Miracle-dz/UniFAR-FTUs huggingface dataset:
Deductive schemas with uncertain premises using qualitative probability expressions

ABSTRACT The new paradigm in the psychology of reasoning redirects the investigation of deduction conceptually and methodologically because the premises and the conclusion of the inferences are assumed to be uncertain. A probabilistic counterpart of the concept of logical validity and a method to assess whether individuals comply with it must be defined. Conceptually, we used de Finetti's coherence as a normative framework to assess individuals' performance. Methodologically, we presented inference schemas whose premises had various levels of probability that contained non-numerical expressions (e.g., “the chances are high”) and, as a control, sure levels. Depending on the inference schemas, from 60% to 80% of the participants produced coherent conclusions when the premises were uncertain. The data also show that (1) except for schemas involving conjunction, performance was consistently lower with certain than uncertain premises, (2) the rate of conjunction fallacy was consistently low (not exceeding 20%, even with sure premises), and (3) participants' interpretation of the conditional agreed with de Finetti's “conditional event” but not with the material conditional.
$\pi2\text{vec}$: Policy Representations with Successor Features

This paper describes $\pi2\text{vec}$, a method for representing behaviors of black box policies as feature vectors. The policy representations capture how the statistics of foundation model features change in response to the policy behavior in a task agnostic way, and can be trained from offline data, allowing them to be used in offline policy selection. This work provides a key piece of a recipe for fusing together three modern lines of research: Offline policy evaluation as a counterpart to offline RL, foundation models as generic and powerful state representations, and efficient policy selection in resource constrained environments.
Generic Forward Curve Dynamics for Commodity Derivatives

This article presents a generic framework for modeling the dynamics of forward curves in commodity market as commodity derivatives are typically traded by futures or forwards. We have theoretically demonstrated that commodity prices are driven by multiple components. As such, the model can better capture the forward price and volatility dynamics. Empirical study shows that the model prices are very close to the market prices, indicating prima facie that the model performs quite well.
MP3: Movement Primitive-Based (Re-)Planning Policy

We introduce a novel deep reinforcement learning (RL) approach called Movement Primitive-based Planning Policy (MP3). By integrating movement primitives (MPs) into the deep RL framework, MP3 enables the generation of smooth trajectories throughout the whole learning process while effectively learning from sparse and non-Markovian rewards. Additionally, MP3 maintains the capability to adapt to changes in the environment during execution. Although many early successes in robot RL have been achieved by combining RL with MPs, these approaches are often limited to learning single stroke-based motions, lacking the ability to adapt to task variations or adjust motions during execution. Building upon our previous work, which introduced an episode-based RL method for the non-linear adaptation of MP parameters to different task variations, this paper extends the approach to incorporating replanning strategies. This allows adaptation of the MP parameters throughout motion execution, addressing the lack of online motion adaptation in stochastic domains requiring feedback. We compared our approach against state-of-the-art deep RL and RL with MPs methods. The results demonstrated improved performance in sophisticated, sparse reward settings and in domains requiring replanning.
On-line reinforcement learning for optimization of real-life energy trading strategy

An increasing share of energy is produced from renewable sources by many small producers. The efficiency of those sources is volatile and, to some extent, random, exacerbating the problem of energy market balancing. In many countries, this balancing is done on the day-ahead (DA) energy markets. This paper considers automated trading on the DA energy market by a medium-sized prosumer. We model this activity as a Markov Decision Process and formalize a framework in which an applicable in real-life strategy can be optimized with off-line data. We design a trading strategy that is fed with the available environmental information that can impact future prices, including weather forecasts. We use state-of-the-art reinforcement learning (RL) algorithms to optimize this strategy. For comparison, we also synthesize simple parametric trading strategies and optimize them with an evolutionary algorithm. Results show that our RL-based strategy generates the highest market profits.
DGC-GNN: Leveraging Geometry and Color Cues for Visual Descriptor-Free 2D-3D Matching

Matching 2D keypoints in an image to a sparse 3D point cloud of the scene without requiring visual descriptors has garnered increased interest due to its low memory requirements, inherent privacy preservation, and reduced need for expensive 3D model maintenance compared to visual descriptor-based methods. However, existing algorithms often compromise on performance, resulting in a significant deterioration compared to their descriptor-based counterparts. In this paper, we introduce DGC-GNN, a novel algorithm that employs a global-to-local Graph Neural Network (GNN) that progressively exploits geometric and color cues to represent keypoints, thereby improving matching accuracy. Our procedure encodes both Euclidean and angular relations at a coarse level, forming the geometric embedding to guide the point matching. We evaluate DGC-GNN on both indoor and outdoor datasets, demonstrating that it not only doubles the accuracy of the state-of-the-art visual descriptor-free algorithm but also substantially narrows the performance gap between descriptor-based and descriptor-free methods.
Siamese SIREN: Audio Compression with Implicit Neural Representations

Implicit Neural Representations (INRs) have emerged as a promising method for representing diverse data modalities, including 3D shapes, images, and audio. While recent research has demonstrated successful applications of INRs in image and 3D shape compression, their potential for audio compression remains largely unexplored. Motivated by this, we present a preliminary investigation into the use of INRs for audio compression. Our study introduces Siamese SIREN, a novel approach based on the popular SIREN architecture. Our experimental results indicate that Siamese SIREN achieves superior audio reconstruction fidelity while utilizing fewer network parameters compared to previous INR architectures.
On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling

Graph Neural Networks (GNNs) have been successfully applied to graph-level tasks in various fields such as biology, social networks, computer vision, and natural language processing. For the graph-level representations learning of GNNs, graph pooling plays an essential role. Among many pooling techniques, node drop pooling has garnered significant attention and is considered as a leading approach. However, existing node drop pooling methods, which typically retain the top-k nodes based on their significance scores, often overlook the diversity inherent in node features and graph structures. This limitation leads to suboptimal graph-level representations. To overcome this, we introduce a groundbreaking plug-and-play score scheme, termed MID. MID comprises a Multidimensional score space and two key operations: flIpscore and Dropscore. The multidimensional score space depicts the significance of nodes by multiple criteria; the flipscore process promotes the preservation of distinct node features; the dropscore compels the model to take into account a range of graph structures rather than focusing on local structures. To evaluate the effectiveness of our proposed MID, we have conducted extensive experiments by integrating it with a broad range of recent node drop pooling methods, such as TopKPool, SAGPool, GSAPool, and ASAP. In particular, MID has proven to bring a significant average improvement of approximately 2.8% over the four aforementioned methods when tested on 17 real-world graph classification datasets. Code is available at https://github.com/whuchuang/mid.
Ask the machine: systematic detection of wind-type outflows in low-mass X-ray binaries

The systematic discovery of outflows in the optical spectra of low-mass X-ray binaries opened a new avenue for the study of the outburst evolution in these extreme systems. However, the efficient detection of such features in a continuously growing data base requires the development of new analysis techniques with a particular focus on scalability, adaptability, and automatization. In this pilot study, we explore the use of machine learning algorithms to perform the identification of outflows in spectral line profiles observed in the optical range. We train and test the classifier on a simulated data base constructed through a combination of disc emission line profiles and outflow signatures, emulating typical observations of low-mass X-ray binaries. The final, trained classifier is applied to two sets of spectra taken during two bright outbursts that were particularly well covered, those of V404 Cyg (2015) and MAXI J1820+070 (2018). The resulting classification gained by this novel approach is overall consistent with that obtained through traditional techniques, while simultaneously providing a number of key advantages over the latter, including the access to low-velocity outflows. This study sets the foundations for future studies on large samples of spectra from low-mass X-ray binaries and other compact binaries.
Decidable Exponentials in Nonassociative Noncommutative Linear Logic

The use of exponentials in linear logic greatly enhances its expressive power. In this paper we focus on nonassociative noncommutative multiplicative linear logic, and systematically explore modal axioms K, T, and 4 as well as the structural rules of contraction and weakening. We give sequent systems for each subset of these axioms; these enjoy cut elimination and have analogues in more structural logics. We then appeal to work of Bulinska extending work of Buszkowski to show that several of these logics are PTIME decidable and generate context free languages as categorial grammars. This contrasts associative systems where similar logics are known to generate all recursively enumerable languages, and are thus in particular undecidable.
Impacts and Risk of Generative AI Technology on Cyber Defense

Generative Artificial Intelligence (GenAI) has emerged as a powerful technology capable of autonomously producing highly realistic content in various domains, such as text, images, audio, and videos. With its potential for positive applications in creative arts, content generation, virtual assistants, and data synthesis, GenAI has garnered significant attention and adoption. However, the increasing adoption of GenAI raises concerns about its potential misuse for crafting convincing phishing emails, generating disinformation through deepfake videos, and spreading misinformation via authentic-looking social media posts, posing a new set of challenges and risks in the realm of cybersecurity. To combat the threats posed by GenAI, we propose leveraging the Cyber Kill Chain (CKC) to understand the lifecycle of cyberattacks, as a foundational model for cyber defense. This paper aims to provide a comprehensive analysis of the risk areas introduced by the offensive use of GenAI techniques in each phase of the CKC framework. We also analyze the strategies employed by threat actors and examine their utilization throughout different phases of the CKC, highlighting the implications for cyber defense. Additionally, we propose GenAI-enabled defense strategies that are both attack-aware and adaptive. These strategies encompass various techniques such as detection, deception, and adversarial training, among others, aiming to effectively mitigate the risks posed by GenAI-induced cyber threats.
Generative Multimodal Entity Linking

Multimodal Entity Linking (MEL) is the task of mapping mentions with multimodal contexts to the referent entities from a knowledge base. Existing MEL methods mainly focus on designing complex multimodal interaction mechanisms and require fine-tuning all model parameters, which can be prohibitively costly and difficult to scale in the era of Large Language Models (LLMs). In this work, we propose GEMEL, a Generative Multimodal Entity Linking framework based on LLMs, which directly generates target entity names. We keep the vision and language model frozen and only train a feature mapper to enable cross-modality interactions. To adapt LLMs to the MEL task, we leverage the in-context learning capability of LLMs by retrieving multimodal instances as demonstrations. Extensive experiments show that, with only ~0.3% of the model parameters fine-tuned, GEMEL achieves state-of-the-art results on two well-established MEL datasets (7.7% accuracy gains on WikiDiverse and 8.8% accuracy gains on WikiMEL). The performance gain stems from mitigating the popularity bias of LLM predictions and disambiguating less common entities effectively. Further analysis verifies the generality and scalability of GEMEL. Our framework is compatible with any off-the-shelf language model, paving the way towards an efficient and general solution for utilizing LLMs in the MEL task. Our code is available at https://github.com/HITsz-TMG/GEMEL.
The twist-3 gluon contribution to Sivers asymmetry in $J/\psi$ production in semi-inclusive deep inelastic scattering

We carry out the first calculation for the twist-3 gluon contribution to the single transverse-spin asymmetry(SSA) in $J/\psi$ production in semi-inclusive deep inelastic scattering. Our result shows that the $J/\psi$ SSA is an ideal observable to pin down the $C$-even type twist-3 gluon distribution that has a direct relationship with the gluon transverse-momentum-dependent distribution function. We also perform some numerical simulations of the $J/\psi$ SSA for the kinematics accessible at the future electron-ion-collider experiment. For color-singlet contribution, the hadronization effect of $J/\psi$ is completely canceled at the level of the SSA and the spin-dependent structure functions directly reflect the behavior of the $C$-even twist-3 gluon distribution.
Memristive Reservoirs Learn to Learn

Memristive reservoirs draw inspiration from a novel class of neuromorphic hardware known as nanowire networks. These systems display emergent brain-like dynamics, with optimal performance demonstrated at dynamical phase transitions. In these networks, a limited number of electrodes are available to modulate system dynamics, in contrast to the global controllability offered by neuromorphic hardware through random access memories. We demonstrate that the learn-to-learn framework can effectively address this challenge in the context of optimization. Using the framework, we successfully identify the optimal hyperparameters for the reservoir. This finding aligns with previous research, which suggests that the optimal performance of a memristive reservoir occurs at the 'edge of formation' of a conductive pathway. Furthermore, our results show that these systems can mimic membrane potential behavior observed in spiking neurons, and may serve as an interface between spike-based and continuous processes.
First-principles study of point defects in chalcopyrite ZnSnP 2

Chalcopyrite ${\mathrm{ZnSnP}}_{2}$ is an alternative photoabsorber material for solar cells because of its controllable band gap, high absorption coefficient, and earth abundant constituents. In this study we systematically investigate its native point defects including vacancies, interstitials, and antisites using first-principles calculations with the Heyd-Scuseria-Ernzerhof hybrid functional. We evaluate the defect formation energies and defect single-particle levels at the dilute limit using finite-size image-charge corrections and compare them with those reported for ${\mathrm{CuInSe}}_{2}$ and ${\mathrm{CuGaSe}}_{2}$. The most likely donors and acceptors are cation antisites, Sn-on-Zn and Zn-on-Sn, respectively. Because of their significantly low formation energies, they lead to Fermi level pinning in the band gap under any growth condition, and constrain the carrier concentration. The Sn-on-Zn antisite in the neutral charge state becomes an intrinsic DX center, a complex of the Sn interstitial and Zn vacancy, and shows a deep donor level as reported for ${\mathrm{CuGaSe}}_{2}$.
Quadratic forms and quaternion algebras: algorithms and arithmetic

This thesis comes in two parts which can be read independently of one another. In the first part, we prove a result concerning representation of primes by quadratic forms. Jagy and Kaplansky exhibited a table of 68 pairs of positive definite binary quadratic forms that represent the same odd primes and conjectured that their list is complete outside of “trivial” pairs. We confirm their conjecture, and in fact find all pairs of such forms that represent the same primes outside of a finite set. In the second part, we investigate a constellation of results concerning algorithms for quaternion algebras and their application to Shimura curves. Let A be a quaternion algebra over a number field F. We discuss the computational complexity and, in many cases, give effective algorithms to solve the following problems: (1) Determine if A ≅ M2(F), and if so, exhibit an isomorphism; (2) Find a maximal order O⊂A ; and (3) Determine if a right ideal I⊂O is principal, and if so, exhibit a generator ξ. We then present fast methods for computing the value of hypergeometric series to large precision. Putting these together, we are able to compute special values of the map j : Γ \ H→P1C for Γ a compact triangle group, which we may recognize as putative algebraic numbers by also computing their Galois conjugates. We apply this to construct the canonical polynomial FN (x, y) for the curve X 0( N ) and to find nontorsion points on some elliptic curves over number fields.
Advancements in Research Mathematics through AI: A Framework for Conjecturing

In the words of the esteemed mathematician Paul Erd\"os, the mathematician's task is to \emph{prove and conjecture}. These two processes form the bedrock of all mathematical endeavours, and in the recent years, the mathematical community has increasingly sought the assistance of computers to bolster these tasks. This paper is a testament to that pursuit; it presents a robust framework enabling a computer to automatically generate conjectures - particularly those conjectures that mathematicians might deem substantial and elegant. More specifically, we outline our framework and provide evidence in the mathematical literature demonstrating its use in generating publishable research and surprising mathematics. We suspect our simple description of computer-assisted mathematical conjecturing will catalyze further research into this area and encourage the development of more advanced techniques than the ones presented herein.
RXFOOD: Plug-in RGB-X Fusion for Object of Interest Detection

The emergence of different sensors (Near-Infrared, Depth, etc.) is a remedy for the limited application scenarios of traditional RGB camera. The RGB-X tasks, which rely on RGB input and another type of data input to resolve specific problems, have become a popular research topic in multimedia. A crucial part in two-branch RGB-X deep neural networks is how to fuse information across modalities. Given the tremendous information inside RGB-X networks, previous works typically apply naive fusion (e.g., average or max fusion) or only focus on the feature fusion at the same scale(s). While in this paper, we propose a novel method called RXFOOD for the fusion of features across different scales within the same modality branch and from different modality branches simultaneously in a unified attention mechanism. An Energy Exchange Module is designed for the interaction of each feature map's energy matrix, who reflects the inter-relationship of different positions and different channels inside a feature map. The RXFOOD method can be easily incorporated to any dual-branch encoder-decoder network as a plug-in module, and help the original backbone network better focus on important positions and channels for object of interest detection. Experimental results on RGB-NIR salient object detection, RGB-D salient object detection, and RGBFrequency image manipulation detection demonstrate the clear effectiveness of the proposed RXFOOD.
Ajtai's Completeness Theorem for Nonstandard Finite Structures

Ajtai's generalization of Godel's completeness theorem is a tool that can be used to construct an extension of a given pseudo-nite structure into a model of a given theory. The existence of such model extensions is closely related to questions in complexity theory. In this paper we give a new proof of Ajtai's theorem using basic techniques of model theory.
Almost sure detection of the presence of malicious components in cyber-physical systems

This article studies a fundamental problem of security of cyber-physical systems (CPSs): that of detecting, almost surely, the presence of malicious components in the CPS. We assume that some of the actuators may be malicious while all sensors are honest. We introduce a novel idea of separability of state trajectories generated by CPSs in two situations: those under the nominal no-attack situation and those under the influence of an attacker. We establish its connection to security of CPSs in the context of detecting the presence of malicious actuators (if any) in them. As primary contributions we establish necessary and sufficient conditions for the aforementioned detection in CPSs modeled as Markov decision processes (MDPs). Moreover, we focus on the mechanism of perturbing the pre-determined control policies of the honest agents in CPSs modeled as stochastic linear systems, by injecting a certain class of random process called private excitation; sufficient conditions for detectability and non-detectability of the presence of malicious actuators assuming that the policies are randomized history dependent and randomized Markovian, are established. Several technical aspects of our results are discussed extensively.
Self‐Compression of Ultrahigh‐Peak‐Power Lasers

Pulse self‐compression can offer a more compact and efficient solution for improving the peak power of ultraintense laser pulses. By solving a modified nonlinear Schrödinger equation considering the fifth‐order susceptibility, it is found that self‐compression occurred even in a normally dispersive medium owing to the negative fifth‐order susceptibility inducing a mass of negative frequency chirp. Furthermore, negatively prechirped pulses enabled self‐compression at lower intensities, thus preventing damage to the medium. The optimal choices for prechirp, input intensity, and medium length are numerically analyzed. A proof‐of‐principle experiment confirmed the aforementioned theoretical findings. It is expected that petawatt or even exawatt laser pulses with 25 fs/15 fs transform‐limited pulse duration can be self‐compressed to ≈ 9.9 fs/7.6 fs in normally dispersive media, such as fused silica glass plate.