Miracle-dz/UniFAR-FTUs huggingface dataset:
A POMDP Model for Safe Geological Carbon Sequestration

Geological carbon capture and sequestration (CCS), where CO$_2$ is stored in subsurface formations, is a promising and scalable approach for reducing global emissions. However, if done incorrectly, it may lead to earthquakes and leakage of CO$_2$ back to the surface, harming both humans and the environment. These risks are exacerbated by the large amount of uncertainty in the structure of the storage formation. For these reasons, we propose that CCS operations be modeled as a partially observable Markov decision process (POMDP) and decisions be informed using automated planning algorithms. To this end, we develop a simplified model of CCS operations based on a 2D spillpoint analysis that retains many of the challenges and safety considerations of the real-world problem. We show how off-the-shelf POMDP solvers outperform expert baselines for safe CCS planning. This POMDP model can be used as a test bed to drive the development of novel decision-making algorithms for CCS operations.
An Edge Alignment-Based Orientation Selection Method for Neutron Tomography

Neutron computed tomography (nCT) is a 3D char-acterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences. A typical workflow involves placing the sample in the path of a neutron beam, acquiring projection data at a predefined set of orientations, and processing the resulting data using an analytic reconstruction algorithm. Typical nCT scans require hours to days to complete and are then processed using conventional filtered back-projection (FBP), which performs poorly with sparse views or noisy data. Hence, the main methods in order to reduce overall acquisition time are the use of an improved sampling strategy combined with the use of advanced reconstruction methods such as model-based iterative reconstruction (MBIR). In this paper, we propose an adaptive orientation selection method in which an MBIR reconstruction on previously-acquired measurements is used to define an objective function on orientations that balances a data-fitting term promoting edge alignment and a regularization term promoting orientation diversity. Using simulated and experimental data, we demonstrate that our method produces high-quality reconstructions using significantly fewer total measurements than the conventional approach.
A Light-weight, Effective and Efficient Model for Label Aggregation in Crowdsourcing

Due to the noises in crowdsourced labels, label aggregation (LA) has emerged as a standard procedure to post-process crowdsourced labels. LA methods estimate true labels from crowdsourced labels by modeling worker qualities. Most existing LA methods are iterative in nature. They need to traverse all the crowdsourced labels multiple times in order to jointly and iteratively update true labels and worker qualities until convergence. Consequently, these methods have high space and time complexities. In this paper, we treat LA as a dynamic system and model it as a Dynamic Bayesian network. From the dynamic model we derive two light-weight algorithms, LA\textsuperscript{onepass} and LA\textsuperscript{twopass}, which can effectively and efficiently estimate worker qualities and true labels by traversing all the labels at most twice. Due to the dynamic nature, the proposed algorithms can also estimate true labels online without re-visiting historical data. We theoretically prove the convergence property of the proposed algorithms, and bound the error of estimated worker qualities. We also analyze the space and time complexities of the proposed algorithms and show that they are equivalent to those of majority voting. Experiments conducted on 20 real-world datasets demonstrate that the proposed algorithms can effectively and efficiently aggregate labels in both offline and online settings even if they traverse all the labels at most twice.
An Augmented Reality Application and User Study for Understanding and Learning Spatial Transformation Matrices

Understanding spatial transformations and their mathematical representations are essential in computer-aided design, robotics, etc. This research has developed and tested an Augmented Reality (AR) application (BRICKxAR/T) to enhance students' learning of spatial transformation matrices. BRICKxAR/T leverages AR features, including information augmentation, physical-virtual object interplay, and embodied learning, to create a novel and effective visualization experience for learning. BRICKxAR T has been evaluated as a learning intervention using LEGO models as example physical and virtual manipulatives in a user study to assess students' learning gains. The study compared AR (N=29) vs. non-AR (N=30) learning workshops with pre- and post-tests on Purdue Visualization of Rotations Test and math questions. Students' math scores significantly improved after participating in both workshops with the AR workshop tending to show greater improvements. The post-workshop survey showed students were inclined to think BRICKxAR/T an interesting and useful application, and they spent more time learning in AR than non-AR.
Reflections on reflections

Analog Hawking radiation emitted by a perfectly reflecting mirror in (1+3)-dimensional flat spacetime is investigated. This is accomplished by studying the reflected frequency and momentum based on Einstein mirror, instead of the canonical way of solving, if possible, wave equations subjected to a dynamical Dirichlet boundary condition. In the case of a finite-size mirror, diffraction pattern appears in the radiation spectrum. Based on the relevant parameters in the proposed AnaBHEL experiment, where the Hawking temperature TH = 0.03 eV and the mirror area A = 0.1 mm2, the Hawking photon yield is estimated to be N = 16 per laser shot (assuming a high reflectivity mirror can be generated in the proposed AnaBHEL experiment).
Mutual Information-based Generalized Category Discovery

We introduce an information-maximization approach for the Generalized Category Discovery (GCD) problem. Speciﬁcally, we explore a parametric family of loss functions evaluating the mutual information between the features and the labels, and ﬁnd automatically the one that maximizes the predictive performances. Furthermore, we introduce the Elbow Maximum Centroid-Shift (EMaCS) technique, which estimates the number of classes in the unlabeled set. We report comprehensive experiments, which show that our mutual information-based approach (MIB) is both versatile and highly competitive under various GCD scenarios. The gap between the proposed approach and the existing methods is signiﬁcant, more so when dealing with ﬁne-grained classiﬁcation problems. Our code:
Unite and Conquer: Cross Dataset Multimodal Synthesis using Diffusion Models

Generating photos satisfying multiple constraints find broad utility in the content creation industry. A key hurdle to accomplishing this task is the need for paired data consisting of all modalities (i.e., constraints) and their corresponding output. Moreover, existing methods need retraining using paired data across all modalities to introduce a new condition. This paper proposes a solution to this problem based on denoising diffusion probabilistic models (DDPMs). Our motivation for choosing diffusion models over other generative models comes from the flexible internal structure of diffusion models. Since each sampling step in the DDPM follows a Gaussian distribution, we show that there exists a closed-form solution for generating an image given various constraints. Our method can unite multiple diffusion models trained on multiple sub-tasks and conquer the combined task through our proposed sampling strategy. We also introduce a novel reliability parameter that allows using different off-the-shelf diffusion models trained across various datasets during sampling time alone to guide it to the desired outcome satisfying multiple constraints. We perform experiments on various standard multimodal tasks to demonstrate the effectiveness of our approach. More details can be found in https://nithin-gk.github. io/projectpages/Multidiff/index.html
A generating-function approach to modelling complex contagion on clustered networks with multi-type branching processes

Understanding cascading processes on complex network topologies is paramount for modelling how diseases, information, fake news and other media spread. In this paper, we extend the multi-type branching process method developed in Keating et al., 2022, which relies on homogenous network properties, to a more general class of clustered networks. Using a model of socially-inspired complex contagion we obtain results, not just for the average behaviour of the cascades but for full distributions of the cascade properties. We introduce a new method for the inversion of probability generating functions to recover their underlying probability distributions; this derivation naturally extends to higher dimensions. This inversion technique is used along with the multi-type branching process to obtain univariate and bivariate distributions of cascade properties. Finally, using clique cover methods, we apply the methodology to synthetic and real-world networks and compare the theoretical distribution of cascade sizes with the results of extensive numerical simulations.
Consensus of hierarchical multi-agent systems with a time-varying set of active agents

Time-varying hierarchical multi-agent systems are common in many applications. A well-known solution to control these systems is to use state feedback controllers that depend on the adjacency matrix to reach consensus. This solution has been applied so far to multi-agent systems with fixed or time-varying communication topologies. In this paper, we consider single-integrator multi-agent systems where only a subset of the agents are active at any given time and the set of active agents is time-varying. This type of multi-agent system is relevant in applications such as modular multilevel converters and water pumping systems. We develop a switching algorithm that periodically selects a set of active agents through a set of graphs that connect the follower agents with a leader agent. We further prove that the developed switching algorithm combined with a classic consensus protocol yields convergence to a consensus state defined by the leader despite a time-varying set of active agents. The effectiveness of the switching algorithm is illustrated in two benchmark examples: a modular multilevel converter and a water pumping system.
CUNI Non-Autoregressive System for the WMT 22 Efficient Translation Shared Task

We present a non-autoregressive system submission to the WMT 22 Efficient Translation Shared Task. Our system was used by Helcl et al. (2022) in an attempt to provide fair comparison between non-autoregressive and autoregressive models. This submission is an effort to establish solid baselines along with sound evaluation methodology, particularly in terms of measuring the decoding speed. The model itself is a 12-layer Transformer model trained with connectionist temporal classification on knowledge-distilled dataset by a strong autoregressive teacher model.
Word Alignment in the Era of Deep Learning: A Tutorial

The word alignment task, despite its prominence in the era of statistical machine translation (SMT), is niche and under-explored today. In this two-part tutorial, we argue for the continued relevance for word alignment. The first part provides a historical background to word alignment as a core component of the traditional SMT pipeline. We zero-in on GIZA++, an unsupervised, statistical word aligner with surprising longevity. Jumping forward to the era of neural machine translation (NMT), we show how insights from word alignment inspired the attention mechanism fundamental to present-day NMT. The second part shifts to a survey approach. We cover neural word aligners, showing the slow but steady progress towards surpassing GIZA++ performance. Finally, we cover the present-day applications of word alignment, from cross-lingual annotation projection, to improving translation.
Are you using test log-likelihood correctly?

Test log-likelihood is commonly used to compare different models of the same data or different approximate inference algorithms for fitting the same probabilistic model. We present simple examples demonstrating how comparisons based on test log-likelihood can contradict comparisons according to other objectives. Specifically, our examples show that (i) approximate Bayesian inference algorithms that attain higher test log-likelihoods need not also yield more accurate posterior approximations and (ii) conclusions about forecast accuracy based on test log-likelihood comparisons may not agree with conclusions based on root mean squared error.
Improved sparse Bayesian learning method for direction-of-arrival estimation in non-uniform noise

The estimation of direction-of-arrival (DOA) in the presence of non-uniform noise in array signal processing is investigated in this study. The noise covariance matrix is regarded as an arbitrary diagonal matrix in the estimation. The spatial sparsity of the incident signals in different numbers of snapshots is introduced. The signal power spectrum and noise covariance matrix are then estimated through the improved sparse Bayesian learning (SBL) method. Finally, a high-precision DOA estimation of the incident signals is achieved. The proposed method can be viewed as a further expansion of the SBL-based DOA estimator. Computer simulations show the validity of the proposed method.
Motion Informed Object Detection of Small Insects in Time-lapse Camera Recordings

Insects as pollinators play a crucial role in ecosystem management and world food production. However, insect populations are declining, calling for efficient methods of insect monitoring. Existing methods analyze video or time-lapse images of insects in nature, but the analysis is challenging since insects are small objects in complex and dynamic scenes of natural vegetation. In this work, we provide a dataset of primary honeybees visiting three different plant species during two months of the summer period. The dataset consists of 107,387 annotated time-lapse images from multiple cameras, including 9,423 annotated insects. We present a method pipeline for detecting insects in time-lapse RGB images. The pipeline consists of a two-step process. Firstly, the time-lapse RGB images are preprocessed to enhance insects in the images. This Motion-Informed-Enhancement technique uses motion and colors to enhance insects in images. Secondly, the enhanced images are subsequently fed into a Convolutional Neural network (CNN) object detector. The method improves the deep learning object detectors You Only Look Once (YOLO) and Faster Region-based CNN (Faster R-CNN). Using Motion-Informed-Enhancement, the YOLO-detector improves the average micro F1-score from 0.49 to 0.71, and the Faster R-CNN-detector improves the average micro F1-score from 0.32 to 0.56 on the dataset. Our dataset and proposed method provide a step forward to automate the time-lapse camera monitoring of flying insects. The dataset is published on: https://vision.eng.au.dk/mie/
Regularization with Fake Features

Recent successes of massively overparameterized models have inspired a new line of work investigating the underlying conditions that enable overparameterized models to generalize well. This paper considers a framework where the possibly overparametrized model includes fake features, i.e., features that are present in the model but not in the data. We present a non-asymptotic high-probability bound on the generalization error of the ridge regression problem under the model misspeciﬁcation of having fake features. Our high-probability results characterize the interplay between the implicit regularization provided by the fake features and the explicit regularization provided by the ridge parameter. We observe that fake features may improve the generalization error, even though they are irrelevant to the data.
On the Effective Usage of Priors in RSS-based Localization

—In this paper, we study the localization problem in dense urban settings. In such environments, Global Navigation Satellite Systems fail to provide good accuracy due to low likelihood of line-of-sight (LOS) links between the receiver (Rx) to be located and the satellites, due to the presence of obstacles like the buildings. Thus, one has to resort to other technologies, which can reliably operate under non-line-of-sight (NLOS) conditions. Recently, we proposed a Received Signal Strength (RSS) ﬁngerprint and convolutional neural network- based algorithm, LocUNet , and demonstrated its state-of-the-art localization performance with respect to the widely adopted k- nearest neighbors (kNN) algorithm, and to state-of-the-art time of arrival (ToA) ranging-based methods. In the current work, we ﬁrst recognize LocUNet’s ability to learn the underlying prior distribution of the Rx position or Rx and transmitter (Tx) association preferences from the training data, and attribute its high performance to these. Conversely, we demonstrate that classical methods based on probabilistic approach, can greatly beneﬁt from an appropriate incorporation of such prior infor- mation. Our studies also numerically prove LocUNet’s close to optimal performance in many settings, by comparing it with the theoretically optimal formulations.
Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Study

We present the results of a 12-week longitudinal user study wherein the participants, 110 subjects from Southern Europe, received on a daily basis Electronic Music (EM) diversified recommendations. By analyzing their explicit and implicit feedback, we show that exposure to specific levels of music recommendation diversity may be responsible for long-term impacts on listeners’ attitudes. In particular, we highlight the function of diversity in increasing the openness in listening to EM, a music genre not particularly known or liked by the participants previous to their participation in the study. Moreover, we demonstrate that recommendations may help listeners in removing positive and negative attachments towards EM, deconstructing pre-existing implicit associations but also stereotypes associated with this music. In addition, our results show the significant influence that recommendation diversity has in generating curiosity in listeners.
Near Sample-Optimal Reduction-based Policy Learning for Average Reward MDP

This work considers the sample complexity of obtaining an $\varepsilon$-optimal policy in an average reward Markov Decision Process (AMDP), given access to a generative model (simulator). When the ground-truth MDP is weakly communicating, we prove an upper bound of $\widetilde O(H \varepsilon^{-3} \ln \frac{1}{\delta})$ samples per state-action pair, where $H := sp(h^*)$ is the span of bias of any optimal policy, $\varepsilon$ is the accuracy and $\delta$ is the failure probability. This bound improves the best-known mixing-time-based approaches in [Jin&Sidford 2021], which assume the mixing-time of every deterministic policy is bounded. The core of our analysis is a proper reduction bound from AMDP problems to discounted MDP (DMDP) problems, which may be of independent interests since it allows the application of DMDP algorithms for AMDP in other settings. We complement our upper bound by proving a minimax lower bound of $\Omega(|\mathcal S| |\mathcal A| H \varepsilon^{-2} \ln \frac{1}{\delta})$ total samples, showing that a linear dependent on $H$ is necessary and that our upper bound matches the lower bound in all parameters of $(|\mathcal S|, |\mathcal A|, H, \ln \frac{1}{\delta})$ up to some logarithmic factors.
Open Relation and Event Type Discovery with Type Abstraction

Conventional “closed-world” information extraction (IE) approaches rely on human ontologies to define the scope for extraction. As a result, such approaches fall short when applied to new domains. This calls for systems that can automatically infer new types from given corpora, a task which we refer to as type discovery.To tackle this problem, we introduce the idea of type abstraction, where the model is prompted to generalize and name the type. Then we use the similarity between inferred names to induce clusters. Observing that this abstraction-based representation is often complementary to the entity/trigger token representation, we set up these two representations as two views and design our model as a co-training framework. Our experiments on multiple relation extraction and event extraction datasets consistently show the advantage of our type abstraction approach.
Modeling Complex Dialogue Mappings via Sentence Semantic Segmentation Guided Conditional Variational Auto-Encoder

Complex dialogue mappings (CDM), including one-to-many and many-to-one mappings, tend to make dialogue models generate incoherent or dull responses, and modeling these mappings remains a huge challenge for neural dialogue systems. To alleviate these problems, methods like introducing external information, reconstructing the optimization function, and manipulating data samples are proposed, while they primarily focus on avoiding training with CDM, inevitably weakening the model's ability of understanding CDM in human conversations and limiting further improvements in model performance. This paper proposes a Sentence Semantic \textbf{Seg}mentation guided \textbf{C}onditional \textbf{V}ariational \textbf{A}uto-\textbf{E}ncoder (SegCVAE) method which can model and take advantages of the CDM data. Specifically, to tackle the incoherent problem caused by one-to-many, SegCVAE uses response-related prominent semantics to constrained the latent variable. To mitigate the non-diverse problem brought by many-to-one, SegCVAE segments multiple prominent semantics to enrich the latent variables. Three novel components, Internal Separation, External Guidance, and Semantic Norms, are proposed to achieve SegCVAE. On dialogue generation tasks, both the automatic and human evaluation results show that SegCVAE achieves new state-of-the-art performance.
Research on the application of contrastive learning in multi-label text classification

The eﬀective application of contrastive learning technology in natural language processing tasks shows the superiority of contrastive learning in text analysis tasks. How to construct positive and negative samples correctly and reasonably is the core challenge of contrastive learning. Since it is diﬃcult to construct contrastive objects in multi-label multi-classiﬁcation tasks, there are few contrastive losses for multi-label multi-classiﬁcation text classiﬁcation. In this paper, we propose ﬁve contrastive losses for multi-label multi-classiﬁcation tasks. They are Strict Contrastive Loss (SCL), Intra-label Contrastive Loss (ICL), Jaccard Similarity Contrastive Loss (JSCL), and Jaccard Similarity Probability Contrastive Loss (JSPCL) and Stepwise Label Contrastive Loss (SLCL). We explore the eﬀectiveness of contrastive learning for multi-label multi-classiﬁcation tasks under different strategies, and provide a set of baseline methods for contrastive learning techniques on multi-label classiﬁcation tasks. We also perform an interpretability analysis of our approach to show how diﬀerent contrastive learning meth-ods play their roles. The experimental results in this paper demonstrate that our proposed contrastive losses can bring some improvement for multi-label multi-classiﬁcation
Auxiliary Learning as a step towards Artificial General Intelligence

Auxiliary Learning is a machine learning approach in which the model acknowledges the existence of objects that do not come under any of its learned categories.The name “Auxiliary learning” was chosen due to the introduction of an auxiliary class. The paper focuses on increasing the generality of existing narrow purpose neural networks and also highlights the need to handle unknown objects. The Cat & Dog binary classiﬁer is taken as an example throughout the paper