Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Prototype Adaptation with Distillation for Few-shot Point Cloud Segmentation

Few-shot point cloud segmentation seeks to generate per-point masks for previously unseen categories, using only a minimal set of annotated point clouds as reference. Existing prototype-based methods rely on support prototypes to guide the segmentation of query point clouds, but they encounter challenges when significant object variations exist between the support prototypes and query features. In this work, we present dynamic prototype adaptation (DPA), which explicitly learns task-specific prototypes for each query point cloud to tackle the object variation problem. DPA achieves the adaptation through prototype rectification, aligning vanilla prototypes from support with the query feature distribution, and prototype-to-query attention, extracting task-specific context from query point clouds. Furthermore, we introduce a prototype distillation regularization term, enabling knowledge transfer between early-stage prototypes and their deeper counterparts during adaption. By iteratively applying these adaptations, we generate task-specific prototypes for accurate mask predictions on query point clouds. Extensive experiments on two popular benchmarks show that DPA surpasses state-of-the-art methods by a significant margin, e.g., 7.43\% and 6.39\% under the 2-way 1-shot setting on S3DIS and ScanNet, respectively. Code is available at https://github.com/jliu4ai/DPA.
Unveiling the Quantum Toroidal Dipole in Nanosystems: Quantization, Interaction Energy, and Measurement

We investigate the physical properties of a charged quantum particle confined to a toroidal surface in the presence of a filiform current along the system's rotational axis. Our analysis reveals that the interaction between the particle and the current induces a non-zero toroidal dipole in the particle's stationary states. We demonstrate that the differences between the toroidal dipole projections for different energy levels can be quantized in units of $\hbar R/(4m_p)$ (where $R$ is the major radius of the torus and $m_p$ is the particle mass), suggesting the existence of toroidal dipole quanta. Furthermore, we find that both the toroidal dipole projection and the energy eigenvalues exhibit periodic behavior with respect to the current intensity, with a period that depends solely on the torus's aspect ratio $R/r$, where r is the minor radius. This periodicity opens up the possibility of using the current intensity to manipulate and measure the toroidal dipole projection. We also observe abrupt changes in the toroidal dipole projection and energy eigenvalues around integer multiples of the current half-period. These changes provide further evidence for the quantization of the toroidal dipole in such systems. The interaction energy between the particle and the current follows the classical electrodynamics form, suggesting a potential method for measuring and manipulating the toroidal dipole projection along the current axis. The quantization rules we have identified represent hallmarks of the quantum toroidal dipole in nanosystems and could lead to the development of novel devices based on this fundamental property.
A Study of Acquisition Functions for Medical Imaging Deep Active Learning

The Deep Learning revolution has enabled groundbreaking achievements in recent years. From breast cancer detection to protein folding, deep learning algorithms have been at the core of very important advancements. However, these modern advancements are becoming more and more data-hungry, especially on labeled data whose availability is scarce: this is even more prevalent in the medical context. In this work, we show how active learning could be very effective in data scarcity situations, where obtaining labeled data (or annotation budget is very limited). We compare several selection criteria (BALD, MeanSTD, and MaxEntropy) on the ISIC 2016 dataset. We also explored the effect of acquired pool size on the model's performance. Our results suggest that uncertainty is useful to the Melanoma detection task, and confirms the hypotheses of the author of the paper of interest, that \textit{bald} performs on average better than other acquisition functions. Our extended analyses however revealed that all acquisition functions perform badly on the positive (cancerous) samples, suggesting exploitation of class unbalance, which could be crucial in real-world settings. We finish by suggesting future work directions that would be useful to improve this current work. The code of our implementation is open-sourced at \url{https://github.com/bonaventuredossou/ece526_course_project}
Meta-Learning for Neural Network-based Temporal Point Processes

Human activities generate various event sequences such as taxi trip records, bike-sharing pick-ups, crime occurrence, and infectious disease transmission. The point process is widely used in many applications to predict such events related to human activities. However, point processes present two problems in predicting events related to human activities. First, recent high-performance point process models require the input of sufficient numbers of events collected over a long period (i.e., long sequences) for training, which are often unavailable in realistic situations. Second, the long-term predictions required in real-world applications are difficult. To tackle these problems, we propose a novel meta-learning approach for periodicity-aware prediction of future events given short sequences. The proposed method first embeds short sequences into hidden representations (i.e., task representations) via recurrent neural networks for creating predictions from short sequences. It then models the intensity of the point process by monotonic neural networks (MNNs), with the input being the task representations. We transfer the prior knowledge learned from related tasks and can improve event prediction given short sequences of target tasks. We design the MNNs to explicitly take temporal periodic patterns into account, contributing to improved long-term prediction performance. Experiments on multiple real-world datasets demonstrate that the proposed method has higher prediction performance than existing alternatives.
Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning

In the ambitious realm of space AI, the integration of federated learning (FL) with low Earth orbit (LEO) satellite constellations holds immense promise. However, many challenges persist in terms of feasibility, learning efficiency, and convergence. These hurdles stem from the bottleneck in communication, characterized by sporadic and irregular connectivity between LEO satellites and ground stations, coupled with the limited computation capability of satellite edge computing (SEC). This paper proposes a novel FL-SEC framework that empowers LEO satellites to execute large-scale machine learning (ML) tasks onboard efficiently. Its key components include i) personalized learning via divide-and-conquer, which identifies and eliminates redundant satellite images and converts complex multi-class classification problems to simple binary classification, enabling rapid and energy-efficient training of lightweight ML models suitable for IoT/edge devices on satellites; ii) orbital model retraining, which generates an aggregated"orbital model"per orbit and retrains it before sending to the ground station, significantly reducing the required communication rounds. We conducted experiments using Jetson Nano, an edge device closely mimicking the limited compute on LEO satellites, and a real satellite dataset. The results underscore the effectiveness of our approach, highlighting SEC's ability to run lightweight ML models on real and high-resolution satellite imagery. Our approach dramatically reduces FL convergence time by nearly 30 times, and satellite energy consumption down to as low as 1.38 watts, all while maintaining an exceptional accuracy of up to 96%.
Quantifying Stereotypes in Language

A stereotype is a generalized perception of a specific group of humans. It is often potentially encoded in human language, which is more common in texts on social issues. Previous works simply define a sentence as stereotypical and anti-stereotypical. However, the stereotype of a sentence may require fine-grained quantification. In this paper, to fill this gap, we quantify stereotypes in language by annotating a dataset. We use the pre-trained language models (PLMs) to learn this dataset to predict stereotypes of sentences. Then, we discuss stereotypes about common social issues such as hate speech, sexism, sentiments, and disadvantaged and advantaged groups. We demonstrate the connections and differences between stereotypes and common social issues, and all four studies validate the general findings of the current studies. In addition, our work suggests that fine-grained stereotype scores are a highly relevant and competitive dimension for research on social issues. The models and datasets used in this paper are available at https://anonymous.4open. science/r/quantifying_stereotypes_in_language.
Quantifying Surface Energy Fluxes and Evaporation over a Significant Expanding Endorheic Lake in the Central Tibetan Plateau

In this study, energy and water vapor exchange between the lake and atmosphere over the largest lake in Tibet, Lake Serling Co, was measured by an eddy covariance system from April 26 until September 26, 2014. The results demonstrated that the diurnal variations of the sensible heat flux (H) and latent heat flux (LE) were different from that of the net radiation (R-n). R-n reached its peak value at the local noon, whereas H peaked in the morning and LE peaked in the afternoon. On a seasonal scale, H and LE were also different from R-n. The maximum value of R-n occurred in June, while the maxima of H and LE were observed in September. Lake evaporation was quantified with a daily mean value of 2.7 mm d(-1) and a total amount of 417.0 mm during the study period. In addition, evaporation from Lake Serling Co was compared with two types of pan evaporation (D20 pan and E601B pan) and potential evaporation on the land surface. The variability of conversion coefficients between lake evaporation, D20/E601B pan and potential evaporation indicate that coefficients varied depending on the month and could not be defined as a single experimental value.
Cross-Layer Performance Evaluation of C-V2X

As self-driving cars increasingly penetrate our daily lives, vehicle-to-everything (V2X) communications are emerging as one of the key enabler technologies. However, the dynamicity of vehicles (one of whose causes is the mobility of vehicles) often complicates it even further to evaluate the performance of a V2X system. We have been building a system-level simulator dedicated to assessing the performance of V2X communications. We highlight that the simulator features the incorporation of (i) intelligent transportation system (ITS) scenarios in geographical setup and (ii) physical (PHY) and radio resource control (RRC) cross-layer performance evaluation capability. In particular, this abstract reports the status of our implementation of the modulation and coding scheme (MCS).
Resonant helical multi-edge transport in Sierpi\'nski carpets

In recent years, synthesis and experimental research of fractalized materials has evolved in a paradigmatic crossover with topological phases of matter. We present here a theoretical investigation of the helical edge transport in Sierpinski carpets (SCs), combining the Bernevig-Hughes-Zhang (BHZ) model and the Landauer approach. Starting from a pristine two-dimensional topological insulator (2DTI), according to the BHZ model, our results reveal resonant transport modes when the SC fractal generation reaches the same scale as the space discretization; these modes are analyzed within a contour plot mapping of the local spin-polarized currents, shown spanned and assisted by inner-edge channels. From such a deeply fractalized SC building block, we introduce a rich tapestry formed by superior SC hierarchies, enlightening intricate patterns and unique fingerprints that offer valuable insights into how helical edge transport occurs in these fractal dimensions.
The Effects of Transmission-Rights Pricing on Multi-Stage Electricity Markets

Cross-border transmission infrastructure is pivotal in balancing modern power systems, but requires fair allocation of cross-border transmission capacity, possibly via fair pricing thereof. This requirement can be implemented using multi-stage market mechanisms for Physical Transmission Rights (PTRs). We analyse the related dynamics, and show prisoner's dilemma arises. Understanding these dynamics enables the development of novel market-settlement mechanisms to enhance market efficiency and incentivize renewable energy use.
FPIA: Field-Programmable Ising Arrays with In-Memory Computing

Ising Machine is a promising computing approach for solving combinatorial optimization problems. It is naturally suited for energy-saving and compact in-memory computing implementations with emerging memories. A na\"ive in-memory computing implementation of a quadratic Ising Machine requires an array of coupling weights that grows quadratically with problem size. However, the resources in such an approach are used inefficiently due to sparsity in practical optimization problems. We first show that this issue can be addressed by partitioning a coupling array into smaller sub-arrays. This technique, however, requires interconnecting subarrays; hence, we developed in-memory computing architecture for quadratic Ising Machines inspired by island-type field programmable gate arrays, which is the main contribution of our paper. We adapt open-source tools to optimize problem embedding and model routing overhead. Modeling results of benchmark problems for the developed architecture show up to 60x area improvement and faster operation than the baseline approach. Finally, we discuss algorithm/circuit co-design techniques for further improvements.
Importance-Aware Data Augmentation for Document-Level Neural Machine Translation

Document-level neural machine translation (DocNMT) aims to generate translations that are both coherent and cohesive, in contrast to its sentence-level counterpart. However, due to its longer input length and limited availability of training data, DocNMT often faces the challenge of data sparsity. To overcome this issue, we propose a novel Importance-Aware Data Augmentation (IADA) algorithm for DocNMT that augments the training data based on token importance information estimated by the norm of hidden states and training gradients. We conduct comprehensive experiments on three widely-used DocNMT benchmarks. Our empirical results show that our proposed IADA outperforms strong DocNMT baselines as well as several data augmentation approaches, with statistical significance on both sentence-level and document-level BLEU.
An Information Aggregation Operator

This study explores a new mathematical operator, symbolized as $\cupplus$, for information aggregation, aimed at enhancing traditional methods by directly amalgamating probability distributions. This operator facilitates the combination of probability densities, contributing a nuanced approach to probabilistic analysis. We apply this operator to a personalized incentive scenario, illustrating its potential in a practical context. The paper's primary contribution lies in introducing this operator and elucidating its elegant mathematical properties. This exploratory work marks a step forward in the field of information fusion and probabilistic reasoning.
Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detection

With the improvements in generative models, the issues of producing hallucinations in various domains (e.g., law, writing) have been brought to people’s attention due to concerns about misinformation. In this paper, we focus on neural fake news, which refers to content generated by neural networks aiming to mimic the style of real news to deceive people. To prevent harmful disinformation spreading fallaciously from malicious social media (e.g., content farms), we propose a novel verification framework, Style-News, using publisher metadata to imply a publisher’s template with the corresponding text types, political stance, and credibility. Based on threat modeling aspects, a style-aware neural news generator is introduced as an adversary for generating news content conditioning for a specific publisher, and style and source discriminators are trained to defend against this attack by identifying which publisher the style corresponds with, and discriminating whether the source of the given news is human-written or machine-generated. To evaluate the quality of the generated content, we integrate various dimensional metrics (language fluency, content preservation, and style adherence) and demonstrate that Style-News significantly outperforms the previous approaches by a margin of 0.35 for fluency, 15.24 for content, and 0.38 for style at most. Moreover, our discriminative model outperforms state-of-the-art baselines in terms of publisher prediction (up to 4.64%) and neural fake news detection (+6.94% 31.72%). We plan to release our Style-News publicly, with the aim of improving neural fake news detection.
Employing Iterative Feature Selection in Fuzzy Rule-Based Binary Classification

The feature selection in a traditional binary classification algorithm is always used in the stage of dataset preprocessing, which makes the obtained features not necessarily the best ones for the classification algorithm, thus affecting the classification performance. For a traditional rule-based binary classification algorithm, classification rules are usually deterministic, which results in the fuzzy information contained in the rules being ignored. To do so, this paper employs iterative feature selection in fuzzy rule-based binary classification. The proposed algorithm combines feature selection based on fuzzy correlation family with rule mining based on biclustering. It first conducts biclustering on the dataset after feature selection. Then it conducts feature selection again for the biclusters according to the feedback of biclusters evaluation. In this way, an iterative feature selection framework is build. During the iteration process, it stops until the obtained bicluster meets the requirements. In addition, the rule membership function is introduced to extract vectorized fuzzy rules from the bicluster and construct weak classifiers. The weak classifiers with good classification performance are selected by Adaptive Boosting and the strong classifier is constructed by"weighted average". Finally, we perform the proposed algorithm on different datasets and compare it with other peers. Experimental results show that it achieves good classification performance and outperforms its peers.
All-electrical driving and probing of dressed states in a single spin

The sub-nanometer distance between tip and sample in a scanning tunneling microscope (STM) enables the application of very large electric fields with a strength as high as ~ 1 GV/m. This has allowed for efficient electrical driving of Rabi oscillations of a single spin on a surface at a moderate radio-frequency (RF) voltage of the order of tens of millivolts. Here, we demonstrate the creation of dressed states of a single electron spin localized in the STM tunnel junction by using resonant RF driving voltages. The read-out of these dressed states was achieved all-electrical by a weakly coupled probe spin. Our work highlights the strength of the atomic-scale geometry inherent to the STM that facilitates creation and control of dressed states, which are promising for a design of atomically well-defined single spin quantum devices on surfaces.
On F-polynomials for generalized quantum cluster algebras and Gupta's formula

We show the existence of $F$-polynomials for generalized quantum cluster algebras and obtain the associated separation formulas under a mild condition. Along the way, we obtain Gupta's formulas of $F$-polynomials for generalized quantum cluster algebras. These formulas specialize to Gupta's formulas for quantum cluster algebras and cluster algebras respectively. Finally, a generalization of Gupta's formula has also been discussed in the setting of generalized cluster algebras.
Semantic Properties of cosine based bias scores for word embeddings

Plenty of works have brought social biases in language models to attention and proposed methods to detect such biases. As a result, the literature contains a great deal of different bias tests and scores, each introduced with the premise to uncover yet more biases that other scores fail to detect. What severely lacks in the literature, however, are comparative studies that analyse such bias scores and help researchers to understand the benefits or limitations of the existing methods. In this work, we aim to close this gap for cosine based bias scores. By building on a geometric definition of bias, we propose requirements for bias scores to be considered meaningful for quantifying biases. Furthermore, we formally analyze cosine based scores from the literature with regard to these requirements. We underline these findings with experiments to show that the bias scores' limitations have an impact in the application case.
RABBIT: A Robot-Assisted Bed Bathing System with Multimodal Perception and Integrated Compliance

This paper introduces RABBIT, a novel robot-assisted bed bathing system designed to address the growing need for assistive technologies in personal hygiene tasks. It combines multimodal perception and dual (software and hardware) compliance to perform safe and comfortable physical human-robot interaction. Using RGB and thermal imaging to segment dry, soapy, and wet skin regions accurately, RABBIT can effectively execute washing, rinsing, and drying tasks in line with expert caregiving practices. Our system includes custom-designed motion primitives inspired by human caregiving techniques, and a novel compliant end-effector called Scrubby, optimized for gentle and effective interactions. We conducted a user study with 12 participants, including one participant with severe mobility limitations, demonstrating the system's effectiveness and perceived comfort. Supplementary material and videos can be found on our website https://emprise.cs. cornell.edu/rabbit.
Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data

Long-term fetal heart rate (FHR) monitoring during the antepartum period, increasingly popularized by electronic FHR monitoring, represents a growing approach in FHR monitoring. This kind of continuous monitoring, in contrast to the short-term one, collects an extended period of fetal heart data. This offers a more comprehensive understanding of fetus's conditions. However, the interpretation of long-term antenatal fetal heart monitoring is still in its early stages, lacking corresponding clinical standards. Furthermore, the substantial amount of data generated by continuous monitoring imposes a significant burden on clinical work when analyzed manually. To address above challenges, this study develops an automatic analysis system named LARA (Long-term Antepartum Risk Analysis system) for continuous FHR monitoring, combining deep learning and information fusion methods. LARA's core is a well-established convolutional neural network (CNN) model. It processes long-term FHR data as input and generates a Risk Distribution Map (RDM) and Risk Index (RI) as the analysis results. We evaluate LARA on inner test dataset, the performance metrics are as follows: AUC 0.872, accuracy 0.816, specificity 0.811, sensitivity 0.806, precision 0.271, and F1 score 0.415. In our study, we observe that long-term FHR monitoring data with higher RI is more likely to result in adverse outcomes (p=0.0021). In conclusion, this study introduces LARA, the first automated analysis system for long-term FHR monitoring, initiating the further explorations into its clinical value in the future.