Miracle-dz/UniFAR-FTUs huggingface dataset:
Examining Audio Communication Mechanisms for Supervising Fleets of Agricultural Robots

Agriculture is facing a labor crisis, leading to increased interest in fleets of small, under-canopy robots (agbots) that can perform precise, targeted actions (e.g., crop scouting, weeding, fertilization), while being supervised by human operators remotely. However, farmers are not necessarily experts in robotics technology and will not adopt technologies that add to their workload or do not provide an immediate payoff. In this work, we explore methods for communication between a remote human operator and multiple agbots and examine the impact of audio communication on the operator’s preferences and productivity. We develop a simulation platform where agbots are deployed across a field, randomly encounter failures, and call for help from the operator. As the agbots report errors, various audio communication mechanisms are tested to convey which robot failed and what type of failure occurs. The human is tasked with verbally diagnosing the failure while completing a secondary task. A user study was conducted to test three audio communication methods: earcons, single-phrase commands, and full sentence communication. Each participant completed a survey to determine their preferences and each method’s overall effectiveness. Our results suggest that the system using single phrases is the most positively perceived by participants and may allow for the human to complete the secondary task more efficiently. The code is available at: https://github.com/akamboj2/Agbot-Sim.
Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering

Multi-hop reasoning requires aggregating multiple documents to answer a complex question. Existing methods usually decompose the multi-hop question into simpler single-hop questions to solve the problem for illustrating the explainable reasoning process. However, they ignore grounding on the supporting facts of each reasoning step, which tends to generate inaccurate decompositions. In this paper, we propose an interpretable stepwise reasoning framework to incorporate both single-hop supporting sentence identification and single-hop question generation at each intermediate step, and utilize the inference of the current hop for the next until reasoning out the final result. We employ a unified reader model for both intermediate hop reasoning and final hop inference and adopt joint optimization for more accurate and robust multi-hop reasoning. We conduct experiments on two benchmark datasets HotpotQA and 2WikiMultiHopQA. The results show that our method can effectively boost performance and also yields a better interpretable reasoning process without decomposition supervision.
Single Spin Detection with a Carbon Nanotube Double Quantum Dot

Spin qubits defined in carbon nanotube quantum dots are of considerable interest for encoding and manipulating quantum information because of the long electron spin coherence times expected. However, before carbon nanotubes can find applications in quantum information processing schemes, we need to understand and control the coupling between individual electron spins and the interaction between the electron spins and their environment. Here we make use of spin selection rules to directly measure - and demonstrate control of - the singlet-triplet exchange coupling between two carbon nanotube quantum dots. We furthermore elucidate the effects of spin-orbit interaction on the electron transitions and investigate the interaction of the quantum dot system with a single impurity spin - the ultimate limit in spin sensitivity. A powerful method to probe the spin dynamics of quantum dots is by measuring electron transport in a double quantum dot device in the spin blockade regime [1]. In this transport regime, the tunneling of an electron between the two quantum dots is forbidden by spin selection rules and hence the current is suppressed. However, spin blockade can be lifted by the interaction of the electron spins with their environment and a measurement of the (leakage) current thus directly probes these interactions [2–4]. The main spin relaxation and decoherence modes in carbon nanotube that have been considered so far are hyperfine and spin-orbit coupling. The importance of hyperfine interaction in the electron spin dynamics of carbon nanotubes has recently been demonstrated in 13 C enriched nanotube quantum dots [5, 6]. The sig
Energy-Aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning

The design of Open Radio Access Network (O-RAN) compliant systems for configuring the virtualized Base Stations (vBSs) is of paramount importance for network operators. This task is challenging since optimizing the vBS scheduling procedure requires knowledge of parameters, which are erratic and demanding to obtain in advance. In this paper, we propose an online learning algorithm for balancing the performance and energy consumption of a vBS. This algorithm provides performance guarantees under unforeseeable conditions, such as non-stationary traffic and network state, and is oblivious to the vBS operation profile. We study the problem in its most general form and we prove that the proposed technique achieves sub-linear regret (i.e., zero average optimality gap) even in a fast-changing environment. By using real-world data and various trace-driven evaluations, our findings indicate savings of up to 74.3% in the power consumption of a vBS in comparison with state-of-the-art benchmarks.
Co-optimizing Distributed Energy Resources in Linear Complexity under Net Energy Metering

The co-optimization of behind-the-meter distributed energy resources is considered for prosumers under the net energy metering tariff. The distributed energy resources considered include renewable generations, flexible demands, and battery energy storage systems. An energy management system co-schedules the consumptions and battery storage based on locally available stochastic renewables by maximizing the expected operation surplus. A stochastic dynamic programming formulation is introduced for which structural properties of the dynamic optimization are derived. A closed-form myopic co-optimization algorithm is proposed, which achieves optimality when the storage capacity constraints are nonbinding. The proposed co-optimization algorithm has linear computation complexity and can be implemented in a decentralized fashion. The myopic co-optimization algorithm's performance and the economic benefits of the co-optimization policy to prosumers and grid operations are evaluated in numerical simulations.
Minimizing the Effect of Noise and Limited Dataset Size in Image Classification Using Depth Estimation as an Auxiliary Task with Deep Multitask Learning

Generalizability is the ultimate goal of Machine Learning (ML) image classifiers, for which noise and limited dataset size are among the major concerns. We tackle these challenges through utilizing the framework of deep Multitask Learning (dMTL) and incorporating image depth estimation as an auxiliary task. On a customized and depth-augmented derivation of the MNIST dataset, we show a) multitask loss functions are the most effective approach of implementing dMTL, b) limited dataset size primarily contributes to classification inaccuracy, and c) depth estimation is mostly impacted by noise. In order to further validate the results, we manually labeled the NYU Depth V2 dataset for scene classification tasks. As a contribution to the field, we have made the data in python native format publicly available as an open-source dataset and provided the scene labels. Our experiments on MNIST and NYU-Depth-V2 show dMTL improves generalizability of the classifiers when the dataset is noisy and the number of examples is limited.
How many singlets are needed to create a bipartite state using LOCC

One-shot entanglement dilution is the protocol by which two distant parties convert a finite number of singlets, which they initially share, to a given bipartite target state, using local operations and classical communication. We refer to the minimum number of singlets needed to achieve this conversion as the one-shot entanglement cost of the target state. In this paper we evaluate this quantity in the most general case where dilution is achieved with a fixed accuracy. For the case of exact dilution, the expression for the cost reduces to a simple form analogous to the entanglement of formation. Extending our result to the asymptotic scenario yields an alternative proof of the equivalence between the asymptotic entanglement cost and the regularized entanglement of formation.
Efficient Utility Function Learning for Multi-Objective Parameter Optimization with Prior Knowledge

The current state-of-the-art in multi-objective optimization assumes either a given utility function, learns a utility function interactively or tries to determine the complete Pareto front, requiring a post elicitation of the preferred result. However, result elicitation in real world problems is often based on implicit and explicit expert knowledge, making it difficult to define a utility function, whereas interactive learning or post elicitation requires repeated and expensive expert involvement. To mitigate this, we learn a utility function offline, using expert knowledge by means of preference learning. In contrast to other works, we do not only use (pairwise) result preferences, but also coarse information about the utility function space. This enables us to improve the utility function estimate, especially when using very few results. Additionally, we model the occurring uncertainties in the utility function learning task and propagate them through the whole optimization chain. Our method to learn a utility function eliminates the need of repeated expert involvement while still leading to high-quality results. We show the sample efficiency and quality gains of the proposed method in 4 domains, especially in cases where the surrogate utility function is not able to exactly capture the true expert utility function. We also show that to obtain good results, it is important to consider the induced uncertainties and analyze the effect of biased samples, which is a common problem in real world domains.
Competition for popularity and identification of interventions on a Chinese microblogging site

Microblogging sites are important vehicles for the users to obtain information and shape public opinion thus they are arenas of continuous competition for popularity. Most popular topics are usually indicated on ranking lists. In this study, we investigate the public attention dynamics through the Hot Search List (HSL) of the Chinese microblog Sina Weibo, where trending hashtags are ranked based on a multi-dimensional search volume index. We characterize the rank dynamics by the time spent by hashtags on the list, the time of the day they appear there, the rank diversity, and by the ranking trajectories. We show how the circadian rhythm affects the popularity of hashtags, and observe categories of their rank trajectories by a machine learning classification algorithm. By analyzing patterns of the ranking dynamics we identify anomalies that are likely to result from the platform provider’s intervention into the ranking, including the anchoring of hashtags to certain ranks on the HSL. We propose a simple model of ranking that explains the mechanism of this anchoring effect.
On parametric resonance in the laser action

We consider the selfconsistent semiclassical Maxwell--Schr\"odinger system for the solid state laser which consists of the Maxwell equations coupled to $N\sim 10^{20}$ Schr\"odinger equations for active molecules. The system contains time-periodic pumping and a weak dissipation. We introduce the corresponding Poincar\'e map $P$ and consider the differential $DP(Y^0)$ at suitable stationary state $Y^0$. We conjecture that the {\it stable laser action} is due to the {\it parametric resonance} (PR) which means that the maximal absolute value of the corresponding multipliers is greater than one. The multipliers are defined as eigenvalues of $DP(Y^0)$. The PR makes the stationary state $Y^0$ highly unstable, and we suppose that this instability maintains the {\it coherent laser radiation}. We prove that the spectrum Spec$\,DP(Y^0)$ is approximately symmetric with respect to the unit circle $|\mu|=1$ if the dissipation is sufficiently small. More detailed results are obtained for the Maxwell--Bloch system. We calculate the corresponding Poincar\'e map $P$ by successive approximations. The key role in calculation of the multipliers is played by the sum of $N$ positive terms arising in the second-order approximation for the total current. This fact can be interpreted as the {\it synchronization of molecular currents} in all active molecules, which is provisionally in line with the role of {\it stimulated emission} in the laser action. The calculation of the sum relies on probabilistic arguments which is one of main novelties of our approach. Other main novelties are i) the calculation of the differential $DP(Y^0)$ in the"Hopf representation", ii) the block structure of the differential, and iii) the justification of the"rotating wave approximation"by a new estimate for the averaging of slow rotations.
An Input-Aware Mimic Defense Theory and its Practice

The current security problems in cyberspace are characterized by strong and complex threats. Defenders face numerous problems such as lack of prior knowledge, various threats, and unknown vulnerabilities, which urgently need new fundamental theories to support. To address these issues, this article proposes a generic theoretical model for cyberspace defense and a new mimic defense framework, that is, Spatiotemporally heterogeneous, Input aware, and Dynamically updated Mimic Defense (SIDMD). We make the following contributions: (1) We first redefine vulnerabilities from the input space perspective to normalize the diverse cyberspace security problem. (2) We propose a novel unknown vulnerability discovery method and a dynamic scheduling strategy considering temporal and spatial dimensions without prior knowledge. Theoretical analysis and experimental results show that SIDMD has the best security performance in complex attack scenarios, and the probability of successful attacks is greatly reduced compared to the state-of-the-art.
Star formation at the smallest scales; A JWST study of the clump populations in SMACS0723

We present the clump populations detected in 18 lensed galaxies at redshifts 1 to 8.5 within the lensing cluster field SMACS0723. The recent JWST Early Release Observations of this poorly known region of the sky have revealed numerous point-like sources within and surrounding their host galaxies, undetected in the shallower HST images. We use JWST multiband photometry and the lensing model of this galaxy cluster to estimate the intrinsic sizes and magnitudes of the stellar clumps. We derive optical restframe effective radii from <10 to hundreds pc and masses ranging from ∼105 to 109 M⊙, overlapping with massive star clusters in the local universe. Clump ages range from 1 Myr to 1 Gyr. We compare the crossing time to the age of the clumps and determine that between 45 and 60 per cent of the detected clumps are consistent with being gravitationally bound. On average, the dearth of Gyr old clumps suggests that the dissolution time scales are shorter than 1 Gyr. We see a significant increase in the luminosity (mass) surface density of the clumps with redshift. Clumps in reionisation era galaxies have stellar densities higher than star clusters in the local universe. We zoom in into single galaxies at redshift <6 and find for two galaxies, the Sparkler and the Firework, that their star clusters/clumps show distinctive colour distributions and location surrounding their host galaxy that are compatible with being accredited or formed during merger events. The ages of some of the compact clusters are between 1 and 4 Gyr, e.g. globular cluster precursors formed around 9-12 Gyr ago. Our study, conducted on a small sample of galaxies, shows the potential of JWST observations for understanding the conditions under which star clusters form in rapidly evolving galaxies.
DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting

Text rewriting with differential privacy (DP) provides concrete theoretical guarantees for protecting the privacy of individuals in textual documents. In practice, existing systems may lack the means to validate their privacy-preserving claims, leading to problems of transparency and reproducibility. We introduce DP-Rewrite, an open-source framework for differentially private text rewriting which aims to solve these problems by being modular, extensible, and highly customizable. Our system incorporates a variety of downstream datasets, models, pre-training procedures, and evaluation metrics to provide a flexible way to lead and validate private text rewriting research. To demonstrate our software in practice, we provide a set of experiments as a case study on the ADePT DP text rewriting system, detecting a privacy leak in its pre-training approach. Our system is publicly available, and we hope that it will help the community to make DP text rewriting research more accessible and transparent.
Security Implications of Large Language Model Code Assistants: A User Study

Large Language Models (LLMs) such as OpenAI Codex are increasingly being used as AI-based coding assistants. Understanding the impact of these tools on developers’ code is paramount, especially as recent work showed that LLMs may suggest cybersecurity vulnerabilities. We conduct a security-driven user study (N=58) to assess code written by student programmers when assisted by LLMs. Given the potential severity of low-level bugs as well as their relative frequency in real-world projects, we tasked participants with implementing a singly-linked ‘shopping list’ structure in C. Our results indicate that the security impact in this setting is small: AI-assisted users produce critical security bugs at a rate no greater than 10% more than the control, indicating the use of LLMs does not introduce new security risks.
The plectic conjecture over local fields

Using a mixed-characteristic incarnation of fusion, we prove an analog of Nekov\'a\v{r}-Scholl's plectic conjecture for local Shimura varieties. We apply this to obtain results on the plectic conjecture for (global) Shimura varieties after restricting to a decomposition group. Along the way, we prove a $p$-adic uniformization theorem for the basic locus of abelian type Shimura varieties at hyperspecial level, which is of independent interest.
Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing

Fine-grained entity typing (FET) aims to deduce specific semantic types of the entity mentions in the text. Modern methods for FET mainly focus on learning what a certain type looks like. And few works directly model the type differences, that is, let models know the extent that which one type is different from others. To alleviate this problem, we propose a type-enriched hierarchical contrastive strategy for FET. Our method can directly model the differences between hierarchical types and improve the ability to distinguish multi-grained similar types. On the one hand, we embed type into entity contexts to make type information directly perceptible. On the other hand, we design a constrained contrastive strategy on the hierarchical structure to directly model the type differences, which can simultaneously perceive the distinguishability between types at different granularity. Experimental results on three benchmarks, BBN, OntoNotes, and FIGER show that our method achieves significant performance on FET by effectively modeling type differences.
Combining Density-Functional Theory with Low-Temperature, Polarized Terahertz Spectroscopy of Single Crystals Explicates the Fundamental Modes of L-Alanine

While the fundamental modes of amino acids are accessible in principle by terahertz spectroscopy, reports to date have several shortcomings: (a) material of uncertain purity and morphology and diluted in a binder material is employed; (b) consequently, vibrations along all crystal axes are excited simultaneously; (c) data is restricted to room temperature, where resonances are broad and the background dominant; (d) comparison with theory has been unsatisfactory (in part because the theory assumes zero temperature). Here, we overcome all four obstacles, in reporting detailed polarized THz spectra of single-crystal l -alanine, assigning vibrational modes using density functional theory, and comparing the calculated dipole moment vector direction to the electric ﬁeld polarization of the measured spectra. The direct and detailed comparison of theory with experiment has corrected previous mode assignments for l -alanine, as well as revealed unreported modes, previously obscured by closely-spaced spectral absorptions. The fundamental modes are thereby determined.
Iterative-Free Quantum Approximate Optimization Algorithm Using Neural Networks

The quantum approximate optimization algorithm (QAOA) is a leading iterative variational quantum algorithm for heuris-tically solving combinatorial optimization problems. A large portion of the computational eﬀort in QAOA is spent by the optimization steps, which require many executions of the quantum circuit. Therefore, there is active research focusing on ﬁnding better initial circuit parameters, which would reduce the number of required iterations and hence the overall execution time. While existing methods for parameter initialization have shown great success, they often oﬀer a single set of parameters for all problem instances. We propose a practical method that uses a simple, fully connected neural network that leverages previous executions of QAOA to ﬁnd better initialization parameters tailored to a new given problem instance. We benchmark state-of-the-art initialization methods for solving the MaxCut problem of Erdős–Rényi graphs using QAOA and show that our method is consistently the fastest to converge while also yielding the best ﬁnal result. Furthermore, the parameters predicted by the neural network are shown to match very well with the fully optimized parameters, to the extent that no iterative steps are required, thereby eﬀectively re-alizing an iterative-free QAOA scheme.
Meta-Learning Online Control for Linear Dynamical Systems

In this work, we consider the problem of finding a meta-learning online control algorithm that can learn across the tasks when faced with a sequence of N (similar) control tasks. Each task involves controlling a linear dynamical system for T time steps. The cost function and system noise at each time step are adversarial and unknown to the algorithm before taking the control action. The goal of a meta-learning algorithm is to sequentially prescribe the individual online control policies for each new task by exploiting the information from previous tasks and the property of task similarity. We propose a meta-learning online control algorithm for this setting and characterize its performance using the metric of meta-regret, which is the average cumulative regret of the tasks. We show that, when the number of tasks are sufficiently large, the meta-regret of our proposed approach is smaller by a factor D/D* compared to an independent-learning online control algorithm which does not perform learning across the task, where D is a problem constant and D* is a scalar that decreases with increase in task similarity. Thus, when the sequence of tasks are similar, the regret of the proposed meta-learning online control is significantly lower than that of the naive approaches without meta-learning. We also present numerical results to demonstrate the superior performance achieved by our meta-learning algorithm.