a given interval of time when both rate and impulse rewards are present. As special cases, we obtain an algorithm to handle models for which only rate rewards occur and another algorithm for the case when only impulse rewards are present. The development is based purely on probabilistic arguments, and the recursions obtained are simple and have a low computational cost.