Miracle-dz/UniFAR-FTUs huggingface dataset:
Derivation of the mechanical and thermodynamic potentials from the generalized BMP model under shear-banding flow

Abstract In this work, we demonstrate that the thermodynamic potential calculated from the steady-state flow curve using the definition of free energy from irreversible thermodynamics, and the mechanical potential derived from the generalized BMP constitutive equation provide the same information in the unstable and metastable regions of the flow curve. The contribution of normal stresses in both potentials as well as its weight on the position of the stress plateau are explicitly exposed. The plateau stress is univocally defined by the location of the critical shear rates corresponding to the minima in the potential. This demonstration is carried out using experimental data of wormlike micellar solutions for various concentrations and temperatures, including regions close to the non-equilibrium critical point. A method to accurately determine the non-equilibrium critical point (or the critical temperature) in a direct form is provided here. Bifurcation points are defined, notably one located at the high shear-rate branch of the flow curve separating the regions of one real solution and three real solutions. The first normal stress difference exhibits three real solutions as sources of elastic instabilities in the high shear band. The contribution of the second normal-stress difference in both the mechanical and thermodynamic potentials is clearly exposed. These results demonstrate that the non-equilibrium phase transition and the mechanical instability as sources of the banded flow are essentially two manifestations of the same reality.
Learning Complicated Manipulation Skills Via Deterministic Policy with Limited Demonstrations

Deep reinforcement learning, when combined with demonstrations, can effectively formulate policies for manipulators. However, the practical collection of ample high-quality demonstrations is time-consuming, and demonstrations generated by humans may not perfectly correspond with the operational demands of robots. These challenges are intensified by issues such as non-Markovian processes and excessive reliance on demonstrations. Our study indicates that in manipulation tasks, reinforcement learning (RL) agents are sensitive to the quality of demonstrations and struggle to adapt to those derived from humans. As a result, leveraging low-quality or scarce demonstrations to assist reinforcement learning in developing superior policies presents a significant challenge. In some cases, dependence on limited demonstrations may paradoxically impair performance. To address these challenges, we propose a novel algorithm, TD3fG (TD3 learning from a generator). This algorithm facilitates a seamless transition from learning from experts to learning from experience, enabling agents to assimilate prior knowledge while mitigating the negative impacts of the demonstrations. Our algorithm demonstrates notable improvement in the Adroit manipulator and MuJoCo tasks, even with limited demonstrations of mixed failure trajectory.
Photometric LiDAR and RGB-D Bundle Adjustment

The joint optimization of the sensor trajectory and 3D map is a crucial characteristic of Simultaneous Localization and Mapping (SLAM) systems. To achieve this, the gold standard is Bundle Adjustment (BA). Modern 3D LiDARs now retain higher resolutions that enable the creation of point cloud images resembling those taken by conventional cameras. Nevertheless, the typical effective global refinement techniques employed for RGB-D sensors are not widely applied to LiDARs. This letter presents a novel BA photometric strategy that accounts for both RGB-D and LiDAR in the same way. Our work can be used on top of any SLAM/GNSS estimate to improve and refine the initial trajectory. We conducted different experiments using these two depth sensors on public benchmarks. Our results show that our system performs on par or better compared to other state-of-the-art ad-hoc SLAM/BA strategies, free from data association and without making assumptions about the environment. In addition, we present the benefit of jointly using RGB-D and LiDAR within our unified method.
A new zero-free region for Rankin-Selberg $L$-functions

Let $\pi$ and $\pi'$ be cuspidal automorphic representations of $\mathrm{GL}(n)$ and $\mathrm{GL}(n')$ with unitary central characters. We establish a new zero-free region for all $\mathrm{GL}(1)$-twists of the Rankin-Selberg $L$-function $L(s,\pi\times\pi')$, generalizing Siegel's celebrated work on Dirichlet $L$-functions. As an application, we prove the first unconditional Siegel-Walfisz theorem for the Dirichlet coefficients of $-L'(s,\pi\times\pi')/L(s,\pi\times\pi')$. Also, for $n\leq 8$, we extend the region of holomorphy and nonvanishing for the twisted symmetric power $L$-functions $L(s,\pi,\mathrm{Sym}^n\otimes\chi)$ of any cuspidal automorphic representation of $\mathrm{GL}(2)$.
Fairlearn: Assessing and Improving Fairness of AI Systems

Fairlearn is an open source project to help practitioners assess and improve fairness of artificial intelligence (AI) systems. The associated Python library, also named fairlearn, supports evaluation of a model's output across affected populations and includes several algorithms for mitigating fairness issues. Grounded in the understanding that fairness is a sociotechnical challenge, the project integrates learning resources that aid practitioners in considering a system's broader societal context.
Lifting Uniform Learners via Distributional Decomposition

We show how any PAC learning algorithm that works under the uniform distribution can be transformed, in a blackbox fashion, into one that works under an arbitrary and unknown distribution D. The efficiency of our transformation scales with the inherent complexity of D, running in (n, (md)d) time for distributions over n whose pmfs are computed by depth-d decision trees, where m is the sample complexity of the original algorithm. For monotone distributions our transformation uses only samples from D, and for general ones it uses subcube conditioning samples. A key technical ingredient is an algorithm which, given the aforementioned access to D, produces an optimal decision tree decomposition of D: an approximation of D as a mixture of uniform distributions over disjoint subcubes. With this decomposition in hand, we run the uniform-distribution learner on each subcube and combine the hypotheses using the decision tree. This algorithmic decomposition lemma also yields new algorithms for learning decision tree distributions with runtimes that exponentially improve on the prior state of the art—results of independent interest in distribution learning.
Thistle: A Vector Database in Rust

We present Thistle, a fully functional vector database. Thistle is an entry into the domain of latent knowledge use in answering search queries, an ongoing research topic at both start-ups and search engine companies. We implement Thistle with several well-known algorithms, and benchmark results on the MS MARCO dataset. Results help clarify the latent knowledge domain as well as the growing Rust ML ecosystem.
Certified Hardness vs. Randomness for Log-Space

Let $\mathcal{L}$ be a language that can be decided in linear space and let $\epsilon \gt 0$ be any constant. Let $\mathcal{A}$ be the exponential hardness assumption that for every n, membership in $\mathcal{L}$ for inputs of length n cannot be decided by circuits of size smaller than $2^{\epsilon n}$. We prove that for every function $f:\{0,1\}^{*} \rightarrow\{0,1\}$, computable by a randomized logspace algorithm R, there exists a deterministic logspace algorithm D (attempting to compute f), such that on every input x of length n, the algorithm D outputs one of the following:1)The correct value $f(x)$. 2)The string: “I am unable to compute $f(x)$ because the hardness assumption $\mathcal{A}$ is false”, followed by a (provenly correct) circuit of size smaller than $2^{\epsilon n^{\prime}}$ for membership in $\mathcal{L}$ for inputs of length $n^{\prime}$, for some $n^{\prime}=\Theta(\log n)$; that is, a circuit that refutes $\mathcal{A}$. Moreover, D is explicitly constructed, given R.We note that previous works on the hardness-versus-randomness paradigm give derandomized algorithms that rely blindly on the hardness assumption. If the hardness assumption is false, the algorithms may output incorrect values, and thus a user cannot trust that an output given by the algorithm is correct. Instead, our algorithm D verifies the computation so that it never outputs an incorrect value. Thus, if D outputs a value for $f(x)$, that value is certified to be correct. Moreover, if D does not output a value for $f(x)$, it alerts that the hardness assumption was found to be false, and refutes the assumption.Our next result is a universal derandomizer for BPL (the class of problems solvable by bounded-error randomized logspace algorithms)1: We give a deterministic algorithm U that takes as an input a randomized logspace algorithm R and an input x and simulates the computation of R on x, deteriministically. Under the widely believed assumption $\mathbf{BPL}=\mathbf{L}$, the space used by U is at most $C_{R} \cdot \log n$ (where $C_{R}$ is a constant depending on R). Moreover, for every constant $c \geq 1$, if $\operatorname{BPL} \subseteq \operatorname{SPACE}\left[(\log (n))^{c}\right]$ then the space used by U is at most $C_{R} \cdot(\log (n))^{c}$. Finally, we prove that if optimal hitting sets for ordered branching programs exist then there is a deterministic logspace algorithm that, given a black-box access to an ordered branching program B of size n, estimates the probability that B accepts on a uniformly random input. This extends the result of (Cheng and Hoza CCC 2020), who proved that an optimal hitting set implies a white-box two-sided derandomization.1Our result is stated and proved for promise-BPL, but we ignore this difference in the abstract.
A Simple model of the integrable Hamiltonian equation

A method of analysis of the infinite‐dimensional Hamiltonian equations which avoids the introduction of the Backlund transformation or the use of the Lax equation is suggested. This analysis is based on the possibility of connecting in several ways the conservation laws of special Hamiltonian equations with their symmetries by using symplectic operators. It leads to a simple and sufficiently general model of integrable Hamiltonian equation, of which the Korteweg–de Vries equation, the modified Korteweg–de Vries equation, the nonlinear Schrodinger equation and the so‐called Harry Dym equation turn out to be particular examples.
Communication Load Balancing via Efficient Inverse Reinforcement Learning

Communication load balancing aims to balance the load between different available resources, and thus improve the quality of service for network systems. After formulating the load balancing (LB) as a Markov decision process problem, reinforcement learning (RL) has recently proven effective in addressing the LB problem. To leverage the benefits of classical RL for load balancing, however, we need an explicit reward definition. Engineering this reward function is challenging, because it involves the need for expert knowledge and there lacks a general consensus on the form of an optimal reward function. In this work, we tackle the communication load balancing problem from an inverse reinforcement learning (IRL) approach. To the best of our knowledge, this is the first time IRL has been successfully applied in the field of communication load balancing. Specifically, first, we infer a reward function from a set of demonstrations, and then learn a reinforcement learning load balancing policy with the inferred reward function. Compared to classical RL-based solution, the proposed solution can be more general and more suitable for real-world scenarios. Experimental evaluations implemented on different simulated traffic scenarios have shown our method to be effective and better than other baselines by a considerable margin.
Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization

Exploiting partial first-order information in a cyclic way is arguably the most natural strategy to obtain scalable first-order methods. However, despite their wide use in practice, cyclic schemes are far less understood from a theoretical perspective than their randomized counterparts. Motivated by a recent success in analyzing an extrapolated cyclic scheme for generalized variational inequalities, we propose an Accelerated Cyclic Coordinate Dual Averaging with Extrapolation (A-CODER) method for composite convex optimization, where the objective function can be expressed as the sum of a smooth convex function accessible via a gradient oracle and a convex, possibly nonsmooth, function accessible via a proximal oracle. We show that A-CODER attains the optimal convergence rate with improved dependence on the number of blocks compared to prior work. Furthermore, for the setting where the smooth component of the objective function is expressible in a finite sum form, we introduce a variance-reduced variant of A-CODER, VR-A-CODER, with state-of-the-art complexity guarantees. Finally, we demonstrate the effectiveness of our algorithms through numerical experiments.
Towards Understanding the Effect of Pretraining Label Granularity

In this paper, we study how the granularity of pretraining labels affects the generalization of deep neural networks in image classification tasks. We focus on the"fine-to-coarse"transfer learning setting, where the pretraining label space is more fine-grained than that of the target problem. Empirically, we show that pretraining on the leaf labels of ImageNet21k produces better transfer results on ImageNet1k than pretraining on other coarser granularity levels, which supports the common practice used in the community. Theoretically, we explain the benefit of fine-grained pretraining by proving that, for a data distribution satisfying certain hierarchy conditions, 1) coarse-grained pretraining only allows a neural network to learn the"common"or"easy-to-learn"features well, while 2) fine-grained pretraining helps the network learn the"rarer"or"fine-grained"features in addition to the common ones, thus improving its accuracy on hard downstream test samples in which common features are missing or weak in strength. Furthermore, we perform comprehensive experiments using the label hierarchies of iNaturalist 2021 and observe that the following conditions, in addition to proper choice of label granularity, enable the transfer to work well in practice: 1) the pretraining dataset needs to have a meaningful label hierarchy, and 2) the pretraining and target label functions need to align well.
Robust Recovery of Robinson Property in $L^p$-Graphons: A Cut-Norm Approach

This paper investigates the Robinson graphon completion/recovery problem within the class of $L^p$-graphons, focusing on the range $5<p\leq \infty$. A graphon $w$ is Robinson if it satisfies the Robinson property: if $x\leq y\leq z$, then $w(x,z)\leq \min\{w(x,y),w(y,z)\}$. We demonstrate that if a graphon possesses localized near-Robinson characteristics, it can be effectively approximated by a Robinson graphon in terms of cut-norm. To achieve this recovery result, we introduce a function $\Lambda$, defined on the space of $L^p$-graphons, which quantifies the degree to which a graphon $w$ adheres to the Robinson property. We prove that $\Lambda$ is a suitable gauge for measuring the Robinson property when proximity of graphons is understood in terms of cut-norm. Namely, we show that (1) $\Lambda(w)=0$ precisely when $w$ is Robinson; (2) $\Lambda$ is cut-norm continuous, in the sense that if two graphons are close in the cut-norm, then their $\Lambda$ values are close; and (3) for $p>5$, any $L^p$-graphon $w$ can be approximated by a Robinson graphon, with error of the approximation bounded in terms of $\Lambda(w)$. When viewing $w$ as a noisy version of a Robinson graphon, our method provides a concrete recipe for recovering a cut-norm approximation of a noiseless w. Given that any symmetric matrix is a special type of graphon, our results can be applicable to symmetric matrices of any size. Our work extends and improves previous results, where a similar question for the special case of $L^\infty$-graphons was answered.
Dynamics of a binary system around a supermassive black hole

We discuss motion of a binary system around a supermassive black hole. Using Fermi-Walker transport, we construct a local inertial reference frame and set up a Newtonian binary system. Assuming a circular geodesic observer around a Schwarzschild black hole, we write down the equations of motion of a binary. Introducing a small acceleration of the observer, we remove the interaction terms between the center of mass (CM) of a binary and its relative coordinates. The CM follows the observer's orbit, but its motion deviates from an exact circular geodesic. We first solve the relative motion of a binary system, and then find the motion of the CM by the perturbation equations with the small acceleration. We show that there appears the Kozai-Lidov (KL) oscillations when a binary is compact and the initial inclination is larger than a critical angle. In a hard binary system, KL oscillations are regular, whereas in a soft binary system, oscillations are irregular both in period and in amplitude, although stable. We find an orbital flip when the initial inclination is large. As for the motion of the CM, the radial deviations from a circular orbit become stable oscillations with very small amplitude.
NoSQL Schema Design for Time-Dependent Workloads

In this paper, we propose a schema optimization method for time-dependent workloads for NoSQL databases. In our proposed method, we migrate schema according to changing workloads, and the estimated cost of execution and migration are formulated and minimized as a single integer linear programming problem. Furthermore, we propose a method to reduce the number of optimization candidates by iterating over the time dimension abstraction and optimizing the workload while updating constraints.
AirLine: Efficient Learnable Line Detection with Local Edge Voting

Line detection is widely used in many robotic tasks such as scene recognition, 3D reconstruction, and simultaneous localization and mapping (SLAM). Compared to points, lines can provide both low-level and high-level geometrical information for downstream tasks. In this paper, we propose a novel learnable edge-based line detection algorithm, AirLine, which can be applied to various tasks. In contrast to existing learnable endpoint-based methods, which are sensitive to the geometrical condition of environments, AirLine can extract line segments directly from edges, resulting in a better generalization ability for unseen environments. To balance efficiency and accuracy, we introduce a region-grow algorithm and a local edge voting scheme for line parameterization. To the best of our knowledge, AirLine is one of the first learnable edge-based line detection methods. Our extensive experiments have shown that it retains state-of-the-art-Ievel precision, yet with a $3-80\times$ runtime acceleration compared to other learning-based methods, which is critical for low-power robots.
Primary tracheal small‐cell carcinoma detected 11 months after surgery for pulmonary large‐cell neuroendocrine carcinoma: A case report

Primary tracheal small‐cell carcinoma is rare, and is often treated using small‐cell lung cancer guidelines given that no standard treatment has been established for it. We report a patient in whom nodules appeared in the trachea and left main bronchus 11 months after surgery for pulmonary large‐cell neuroendocrine carcinoma; a biopsy revealed small‐cell carcinoma. Given the absence of malignant lesions elsewhere in the body, the lesions were diagnosed as primary tracheal small‐cell carcinoma. Respiratory failure progressed rapidly owing to airway stenosis caused by the growing lesion, and the patient required nasal high‐flow therapy. However, the lesions shrank a few days after commencing first‐line chemotherapy, and his respiratory failure resolved. Accelerated hyperfractionated radiotherapy was administered in conjunction with the third course of chemotherapy, and the patient ultimately achieved a complete response. Although the lesions were initially suspected of being postoperative recurrence of pulmonary large‐cell neuroendocrine carcinoma, the fact that the biopsy revealed them to be primary tracheal small‐cell carcinoma indicates that intra‐airway nodules that appear after lung cancer surgery may possibly be primary tracheal tumors.
ELECTROMECHANICAL COUPLING COEFFICIENT FOR SURFACE ACOUSTIC WAVES IN PROTON-EXCHANGED 128-ROTATED Y-CUT LITHIUM NIOBATE

The attenuation of surface acoustic waves propagating along crystallographic X axis in proton-exchanged 128° rotated Y-cut lithium niobate during the evaporation of copper film on the crystal surface has been measured as a function of sheet resistivity of the film. From the magnitude of attenuation maxima, the effective electromechanical coupling coefficients for samples with different thicknesses of HxLi1−xNbO3 layers have been evaluated, and from the position of maxima in the resistivity scale, the effective dielectric permittivities of the structure have been estimated. The electromechanical coupling coefficient is found to be reduced practically to zero with increasing thickness of protonated layer and acoustic frequency, and the dielectric permittivity is not significantly affected by the proton exchange.
Ketamine for Treatment‐Resistant bipolar depression—A reality check!

Ketamine is a novel drug used for treatment- resistant depression. The initial randomized controlled trials (RCTs) established intravenous (IV) ketamine's rapid antidepressant effects for refrac-tory major depression, leading to its widespread use. However, the data for ketamine's efficacy in treatment- resistant bipolar depression (TRBD) remain minimal. There are 3 single infusion RCTs ( n = 49) suggesting antidepressant effect lasting for 3 days. 1 Although serial IV ketamine infusion studies have shown to prolong the antidepressant effect in treatment- resistant depression
The Virgo detector

The Virgo Experiment is a gravitational wave interferometric detector. It consists in a Michelson interferometer with two 3 km long Fabry‐Perot cavities as orthogonal arms. The installation of the detector has been completed in September 2003 and presently the apparatus is under commissioning. In this article an overview of the detector status is presented.