Miracle-dz/UniFAR-FTUs huggingface dataset:
Constant-delay enumeration for SLP-compressed documents

We study the problem of enumerating results from a query over a compressed document. The model we use for compression are straight-line programs (SLPs), which are defined by a context-free grammar that produces a single string. For our queries, we use a model called Annotated Automata, an extension of regular automata that allows annotations on letters. This model extends the notion of Regular Spanners as it allows arbitrarily long outputs. Our main result is an algorithm that evaluates such a query by enumerating all results with output-linear delay after a preprocessing phase which takes linear time on the size of the SLP, and cubic time over the size of the automaton. This is an improvement over Schmid and Schweikardt's result, which, with the same preprocessing time, enumerates with a delay that is logarithmic on the size of the uncompressed document. We achieve this through a persistent data structure named Enumerable Compact Sets with Shifts which guarantees output-linear delay under certain restrictions. These results imply constant-delay enumeration algorithms in the context of regular spanners. Further, we use an extension of annotated automata which utilizes succinctly encoded annotations to save an exponential factor from previous results that dealt with constant-delay enumeration over vset automata. Lastly, we extend our results in the same fashion Schmid and Schweikardt did to allow complex document editing while maintaining the constant delay guarantee.
The star formation history of the Large Magellanic Cloud as seen by star clusters and stars

The aim of this work is to test to what extent the star cluster population of a galaxy can be utilized to constrain or estimate the star formation history, with the Large Magellanic Cloud as our test bed. We follow two methods to extract information about the star formation rate from star clusters, either using only the most massive clusters or using the whole cluster population, albeit this is only possible for a shorter age span. We compare these results with the star formation history derived from colour-magnitude diagrams and find good overall agreement for the most recent ≈ 1 Gyr. For later ages, and especially during the 'cluster age gap', there is a deficiency of star clusters in relation to the star formation rate derived from the colour-magnitude diagram. The star formation rates following from the whole cluster population lie a factor of ≈10 lower than the star formation rates deduced from the most massive clusters or from the colour-magnitude diagram, suggesting that only ≈10 per cent of all stars form in long-lived bound star clusters.
GLOBAL MODELING OF RADIATIVELY DRIVEN ACCRETION OF METALS FROM COMPACT DEBRIS DISKS ONTO WHITE DWARFS

Recent infrared observations have revealed the presence of compact (radii ≲ R☉) debris disks around more than a dozen metal-rich white dwarfs (WDs), likely produced by a tidal disruption of asteroids. Accretion of high-Z material from these disks may account for the metal contamination of these WDs. It was previously shown using local calculations that the Poynting–Robertson (PR) drag acting on the dense, optically thick disk naturally drives metal accretion onto the WD at the typical rate g s−1. Here we extend this local analysis by exploring the global evolution of the debris disk under the action of the PR drag for a variety of assumptions about the disk properties. We find that massive disks (mass ≳ 1020 g), which are optically thick to incident stellar radiation, inevitably give rise to metal accretion at rates . The magnitude of and its time evolution are determined predominantly by the initial pattern of the radial distribution of the debris (i.e., ring-like versus disk-like) but not by the total mass of the disk. The latter determines only the disk lifetime, which can be several Myr or longer. The evolution of an optically thick disk generically results in the development of a sharp outer edge of the disk. We also find that the low-mass (≲ 1020 g), optically thin disks exhibit and evolve on a characteristic timescale ∼105–106 yr, independent of their total mass.
A Case Report On The "A.I. Locked-In Problem": social concerns with modern NLP

Modern NLP models are becoming better conversational agents than their predecessors. Recurrent Neural Networks (RNNs) and especially Long-Short Term Memory (LSTM) features allow the agent to better store and use information about semantic content, a trend that has become even more pronounced with the Transformer Models. Large Language Models (LLMs) such as GPT-3 by OpenAI have become known to be able to construct and follow a narrative, which enables the system to adopt personas on the go, adapt them and play along in conversational stories. However, practical experimentation with GPT-3 shows that there is a recurring problem with these modern NLP systems, namely that they can “get stuck” in the narrative so that further conversations, prompt executions or commands become futile. This is here referred to as the “Locked-In Problem” and is exemplified with an experimental case report, followed by practical and social concerns that are accompanied with this problem.
$O(T^{-1})$ Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games

We prove that optimistic-follow-the-regularized-leader (OFTRL), together with smooth value updates, finds an $O(T^{-1})$-approximate Nash equilibrium in $T$ iterations for two-player zero-sum Markov games with full information. This improves the $\tilde{O}(T^{-5/6})$ convergence rate recently shown in the paper Zhang et al (2022). The refined analysis hinges on two essential ingredients. First, the sum of the regrets of the two players, though not necessarily non-negative as in normal-form games, is approximately non-negative in Markov games. This property allows us to bound the second-order path lengths of the learning dynamics. Second, we prove a tighter algebraic inequality regarding the weights deployed by OFTRL that shaves an extra $\log T$ factor. This crucial improvement enables the inductive analysis that leads to the final $O(T^{-1})$ rate.
Multistage Large Segment Imputation Framework Based on Deep Learning and Statistic Metrics

Missing value is a very common and unavoidable problem in sensors, and researchers have made numerous attempts for missing value imputation, particularly in deep learning models. However, for real sensor data, the specific data distribution and data periods are rarely considered, making it difficult to choose the appropriate evaluation indexes and models for different sensors. To address this issue, this study proposes a multistage imputation framework based on deep learning with adaptability for missing value imputation. The model presents a mixture measurement index of low- and higher-order statistics for data distribution and a new perspective on data imputation performance metrics, which is more adaptive and effective than the traditional mean squared error. A multistage imputation strategy and dynamic data length are introduced into the imputation process for data periods. Experimental results on different types of sensor data show that the multistage imputation strategy and the mixture index are superior and that the effect of missing value imputation has been improved to some extent, particularly for the large segment imputation problem. The codes and experimental results have been uploaded to GitHub.
DEFT: Diverse Ensembles for Fast Transfer in Reinforcement Learning

Deep ensembles have been shown to extend the positive effect seen in typical ensemble learning to neural networks and to reinforcement learning (RL). However, there is still much to be done to improve the efficiency of such ensemble models. In this work, we present Diverse Ensembles for Fast Transfer in RL (DEFT), a new ensemble-based method for reinforcement learning in highly multimodal environments and improved transfer to unseen environments. The algorithm is broken down into two main phases: training of ensemble members, and synthesis (or fine-tuning) of the ensemble members into a policy that works in a new environment. The first phase of the algorithm involves training regular policy gradient or actor-critic agents in parallel but adding a term to the loss that encourages these policies to differ from each other. This causes the individual unimodal agents to explore the space of optimal policies and capture more of the multimodality of the environment than a single actor could. The second phase of DEFT involves synthesizing the component policies into a new policy that works well in a modified environment in one of two ways. To evaluate the performance of DEFT, we start with a base version of the Proximal Policy Optimization (PPO) algorithm and extend it with the modifications for DEFT. Our results show that the pretraining phase is effective in producing diverse policies in multimodal environments. DEFT often converges to a high reward significantly faster than alternatives, such as random initialization without DEFT and fine-tuning of ensemble members. While there is certainly more work to be done to analyze DEFT theoretically and extend it to be even more robust, we believe it provides a strong framework for capturing multimodality in environments while still using RL methods with simple policy representations.
Spin imbalance effect on the Larkin-Ovchinnikov-Fulde-Ferrel state

We study spin imbalance effects on the Larkin-Ovchinnikov-Fulde-Ferrel (LOFF) state relevant for superconductors under a strong magnetic field and spin polarized ultracold Fermi gas. We obtain the exact solution for the condensates with arbitrary spin imbalance and the fermion spectrum perturbatively in the presence of small spin imbalance. We also obtain fermion zero mode exactly without perturbation theory.
Asymmetric Laplace scale mixtures for the distribution of cryptocurrency returns

Recent studies about cryptocurrency returns show that its distribution can be highly-peaked, skewed, and heavy-tailed, with a large excess kurtosis. To accommodate all these peculiarities, we propose the asymmetric Laplace scale mixture (ALSM) family of distributions. Each member of the family is obtained by dividing the scale parameter of the conditional asymmetric Laplace (AL) distribution by a convenient mixing random variable taking values on all or part of the positive real line and whose distribution depends on a parameter vector θ providing greater ﬂexibility to the resulting ALSM. Advantageously with respect to the AL distribution, the members of our family allow for a wider range of values for skewness and kurtosis. For illustrative purposes, we consider diﬀerent mixing distributions; they give rise to ALSMs having a closed-form probability density function where the AL distribution is obtained as a special case under a convenient choice of θ . We examine some properties of our ALSMs such as hierarchical and stochastic representations and moments of practical interest. We describe an EM algorithm to obtain maximum likelihood estimates of the parameters for all the considered ALSMs. We ﬁt these models to the returns of two cryptocurrencies, considering several classical distributions for comparison. The analysis shows how our models represent a valid alternative to the considered competitors in terms of AIC, BIC, and likelihood-ratio tests. for
A Direct Time-of-Flight Image Sensor With In-Pixel Surface Detection and Dynamic Vision

3D flash LIDAR is an alternative to the traditional scanning LIDAR systems, promising precise depth imaging in a compact form factor, and free of moving parts, for applications such as self-driving cars, robotics and augmented reality (AR). Typically implemented using single-photon, direct time-of-flight (dToF) receivers in image sensor format, the operation of the devices can be hindered by the large number of photon events needing to be processed and compressed in outdoor scenarios, limiting frame rates and scalability to larger arrays. We here present a <inline-formula><tex-math notation="LaTeX">$64 \times 32$</tex-math></inline-formula> pixel (<inline-formula><tex-math notation="LaTeX">$256 \times 128$</tex-math></inline-formula> SPAD) dToF imager that overcomes these limitations by using pixels with embedded histogramming, which lock onto and track the return signal. This reduces the size of output data frames considerably, enabling maximum frame rates in the 10 kFPS range or 100 kFPS for direct depth readings. The sensor offers selective readout of pixels detecting surfaces, or those sensing motion, akin to dynamic vision sensors that report changes in light intensity, leading to reduced power consumption and off-chip processing requirements. We demonstrate the application of the sensor in mid-range LIDAR.
Towards Efficient RRAM-based Quantized Neural Networks Hardware: State-of-the-art and Open Issues

The increasing amount of data processed on edge and demand for reducing the energy consumption for large neural network architectures have initiated the transition from traditional von Neumann architectures towards in-memory computing paradigms. Quantization is one of the methods to reduce power and computation requirements for neural networks by limiting bit precision. Resistive Random Access Memory (RRAM) devices are great candidates for Quantized Neural Networks (QNN) implementations. As the number of possible conductive states in RRAMs is limited, a certain level of quantization is always considered when designing RRAM-based neural networks. In this work, we provide a comprehensive analysis of state-of-the-art RRAM-based QNN implementations, showing where RRAMs stand in terms of satisfying the criteria of efficient QNN hardware. We cover hardware and device challenges related to QNNs and show the main unsolved issues and possible future research directions.
Multiangle QAOA Does Not Always Need All Its Angles

Introducing additional tunable parameters to quantum circuits is a powerful way of improving per-formance without increasing hardware requirements. A recently introduced multiangle extension of the quantum approximate optimization algorithm (ma-QAOA) signifi-cantly improves the solution quality compared with QAOA by allowing the parameters for each term in the Hamilto-nian to vary independently. Prior results suggest, however, considerable redundancy in parameters, the removal of which would reduce the cost of parameter optimization. In this work we show numerically the connection between the problem symmetries and the parameter redundancy by demonstrating that symmetries can be used to reduce the number of parameters used by ma-QAOA without decreasing the solution quality. We study Max-Cut on all 7,565 connected, non-isomorphic 8-node graphs with a nontrivial symmetry group and show numerically that in 67.4% of these graphs, symmetry can be used to reduce the number of parameters with no decrease in the objective, with the average ratio of parameters reduced by 28.1%. Moreover, we show that in 35.9% of the graphs this reduction can be achieved by simply using the largest symmetry. For the graphs where reducing the number of parameters leads to a decrease in the objective, the largest symmetry can be used to reduce the parameter count by 37.1% at the cost of only a 6.1% decrease in the objective. We demonstrate the central role of symmetries by showing that a random parameter reduction strategy leads to much worse performance.
Model Predictive Monitoring of Dynamic Systems for Signal Temporal Logic Specifications

Online monitoring aims to evaluate or to predict, at runtime, whether or not the behaviors of a system satisfy some desired specification. It plays a key role in safety-critical cyber-physical systems. In this work, we propose a new monitoring approach, called model predictive monitoring, for specifications described by Signal Temporal Logic (STL) formulae. Specifically, we assume that the observed state traces are generated by an underlying dynamic system whose model is known. The main idea is to use the dynamic of the system to predict future states when evaluating the satisfaction of the STL formulae. To this end, effective approaches for the computation of feasible sets of STL formulae are provided. We show that, by explicitly utilizing the model information of the dynamic system, the proposed online monitoring algorithm can falsify or certify of the specification in advance compared with existing algorithms, where no model information is used. We also demonstrate the proposed monitoring algorithm by several real world case studies.
Environmental and Social Sustainability of Creative-Ai

The recent developments of artificial intelligence increase its capability for the creation of arts in both largely autonomous and collaborative contexts. In both contexts, Ai aims to imitate, combine, and extend existing artistic styles, and can transform creative practices. In our ongoing research, we investigate such Creative-Ai from sustainability and ethical perspectives. The two main focus areas are understanding the environmental sustainability aspects (material, practices) in the context of artistic processes that involve Creative-Ai, and ethical issues related to who gets to be involved in the creation process (power, authorship, ownership). This paper provides an outline of our ongoing research in these two directions. We will present our interdisciplinary approach, which combines interviews, workshops, online ethnography, and energy measurements, to address our research questions: How is Creative-Ai currently used by artist communities, and which future applications do artists imagine? When Ai is applied to creating art, how might it impact the economy and environment? And, how can answers to these questions guide requirements for intellectual property regimes for Creative-Ai?
The Interplay of AI and Digital Twin: Bridging the Gap between Data-Driven and Model-Driven Approaches

The evolution of network virtualization and native artificial intelligence (AI) paradigms have conceptualized the vision of future wireless networks as a comprehensive entity operating in whole over a digital platform, with smart interaction with the physical domain, paving the way for the blooming of the Digital Twin (DT) concept. The recent interest in the DT networks is fueled by the emergence of novel wireless technologies and use-cases, that exacerbate the level of complexity to orchestrate the network and to manage its resources. Driven by AI, the key principle of the DT is to create a virtual twin for the physical entities and network dynamics, where the virtual twin will be leveraged to generate synthetic data and offer an on-demand platform for AI model training. Despite the common understanding that AI is the seed for DT, we anticipate that the DT and AI will be enablers for each other, in a way that overcome their limitations and complement each other benefits. In this article, we dig into the fundamentals of DT, where we reveal the role of DT in unifying model-driven and data-driven approaches, and explore the opportunities offered by DT in order to achieve the optimistic vision of 6G networks. We further unfold the essential role of the theoretical underpinnings in unlocking further opportunities by AI, and hence, we unveil their pivotal impact on the realization of reliable, efficient, and low-latency DT.
Systematic Mapping Protocol - Agile strategies for software development according to technical and environmental complexity factors

Software development projects management is a complex endeavor because it requires dealing with numerous unforeseen events that constantly arise along the way and that go against the expectations that had been established at the beginning. A good project leader is not so much who carries out what is planned, but rather who is able to deal with all the inconveniences that arise and, in the end, achieve a result that is as close as possible to what was expected [1]. In other words, what is most valued is the ability to adapt to changes, to face unforeseen events, to make the best decisions regarding a reality that is imposed as the project progresses. On the other hand, those of us who dedicate ourselves to IT projects have the tendency to cling to the tools that gave us the best results, although many times they are not the most appropriate for the case or, if they are, we continue to maintain them even when the context of the project has changed. We are interested in portraying the state of the art of software development project management with respect to the selection of management strategies based on the complexity of the problem to be faced. Our goal is to collect all the available evidence, analyze it, and study the possibility of guiding in the selection of most appropriate approaches, techniques, and tools, depending on the complexity of the problem, to better manage projects. In this document we detail the planning phase of a Systematic Mapping Study, used to structure the findings on a research area, based on the guidelines from Petersen et al. Our goal is twofold: to identify the evidence present in the scientific literature about criteria to select agile or plan-based project management approaches, techniques and tools, and the frameworks used to characterize the projects, in case they are used. We will perform a systematic mapping study of the literature for this purpose.
Parity nonconservation in Fr-like actinide and Cs-like rare-earth-metal ions

Parity-nonconservation (PNC) amplitudes are calculated for the 7s-6d(3/2) transitions of the francium isoelectronic sequence (Fr, Ra+, Ac2+, Th3+, Pa4+, U5+, and Np6+) and for the 6s-5d(3/2) transitions of the cesium isoelectronic sequence (Cs, Ba+, La2+, Ce3+, and Pr4+). We show in particular that isotopes of La2+, Ac2+, and Th3+ ions have strong potential in the search for new physics beyond the standard model: The PNC amplitudes are large, the calculations are accurate, and the nuclei are practically stable. In addition, Th-232(3+) ions have recently been trapped and cooled [Campbell et al., Phys. Rev. Lett. 102, 233004 (2009)]. We also extend previous works by calculating the s-s PNC transitions in Ra+ and Ba+ and provide calculations of several energy levels, and electric dipole and quadrupole transition amplitudes for the Fr-like actinide ions.
EasyRec: An easy-to-use, extendable and efficient framework for building industrial recommendation systems

We present EasyRec, an easy-to-use, extendable and efficient recommendation framework for building industrial recommendation systems. Our EasyRec framework is superior in the following aspects:first, EasyRec adopts a modular and pluggable design pattern to reduce the efforts to build custom models; second, EasyRec implements hyper-parameter optimization and feature selection algorithms to improve model performance automatically; third, EasyRec applies online learning to adapt to the ever-changing data distribution. The code is released: https://github.com/alibaba/EasyRec.
Lower Bound Proof for the Size of BDDs representing a Shifted Addition

Decision Diagrams(DDs) are one of the most popular representations for boolean functions. They are widely used in the design and verification of circuits. Different types of DDs have been proven to represent important functions in polynomial space and some types (like Binary Decision Diagrams(BDDs)) also allow operations on diagrams in polynomial time. However, there is no type which was proven capable of representing arbitrary boolean functions in polynomial space with regard to the input size. In particular for BDDs it is long known that integer multiplication is one of the functions, where the output BDDs have exponential size. In this paper, we show that this also holds for an integer addition where one of the operands is shifted to the right by an arbitrary value. We call this function the Shifted Addition. Our interest in this function is motivated through its occurrence during the floating point addition.