Miracle-dz/UniFAR-FTUs huggingface dataset:
Compliant finray-effect gripper for high-speed robotic assembly of electrical components

Fine assembly tasks such as electrical connector insertion have tight tolerances and sensitive components, limiting the speed and robustness of robot assembly, even when using vision, tactile, or force sensors. Connector insertion is a common industrial task, requiring horizontal alignment errors to be compensated with minimal force, then sufficient force to be brought in the insertion direction. The ability to handle a variety of objects, achieve high-speeds, and handle a wide range in object position variation are also desired. Soft grippers can allow the gripping of parts with variation in surface geometry, but often focus on gripping alone and may not be able to bring the assembly forces required. To achieve high-speed connector insertion, this paper proposes monolithic fingers with structured compliance and form-closure features. A finray-effect gripper is adapted to realize structured (i.e. directional) stiffness that allows high-speed mechanical search, self-alignment in insertion, and sufficient assembly force. The design of the finray ribs and fingertips are investigated, with a final design allowing plug insertion with a tolerance window of up to 7.5 mm at high speed.
Batch Prompting: Efficient Inference with Large Language Model APIs

Performing inference on large volumes of samples with large language models (LLMs) can be computationally and financially costly in industry and real-world use. We propose batch prompting, a simple yet effective prompting approach that enables the LLM to run inference in batches, instead of one sample at a time. Our method reduces both token and time costs while retaining downstream performance. We theoretically demonstrate that under a few-shot in-context learning setting, the inference costs decrease almost inverse linearly with the number of samples in each batch. We extensively validate the effectiveness of batch prompting on ten datasets across commonsense QA, arithmetic reasoning, and NLI/NLU: batch prompting significantly~(up to 5x with six samples in batch) reduces the LLM (Codex) inference token and time costs while achieving better or comparable performance. For state-of-the-art Chat-based LLMs, e.g., GPT-3.5 and GPT-4, we show the benefits of batch prompting also hold. Further analysis shows that the number of samples in each batch and the complexity of tasks affect its performance. Moreover, batch prompting can be applied across different reasoning methods using LLMs. Our code can be found at the site https://github.com/xlang-ai/batch-prompting.
Control and amplification of Bloch oscillations via photon-mediated interactions

We propose a scheme to control and enhance atomic Bloch oscillations via photon-mediated interactions in an optical lattice supported by a standing-wave cavity with incommensurate lattice and cavity wavelengths. Our scheme uses position-dependent atom-light couplings in an optical cavity to spatially prepare an array of atoms at targeted lattice sites starting from a thermal gas. On this initial state we take advantage of dispersive position-dependent atom-cavity couplings to perform non-destructive measurements of single-particle Bloch oscillations, and to generate long-range interactions self-tuned by atomic motion. The latter leads to the generation of dynamical phase transitions in the deep lattice regime and the amplification of Bloch oscillations in the shallow lattice regime. Our work introduces new possibilities accessible in state-of-the-art cavity QED experiments for the exploration of many-body dynamics in self-tunable potentials.
ETLP: Event-based Three-factor Local Plasticity for online learning with neuromorphic hardware

Neuromorphic perception with event-based sensors, asynchronous hardware and spiking neurons is showing promising results for real-time and energy-efficient inference in embedded systems. The next promise of brain-inspired computing is to enable adaptation to changes at the edge with online learning. However, the parallel and distributed architectures of neuromorphic hardware based on co-localized compute and memory imposes locality constraints to the on-chip learning rules. We propose in this work the Event-based Three-factor Local Plasticity (ETLP) rule that uses (1) the pre-synaptic spike trace, (2) the post-synaptic membrane voltage and (3) a third factor in the form of projected labels with no error calculation, that also serve as update triggers. We apply ETLP with feedforward and recurrent spiking neural networks on visual and auditory event-based pattern recognition, and compare it to Back-Propagation Through Time (BPTT) and eProp. We show a competitive performance in accuracy with a clear advantage in the computational complexity for ETLP. We also show that when using local plasticity, threshold adaptation in spiking neurons and a recurrent topology are necessary to learn spatio-temporal patterns with a rich temporal structure. Finally, we provide a proof of concept hardware implementation of ETLP on FPGA to highlight the simplicity of its computational primitives and how they can be mapped into neuromorphic hardware for online learning with low-energy consumption and real-time interaction.
A reconfigurable silicon photonics chip for the generation of frequency bin entangled qudits

Quantum optical microcombs in integrated ring resonators generate entangled photon pairs over many spectral modes, and allow the preparation of high dimensional qudit states. Ideally, those sources should be programmable and have a high generation rate, with comb lines tightly spaced for the implementation of efficient qudit gates based on electro-optic frequency mixing. While these requirements cannot all be satisfied by a single resonator device, for which there is a trade-off between high generation rate and tight bin spacing, a promising strategy is the use of multiple resonators, each generating photon pairs in specific frequency bins via spontaneous four-wave mixing. Based on this approach we present a programmable silicon photonics device for the generation of frequency bin entangled qudits, in which bin spacing, qudit dimension, and bipartite quantum state can be reconfigured on-chip. Using resonators with a radius of 22 microns, we achieve a high brightness (MHz/(mW)^2) per comb line with a bin spacing of 15 GHz, and fidelities above 85% with maximally entangled Bell states up to a Hilbert space dimension of sixteen. By individually addressing each spectral mode, we realize states that can not be generated on-chip using a single resonator. We measure the correlation matrices of maximally entangled two-qubit and two-qutrit states on a set of mutually unbiased bases, finding fidelities exceeding 98%, and indicating that the source can find application in high-dimensional secure communication protocols.
Continuous Newton-like Methods Featuring Inertia and Variable Mass

We introduce a new dynamical system, at the interface between second-order dynamics with inertia and Newton's method. This system extends the class of inertial Newton-like dynamics by featuring a time-dependent parameter in front of the acceleration, called variable mass. For strongly convex optimization, we provide guarantees on how the Newtonian and inertial behaviors of the system can be non-asymptotically controlled by means of this variable mass. A connection with the Levenberg--Marquardt (or regularized Newton's) method is also made. We then show the effect of the variable mass on the asymptotic rate of convergence of the dynamics, and in particular, how it can turn the latter into an accelerated Newton method. We provide numerical experiments supporting our findings. This work represents a significant step towards designing new algorithms that benefit from the best of both first- and second-order optimization methods.
Generators of existence varieties of regular rings and complemented Arguesian lattices

We proved in an earlier work that any existence variety of regular algebras is generated by its simple unital Artinian members, while any existence variety of Arguesian sectionally complemented lattices is generated by its simple members of finite length. A characterization of the class of simple unital Artinian members [members of finite length, respectively] of such varieties is given in the present paper.
Force-field-enhanced neural network interactions: from local equivariant embedding to atom-in-molecule properties and long-range effects

We introduce FENNIX (Force-Field-Enhanced Neural Network InteraXions), a hybrid approach between machine-learning and force-fields. We leverage state-of-the-art equivariant neural networks to predict local energy contributions and multiple atom-in-molecule properties that are then used as geometry-dependent parameters for physically-motivated energy terms which account for long-range electrostatics and dispersion. Using high-accuracy ab initio data (small organic molecules/dimers), we trained a first version of the model. Exhibiting accurate gas-phase energy predictions, FENNIX is transferable to the condensed phase. It is able to produce stable Molecular Dynamics simulations, including nuclear quantum effects, for water predicting accurate liquid properties. The extrapolating power of the hybrid physically-driven machine learning FENNIX approach is exemplified by computing: (i) the solvated alanine dipeptide free energy landscape; (ii) the reactive dissociation of small molecules.
Interaction of thin tungsten and tantalum films with ultrashort laser pulses: calculations from first principles

The interaction of ultrashort laser pulses with thin tungsten and tantalum ﬁlms is investigated through the full-potential band-structure calculations. Our calculations show that at relatively low absorbed energies (the electron temperature T e . 7 kK), the lattice of tantalum undergoes noticeable hardening. The hardening leads to the change of the tantalum complete melting threshold under these conditions. Calculations suggest that for the isochorically heated Ta ﬁlm, if such hardening really occurs, the complete melting threshold will be at least 25% higher. It is also shown that the body-centered cubic structures of W and Ta crystals become dynamically unstable when the electronic subsystem is heated to suﬃciently high temperatures ( T e > 22 kK). This lead to their complete melting on the sub-picosecond time scale.
Which Features are Learned by CodeBert: An Empirical Study of the BERT-based Source Code Representation Learning

The Bidirectional Encoder Representations from Transformers (BERT) were proposed in the natural language process (NLP) and shows promising results. Recently researchers applied the BERT to source-code representation learning and reported some good news on several downstream tasks. However, in this paper, we illustrated that current methods cannot effectively understand the logic of source codes. The representation of source code heavily relies on the programmer-defined variable and function names. We design and implement a set of experiments to demonstrate our conjecture and provide some insights for future works.
Space-charge region recombination in monocrystalline silicon-based barrier structures with long lifetimes and its impact on key characteristics of high-efficiency solar cells

The recombination rate in the space charge region (SCR) of a silicon-based barrier structure with long Shockley-Reed-Hall lifetime is calculated theoretically taking into account the concentration gradient of excess electron-hole pairs in the base region. The effects of the SCR lifetime and the applied voltage on the structure's ideality factor are analyzed. The ideality factor is significantly reduced by the concentration gradient of electron-hole pairs. This mechanism provides an increase of the effective lifetime compared to the case when it is insignificant, which is realized at sufficiently low pair concentrations. The theoretical results are shown to be in agreement with experimental data.
Low-energy excitations and transport functions of the one-dimensional Kondo insulator

Using variational matrix product states, we analyze the finite temperature behavior of a half-filled periodic Anderson model in one dimension, a prototypical model of a Kondo insulator. We present an extensive analysis of single-particle Green’s functions, two-particle Green’s functions, and transport functions creating a broad picture of the low-temperature properties. We confirm the existence of energetically low-lying spin excitations in this model and study their energy-momentum dispersion and temperature dependence. We demonstrate that charge-charge correlations at the Fermi energy exhibit a different temperature dependence than spin-spin correlations. While energetically low-lying spin excitations emerge approximately at the Kondo temperature, which exponentially depends on the interaction strength, charge correlations vanish already at high temperatures. Furthermore, we analyze the charge and thermal conductivity at finite temperatures by calculating the time-dependent current-current correlation functions. While both charge and thermal conductivity can be fitted for all interaction strengths by gapped systems with a renormalized band gap, the gap in the system describing the thermal conductivity is generally smaller than the system describing the charge conductivity. Thus, two-particle correlations affect the charge and heat conductivities in a different way resulting in a temperature region where the charge conductivity of this one-dimensional Kondo insulator is already decreasing while the heat conductivity is still increasing.
Can Continuous Aperture MIMO Achieve Much Better Performance than Discrete MIMO?

—The concept of continuous-aperture multiple- input multiple-output (CAP-MIMO) technology has been proposed recently, which aims at achieving high spectrum density by deploying extremely dense antennas or even continuous antennas in a given aperture. The fundamental question of CAP-MIMO is whether it can achieve much better performance than the traditional discrete MIMO system. In this paper, to model the CAP-MIMO, we use self-adjoint operators to depict the structural characteristics of the continuous random electromagnetic ﬁelds from physical laws. Then, we propose a non-asymptotic performance comparison scheme between continuous and discrete MIMO systems based on the analysis of mutual information. We show the consistency of the proposed scheme by proving that the mutual information between discretized transceivers converges to that between continuous transceivers. Numerical analysis veriﬁes the theoretical results, and suggests that the mutual information obtained from the discrete MIMO with widely adopted half-wavelength spaced antennas al- most achieves the mutual information obtained from CAP-MIMO.
Estimation of Large Financial Covariances: A Cross-Validation Approach

We introduce a novel covariance estimator for portfolio selection that adapts to the non-stationary or persistent heteroskedastic environments of ﬁnancial time series by employing exponentially weighted averages and nonlinearly shrinking the sample eigenvalues through cross-validation. Our estimator is structure agnostic, transparent, and computationally feasible in large dimensions. By correcting the biases in the sample eigenvalues and aligning our estimator to more recent risk, we demonstrate that our estimator performs well in large dimensions against existing state-of-the-art static and dynamic covariance shrinkage estimators through simulations and with an empirical application in active portfolio management.
Voter coalitions in Decentralized Autonomous Organization (DAO): Evidence from MakerDAO 1

Decentralized Autonomous Organization (DAO) provides a decentralized governance solution through blockchain, where decision-making process relies on on-chain voting and follows majority rule. This paper focuses on MakerDAO, and we find five voter coalitions after applying clustering algorithm to voting history. The emergence of a dominant voter coalition is a signal of governance centralization in DAO, and voter coalitions have complicated influence on Maker protocol, which is governed by MakerDAO. This paper presents empirical evidence of multicoalition democracy in DAO and further contributes to the contemporary debate on whether decentralized governance is possible.
The Lost Art of Mathematical Modelling

We provide a critique of mathematical biology in light of rapid developments in modern machine learning. We argue that out of the three modelling activities - (1) formulating models; (2) analysing models; and (3) fitting or comparing models to data - inherent to mathematical biology, researchers currently focus too much on activity (2) at the cost of (1). This trend, we propose, can be reversed by realising that any given biological phenomenon can be modelled in an infinite number of different ways, through the adoption of an pluralistic approach, where we view a system from multiple, different points of view. We explain this pluralistic approach using fish locomotion as a case study and illustrate some of the pitfalls - universalism, creating models of models, etc. - that hinder mathematical biology. We then ask how we might rediscover a lost art: that of creative mathematical modelling.
Nonlinear Functionals of Hyperbolic Random Waves: the Wiener Chaos Approach

We consider Gaussian random waves on hyperbolic spaces and establish variance asymptotics and central limit theorems for a large class of their integral functionals, both in the high-frequency and large domain limits. Our strategy of proof relies on a fine analysis of Wiener chaos expansions, which in turn requires us to analytically assess the fluctuations of integrals involving mixed moments of covariance kernels. Our results complement several recent findings on non-linear transforms of planar and arithmetic random waves, as well as of random spherical harmonics. In the particular case of 2-dimensional hyperbolic spaces, our analysis reveals an intriguing discrepancy between the high-frequency and large domain fluctuations of the so-called fourth polyspectra -- a phenomenon that has no counterpart in the Euclidean setting. We develop applications of a geometric flavor, most notably to excursion volumes and occupation densities.
Search for the decay $B^0_s \rightarrow \pi^0 \pi^0$ at Belle

We report the results of the first search for the decay $B_s^0\rightarrow\pi^0\pi^0$ using $121.4\ \rm fb^{-1}$ of data collected at the $\Upsilon(5\rm S)$ resonance with the Belle detector at the KEKB asymmetric-energy $e^+e^-$ collider. We observe no signal and set a 90\% confidence level upper limit of $7.7\times 10^{-6}$ on the $B_s^0\rightarrow\pi^0\pi^0$ decay branching fraction.
Worst Case Resistance Testing: A Nonresponse Bias Solution for Today's Behavioral Research Realities

This study proposes a method of nonresponse assessment based on meta-analytical file-drawer techniques, also known as worst-case resistance testing (WCRT), and suitable for a wide range of data collection scenarios. A general method is devised to estimate the number of significantly different nonrespondents it would take to significantly alter the results of an analysis. Estimates of nonrespondents can be plotted against effect sizes using “n-curves”, with similar interpretation to p-curves or power curves. Variants of the general method are derived for tests of means and correlations. A sample using a well-established survey instrument from previous behavioral research is used to test the method. The results suggest that employing worst-case resistance testing can be used on its own or in conjunction with wave analysis to precisely flag nonresponse risks.
LOCAL GEOMETRY OF SELF-SIMILAR SETS: TYPICAL BALLS, TANGENT MEASURES AND ASYMPTOTIC SPECTRA

We analyze the local geometric structure of self-similar sets with open set condition through the study of the properties of a distinguished family of spherical neighborhoods, the typical balls. We quantify the complexity of the local geometry of self-similar sets, showing that there are uncountably many classes of spherical neighborhoods that are not equivalent under similitudes. We show that at a tangent level, the uniformity of the Euclidean space is recuperated in the sense that any typical ball is a tangent measure of the measure [Formula: see text] at [Formula: see text]-a.e. point, where [Formula: see text] is any self-similar measure. We characterize the spectrum of asymptotic densities of metric measures in terms of the packing and centered Hausdorff measures. As an example, we compute the spectrum of asymptotic densities of the Sierpiński gasket.
ON NON-NEWTONIAN FLUIDS WITH A PROPERTY OF RAPID THICKENING UNDER DIFFERENT STIMULUS

The paper concerns the model of a flow of non-Newtonian fluid with nonstandard growth conditions of the Cauchy stress tensor. Contrary to standard power-law type rheology, we propose the formulation with the help of the spatially-dependent convex function. This framework includes e.g. rapidly shear thickening and magnetorheological fluids. We provide the existence of weak solutions. The nonstandard growth conditions yield the analytical formulation of the problem in generalized Orlicz spaces. Basing on the energy equality, we exploit the tools of Young measures.
Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning Systems

Solving the problem of cooperation is of fundamental importance to the creation and maintenance of functional societies, with examples of cooperative dilemmas ranging from navigating busy road junctions to negotiating carbon reduction treaties. As the use of AI becomes more pervasive throughout society, the need for socially intelligent agents that are able to navigate these complex cooperative dilemmas is becoming increasingly evident. In the natural world, direct punishment is an ubiquitous social mechanism that has been shown to benefit the emergence of cooperation within populations. However no prior work has investigated its impact on the development of cooperation within populations of artificial learning agents experiencing social dilemmas. Additionally, within natural populations the use of any form of punishment is strongly coupled with the related social mechanisms of partner selection and reputation. However, no previous work has considered the impact of combining multiple social mechanisms on the emergence of cooperation in multi-agent systems. Therefore, in this paper we present a comprehensive analysis of the behaviours and learning dynamics associated with direct punishment in multi-agent reinforcement learning systems and how it compares to third-party punishment, when both are combined with the related social mechanisms of partner selection and reputation. We provide an extensive and systematic evaluation of the impact of these key mechanisms on the dynamics of the strategies learned by agents. Finally, we discuss the implications of the use of these mechanisms on the design of cooperative AI systems.