Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Line Emission from Photodissociation Regions

We present a parameter study for the optical, low-ionization [O I] λ6300, λ6363, and λ5577 and the [S II] λ6731 and λ6716 lines emitted by photodissociation regions (PDRs). We assume in these models that the PDR has a plane-parallel, semi-infinite geometry and is illuminated from one side. The PDR models are characterized by the total hydrogen density 104 cm-3 ≤ n ≤ 107 cm-3 and the strength of the FUV field G0 in terms of the Habing field 103 < G0 < 107. Oxygen and singly ionized sulfur are excited thermally in the outer hot PDR layers due to collisions with electrons and atomic hydrogen. [O I] photons are also produced in the photodissociation of OH. We apply the results of the parameter study to the protoplanetary disks in Orion, layers between H II regions and molecular clouds, low-ionization nuclear emission-line regions, T Tauri stars, and planetary nebulae. We discuss the extent to which PDRs dominate or contribute to the observed [O I] and [S II] emission in these different objects.
Voter Coalitions and democracy in Decentralized Finance: Evidence from MakerDAO

Decentralized Autonomous Organization (DAO) provides a decentralized governance solution through blockchain, where decision-making process relies on on-chain voting and follows majority rule. This paper focuses on MakerDAO, and we find three voter coalitions after applying clustering algorithm to voting history. The emergence of a dominant voter coalition is a signal of governance centralization in DAO, and voter coalitions have complicated influence on Maker protocol, which is governed by MakerDAO. This paper presents empirical evidence of multicoalition democracy in DAO and further contributes to the contemporary debate on whether decentralized governance is possible.
Imbalanced Semi-supervised Learning with Bias Adaptive Classifier

Pseudo-labeling has proven to be a promising semi-supervised learning (SSL) paradigm. Existing pseudo-labeling methods commonly assume that the class distributions of training data are balanced. However, such an assumption is far from realistic scenarios and thus severely limits the performance of current pseudo-labeling methods under the context of class-imbalance. To alleviate this problem, we design a bias adaptive classifier that targets the imbalanced SSL setups. The core idea is to automatically assimilate the training bias caused by class imbalance via the bias adaptive classifier, which is composed of a novel bias attractor and the original linear classifier. The bias attractor is designed as a light-weight residual network and optimized through a bi-level learning framework. Such a learning strategy enables the bias adaptive classifier to fit imbalanced training data, while the linear classifier can provide unbiased label prediction for each class. We conduct extensive experiments under various imbalanced semi-supervised setups, and the results demonstrate that our method can be applied to different pseudo-labeling models and is superior to current state-of-the-art methods.
Synthetic Misinformers: Generating and Combating Multimodal Misinformation

With the expansion of social media and the increasing dissemination of multimedia content, the spread of misinformation has become a major concern. This necessitates effective strategies for multimodal misinformation detection (MMD) that detect whether the combination of an image and its accompanying text could mislead or misinform. Due to the data-intensive nature of deep neural networks and the labor-intensive process of manual annotation, researchers have been exploring various methods for automatically generating synthetic multimodal misinformation - which we refer to as Synthetic Misinformers - in order to train MMD models. However, limited evaluation on real-world misinformation and a lack of comparisons with other Synthetic Misinformers makes difficult to assess progress in the field. To address this, we perform a comparative study on existing and new Synthetic Misinformers that involves (1) out-of-context (OOC) image-caption pairs, (2) cross-modal named entity inconsistency (NEI) as well as (3) hybrid approaches and we evaluate them against real-world misinformation; using the COSMOS benchmark. The comparative study showed that our proposed CLIP-based Named Entity Swapping can lead to MMD models that surpass other OOC and NEI Misinformers in terms of multimodal accuracy and that hybrid approaches can lead to even higher detection accuracy. Nevertheless, after alleviating information leakage from the COSMOS evaluation protocol, low Sensitivity scores indicate that the task is significantly more challenging than previous studies suggested. Finally, our findings showed that NEI-based Synthetic Misinformers tend to suffer from a unimodal bias, where text-only models can outperform multimodal ones.
Optimal transfer protocol by incremental layer defrosting

Transfer learning is a powerful tool enabling model training with limited amounts of data. This technique is particularly useful in real-world problems where data availability is often a serious limitation. The simplest transfer learning protocol is based on ``freezing"the feature-extractor layers of a network pre-trained on a data-rich source task, and then adapting only the last layers to a data-poor target task. This workflow is based on the assumption that the feature maps of the pre-trained model are qualitatively similar to the ones that would have been learned with enough data on the target task. In this work, we show that this protocol is often sub-optimal, and the largest performance gain may be achieved when smaller portions of the pre-trained network are kept frozen. In particular, we make use of a controlled framework to identify the optimal transfer depth, which turns out to depend non-trivially on the amount of available training data and on the degree of source-target task correlation. We then characterize transfer optimality by analyzing the internal representations of two networks trained from scratch on the source and the target task through multiple established similarity measures.
Chip-scale, CMOS-compatible, high energy passively Q-switched laser

Chip-scale, high-energy optical pulse generation is becoming increasingly important as we expand activities into hard to reach areas such as space and deep ocean. Q-switching of the laser cavity is the best known technique for generating high-energy pulses, and typically such systems are in the realm of large bench-top solid-state lasers and fiber lasers, especially in the long wavelength range>1.8 um, thanks to their large energy storage capacity. However, in integrated photonics, the very property of tight mode confinement, that enables a small form factor, becomes an impediment to high energy application due to small optical mode cross-section. In this work, we demonstrate complementary metal-oxide-semiconductor (CMOS) compatible, rare-earth gain based large mode area (LMA) passively Q-switched laser in a compact footprint. We demonstrate high on-chip output pulse energy of>150 nJ in single transverse fundamental mode in the eye-safe window (1.9 um), with a slope efficiency ~ 40% in a footprint of ~9 mm2. The high energy pulse generation demonstrated in this work is comparable or in many cases exceeds Q-switched fiber lasers. This bodes well for field applications in medicine and space.
Some unlikely intersections beyond André–Oort

Abstract According to the André–Oort conjecture, an algebraic curve in Y (1)n that is not equal to a special subvariety contains only finitely many points which correspond to ann-tuple of elliptic curves with complex multiplication. Pink’s conjecture generalizes the André–Oort conjecture to the extent that if the curve is not contained in a special subvariety of positive codimension, then it is expected to meet the union of all special subvarieties of codimension two in only finitely many points. We prove this for a large class of curves in Y (1)n. When restricting to special subvarieties of codimension two that are not strongly special we obtain finiteness for all curves defined over $\overline {\mathbb Q}$. Finally, we formulate and prove a variant of the Mordell–Lang conjecture for subvarieties of Y (1)n.
Preference Transformer: Modeling Human Preferences using Transformers for RL

Preference-based reinforcement learning (RL) provides a framework to train agents using human preferences between two behaviors. However, preference-based RL has been challenging to scale since it requires a large amount of human feedback to learn a reward function aligned with human intent. In this paper, we present Preference Transformer, a neural architecture that models human preferences using transformers. Unlike prior approaches assuming human judgment is based on the Markovian rewards which contribute to the decision equally, we introduce a new preference model based on the weighted sum of non-Markovian rewards. We then design the proposed preference model using a transformer architecture that stacks causal and bidirectional self-attention layers. We demonstrate that Preference Transformer can solve a variety of control tasks using real human preferences, while prior approaches fail to work. We also show that Preference Transformer can induce a well-specified reward and attend to critical events in the trajectory by automatically capturing the temporal dependencies in human decision-making. Code is available on the project website: https://sites.google. com/view/preference-transformer.
CTRLStruct: Dialogue Structure Learning for Open-Domain Response Generation

Dialogue structure discovery is essential in dialogue generation. Well-structured topic flow can leverage background information and predict future topics to help generate controllable and explainable responses. However, most previous work focused on dialogue structure learning in task-oriented dialogue other than open-domain dialogue which is more complicated and challenging. In this paper, we present a new framework CTRLStruct for dialogue structure learning to effectively explore topic-level dialogue clusters as well as their transitions with unlabelled information. Precisely, dialogue utterances encoded by bi-directional Transformer are further trained through a special designed contrastive learning task to improve representation. Then we perform clustering to utterance-level representations and form topic-level clusters that can be considered as vertices in dialogue structure graph. The edges in the graph indicating transition probability between vertices are calculated by mimicking expert behavior in datasets. Finally, dialogue structure graph is integrated into dialogue model to perform controlled response generation. Experiments on two popular open-domain dialogue datasets show our model can generate more coherent responses compared to some excellent dialogue models, as well as outperform some typical sentence embedding methods in dialogue utterance representation. Code is available in GitHub1.
Interpretable System Identification and Long-term Prediction on Time-Series Data

Time-series prediction has drawn considerable attention during the past decades fueled by the emerging advances of deep learning methods. However, most neural network based methods lack interpretability and fail in extracting the hidden mechanism of the targeted physical system. To overcome these shortcomings, an interpretable sparse system identification method without any prior knowledge is proposed in this study. This method adopts the Fourier transform to reduces the irrelevant items in the dictionary matrix, instead of indiscriminate usage of polynomial functions in most system identification methods. It shows an interpretable system representation and greatly reduces computing cost. With the adoption of $l_1$ norm in regularizing the parameter matrix, a sparse description of the system model can be achieved. Moreover, Three data sets including the water conservancy data, global temperature data and financial data are used to test the performance of the proposed method. Although no prior knowledge was known about the physical background, experimental results show that our method can achieve long-term prediction regardless of the noise and incompleteness in the original data more accurately than the widely-used baseline data-driven methods. This study may provide some insight into time-series prediction investigations, and suggests that an white-box system identification method may extract the easily overlooked yet inherent periodical features and may beat neural-network based black-box methods on long-term prediction tasks.
A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks

The large number of antennas in massive MIMO systems allows the base station to communicate with multiple users at the same time and frequency resource with multi-user beamforming. However, highly correlated user channels could drastically impede the spectral efficiency that multi-user beamforming can achieve. As such, it is critical for the base station to schedule a suitable group of users in each time and frequency resource block to achieve maximum spectral efficiency while adhering to fairness constraints among the users. In this paper, we consider the resource scheduling problem for massive MIMO systems with its optimal solution known to be NP-hard. Inspired by recent achievements in deep reinforcement learning (DRL) to solve problems with large action sets, we propose SMART, a dynamic scheduler for massive MIMO based on the state-of-the-art Soft Actor-Critic (SAC) DRL model and the K-Nearest Neighbors (KNN) algorithm. Through comprehensive simulations using realistic massive MIMO channel models as well as real-world datasets from channel measurement experiments, we demonstrate the effectiveness of our proposed model in various channel conditions. Our results show that our proposed model performs very close to the optimal proportionally fair (Opt-PF) scheduler in terms of spectral efficiency and fairness with more than one order of magnitude lower computational complexity in medium network sizes where Opt-PF is computationally feasible. Our results also show the feasibility and high performance of our proposed scheduler in networks with a large number of users and resource blocks.
Survival modelling of smartphone trigger data for earthquake parameter estimation in early warning. With applications to 2023 Turkish-Syrian and 2019 Ridgecrest events

Crowdsourced smartphone-based earthquake early warning systems recently emerged as reliable alternatives to the more expensive solutions based on scientific-grade instruments. For instance, during the 2023 Turkish-Syrian deadly event, the system implemented by the Earthquake Network citizen science initiative provided a forewarning up to 25 seconds. We develop a statistical methodology based on a survival mixture cure model which provides full Bayesian inference on epicentre, depth and origin time, and we design an efficient tempering MCMC algorithm to address multi-modality of the posterior distribution. The methodology is applied to data collected by the Earthquake Network, including the 2023 Turkish-Syrian and 2019 Ridgecrest events.
Kullback-Leibler Divergence-Based Out-of-Distribution Detection With Flow-Based Generative Models

Recent research has revealed that deep generative models including flow-based models and Variational Autoencoders may assign higher likelihoods to out-of-distribution (OOD) data than in-distribution (ID) data. However, we cannot sample OOD data from the model. This counterintuitive phenomenon has not been satisfactorily explained and brings obstacles to OOD detection with flow-based models. In this article, we prove theorems to investigate the Kullback-Leibler divergence in flow-based model and give two explanations for the above phenomenon. Based on our theoretical analysis, we propose a new method KLODS to leverage KL divergence and local pixel dependence of representations to perform anomaly detection. Experimental results on prevalent benchmarks demonstrate the effectiveness and robustness of our method. For group anomaly detection, our method achieves 98.1% AUROC on average with a small batch size of 5. On the contrary, the baseline typicality test-based method only achieves 64.6% AUROC on average due to its failure on challenging problems. Our method also outperforms the state-of-the-art method by 9.1% AUROC. For point-wise anomaly detection, our method achieves 90.7% AUROC on average and outperforms the baseline by 5.2% AUROC. Besides, our method has the least notable failures and is the most robust one.
MuscleMap: Towards Video-based Activated Muscle Group Estimation

In this paper, we tackle the new task of video-based A ctivated M uscle G roup E stimation (AMGE) aiming at identifying active muscle regions during physical activity. To this intent, we provide the MuscleMap136 dataset featuring > 15 K video clips with 136 different activities and 20 labeled muscle groups. This dataset opens the vistas to multiple video-based applications in sports and rehabilitation medicine. We further complement the main MuscleMap136 dataset, which speciﬁcally targets physical exercise, with Muscle-UCF90 and Muscle-HMDB41, which are new variants of the well-known activity recognition benchmarks extended with AMGE annotations. To make the AMGE model applicable in real-life situations, it is crucial to ensure that the model can generalize well to types of physical activities not present during training and involving new combinations of activated muscles. To achieve this, our benchmark also covers an evaluation setting where the model is exposed to activity types excluded from the training set. Our experiments reveal that generalizability of existing architectures adapted for the AMGE task remains a challenge. Therefore, we also propose a new approach, T RANS M 3 E , which employs a transformer-based model with cross-modal multi-label knowledge distillation and surpasses all popular video classiﬁcation models when dealing with both, previously seen and new types of physical activities. 1
Investigating biomechanical determinants of endothelial permeability in a modified hollow fibre bioreactor

Effects of mechanical stress on the permeability of vascular endothelium are important to normal physiology and may be critical in the development of atherosclerosis, where they can account for the patchy arterial distribution of the disease. Such properties are frequently investigated in vitro. Here we evaluate and use the hollow fibre bioreactor for this purpose; in this system, endothelial cells form a confluent monolayer lining numerous plastic capillaries with porous walls, contained in a cartridge. The capillaries were perfused with a near-aortic waveform by an external pump, and permeability was assessed by the movement of rhodamine-labelled albumin from the intracapillary space to the extracapillary space. Confluence and quiescence of the cells was confirmed by electron microscopy and measurements of glucose consumption and permeability. The system was able to detect previously established influences on permeability: tracer transport was increased by acute application of shear stress and decreased by chronic shear stress compared to a static control, and was increased by thrombin or an NO synthase inhibitor under chronic shear. Increasing viscosity by addition of xanthan gum reduced permeability under both acute and chronic shear. Addition of damping chambers to reduce flow pulsatility increased permeability. Modifying the cartridge to allow chronic convection across the monolayer increased effective permeability more than could be explained the addition of convective transport alone, indicating that it caused an increase in permeability. The off-the-shelf hollow fibre bioreactor provides an excellent system for investigating the biomechanics of endothelial permeability and its potential is increased by simple modifications.
Frauds Bargain Attack: Generating Adversarial Text Samples via Word Manipulation Process

Recent research has revealed that natural language processing (NLP) models are vulnerable to adversarial examples. However, the current techniques for generating such examples rely on deterministic heuristic rules, which fail to produce optimal adversarial examples. In response, this study proposes a new method called the Fraud's Bargain Attack (FBA), which uses a randomization mechanism to expand the search space and produce high-quality adversarial examples with a higher probability of success. FBA uses the Metropolis-Hasting sampler, a type of Markov Chain Monte Carlo sampler, to improve the selection of adversarial examples from all candidates generated by a customized stochastic process called the Word Manipulation Process (WMP). The WMP method modifies individual words in a contextually-aware manner through insertion, removal, or substitution. Through extensive experiments, this study demonstrates that FBA outperforms other methods in terms of attack success rate, imperceptibility and sentence quality.
Correction to"Well-posedness of semilinear stochastic wave equations with H\"{o}lder continuous coefficients''

We prove that semilinear stochastic abstract wave equations, including wave and plate equations, are well-posed in the strong sense with an $\alpha$-H\"{o}lder continuous drift coefficient, if $\alpha \in (2/3,1)$. The uniqueness may fail for the corresponding deterministic PDE and well-posedness is restored by adding an external random forcing of white noise type. This shows a kind of regularization by noise for the semilinear wave equation. To prove the result we introduce an approach based on backward stochastic differential equations, differentiability along subspaces and control theoretic results. We stress that the well-posedness holds despite the Markov semigroup associated to the linear stochastic wave equation is not strong Feller.
Evolving Deep Neural Network by Customized Moth Flame Optimization Algorithm for Underwater Targets Recognition

This chapter proposes using the Moth Flame Optimization (MFO) algorithm for finetuning a Deep Neural Network to recognize different underwater sonar datasets. Same as other models evolved by metaheuristic algorithms, premature convergence, trapping in local minima, and failure to converge in a reasonable time are three defects MFO confronts in solving problems with high-dimension search space. Spiral flying is the key component of the MFO as it determines how the moths adjust their positions in relation to flames; thereby, the shape of spiral motions can regulate the transition behavior between the exploration and exploitation phases. Therefore, this chapter investigates the efficiency of seven spiral motions with different curvatures and slopes in the performance of the MFO, especially for underwater target classification tasks. To assess the performance of the customized model, in addition to benchmark Sejnowski&Gorman's dataset, two experimental sonar datasets, i.e., the passive sonar and active datasets, are exploited. The results of MFO and its modifications are compared to four novel nature-inspired algorithms, including Heap-Based Optimizer (HBO), Chimp Optimization Algorithm (ChOA), Ant Lion Optimization (ALO), Stochastic Fractals Search (SFS), as well as the classic Particle Swarm Optimization (PSO). The results confirm that the customized MFO shows better performance than the other state-of-the-art models so that the classification rates are increased 1.5979, 0.9985, and 2.0879 for Sejnowski&Gorman, passive, and active datasets, respectively. The results also approve that time complexity is not significantly increased by using different spiral motions.