Miracle-dz/UniFAR-FTUs huggingface dataset:
Strategy Extraction in Single-Agent Games

The ability to continuously learn and adapt to new situations is one where humans are far superior compared to AI agents. We propose an approach to knowledge transfer using behavioural strategies as a form of transferable knowledge influenced by the human cognitive ability to develop strategies. A strategy is defined as a partial sequence of events - where an event is both the result of an agent's action and changes in state - to reach some predefined event of interest. This information acts as guidance or a partial solution that an agent can generalise and use to make predictions about how to handle unknown observed phenomena. As a first step toward this goal, we develop a method for extracting strategies from an agent's existing knowledge that can be applied in multiple contexts. Our method combines observed event frequency information with local sequence alignment techniques to find patterns of significance that form a strategy. We show that our method can identify plausible strategies in three environments: Pacman, Bank Heist and a dungeon-crawling video game. Our evaluation serves as a promising first step toward extracting knowledge for generalisation and, ultimately, transfer learning.
Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting

Multivariate time-series (MTS) forecasting is a paramount and fundamental problem in many real-world applications. The core issue in MTS forecasting is how to effectively model complex spatial-temporal patterns. In this paper, we develop a adaptive, interpretable and scalable forecasting framework, which seeks to individually model each component of the spatial-temporal patterns. We name this framework SCNN, as an acronym of Structured Component-based Neural Network. SCNN works with a pre-defined generative process of MTS, which arithmetically characterizes the latent structure of the spatial-temporal patterns. In line with its reverse process, SCNN decouples MTS data into structured and heterogeneous components and then respectively extrapolates the evolution of these components, the dynamics of which are more traceable and predictable than the original MTS. Extensive experiments are conducted to demonstrate that SCNN can achieve superior performance over state-of-the-art models on three real-world datasets. Additionally, we examine SCNN with different configurations and perform in-depth analyses of the properties of SCNN.
IMBUE: In-Memory Boolean-to-CUrrent Inference ArchitecturE for Tsetlin Machines

In-memory computing for Machine Learning (ML) applications remedies the von Neumann bottlenecks by organizing computation to exploit parallelism and locality. Non-volatile memory devices such as Resistive RAM (ReRAM) offer integrated switching and storage capabilities showing promising performance for ML applications. However, ReRAM devices have design challenges, such as nonlinear digital-analog conversion and circuit overheads. This paper proposes an In-Memory Boolean-to-Current Inference Architecture (IMBUE) that uses ReRAM-transistor cells to eliminate the need for such conversions. IMBUE processes Boolean feature inputs expressed as digital voltages and generates parallel current paths based on resistive memory states. The proportional column current is then translated back to the Boolean domain for further digital processing. The IMBUE architecture is inspired by the Tsetlin Machine (TM), an emerging ML algorithm based on intrinsically Boolean logic. The IMBUE architecture demonstrates significant performance improvements over binarized convolutional neural networks and digital TM in-memory implementations, achieving up to a 12.99x and 5.28x increase, respectively.
DeCloak: Enable Secure and Cheap Multi-Party Transactions on Legacy Blockchains by a Minimally Trusted TEE Network

The crucial blockchain privacy and scalability demand has boosted off-chain contract execution frameworks for years. Some have recently extended their capabilities to transition blockchain states by off-chain multi-party computation while ensuring public verifiability. This new capability is defined as acrfull mpt. However, existing MPT solutions lack at least one of the following properties crucially valued by communities: data availability, financial fairness, delivery fairness, and delivery atomicity. This paper proposes a novel MPT-enabled off-chain contract execution framework, Decloak. Using TEEs, Decloak solves identified properties with lower gas costs and a weaker assumption. Notably, Decloak is the first to achieve data availability and also achieve all of the above properties. This achievement is coupled with its ability to tolerate all-but-one Byzantine parties and TEE executors. Evaluating 10 MPTs in different businesses, Decloak reduces the gas cost of the SOTA, Cloak, by 65.6%. This efficiency advantage further amplifies with an increasing number of MPT’s parties. Consequently, we establish an elevated level of secure and cheap MPT, being the first to demonstrate the feasibility of achieving gas costs comparable to Ethereum transactions while evaluating MPTs.
OL-Transformer: A Fast and Universal Surrogate Simulator for Optical Multilayer Thin Film Structures

Deep learning-based methods have recently been established as fast and accurate surrogate simulators for optical multilayer thin film structures. However, existing methods only work for limited types of structures with different material arrangements, preventing their applications towards diverse and universal structures. Here, we propose the Opto-Layer (OL) Transformer to act as a universal surrogate simulator for enormous types of structures. Combined with the technique of structure serialization, our model can predict accurate reflection and transmission spectra for up to $10^{25}$ different multilayer structures, while still achieving a six-fold degradation in simulation time compared to physical solvers. Further investigation reveals that the general learning ability comes from the fact that our model first learns the physical embeddings and then uses the self-attention mechanism to capture the hidden relationship of light-matter interaction between each layer.
Sweet-spot operation of a germanium hole spin qubit with highly anisotropic noise sensitivity

Spin qubits defined by valence band hole states comprise an attractive candidate for quantum information processing due to their inherent coupling to electric fields enabling fast and scalable qubit control. In particular, heavy holes in germanium have shown great promise, with recent demonstrations of fast and high-fidelity qubit operations. However, the mechanisms and anisotropies that underlie qubit driving and decoherence are still mostly unclear. Here, we report on the highly anisotropic heavy-hole $g$-tensor and its dependence on electric fields, allowing us to relate both qubit driving and decoherence to an electric modulation of the $g$-tensor. We also confirm the predicted Ising-type hyperfine interaction but show that qubit coherence is ultimately limited by $1/f$ charge noise. Finally, we operate the qubit at low magnetic field and measure a dephasing time of $T_2^*=9.2$ ${\mu}$s, while maintaining a single-qubit gate fidelity of 99.94 %, that remains well above 99 % at an operation temperature T>1 K. This understanding of qubit driving and decoherence mechanisms are key for the design and operation of scalable and highly coherent hole qubit arrays.
Topological data analysis suggests human brain networks reconfiguration in the transition from a resting state to cognitive load

BACKGROUND: The functional network of the brain continually adapts to changing environmental demands. The environmental changes closely connect with changes of active cognitive processes. In recent years, the network approach has emerged as a promising method for analyzing the neurophysiological mechanisms that underlie psychological functions. AIM: This research aimed to explore how the topological features of functional connectomes in the human brain are linked to different cognitive states. The focus was on understanding the dynamic changes in brain networks during working memory tasks, with the goal of identifying associations between specific network characteristics and type of cognitive load. Such insights could improve our understanding of cognitive processes, especially related to working memory. METHODS: The present study examines topological characteristics of functional brain networks in resting state and in cognitive load, provided by the execution of the Sternberg Item Recognition Paradigm (SIRP) based on electroencephalographic data. We propose that the topological properties of the functional networks in the human brain are distinct between cognitive load and resting state with higher integration in the networks during cognitive load. RESULTS: It was shown that topological features of functional connectomes strongly depend on the type of cognitive process performed by the subject and change in accordance with task change. The analysis also demonstrated that functional connectivity during working memory tasks showed a faster emergence of homology groups generators, supporting the idea of a relationship between the initial stages of working memory execution and an increase in faster network integration, with connector hubs playing a crucial role. CONCLUSION: The study found that the topological features of functional brain networks are influenced by the type of cognitive process engaged by individuals and adapt to task changes. During working memory tasks, the functional connectivity exhibited a faster emergence of homology group generators, indicating a connection between the initial stages of working memory execution and enhanced network integration. These results suggest that cognitive states, particularly related to working memory, are associated with distinct topological properties of functional brain networks, highlighting the importance of network dynamics in cognitive processing.
EE-TTS: Emphatic Expressive TTS with Linguistic Information

While Current TTS systems perform well in synthesizing high-quality speech, producing highly expressive speech remains a challenge. Emphasis, as a critical factor in determining the expressiveness of speech, has attracted more attention nowadays. Previous works usually enhance the emphasis by adding intermediate features, but they can not guarantee the overall expressiveness of the speech. To resolve this matter, we propose Emphatic Expressive TTS (EE-TTS), which leverages multi-level linguistic information from syntax and semantics. EE-TTS contains an emphasis predictor that can identify appropriate emphasis positions from text and a conditioned acoustic model to synthesize expressive speech with emphasis and linguistic information. Experimental results indicate that EE-TTS outperforms baseline with MOS improvements of 0.49 and 0.67 in expressiveness and naturalness. EE-TTS also shows strong generalization across different datasets according to AB test results.
Park visitation and walkshed demographics in the United States

A large and growing body of research demonstrates the value of local parks to mental and physical well-being. Recently, researchers have begun using passive digital data sources to investigate equity in usage; exactly who is benefiting from parks? Early studies suggest that park visitation differs according to demographic features, and that the demographic composition of a park's surrounding neighborhood may be related to the utilization a park receives. Employing a data set of park visitations generated by observations of roughly 50 million mobile devices in the US in 2019, we assess the ability of the demographic composition of a park's walkshed to predict its yearly visitation. Predictive models are constructed using Support Vector Regression, LASSO, Elastic Net, and Random Forests. Surprisingly, our results suggest that the demographic composition of a park's walkshed demonstrates little to no utility for predicting visitation.
RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representation from X-Ray Images

Purpose: Self-supervised learning has been gaining attention in the medical field for its potential to improve computer-aided diagnosis. One popular method of self-supervised learning is masked image modeling (MIM), which involves masking a subset of input pixels and predicting the masked pixels. However, traditional MIM methods typically use a random masking strategy, which may not be ideal for medical images that often have a small region of interest for disease detection. To address this issue, this work aims to improve MIM for medical images and evaluate its effectiveness in an open X-ray image dataset. Methods: In this paper, we present a novel method called region-guided masked image modeling (RGMIM) for learning meaningful representation from X-ray images. Our method adopts a new masking strategy that utilizes organ mask information to identify valid regions for learning more meaningful representations. The proposed method was contrasted with five self-supervised learning techniques (MAE, SKD, Cross, BYOL, and, SimSiam). We conduct quantitative evaluations on an open lung X-ray image dataset as well as masking ratio hyperparameter studies. Results: When using the entire training set, RGMIM outperformed other comparable methods, achieving a 0.962 lung disease detection accuracy. Specifically, RGMIM significantly improved performance in small data volumes, such as 5% and 10% of the training set (846 and 1,693 images) compared to other methods, and achieved a 0.957 detection accuracy even when only 50% of the training set was used. Conclusions: RGMIM can mask more valid regions, facilitating the learning of discriminative representations and the subsequent high-accuracy lung disease detection. RGMIM outperforms other state-of-the-art self-supervised learning methods in experiments, particularly when limited training data is used.
A Training and Inference Strategy Using Noisy and Enhanced Speech as Target for Speech Enhancement without Clean Speech

The lack of clean speech is a practical challenge to the development of speech enhancement systems, which means that there is an inevitable mismatch between their training criterion and evaluation metric. In response to this unfavorable situation, we propose a training and inference strategy that additionally uses enhanced speech as a target by improving the previously proposed noisy-target training (NyTT). Because homogeneity between in-domain noise and extraneous noise is the key to the effectiveness of NyTT, we train various student models by remixing 1) the teacher model's estimated speech and noise for enhanced-target training or 2) raw noisy speech and the teacher model's estimated noise for noisy-target training. Experimental results show that our proposed method outperforms several baselines, especially with the teacher/student inference, where predicted clean speech is derived successively through the teacher and final student models.
Diversity and Inclusion in Artificial Intelligence

To date, there has been little concrete practical advice about how to ensure that diversity and inclusion considerations should be embedded within both specific Artificial Intelligence (AI) systems and the larger global AI ecosystem. In this chapter, we present a clear definition of diversity and inclusion in AI, one which positions this concept within an evolving and holistic ecosystem. We use this definition and conceptual framing to present a set of practical guidelines primarily aimed at AI technologists, data scientists and project leaders.
Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategy

Ultra-fine entity typing (UFET) is the task of inferring the semantic types, from a large set of fine-grained candidates, that apply to a given entity mention. This task is especially challenging because we only have a small number of training examples for many of the types, even with distant supervision strategies. State-of-the-art models, therefore, have to rely on prior knowledge about the type labels in some way. In this paper, we show that the performance of existing methods can be improved using a simple technique: we use pre-trained label embeddings to cluster the labels into semantic domains and then treat these domains as additional types. We show that this strategy consistently leads to improved results, as long as high-quality label embeddings are used. We furthermore use the label clusters as part of a simple post-processing technique, which results in further performance gains. Both strategies treat the UFET model as a black box and can thus straightforwardly be used to improve a wide range of existing models.
Density biases and temperature relations for DESIRED HII regions

We present a first study based on the analysis of the DEep Spectra of Ionized REgions Database (DESIRED). This is a compilation of 190 high signal-to-noise ratio optical spectra of H ii regions and other photoionized nebulae, mostly observed with 8-10m telescopes and containing ∼29380 emission lines. We find that the electron density –ne– of the objects is underestimated when [S ii] λ6731/λ6716 and/or [O ii] λ3726/λ3729 are the only density indicators available. This is produced by the non-linear density dependence of the indicators in the presence of density inhomogeneities. The average underestimate is ∼300 cm−3 in extragalactic H ii regions, introducing systematic overestimates of Te([O ii]) and Te([S ii]) compared to Te([N ii]). The high-sensitivity of [O ii] λλ7319 + 20 + 30 + 31/λλ3726 + 29 and [S ii] λλ4069 + 76/λλ6716 + 31 to density makes them more suitable for the diagnosis of the presence of high-density clumps. If Te([N ii]) is adopted, the density underestimate has a small impact in the ionic abundances derived from optical spectra, being limited to up to ∼0.1 dex when auroral [S ii] and/or [O ii] lines are used. However, these density effects are critical for the analysis of infrared fine structure lines, such as those observed by the JWST in local star forming regions, implying strong underestimates of the ionic abundances. We present temperature relations between Te([O iii]), Te([Ar iii]), Te([S iii]) and Te([N ii]) for the extragalactic H ii regions. We confirm a non-linear dependence between Te([O iii])-Te([N ii]) due to a more rapid increase of Te([O iii]) at lower metallicities.
SignSVRG: fixing SignSGD via variance reduction

We consider the problem of unconstrained minimization of finite sums of functions. We propose a simple, yet, practical way to incorporate variance reduction techniques into SignSGD, guaranteeing convergence that is similar to the full sign gradient descent. The core idea is first instantiated on the problem of minimizing sums of convex and Lipschitz functions and is then extended to the smooth case via variance reduction. Our analysis is elementary and much simpler than the typical proof for variance reduction methods. We show that for smooth functions our method gives $\mathcal{O}(1 / \sqrt{T})$ rate for expected norm of the gradient and $\mathcal{O}(1/T)$ rate in the case of smooth convex functions, recovering convergence results of deterministic methods, while preserving computational advantages of SignSGD.
Retrieval-augmented Multi-label Text Classification

Multi-label text classification (MLC) is a challenging task in settings of large label sets, where label support follows a Zipfian distribution. In this paper, we address this problem through retrieval augmentation, aiming to improve the sample efficiency of classification models. Our approach closely follows the standard MLC architecture of a Transformer-based encoder paired with a set of classification heads. In our case, however, the input document representation is augmented through cross-attention to similar documents retrieved from the training set and represented in a task-specific manner. We evaluate this approach on four datasets from the legal and biomedical domains, all of which feature highly skewed label distributions. Our experiments show that retrieval augmentation substantially improves model performance on the long tail of infrequent labels especially so for lower-resource training scenarios and more challenging long-document data scenarios.
Electric current from Schwinger's time-ordered propagator

The electric current density of fermions in the presence of an external electric field is determined through the utilization of a time-ordered Schwinger propagator. The study encompasses the necessary conditions for establishing a well-defined time-ordered propagator within the Schwinger formalism, specifically concentrating on constant and uniform electromagnetic fields. Within this theoretical framework, the introduction of the Schwinger phase induces a chemical potential, resulting in non-zero values for the electric current and charge number density. Consequently, the dependence of electric conductivity on the strength of the electric field and the charge number density is investigated.
Teaching Probabilistic Logical Reasoning to Transformers

In this paper, we evaluate the capability of transformer-based language models in making inferences over uncertain text that includes uncertain rules of reasoning. We cover both Pre-trained Language Models (PLMs) and generative Large Language Models (LLMs). Our evaluation results show that both generations of language models struggle with reasoning over uncertain text. We propose a novel end-to-end fine-tuning approach, Probabilistic Constraint Training (PCT), that utilizes probabilistic logical rules as constraints in the fine-tuning phase without relying on these rules in the inference stage. To assess the effectiveness of PCT, we utilize the related corpora and, additionally, create a new and more challenging benchmark that, unlike the previous ones, uses instance-specific rules. Our study demonstrates that PCT improves the transformer-based language model’s intrinsic reasoning and makes their probabilistic logical reasoning process more explicit and explainable. Furthermore, PCT equips these models to effectively handle novel situations, including higher reasoning depth, new domains, and complex probabilistic structures.
Evaluating Factual Consistency of Texts with Semantic Role Labeling

Automated evaluation of text generation systems has recently seen increasing attention, particularly checking whether generated text stays truthful to input sources.Existing methods frequently rely on an evaluation using task-specific language models, which in turn allows for little interpretability of generated scores.We introduce SRLScore, a reference-free evaluation metric designed with text summarization in mind. Our approach generates fact tuples constructed from Semantic Role Labels, applied to both input and summary texts.A final factuality score is computed by an adjustable scoring mechanism, which allows for easy adaption of the method across domains. Correlation with human judgments on English summarization datasets shows that SRLScore is competitive with state-of-the-art methods and exhibits stable generalization across datasets without requiring further training or hyperparameter tuning.We experiment with an optional co-reference resolution step, but find that the performance boost is mostly outweighed by the additional compute required.Our metric is available online at: https://github.com/heyjing/SRLScore