Miracle-dz/UniFAR-FTUs huggingface dataset:
All solution grown epitaxial magnonic crystal of thulium iron garnet thin film

Magnonics has shown the immense potential of compatibility with CMOS devices and the ability to be utilized in futuristic quantum computing. Therefore, the magnonic crystals, both metallic and insulating, are under extensive exploration. The presence of high spin-orbit interaction induced by the presence of rare-earth elements in thulium iron garnet (TmIG) increases its potential in magnonic applications. Previously, TmIG thin films were grown using ultra-high vacuum-based techniques. Here, we present a cost-effective solution-based approach that enables the excellent quality interface and surface roughness of the epitaxial TmIG/GGG. The deposited TmIG (12.2 nm) thin film's physical and spin dynamic properties are investigated in detail. The confirmation of the epitaxy using X-ray diffraction in $\phi$-scan geometry along with the X-ray reflectivity and atomic force for the thickness and roughness analysis and topography, respectively. The epitaxial TmIG/GGG have confirmed the perpendicular magnetic anisotropy utilizing the polar-magneto-optic Kerr effect. Analyzing the ferromagnetic resonance study of TmIG/GGG thin films provides the anisotropy constant K$_U$ = 20.6$\times$10$^3$ $\pm$ 0.2$\times$10$^3$ N/m$^2$ and the Gilbert damping parameter $\alpha$ = 0.0216 $\pm$ 0.0028. The experimental findings suggest that the solution-processed TmIG/GGG thin films have the potential to be utilized in device applications.
Formalising the Double-Pushout Approach to Graph Transformation

In this paper, we utilize Isabelle/HOL to develop a formal framework for the basic theory of double-pushout graph transformation. Our work includes defining essential concepts like graphs, morphisms, pushouts, and pullbacks, and demonstrating their properties. We establish the uniqueness of derivations, drawing upon Rosens 1975 research, and verify the Church-Rosser theorem using Ehrigs and Kreowskis 1976 proof, thereby demonstrating the effectiveness of our formalisation approach. The paper details our methodology in employing Isabelle/HOL, including key design decisions that shaped the current iteration. We explore the technical complexities involved in applying higher-order logic, aiming to give readers an insightful perspective into the engaging aspects of working with an Interactive Theorem Prover. This work emphasizes the increasing importance of formal verification tools in clarifying complex mathematical concepts.
Comparison of Scapular Kinematics During Active Shoulder Horizontal Adduction Between Subjects With and Without Limited Range of Motion of Shoulder Horizontal Adduction

1) Background: Shoulder horizontal adduction (HA) is performed in many activities of daily living. The limited range of motion (LROM) of HA is affected by the tightness of the posterior deltoid, infraspinatus, teres major, and posterior capsule of glenohumeral joint. The LROM of shoulder HA contributes to excessive scapular abduction. Objects: The aim of this study is to compare the scapular abduction distance and three-dimensional displacement of the scapula during shoulder horizontal adduction between subjects with and without the LROM of shoulder HA. Methods: 24 subjects (12 people in LROM group and 12 people in normal ROM group) participated. Subjects with less than 115° of HA ROM were included in LROM group. Shoulder HA was performed 3 times for measuring scapular abduction distance and three-dimensional displacement of the scapula. Tape measure was used for measuring scapular abduction distance. Scapular abduction distance was normalized by dividing the scapular size. Polhemus Liberty was used for measuring the three-dimensional displacement of the scapula. Results: Normalized scapular abduction distance was significantly greater in LROM group than normal ROM group (p<.001). Three-dimensional displacement of the scapula during shoulder HA was greater in LROM group than normal ROM group (p<.05). Conclusion: LROM group had a greater scapular abduction and three-dimensional displacement of the scapula during shoulder HA compared to normal ROM group.
Learning-To-Rank Approach for Identifying Everyday Objects Using a Physical-World Search Engine

Domestic service robots offer a solution to the increasing demand for daily care and support. A human-in-the-loop approach that combines automation and operator intervention is considered to be a realistic approach to their use in society. Therefore, we focus on the task of retrieving target objects from open-vocabulary user instructions in a human-in-the-loop setting, which we define as the learning-to-rank physical objects (LTRPO) task. For example, given the instruction “Please go to the dining room which has a round table. Pick up the bottle on it,” the model is required to output a ranked list of target objects that the operator/user can select. In this letter, we propose MultiRankIt, which is a novel approach for the LTRPO task. MultiRankIt introduces the Crossmodal Noun Phrase Encoder to model the relationship between phrases that contain referring expressions and the target bounding box, and the Crossmodal Region Feature Encoder to model the relationship between the target object and multiple images of its surrounding contextual environment. Additionally, we built a new dataset for the LTRPO task that consists of instructions with complex referring expressions accompanied by real indoor environmental images that feature various target objects. We validated our model on the dataset and it outperformed the baseline method in terms of the mean reciprocal rank and recall@k. Furthermore, we conducted physical experiments in a setting where a domestic service robot retrieved everyday objects in a standardized domestic environment, based on users' instruction in a human–in–the–loop setting. The experimental results demonstrate that the success rate for object retrieval achieved 80%.
Exploiting the capacity of deep networks only at training stage for nonlinear black-box system identification

To benefit from the modeling capacity of deep models in system identification, without worrying about inference time, this study presents a novel training strategy that uses deep models only at the training stage. For this purpose two separate models with different structures and goals are employed. The first one is a deep generative model aiming at modeling the distribution of system output(s), called the teacher model, and the second one is a shallow basis function model, named the student model, fed by system input(s) to predict the system output(s). That means these isolated paths must reach the same ultimate target. As deep models show a great performance in modeling of highly nonlinear systems, aligning the representation space learned by these two models make the student model to inherit the approximation power of the teacher model. The proposed objective function consists of the objective of each student and teacher model adding up with a distance penalty between the learned latent representations. The simulation results on three nonlinear benchmarks show a comparative performance with examined deep architectures applied on the same benchmarks. Algorithmic transparency and structure efficiency are also achieved as byproducts.
Learning Multi-Step Manipulation Tasks from A Single Human Demonstration

Learning from human demonstrations has exhibited remarkable achievements in robot manipulation. However, the challenge remains to develop a robot system that matches human capabilities and data efficiency in learning and generalizability, particularly in complex, unstructured real-world scenarios. We propose a system that processes RGBD videos to translate human actions to robot primitives and identifies task-relevant key poses of objects using Grounded Segment Anything. We then address challenges for robots in replicating human actions, considering the human-robot differences in kinematics and collision geometry. To test the effectiveness of our system, we conducted experiments focusing on manual dishwashing. With a single human demonstration recorded in a mockup kitchen, the system achieved 50-100% success for each step and up to a 40% success rate for the whole task with different objects in a home kitchen. Videos are available at https://robot-dishwashing.github. io
Detection of microbial populations in petroleum hydrocarbon contaminated aquifers and isolation of denitrifying hydrocarbon degraders

The in situ bioremediation of aquifers contaminated with petroleum hydrocarbons is commonly based on the infiltration of groundwater supplemented with oxidants (e.g. 02, N03) and nutrients (e.g. NH4\ PO43). These additions stimulate the microbial activity in the aquifer and several field studies describing the resulting processes have been published. However, due to the heterogeneity of the subsurface and due to the limited number of observation wells usually available, these field data do not offer a sufficient spatial and temporal resolution. In this study, flow-through columns of 47 cm length equipped with 17 sampling ports were filled with homogeneously contaminated aquifer material from a diesel fuel contaminated in situ bioremediation site. The columns were operated over 96 days at 12°C with artificial groundwater supplemented with 02, NO3and PO43-. Concentration profiles of 02, N03-, N02-, dissolved inorganic and organic carbon (DIC and DOC, respectively), protein, microbial cells and total residual hydrocarbons were measured. Within the first 12 cm, corresponding to a mean groundwater residence time of < 3.6 hours, a steep 02 decrease from 4.6 to <0.3 mg l1, denitrification, a production of DIC and DOC, high microbial cell numbers and a high removal of hydrocarbons were observed. Within a distance of 24 to 40.5 cm from the infiltration, 02 was below 0.1 mg l1 and a denitrifying activity was found. In the presence and in 2
Open-Vocabulary Video Relation Extraction

A comprehensive understanding of videos is inseparable from describing the action with its contextual action-object interactions. However, many current video understanding tasks prioritize general action classification and overlook the actors and relationships that shape the nature of the action, resulting in a superficial understanding of the action. Motivated by this, we introduce Open-vocabulary Video Relation Extraction (OVRE), a novel task that views action understanding through the lens of action-centric relation triplets. OVRE focuses on pairwise relations that take part in the action and describes these relation triplets with natural languages. Moreover, we curate the Moments-OVRE dataset, which comprises 180K videos with action-centric relation triplets, sourced from a multi-label action classification dataset. With Moments-OVRE, we further propose a cross-modal mapping model to generate relation triplets as a sequence. Finally, we benchmark existing cross-modal generation models on the new task of OVRE. Our code and dataset are available at https://github.com/Iriya99/OVRE.
Fairness-Aware Structured Pruning in Transformers

The increasing size of large language models (LLMs) has introduced challenges in their training and inference. Removing model components is perceived as a solution to tackle the large model sizes, however, existing pruning methods solely focus on performance, without considering an essential aspect for the responsible use of LLMs: model fairness. It is crucial to address the fairness of LLMs towards diverse groups, such as women, Black people, LGBTQ+, Jewish communities, among others, as they are being deployed and available to a wide audience. In this work, first, we investigate how attention heads impact fairness and performance in pre-trained transformer-based language models. We then propose a novel method to prune the attention heads that negatively impact fairness while retaining the heads critical for performance, i.e. language modeling capabilities. Our approach is practical in terms of time and resources, as it does not require fine-tuning the final pruned, and fairer, model. Our findings demonstrate a reduction in gender bias by 19%, 19.5%, 39.5%, 34.7%, 23%, and 8% for DistilGPT-2, GPT-2, GPT-Neo of two different sizes, GPT-J, and Llama 2 models, respectively, in comparison to the biased model, with only a slight decrease in performance. WARNING: This work uses language that is offensive in nature.
MotifPiece: A Data-Driven Approach for Effective Motif Extraction and Molecular Representation Learning

Motif extraction is an important task in motif based molecular representation learning. Previously, machine learning approaches employing either rule-based or string-based techniques to extract motifs. Rule-based approaches may extract motifs that aren't frequent or prevalent within the molecular data, which can lead to an incomplete understanding of essential structural patterns in molecules. String-based methods often lose the topological information inherent in molecules. This can be a significant drawback because topology plays a vital role in defining the spatial arrangement and connectivity of atoms within a molecule, which can be critical for understanding its properties and behavior. In this paper, we develop a data-driven motif extraction technique known as MotifPiece, which employs statistical measures to define motifs. To comprehensively evaluate the effectiveness of MotifPiece, we introduce a heterogeneous learning module. Our model shows an improvement compared to previously reported models. Additionally, we demonstrate that its performance can be further enhanced in two ways: first, by incorporating more data to aid in generating a richer motif vocabulary, and second, by merging multiple datasets that share enough motifs, allowing for cross-dataset learning.
ZO-AdaMU Optimizer: Adapting Perturbation by the Momentum and Uncertainty in Zeroth-order Optimization

Lowering the memory requirement in full-parameter training on large models has become a hot research area. MeZO fine-tunes the large language models (LLMs) by just forward passes in a zeroth-order SGD optimizer (ZO-SGD), demonstrating excellent performance with the same GPU memory usage as inference. However, the simulated perturbation stochastic approximation for gradient estimate in MeZO leads to severe oscillations and incurs a substantial time overhead. Moreover, without momentum regularization, MeZO shows severe over-fitting problems. Lastly, the perturbation-irrelevant momentum on ZO-SGD does not improve the convergence rate. This study proposes ZO-AdaMU to resolve the above problems by adapting the simulated perturbation with momentum in its stochastic approximation. Unlike existing adaptive momentum methods, we relocate momentum on simulated perturbation in stochastic gradient approximation. Our convergence analysis and experiments prove this is a better way to improve convergence stability and rate in ZO-SGD. Extensive experiments demonstrate that ZO-AdaMU yields better generalization for LLMs fine-tuning across various NLP tasks than MeZO and its momentum variants.
Detection-based Intermediate Supervision for Visual Question Answering

Recently, neural module networks (NMNs) have yielded ongoing success in answering compositional visual questions, especially those involving multi-hop visual and logical reasoning. NMNs decompose the complex question into several sub-tasks using instance-modules from the reasoning paths of that question and then exploit intermediate supervisions to guide answer prediction, thereby improving inference interpretability. However, their performance may be hindered due to sketchy modeling of intermediate supervisions. For instance, (1) a prior assumption that each instance-module refers to only one grounded object yet overlooks other potentially associated grounded objects, impeding full cross-modal alignment learning; (2) IoU-based intermediate supervisions may introduce noise signals as the bounding box overlap issue might guide the model's focus towards irrelevant objects. To address these issues, a novel method, Detection-based Intermediate Supervision (DIS), is proposed, which adopts a generative detection framework to facilitate multiple grounding supervisions via sequence generation. As such, DIS offers more comprehensive and accurate intermediate supervisions, thereby boosting answer prediction performance. Furthermore, by considering intermediate results, DIS enhances the consistency in answering compositional questions and their sub-questions. Extensive experiments demonstrate the superiority of our proposed DIS, showcasing both improved accuracy and state-of-the-art reasoning consistency compared to prior approaches.
Efficient Asynchronous Federated Learning with Sparsification and Quantization

While data is distributed in multiple edge devices, federated learning (FL) is attracting more and more attention to collaboratively train a machine learning model without transferring raw data. FL generally exploits a parameter server and a large number of edge devices during the whole process of the model training, while several devices are selected in each round. However, straggler devices may slow down the training process or even make the system crash during training. Meanwhile, other idle edge devices remain unused. As the bandwidth between the devices and the server is relatively low, the communication of intermediate data becomes a bottleneck. In this article, we propose time‐efficient asynchronous federated learning with sparsification and quantization, that is, TEASQ‐Fed. TEASQ‐Fed can fully exploit edge devices to asynchronously participate in the training process by actively applying for tasks. We utilize control parameters to choose an appropriate number of parallel edge devices, which simultaneously execute the training tasks. In addition, we introduce a caching mechanism and weighted averaging with respect to model staleness to further improve the accuracy. Furthermore, we propose a sparsification and quantitation approach to compress the intermediate data to accelerate the training. The experimental results reveal that TEASQ‐Fed improves the accuracy (up to 16.67% higher) while accelerating the convergence of model training (up to twice faster).
Automatically Generating Metamorphic Relations via Genetic Programming

Metamorphic testing is a popular approach that aims to alleviate the oracle problem in software testing. At the core of this approach are Metamorphic Relations (MRs), specifying properties that hold among multiple test inputs and corresponding outputs. Deriving MRs is mostly a manual activity, since their automated generation is a challenging and largely unexplored problem. This paper presents GenMorph, a technique to automatically generate MRs for Java methods that involve inputs and outputs that are boolean, numerical, or ordered sequences. GenMorph uses an evolutionary algorithm to search for effective test oracles, i.e., oracles that trigger no false alarms and expose software faults in the method under test. The proposed search algorithm is guided by two fitness functions that measure the number of false alarms and the number of missed faults for the generated MRs. Our results show that GenMorph generates effective MRs for 18 out of 23 methods (mutation score>20%). Furthermore, it can increase Randoop's fault detection capability in 7 out of 23 methods, and Evosuite's in 14 out of 23 methods.
Stochastic mean-shift clustering

In this paper we presented a stochastic version mean-shift clustering algorithm. In the stochastic version the data points"climb"to the modes of the distribution collectively, while in the deterministic mean-shift, each datum"climbs"individually, while all other data points remains in their original coordinates. Stochastic version of the mean-shift clustering is comparison with a standard (deterministic) mean-shift clustering on synthesized 2- and 3-dimensional data distributed between several Gaussian component. The comparison performed in terms of cluster purity and class data purity. It was found the the stochastic mean-shift clustering outperformed in most of the cases the deterministic mean-shift.
Application of the Green function formalism to the interplay between avalanche and multiphoton ionization induced by optical pulses

A fundamental brick of light-matter interaction at large optical intensities is the generation of a plasma. The optically-induced plasma in turn plays a fundamental role in determining the optical propagation. The plasma generation is a result of the interplay between multi-photon, tunnel and avalanche ionization. Here we use the basic rate equations to discuss an analytical model for the interaction between these physical effects. After defining a nonlinear impulse response for the system, we describe how the interplay depends on the features of the optical pulses. Our approach strongly simplifies the modelling of the propagation of ultrashort-pulses, paving the way to a much easier and faster interpretation of experimental observations, with potential impact on the broad fields of ultrafast light-matter interaction and laser micro-machining.
Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It

Fake news detection models are critical to countering disinformation but can be manipulated through adversarial attacks. In this position paper, we analyze how an attacker can compromise the performance of an online learning detector on specific news content without being able to manipulate the original target news. In some contexts, such as social networks, where the attacker cannot exert complete control over all the information, this scenario can indeed be quite plausible. Therefore, we show how an attacker could potentially introduce poisoning data into the training data to manipulate the behavior of an online learning method. Our initial findings reveal varying susceptibility of logistic regression models based on complexity and attack type.
The Fano variety of lines on singular cyclic cubic fourfolds

We study the symplectic resolution of the Fano variety of lines on some singular cyclic cubic fourfolds, i.e. cubic fourfolds arising as cyclic 3:1 cover of $\mathbb{P}^4$ branched along a cubic threefold. In particular we are interested in the geometry of these varieties in the case of cyclic cubic fourfolds branched along a cubic threefold having one isolated singularity of type $A_i$ for $i=2,3,4$. On these symplectic resolutions we find a non-symplectic automorphism of order three induced by the covering automorphism.
Modality-Collaborative Transformer with Hybrid Feature Reconstruction for Robust Emotion Recognition

As a vital aspect of affective computing, Multimodal Emotion Recognition has been an active research area in the multimedia community. Despite recent progress, this field still confronts two major challenges in real-world applications: 1) improving the efficiency of constructing joint representations from unaligned multimodal features, and 2) relieving the performance decline caused by random modality feature missing. In this paper, we propose a unified framework, Modality-Collaborative Transformer with Hybrid Feature Reconstruction (MCT-HFR), to address these issues. The crucial component of MCT is a novel attention-based encoder which concurrently extracts and dynamically balances the intra- and inter-modality relations for all associated modalities. With additional modality-wise parameter sharing, a more compact representation can be encoded with less time and space complexity. To improve the robustness of MCT, we further introduce HFR which consists of two modules: Local Feature Imagination (LFI) and Global Feature Alignment (GFA). During model training, LFI leverages complete features as supervisory signals to recover local missing features, while GFA is designed to reduce the global semantic gap between pairwise complete and incomplete representations. Experimental evaluations on two popular benchmark datasets demonstrate that our proposed method consistently outperforms advanced baselines in both complete and incomplete data scenarios.