Miracle-dz/UniFAR-FTUs huggingface dataset:
The Diffuse Interstellar Bands: an Elderly Astro-Puzzle Rejuvenated

Abstract The interstellar medium constitutes a physically and chemically complex component of galaxies and is important in the cycle of matter and the evolution of stars. From various spectroscopic clues we now know that the interstellar medium is rich in organic compounds. However, identifying the exact nature of all these components remains a challenge. In particular the identification of the so-called diffuse band carriers has been alluding astronomers for almost a century. In recent decades, observational, experimental and theoretical advances have rapidly lead to renewed interest in the diffuse interstellar bands (DIBs). This has been instigated partly by their perceived relation to the infrared aromatic emission bands, the UV extinction bump and far-UV rise, and the growing number of (small) organic molecules identified in space. This chapter gives an overview of the observational properties and behaviour of the DIBs, and their presence throughout the Universe. I will highlight recent progress in identifying their carriers and discuss their potential as tracers and probes of (extra)-Galactic ISM conditions.
The Fundamental Limits of Structure-Agnostic Functional Estimation

Many recent developments in causal inference, and functional estimation problems more generally, have been motivated by the fact that classical one-step (first-order) debiasing methods, or their more recent sample-split double machine-learning avatars, can outperform plugin estimators under surprisingly weak conditions. These first-order corrections improve on plugin estimators in a black-box fashion, and consequently are often used in conjunction with powerful off-the-shelf estimation methods. These first-order methods are however provably suboptimal in a minimax sense for functional estimation when the nuisance functions live in Holder-type function spaces. This suboptimality of first-order debiasing has motivated the development of"higher-order"debiasing methods. The resulting estimators are, in some cases, provably optimal over Holder-type spaces, but both the estimators which are minimax-optimal and their analyses are crucially tied to properties of the underlying function space. In this paper we investigate the fundamental limits of structure-agnostic functional estimation, where relatively weak conditions are placed on the underlying nuisance functions. We show that there is a strong sense in which existing first-order methods are optimal. We achieve this goal by providing a formalization of the problem of functional estimation with black-box nuisance function estimates, and deriving minimax lower bounds for this problem. Our results highlight some clear tradeoffs in functional estimation -- if we wish to remain agnostic to the underlying nuisance function spaces, impose only high-level rate conditions, and maintain compatibility with black-box nuisance estimators then first-order methods are optimal. When we have an understanding of the structure of the underlying nuisance functions then carefully constructed higher-order estimators can outperform first-order estimators.
Set-Type Belief Propagation with Applications to Mapping, MTT, SLAM, and SLAT

—Belief propagation (BP) is a useful probabilistic inference algorithm for efﬁciently computing approximate marginal probability densities of random variables. However, in its standard form, BP is applicable to only the vector-type random variables, while certain applications rely on set-type random variables with an unknown number of vector elements. In this paper, we ﬁrst develop BP rules for set-type random variables and demonstrate that vector-type BP is a special case of set-type BP. We further propose factor graphs with set-factor and set-variable nodes by devising the set-factor nodes that can address the set-variables with random elements and cardinality, while the number of vector elements in vector-type is known. To demonstrate the validity of developed set-type BP, we apply it to the Poisson multi-Bernoulli (PMB) ﬁlter for simultaneous localization and mapping (SLAM), which naturally leads to a new set-type BP-SLAM ﬁlter. Finally, we reveal connections between the vector-type BP-SLAM ﬁlter and the proposed set-type BP-SLAM ﬁlter and show a performance gain of the proposed set-type BP-SLAM ﬁlter in comparison with the vector-type BP-SLAM ﬁlter.
Capacity Achieving Codes for an Erasure Queue-channel

We consider a queue-channel model that captures the waiting time-dependent degradation of information bits as they wait to be transmitted. Such a scenario arises naturally in quantum communications, where quantum bits tend to decohere rapidly. Trailing the capacity results obtained recently for certain queue-channels, this paper aims to construct practical channel codes for the erasure queue-channel (EQC)—a channel characterized by highly correlated erasures, governed by the underlying queuing dynamics. Our main contributions in this paper are twofold: (i) We propose a generic ‘wrapper’ based on interleaving across renewal blocks of the queue to convert any capacity-achieving block code for a memoryless erasure channel to a capacity-achieving code for the EQC. Next, due to the complexity involved in implementing interleaved systems, (ii) we study the performance of LDPC and Polar codes without any interleaving. We show that standard Arıkan’s Polar transform polarizes the EQC for certain restricted class of erasure probability functions. We also highlight some possible approaches and the corresponding challenges involved in proving the polarization of a general EQC.
Accented Text-to-Speech Synthesis With Limited Data

This paper presents an accented text-to-speech (TTS) synthesis framework with limited training data. We study two aspects concerning accent rendering: phonetic (phoneme difference) and prosodic (pitch pattern and phoneme duration) variations. The proposed accented TTS framework consists of two models: an accented front-end for grapheme-to-phoneme (G2P) conversion and an accented acoustic model with integrated pitch and duration predictors for phoneme-to-Mel-spectrogram prediction. The accented front-end directly models the phonetic variation, while the accented acoustic model explicitly controls the prosodic variation. Specifically, both models are first pre-trained on a large amount of data, then only the accent-related layers are fine-tuned on a limited amount of data for the target accent. In the experiments, speech data of three English accents, i.e., General American English, Irish English, and British English Received Pronunciation, are used for pre-training. The pre-trained models are then fine-tuned with Scottish and General Australian English accents, respectively. Both objective and subjective evaluation results show that the accented TTS front-end fine-tuned with a small accented phonetic lexicon ($\text{5}\,k$ words) effectively handles the phonetic variation of accents, while the accented TTS acoustic model fine-tuned with a limited amount of accented speech data (approximately 3 minutes) effectively improves the prosodic rendering including pitch and duration. The overall accent modeling contributes to improved speech quality and accent similarity.
An Overview of AI and Blockchain Integration for Privacy-Preserving

With the widespread attention and application of artificial intelligence (AI) and blockchain technologies, privacy protection techniques arising from their integration are of notable significance. In addition to protecting privacy of individuals, these techniques also guarantee security and dependability of data. This paper initially presents an overview of AI and blockchain, summarizing their combination along with derived privacy protection technologies. It then explores specific application scenarios in data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity methods. Moreover, the paper evaluates five critical aspects of AI-blockchain-integration privacy protection systems, including authorization management, access control, data protection, network security, and scalability. Furthermore, it analyzes the deficiencies and their actual cause, offering corresponding suggestions. This research also classifies and summarizes privacy protection techniques based on AI-blockchain application scenarios and technical schemes. In conclusion, this paper outlines the future directions of privacy protection technologies emerging from AI and blockchain integration, including enhancing efficiency and security to achieve a more comprehensive privacy protection of privacy.
Leveraging Semantic Relationships to Prioritise Indicators of Compromise in Additive Manufacturing Systems

Additive manufacturing (AM) offers numerous benefits, such as manufacturing complex and customised designs quickly and cost-effectively, reducing material waste, and enabling on-demand production. However, several security challenges are associated with AM, making it increasingly attractive to attackers ranging from individual hackers to organised criminal gangs and nation-state actors. This paper addresses the cyber risk in AM to attackers by proposing a novel semantic-based threat prioritisation system for identifying, extracting and ranking indicators of compromise (IOC). The system leverages the heterogeneous information networks (HINs) that automatically extract high-level IOCs from multi-source threat text and identifies semantic relations among the IOCs. It models IOCs with a HIN comprising different meta-paths and meta-graphs to depict semantic relations among diverse IOCs. We introduce a domain-specific recogniser that identifies IOCs in three domains: organisation-specific, regional source-specific, and regional target-specific. A threat assessment uses similarity measures based on meta-paths and meta-graphs to assess semantic relations among IOCs. It prioritises IOCs by measuring their severity based on the frequency of attacks, IOC lifetime, and exploited vulnerabilities in each domain.
Efficient Query Re-optimization with Judicious Subquery Selections

Query re-optimization is an adaptive query processing technique that re-invokes the optimizer at certain points in query execution. The goal is to dynamically correct the cardinality estimation errors using the statistics collected at runtime to adjust the query plan to improve the overall performance. We identify a key weakness in existing re-optimization algorithms: their subquery division and re-optimization trigger strategies rely heavily on the optimizer's initial plan, which can be far away from optimal. We, therefore, propose QuerySplit, a novel re-optimization algorithm that skips the potentially misleading global plan and instead generates subqueries directly from the logical plan as the basic re-optimization units. By developing a cost function that prioritizes the execution of less "damaging" subqueries, QuerySplit successfully postpones (sometimes avoids) the execution of complex large joins to maximize their probability of having smaller input sizes. We implemented QuerySplit in PostgreSQL and compared our solution against four state-of-the-art re-optimization algorithms using the Join Order Benchmark. Our experiments show that QuerySplit reduces the benchmark execution time by 35% compared to the second-best alternative. The performance gap between QuerySplit and an optimal optimizer is within 4%.
Efficient Scenario Generation for Heavy-Tailed Chance Constrained Optimization

We consider a generic class of chance-constrained optimization problems with heavy-tailed (i.e., power-law type) risk factors. As the most popular generic method for solving chance constrained optimization, the scenario approach generates sampled optimization problem as a precise approximation with provable reliability, but the computational complexity becomes intractable when the risk tolerance parameter is small. To reduce the complexity, we sample the risk factors from a conditional distribution given that the risk factors are in an analytically tractable event that encompasses all the plausible events of constraints violation. Our approximation is proven to have optimal value within a constant factor to the optimal value of the original chance constraint problem with high probability, uniformly in the risk tolerance parameter. To the best of our knowledge, our result is the first uniform performance guarantee of this type. We additionally demonstrate the efficiency of our algorithm in the context of solvency in portfolio optimization and insurance networks. Funding: The research of B. Zwart is supported by the NWO (Dutch Research Council) [Grant 639.033. 413]. The research of J. Blanchet is supported by the Air Force Office of Scientific Research [Award FA9550-20-1-0397], the National Science Foundation [Grants 1820942, 1838576, 1915967, and 2118199], Defense Advanced Research Projects Agency [Award N660011824028], and China Merchants Bank.
The Constant in the Theorem of Binev-Dahmen-DeVore-Stevenson and a Generalisation of it

A triangulation of a polytope into simplices is refined recursively. In every refinement round, some simplices which have been marked by an external algorithm are bisected and some others around also must be bisected to retain regularity of the triangulation. The ratio of the total number of marked simplices and the total number of bisected simplices is bounded from above. Binev, Dahmen and DeVore proved under a certain initial condition a bound that depends only on the initial triangulation. This thesis proposes a new way to obtain a better bound in any dimension. Furthermore, the result is proven for a weaker initial condition, invented by Alk\"amper, Gaspoz and Kl\"ofkorn, who also found an algorithm to realise this condition for any regular initial triangulation. Supposably, it is the first proof for a Binev-Dahmen-DeVore theorem in any dimension with always practically realiseable initial conditions without an initial refinement. Additionally, the initialisation refinement proposed by Kossaczk\'y and Stevenson is generalised, and the number of recursive bisections of one single simplex in one refinement round is bounded from above by twice the dimension, sharpening a result of Gallistl, Schedensack and Stevenson.
Getting More out of Large Language Models for Proofs

Large language models have the potential to simplify formal theorem proving and make it more accessible. But how to get the most out of these models is still an open question. To answer this question, we take a step back and explore the failure cases of these models using common prompting-based techniques. Our talk will discuss these failure cases and what they can teach us about how to get more out of these models.
DEFENDER: DTW-Based Episode Filtering Using Demonstrations for Enhancing RL Safety

Deploying reinforcement learning agents in the real world can be challenging due to the risks associated with learning through trial and error. We propose a task-agnostic method that leverages small sets of safe and unsafe demonstrations to improve the safety of RL agents during learning. The method compares the current trajectory of the agent with both sets of demonstrations at every step, and filters the trajectory if it resembles the unsafe demonstrations. We perform ablation studies on different filtering strategies and investigate the impact of the number of demonstrations on performance. Our method is compatible with any stand-alone RL algorithm and can be applied to any task. We evaluate our method on three tasks from OpenAI Gym's Mujoco benchmark and two state-of-the-art RL algorithms. The results demonstrate that our method significantly reduces the crash rate of the agent while converging to, and in most cases even improving, the performance of the stand-alone agent.
Two-Dimensional Channel Parameter Estimation for IRS-Assisted Networks

This paper proposes a pilot decoupling-based two-dimensional channel parameter estimation method for intelligent reflecting surface (IRS)-assisted networks. We exploit the combined effect of Terahertz sparse propagation and the geometrical structure of arrays deployed at the base station, the IRS, and the user equipment to develop a low-complexity channel parameter estimation method. By means of a new pilot design along the horizontal and vertical domains, the overall channel parameter estimation problem is decoupled into different domains. Furthermore, with this decoupling, it is possible to simultaneously sense/estimate the channel parameters and to communicate with the sensed node. Specifically, we derive two estimators by decoupling the global problem into sub-problems and exploiting the built-in tensor structure of the sensing/estimation problem by means of multiple rank-one approximations. The Cram\'er-Rao lower bound is derived to assess the performance of the proposed estimators. We show that the two proposed methods yield accurate parameter estimates and outperform state-of-the-art methods in terms of complexity. The tradeoffs between performance and complexity offered by the proposed methods are discussed and numerically assessed.
Non-Autoregressive Math Word Problem Solver with Unified Tree Structure

Existing MWP solvers employ sequence or binary tree to present the solution expression and decode it from given problem description. However, such structures fail to handle the variants that can be derived via mathematical manipulation, e.g., $(a_1+a_2) * a_3$ and $a_1 * a_3+a_2 * a_3$ can both be possible valid solutions for a same problem but formulated as different expression sequences or trees. The multiple solution variants depicting different possible solving procedures for the same input problem would raise two issues: 1) making it hard for the model to learn the mapping function between the input and output spaces effectively, and 2) wrongly indicating \textit{wrong} when evaluating a valid expression variant. To address these issues, we introduce a unified tree structure to present a solution expression, where the elements are permutable and identical for all the expression variants. We propose a novel non-autoregressive solver, named \textit{MWP-NAS}, to parse the problem and deduce the solution expression based on the unified tree. For evaluating the possible expression variants, we design a path-based metric to evaluate the partial accuracy of expressions of a unified tree. The results from extensive experiments conducted on Math23K and MAWPS demonstrate the effectiveness of our proposed MWP-NAS. The codes and checkpoints are available at: \url{https://github.com/mengqunhan/MWP-NAS}.
Generalised Coupling and An Elementary Algorithm for the Quantum Schur Transform

The quantum Schur transform is a fundamental building block that maps the computational basis to a coupled basis consisting of irreducible representations of the unitary and symmetric groups. Equivalently, it may be regarded as a change of basis from the computational basis to a simultaneous spin eigenbasis of Permutational Quantum Computing (PQC) [Quantum Inf. Comput. , 10, 470-497 (2010)]. By adopting the latter perspective, we present a transparent algorithm for implementing the qubit quantum Schur transform which uses $O(\log(n))$ ancillas and can be decomposed into a sequence of $O(n^3\log(n)\log(\frac{n}{\epsilon}))$ Clifford + T gates, where $\epsilon$ is the accuracy of the algorithm in terms of the trace norm. We discuss the necessity for some applications of implementing this operation as a unitary rather than an isometry, as is often presented. By studying the associated Schur states, which consist of qubits coupled via Clebsch-Gordan coefficients, we introduce the notion of generally coupled quantum states. We present six conditions, which in different combinations ensure the efficient preparation of these states on a quantum computer or their classical simulability (in the sense of computational tractability). It is shown that Wigner 6-j symbols and SU(N) Clebsch-Gordan coefficients naturally fit our framework. Finally, we investigate unitary transformations which preserve the class of computationally tractable states.
Pickup Ion–Mediated Magnetic Reconnection in the Outer Heliosphere

Pickup ions (PUIs) play a crucial role in the heliosphere, contributing to the mediation of large-scale structures such as the distant solar wind, the heliospheric termination shock, and the heliopause. While magnetic reconnection is thought to be a common process in the heliosphere due to the presence of heliospheric current sheets, it is poorly understood how PUIs might affect the evolution of magnetic reconnection. Although it is reasonable to suppose that PUIs decrease the reconnection rate since the plasma beta becomes much larger than 1 when PUIs are included, we show for the first time that such a supposition is invalid and that PUI-induced turbulence, heat conduction, and viscosity can preferentially boost magnetic reconnection in heliospheric current sheets in the distant solar wind. This suggests that it is critical to include the effect of the turbulence, heat conduction, and viscosity caused by PUIs to understand the dynamics of magnetic reconnection in the outer heliosphere.
Adapting Transformer Language Models for Predictive Typing in Brain-Computer Interfaces

Brain-computer interfaces (BCI) are an important mode of alternative and augmentative communication for many people. Unlike keyboards, many BCI systems do not display even the 26 letters of English at one time, let alone all the symbols in more complex systems. Using language models to make character-level predictions, therefore, can greatly speed up BCI typing (Ghosh and Kristensson, 2017). While most existing BCI systems employ character n-gram models or no LM at all, this paper adapts several wordpiece-level Transformer LMs to make character predictions and evaluates them on typing tasks. GPT-2 fares best on clean text, but different LMs react differently to noisy histories. We further analyze the effect of character positions in a word and context lengths.
Spiking neural networks with Hebbian plasticity for unsupervised representation learning

We introduce a novel spiking neural network model for learning distributed internal representations from data in an unsupervised procedure. We achieved this by transforming the non-spiking feedforward Bayesian Confidence Propagation Neural Network (BCPNN) model, employing an online correlation-based Hebbian-Bayesian learning and rewiring mechanism, shown previously to perform representation learning, into a spiking neural network with Poisson statistics and low firing rate comparable to in vivo cortical pyramidal neurons. We evaluated the representations learned by our spiking model using a linear classifier and show performance close to the non-spiking BCPNN, and competitive with other Hebbian-based spiking networks when trained on MNIST and F-MNIST machine learning benchmarks.