Miracle-dz/UniFAR-FTUs huggingface dataset:
Human labeling errors and their impact on ConvNets for satellite image scene classification

Convolutional neural networks (ConvNets) have been successfully applied to satellite image scene classification. Human-labeled training datasets are essential for ConvNets to perform accurate classification. Errors in human-labeled training datasets are unavoidable due to the complexity of satellite images. However, the distribution of human labeling errors on satellite images and their impact on ConvNets have not been investigated. To fill this research gap, this study, for the first time, collected real-world labels from 32 participants and explored how their errors affect three ConvNets (VGG16, GoogleNet and ResNet-50) for high-resolution satellite image scene classification. We found that: (1) human labeling errors have significant class and instance dependence, which is fundamentally different from the simulation noise in previous studies; (2) regarding the overall accuracy of all classes, when human labeling errors in training data increase by one unit, the overall accuracy of ConvNets classification decreases by approximately half a unit; (3) regarding the accuracy of each class, the impact of human labeling errors on ConvNets shows large heterogeneity across classes. To uncover the mechanism underlying the impact of human labeling errors on ConvNets, we further compared it with two types of simulated labeling noise: uniform noise (errors independent of both classes and instances) and class-dependent noise (errors independent of instances but not classes). Our results show that the impact of human labeling errors on ConvNets is similar to that of the simulated class-dependent noise but not to that of the simulated uniform noise, suggesting that the impact of human labeling errors on ConvNets is mainly due to class-dependent errors rather than instance-dependent errors.
Essential protein discovery method based on integration of PPI and gene expression data

A new method for identifying essential proteins based on the integration of PPI and gene expression data named PeC was proposed.For each edge of the network,its edge clustering coefficient(ECC)and Pearson correlation coefficient(PCC)were calculated.And then,the weight of each edge was computed based on ECC and PCC.Then,a protein's PeC value was defined as the sum of weights of the edges connected to it.The experimental results on the yeast protein interaction network show that PeC is obviously higher than other eight centrality measures(DC,BC,CC,SC,EC, IC,LAC and SoECC)in the prediction accuracy of essential proteins.Especially,for less than the top 10%proteins selected as the candidate essential proteins,the prediction accuracy of PeC has 20%higher than those of SC,CC and EC.
User Experience Considered Harmful (for the Planet)

Great user experience is killing us (more or less)! My argument in this provocation is that the excessive focus on user experience (UX) by the tech industry and academic community has a negative impact on the sustainability of ICT devices. I will argue based on two examples, that we need new metrics or extend current UX metrics to also include third order effects and sustainability perspectives. Lastly, I would like us - the (Sustainable) HCI community - to increase our focus on solving the problems that result from our very own creations.
Improving Metrics for Speech Translation

We introduce Parallel Paraphrasing ($\text{Para}_\text{both}$), an augmentation method for translation metrics making use of automatic paraphrasing of both the reference and hypothesis. This method counteracts the typically misleading results of speech translation metrics such as WER, CER, and BLEU if only a single reference is available. We introduce two new datasets explicitly created to measure the quality of metrics intended to be applied to Swiss German speech-to-text systems. Based on these datasets, we show that we are able to significantly improve the correlation with human quality perception if our method is applied to commonly used metrics.
Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning

Probabilistic logical rule learning has shown great strength in logical rule mining and knowledge graph completion. It learns logical rules to predict missing edges by reasoning on existing edges in the knowledge graph. However, previous efforts have largely been limited to only modeling chain-like Horn clauses such as $R_1(x,z)\land R_2(z,y)\Rightarrow H(x,y)$. This formulation overlooks additional contextual information from neighboring sub-graphs of entity variables $x$, $y$ and $z$. Intuitively, there is a large gap here, as local sub-graphs have been found to provide important information for knowledge graph completion. Inspired by these observations, we propose Logical Entity RePresentation (LERP) to encode contextual information of entities in the knowledge graph. A LERP is designed as a vector of probabilistic logical functions on the entity's neighboring sub-graph. It is an interpretable representation while allowing for differentiable optimization. We can then incorporate LERP into probabilistic logical rule learning to learn more expressive rules. Empirical results demonstrate that with LERP, our model outperforms other rule learning methods in knowledge graph completion and is comparable or even superior to state-of-the-art black-box methods. Moreover, we find that our model can discover a more expressive family of logical rules. LERP can also be further combined with embedding learning methods like TransE to make it more interpretable.
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages

Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) -- languages for which NLP re-search is particularly far behind in meeting user needs -- it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather than zero-shot; its focus on user-centric tasks -- tasks with broad adoption by speakers of high-resource languages; and its focus on under-represented languages where this scarce-data scenario tends to be most realistic. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks that are of general utility. We create new datasets for OCR, autocomplete, semantic parsing, and transliteration, and build on and refine existing datasets for other tasks. XTREME-UP provides methodology for evaluating many modeling scenarios including text-only, multi-modal (vision, audio, and text),supervised parameter tuning, and in-context learning. We evaluate commonly used models on the benchmark. We release all code and scripts to train and evaluate models
Z-ordered Range Refinement for Multi-dimensional Range Queries

The z-order curve is a space-filling curve and is now attracting the interest of developers because of its simple and useful features. In the case of key-value stores, because the z-order curve achieves multi-dimensional range queries in one-dimensional z-ordered space, its use has been proposed for both academic and industrial purposes. However, z-ordered range queries suffer from wasteful query regions due to the properties of the z-order curve. Although previous studies have proposed refining z-ordered ranges, doing so is computationally expensive. In this paper, we propose z-ordered range refinement based on jump-in/out algorithms, and then we approximate z-ordered query regions to achieve efficient range refinement. Because the proposed method is lightweight and pluggable, it can be applied to various databases. We implemented our approach using PL/pgSQL in PostgreSQL and evaluated the performance of range refinement and multi-dimensional range queries. The experimental results demonstrate the effectiveness and efficiency of the proposed method.
End-to-End Stable Imitation Learning via Autonomous Neural Dynamic Policies

State-of-the-art sensorimotor learning algorithms offer policies that can often produce unstable behaviors, damaging the robot and/or the environment. Traditional robot learning, on the contrary, relies on dynamical system-based policies that can be analyzed for stability/safety. Such policies, however, are neither flexible nor generic and usually work only with proprioceptive sensor states. In this work, we bridge the gap between generic neural network policies and dynamical system-based policies, and we introduce Autonomous Neural Dynamic Policies (ANDPs) that: (a) are based on autonomous dynamical systems, (b) always produce asymptotically stable behaviors, and (c) are more flexible than traditional stable dynamical system-based policies. ANDPs are fully differentiable, flexible generic-policies that can be used in imitation learning setups while ensuring asymptotic stability. In this paper, we explore the flexibility and capacity of ANDPs in several imitation learning tasks including experiments with image observations. The results show that ANDPs combine the benefits of both neural network-based and dynamical system-based methods.
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation

Reinforcement learning (RL), imitation learning (IL), and task and motion planning (TAMP) have demonstrated impressive performance across various robotic manipulation tasks. However, these approaches have been limited to learning simple behaviors in current real-world manipulation benchmarks, such as pushing or pick-and-place. To enable more complex, long-horizon behaviors of an autonomous robot, we propose to focus on real-world furniture assembly, a complex, long-horizon robot manipulation task that requires addressing many current robotic manipulation challenges to solve. We present FurnitureBench, a reproducible real-world furniture assembly benchmark aimed at providing a low barrier for entry and being easily reproducible, so that researchers across the world can reliably test their algorithms and compare them against prior work. For ease of use, we provide 200+ hours of pre-collected data (5000+ demonstrations), 3D printable furniture models, a robotic environment setup guide, and systematic task initialization. Furthermore, we provide FurnitureSim, a fast and realistic simulator of FurnitureBench. We benchmark the performance of offline RL and IL algorithms on our assembly tasks and demonstrate the need to improve such algorithms to be able to solve our tasks in the real world, providing ample opportunities for future research.
Beyond Flat GelSight Sensors: Simulation of Optical Tactile Sensors of Complex Morphologies for Sim2Real Learning

Recently, several morphologies, each with its advantages, have been proposed for the \textit{GelSight} high-resolution tactile sensors. However, existing simulation methods are limited to flat-surface sensors, which prevents their usage with the newer sensors of non-flat morphologies in Sim2Real experiments. In this paper, we extend a previously proposed GelSight simulation method developed for flat-surface sensors and propose a novel method for curved sensors. In particular, we address the simulation of light rays travelling through a curved tactile membrane in the form of geodesic paths. The method is validated by simulating the finger-shaped GelTip sensor and comparing the generated synthetic tactile images against the corresponding real images. Our extensive experiments show that combining the illumination generated from the geodesic paths, with a background image from the real sensor, produces the best results when compared to the lighting generated by direct linear paths in the same conditions. As the method is parameterised by the sensor mesh, it can be applied in principle to simulate a tactile sensor of any morphology. The proposed method not only unlocks simulating existing optical tactile sensors of complex morphologies but also enables experimenting with sensors of novel morphologies, before the fabrication of the real sensor. Project website: https://danfergo.github. io/geltip-sim
Bound and Optimal Design of Dallenbach Absorber Under Finite-Bandwidth Multiple-Angle Illumination

Dallenbach absorbers are lossy substances attached to a perfect electric conductor sheet. For such a configuration Rozanov derived a sum rule that relates the absorber's efficacy with its thickness and frequency-band of operation. Rozanov's derivation is valid only for layer impinged by a normally incident plane wave. Later, this relation was extended for oblique incidence considering both transverse electric and transverse magnetic polarizations. Here, we follow the same approach and present a sum rule that is valid for multiple and possibly a spectrum of oblique incident waves which are arbitrarily weighted. We recast the design of the Dallenbach absorber as an optimization problem, where optimization is performed over its electromagnetic properties. The optimization problem is applicable for practical implementations where finite spectral bandwidth is considered, as well as for theoretical aspects such as determining the tightness of the sum rule over an infinite bandwidth. We provide a numerical example for a practical case where we perform an optimization procedure for a given weight function and finite bandwidth. Additionally, we demonstrate the effect of the weight function on the optimization results.
Cycle spaces: invariant projections and applications to transportation cost

The paper starts with discussion of applications of cycle spaces to transportation cost. After a short survey of the known results on cycle spaces, we turn to the study of minimal projections onto cycle spaces in the corresponding $\ell_1$-spaces. This study is naturally related to the study of invariant projections on the cycle space, which, in turn, are determined by the properties of representations of the automorphism group of the corresponding graph. The main focus is on discrete tori and Hamming graphs.
Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge

Multimodal Named Entity Recognition (MNER) on social media aims to enhance textual entity prediction by incorporating image-based clues. Existing studies mainly focus on maximizing the utilization of pertinent image information or incorporating external knowledge from explicit knowledge bases. However, these methods either neglect the necessity of providing the model with external knowledge, or encounter issues of high redundancy in the retrieved knowledge. In this paper, we present PGIM -- a two-stage framework that aims to leverage ChatGPT as an implicit knowledge base and enable it to heuristically generate auxiliary knowledge for more efficient entity prediction. Specifically, PGIM contains a Multimodal Similar Example Awareness module that selects suitable examples from a small number of predefined artificial samples. These examples are then integrated into a formatted prompt template tailored to the MNER and guide ChatGPT to generate auxiliary refined knowledge. Finally, the acquired knowledge is integrated with the original text and fed into a downstream model for further processing. Extensive experiments show that PGIM outperforms state-of-the-art methods on two classic MNER datasets and exhibits a stronger robustness and generalization capability.
Registering Neural Radiance Fields as 3D Density Images

No significant work has been done to directly merge two partially overlapping scenes using NeRF representations. Given pre-trained NeRF models of a 3D scene with partial overlapping, this paper aligns them with a rigid transform, by generalizing the traditional registration pipeline, that is, key point detection and point set registration, to operate on 3D density fields. To describe corner points as key points in 3D, we propose to use universal pre-trained descriptor-generating neural networks that can be trained and tested on different scenes. We perform experiments to demonstrate that the descriptor networks can be conveniently trained using a contrastive learning strategy. We demonstrate that our method, as a global approach, can effectively register NeRF models, thus making possible future large-scale NeRF construction by registering its smaller and overlapping NeRFs captured individually.
Generalizable synthetic MRI with physics-informed convolutional networks

BACKGROUND Magnetic resonance imaging (MRI) provides state-of-the-art image quality for neuroimaging, consisting of multiple separately acquired contrasts. Synthetic MRI aims to accelerate examinations by synthesizing any desirable contrast from a single acquisition. PURPOSE We developed a physics-informed deep learning-based method to synthesize multiple brain MRI contrasts from a single 5-min acquisition and investigate its ability to generalize to arbitrary contrasts. METHODS A dataset of 55 subjects acquired with a clinical MRI protocol and a 5-min transient-state sequence was used. The model, based on a generative adversarial network, maps data acquired from the five-minute scan to "effective" quantitative parameter maps (q*-maps), feeding the generated PD, T1 , and T2 maps into a signal model to synthesize four clinical contrasts (proton density-weighted, T1 -weighted, T2 -weighted, and T2 -weighted fluid-attenuated inversion recovery), from which losses are computed. The synthetic contrasts are compared to an end-to-end deep learning-based method proposed by literature. The generalizability of the proposed method is investigated for five volunteers by synthesizing three contrasts unseen during training and comparing these to ground truth acquisitions via qualitative assessment and contrast-to-noise ratio (CNR) assessment. RESULTS The physics-informed method matched the quality of the end-to-end method for the four standard contrasts, with structural similarity metrics above 0.75 ± 0.08 (±std), peak signal-to-noise ratios above 22.4 ± 1.9, representing a portion of compact lesions comparable to standard MRI. Additionally, the physics-informed method enabled contrast adjustment, and similar signal contrast and comparable CNRs to the ground truth acquisitions for three sequences unseen during model training. CONCLUSIONS The study demonstrated the feasibility of physics-informed, deep learning-based synthetic MRI to generate high-quality contrasts and generalize to contrasts beyond the training data. This technology has the potential to accelerate neuroimaging protocols.
Reproducibility Requires Consolidated Artifacts

Machine learning is facing a ‘reproducibility crisis’ where a significant number of works report failures when attempting to reproduce previously published results. We evaluate the sources of reproducibility failures using a meta-analysis of 142 replication studies from ReScience C and 204 code repositories. We find that missing experiment details such as hyperparameters are potential causes of unreproducibility. We experimentally show the bias of different hyperparameter selection strategies and conclude that consolidated artifacts with a unified framework can help support reproducibility.
A Frustratingly Simple Decoding Method for Neural Text Generation

We introduce a frustratingly simple, super efficient and surprisingly effective decoding method, which we call Frustratingly Simple Decoding (FSD), for neural text generation. The idea behind FSD is straightforward: we build an anti-LM based on previously generated text and use this anti-LM to penalize future generation of what has been generated. The anti-LM can be implemented as simple as an n-gram language model or a vectorized variant. In this way, FSD introduces no extra model parameters and negligible computational overhead (FSD can be as fast as greedy search). Despite the simplicity, FSD is surprisingly effective; Experiments show that FSD can outperform the canonical methods to date (i.e., nucleus sampling) as well as several strong baselines that were proposed recently.
2D material-based mode confinement engineering in electro-optic modulators

The ability to modulate light using 2-dimensional (2D) materials is fundamentally challenged by their small optical cross-section leading to miniscule modal confinements in diffraction-limited photonics despite intrinsically high electro-optic absorption modulation (EAM) potential given by their strong exciton binding energies. However the inherent polarization anisotropy in 2D-materials and device tradeoffs lead to additional requirements with respect to electric field directions and modal confinement. A detailed relationship between modal confinement factor and obtainable modulation strength including definitions on bounding limits are outstanding. Here we show that the modal confinement factor is a key parameter determining both the modulation strength and the modulator extinction ratio-to-insertion loss metric. We show that the modal confinement and hence the modulation strength of a single-layer modulated 2D material in a plasmonically confined mode is able to improve by more than 10x compared to diffraction-limited modes. Combined with the strong-index modulation of graphene the modulation strength can be more than 2-orders of magnitude higher compared to Silicon-based EAMs. Furthermore modal confinement was found to be synergistic with performance optimization via enhanced light-matter-interactions. These results show that there is room for scaling 2D material EAMs with respect to modal engineering towards realizing synergistic designs leading to high-performance modulators.
A Study on the Path Planning of Mobile Robot with the Fuzzy Logic Method

Existing problems on path planning based on fuzzy logic for mobile robots are analyzed. The kinematic model of a mobile robot is built up, and a selforganized fuzzy controller applied to path planning in unknown, unstructured environment is designed. Establishment of fuzzy rules accords well with the key idea of greedy algorithm. Some new methods are presented to solve such issues like "symmetric indecision", deadlock estimation, freedom from deadlock state and the judgement whether deadlock is eliminated or not. Simulations in several kinds of environments verify the validity and reasonability of these methods.
Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis

We propose using automatically generated natural language definitions of contextualised word usages as interpretable word and word sense representations.Given a collection of usage examples for a target word, and the corresponding data-driven usage clusters (i.e., word senses), a definition is generated for each usage with a specialised Flan-T5 language model, and the most prototypical definition in a usage cluster is chosen as the sense label. We demonstrate how the resulting sense labels can make existing approaches to semantic change analysis more interpretable, and how they can allow users — historical linguists, lexicographers, or social scientists — to explore and intuitively explain diachronic trajectories of word meaning. Semantic change analysis is only one of many possible applications of the ‘definitions as representations’ paradigm. Beyond being human-readable, contextualised definitions also outperform token or usage sentence embeddings in word-in-context semantic similarity judgements, making them a new promising type of lexical representation for NLP.