Miracle-dz/UniFAR-FTUs huggingface dataset:
Like My Aunt Dorothy: Effects of Conversational Styles on Perceptions, Acceptance and Metaphorical Descriptions of Voice Assistants during Later Adulthood

Little research has investigated the design of conversational styles of voice assistants (VA) for adults in their later adulthood with varying personalities. In this Wizard of Oz experiment, 34 middle-aged (50 to 64 years old) and 24 older adults (65 to 80 years old) participated in a user study at a simulated home, interacting with a VA using either formal or informal language. Older adults with higher agreeableness perceived VA as being more likeable than middle-aged adults. Middle-aged adults showed similar technology acceptance toward the informal and formal VA, and older adults preferred using informal VA, especially those with low agreeableness. Further, while both middle-aged and older adults frequently anthropomorphized VAs by using human metaphors for them, older adults compared formal VA with professionals (e.g., librarians, teachers) and informal VA with their close ones (e.g., spouses, relatives). Overall, the conversational style showed differential effects on the perceptions of middle-aged and older adults, suggesting personalized design implications.
A Real-Time Multi-Task Learning System for Joint Detection of Face, Facial Landmark and Head Pose

Extreme head postures pose a common challenge across a spectrum of facial analysis tasks, including face detection, facial landmark detection (FLD), and head pose estimation (HPE). These tasks are interdependent, where accurate FLD relies on robust face detection, and HPE is intricately associated with these key points. This paper focuses on the integration of these tasks, particularly when addressing the complexities posed by large-angle face poses. The primary contribution of this study is the proposal of a real-time multi-task detection system capable of simultaneously performing joint detection of faces, facial landmarks, and head poses. This system builds upon the widely adopted YOLOv8 detection framework. It extends the original object detection head by incorporating additional landmark regression head, enabling efficient localization of crucial facial landmarks. Furthermore, we conduct optimizations and enhancements on various modules within the original YOLOv8 framework. To validate the effectiveness and real-time performance of our proposed model, we conduct extensive experiments on 300W-LP and AFLW2000-3D datasets. The results obtained verify the capability of our model to tackle large-angle face pose challenges while delivering real-time performance across these interconnected tasks.
The trace fractional Laplacian and the mid-range fractional Laplacian

In this paper we introduce two new fractional versions of the Laplacian. The first one is based on the classical formula that writes the usual Laplacian as the sum of the eigenvalues of the Hessian. The second one comes from looking at the classical fractional Laplacian as the mean value (in the sphere) of the 1-dimensional fractional Laplacians in lines with directions in the sphere. To obtain this second new fractional operator we just replace the mean value by the mid-range of 1-dimensional fractional Laplacians with directions in the sphere. For these two new fractional operators we prove a comparison principle for viscosity sub and supersolutions and then we obtain existence and uniqueness for the Dirichlet problem. We also show that solutions are $C^\gamma$ smooth up to the boundary when the exterior datum is also H\"older continuous. Finally, we prove that for the first operator we recover the classical Laplacian in the limit as $s\nearrow 1$.
Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings.
The length-weight and volume relationships of the Indian oilsardine Sardinella longiceps Val.

The study of length-weight relationship of fishes forms an important aspect of fishery biological investigations. Its practical applications while estimating the weight from the know length and vice versa and also in determining the condition factor or the ponderal index, the spav/ning seasons and the taxonomic differences of fishes (Lc Cren, 1951) hardly need any emphasis. The length-volume study gives an idea on the seasonal changes in their specific gravity {Tester, 1940). There has so far been no published account on the length-weight and volume relationships of the Indian oil sardine, Sardinella longiceps Val, The present communi" cation is a preliminary account of studies on these aspects taken up at Mangalore between October 1959 and November i960.
Online Supervised Training of Spaceborne Vision during Proximity Operations using Adaptive Kalman Filtering

This work presents an Online Supervised Training (OST) method to enable robust vision-based navigation about a non-cooperative spacecraft. Spaceborne Neural Networks (NN) are susceptible to domain gap as they are primarily trained with synthetic images due to the inaccessibility of space. OST aims to close this gap by training a pose estimation NN online using incoming flight images during Rendezvous and Proximity Operations (RPO). The pseudo-labels are provided by adaptive unscented Kalman filter where the NN is used in the loop as a measurement module. Specifically, the filter tracks the target's relative orbital and attitude motion, and its accuracy is ensured by robust on-ground training of the NN using only synthetic data. The experiments on real hardware-in-the-loop trajectory images show that OST can improve the NN performance on the target image domain given that OST is performed on images of the target viewed from a diverse set of directions during RPO.
Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry

The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. We investigated the synergetic pattern between the data-driven methods, empirical domain knowledge, and first-principles simulations. We showed the potential risk of biased results when using data-driven models without causal analysis. Using a case study assessing the implication of several design solutions on the energy consumption of a building, we proved the necessity of causal analysis during the data-driven modeling process. We concluded that: (a) Data-driven models' accuracy assessment or domain knowledge screening may not rule out biased and spurious results; (b) Data-driven models' feature selection should involve careful consideration of causal relationships, especially colliders; (c) Causal analysis results can be used as an aid to first-principles simulation design and parameter checking to avoid cognitive biases. We proved the benefits of causal analysis when applied to data-driven models in building engineering.
Electrons densities and plasma mass densities observed by pulsar dispersion in different locations of our galaxy

Electrons densities in different locations of our galaxy are obtained in pulsar astronomy by dividing the dispersion Measure by the distance of the pulsar to Earth. In order that such measurements will be reliable these distances should be obtained by methods which are independent of the electron density model and the results of such measurements are used in the present article to derive plasma mass densities. Our main analysis is based on the idea that the pulsars measurements are obtained for completely ionized hydrogen plasma. We use the electrons densities to derive the plasma mass densities and compare it with dark matter mass densities which are found to be much larger. But some factors which may reduce this difference are discussed. The properties of the low-density plasma are analyzed by using Saha equation.
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

We present LongLoRA, an efficient fine-tuning approach that extends the context sizes of pre-trained large language models (LLMs), with limited computation cost. Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources. For example, training on the context length of 8192 needs 16x computational costs in self-attention layers as that of 2048. In this paper, we speed up the context extension of LLMs in two aspects. On the one hand, although dense global attention is needed during inference, fine-tuning the model can be effectively and efficiently done by sparse local attention. The proposed shifted sparse attention effectively enables context extension, leading to non-trivial computation saving with similar performance to fine-tuning with vanilla attention. Particularly, it can be implemented with only two lines of code in training, while being optional in inference. On the other hand, we revisit the parameter-efficient fine-tuning regime for context expansion. Notably, we find that LoRA for context extension works well under the premise of trainable embedding and normalization. LongLoRA combines this improved LoRA with S^2-Attn. LongLoRA demonstrates strong empirical results on various tasks on Llama2 models from 7B/13B to 70B. LongLoRA extends Llama2 7B from 4k context to 100k, or Llama2 70B to 32k on a single 8x A100 machine. LongLoRA extends models' context while retaining their original architectures, and is compatible with most existing techniques, like Flash-Attention2. In addition, we further conduct supervised fine-tuning with LongLoRA and our long instruction-following LongAlpaca dataset.
Neural Stochastic Poisson Surface Reconstruction

Reconstructing a surface from a point cloud is an underdetermined problem. We use a neural network to study and quantify this reconstruction uncertainty under a Poisson smoothness prior. Our algorithm addresses the main limitations of existing work and can be fully integrated into the 3D scanning pipeline, from obtaining an initial reconstruction to deciding on the next best sensor position and updating the reconstruction upon capturing more data.
Can We Reliably Improve the Robustness to Image Acquisition of Remote Sensing of PV Systems?

Photovoltaic (PV) energy is crucial for the decarbonization of energy systems. Due to the lack of centralized data, remote sensing of rooftop PV installations is the best option to monitor the evolution of the rooftop PV installed fleet at a regional scale. However, current techniques lack reliability and are notably sensitive to shifts in the acquisition conditions. To overcome this, we leverage the wavelet scale attribution method (WCAM), which decomposes a model's prediction in the space-scale domain. The WCAM enables us to assess on which scales the representation of a PV model rests and provides insights to derive methods that improve the robustness to acquisition conditions, thus increasing trust in deep learning systems to encourage their use for the safe integration of clean energy in electric systems.
GWAK: Gravitational-Wave Anomalous Knowledge with Recurrent Autoencoders

Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such techniques have been traditionally used in searches for compact binary coalescences (CBCs), and have been employed in all known GW detections so far. However, interesting science cases aside from compact mergers do not yet have accurate enough modeling to make matched filtering possible, including core-collapse supernovae and sources where stochasticity may be involved. Therefore the development of techniques to identify sources of these types is of significant interest. In this paper, we present a method of anomaly detection based on deep recurrent autoencoders to enhance the search region to unmodeled transients. We use a semi-supervised strategy that we name Gravitational Wave Anomalous Knowledge (GWAK). While the semi-supervised nature of the problem comes with a cost in terms of accuracy as compared to supervised techniques, there is a qualitative advantage in generalizing experimental sensitivity beyond pre-computed signal templates. We construct a low-dimensional embedded space using the GWAK method, capturing the physical signatures of distinct signals on each axis of the space. By introducing signal priors that capture some of the salient features of GW signals, we allow for the recovery of sensitivity even when an unmodeled anomaly is encountered. We show that regions of the GWAK space can identify CBCs, detector glitches and also a variety of unmodeled astrophysical sources.
On-the-Fly SfM: What you capture is What you get

Over the last decades, ample achievements have been made on Structure from motion (SfM). However, the vast majority of them basically work in an offline manner, i.e., images are firstly captured and then fed together into a SfM pipeline for obtaining poses and sparse point cloud. In this work, on the contrary, we present an on-the-fly SfM: running online SfM while image capturing, the newly taken On-the-Fly image is online estimated with the corresponding pose and points, i.e., what you capture is what you get. Specifically, our approach firstly employs a vocabulary tree that is unsupervised trained using learning-based global features for fast image retrieval of newly fly-in image. Then, a robust feature matching mechanism with least squares (LSM) is presented to improve image registration performance. Finally, via investigating the influence of newly fly-in image's connected neighboring images, an efficient hierarchical weighted local bundle adjustment (BA) is used for optimization. Extensive experimental results demonstrate that on-the-fly SfM can meet the goal of robustly registering the images while capturing in an online way.
Tuning brittleness in multi-component metallic glasses through chemical disorder aging

Shear localization in slowly-driven bulk metallic glasses (BMGs) is typically accompanied by a sharp drop in the bulk stress response as a signature of the plastic yielding transition. It is also observed that the sharpness of this elastic-plastic dynamical transition depends on the extent of local chemical and microstructural orders, as well as the glass preparation protocol ( ie. thermal annealing). Here, we investigate sheared multi-element BMGs in molecular dynamics (MD) simulations, and demonstrate that glass aging, implemented through a hybrid Monte-Carlo(MC)-MD process, sharpens the elastic-plastic transition through a distinct crossover, seen in strain patterns that gradually shift from diffuse features in as-quenched samples to localized (yet system-spanning) patterns in well-annealed glasses. This effect of glass aging on the elastic-plastic transition is found to be correlated to the inherent interplay between aging-induced icosahedra ordering and co-operative formation of shear transformation zones. The observed crossover is quantified through a measure of the age-dependent susceptibility to plastic rearrangements, exhibiting strong (anti-)correlations to local ordering features, and the corresponding spatial correlation length grows with the aging timescale.
A real-time, hardware agnostic framework for close-up branch reconstruction using RGB data

Creating accurate 3D models of tree topology is an important task for tree pruning. The 3D model is used to decide which branches to prune and then to execute the pruning cuts. Previous methods for creating 3D tree models have typically relied on point clouds, which are often computationally expensive to process and can suffer from data defects, especially with thin branches. In this paper, we propose a method for actively scanning along a primary tree branch, detecting secondary branches to be pruned, and reconstructing their 3D geometry using just an RGB camera mounted on a robot arm. We experimentally validate that our setup is able to produce primary branch models with 4-5 mm accuracy and secondary branch models with 15 degrees orientation accuracy with respect to the ground truth model. Our framework is real-time and can run up to 10 cm/s with no loss in model accuracy or ability to detect secondary branches.
TrueLearn: A Python Library for Personalised Informational Recommendations with (Implicit) Feedback

This work describes the TrueLearn Python library, which contains a family of online learning Bayesian models for building educational (or more generally, informational) recommendation systems. This family of models was designed following the"open learner"concept, using humanly-intuitive user representations. For the sake of interpretability and putting the user in control, the TrueLearn library also contains different representations to help end-users visualise the learner models, which may in the future facilitate user interaction with their own models. Together with the library, we include a previously publicly released implicit feedback educational dataset with evaluation metrics to measure the performance of the models. The extensive documentation and coding examples make the library highly accessible to both machine learning developers and educational data mining and learning analytic practitioners. The library and the support documentation with examples are available at https://truelearn.readthedocs. io/en/latest.
Regret and Conservatism of Distributionally Robust Constrained Stochastic Model Predictive Control

We analyse the conservatism and regret of distributionally robust (DR) stochastic model predictive control (SMPC) when using moment-based ambiguity sets for modeling unknown uncertainties. To quantify the conservatism, we compare the deterministic constraint tightening while taking a DR approach against the optimal tightening when the exact distributions of the stochastic uncertainties are known. Furthermore, we quantify the regret by comparing the performance when the distributions of the stochastic uncertainties are known and unknown. Analysing the accumulated sub-optimality of SMPC due to the lack of knowledge about the true distributions of the uncertainties marks the novel contribution of this work.
Interactive Flexible Style Transfer for Vector Graphics

Vector graphics are an industry-standard way to represent and share visual designs. Designers frequently source and incorporate styles from existing designs into their work. Unfortunately, popular design tools are not well suited for this task. We present VST, Vector Style Transfer, a novel design tool for flexibly transferring visual styles between vector graphics. The core of VST lies in leveraging automation while respecting designers’ tastes and the subjectivity inherent to style transfer. In VST, designers tune a cross-design element correspondence and customize which style attributes to change. We report results from a user study in which designers used VST to control style transfer between several designs, including designs participants created with external tools beforehand. VST shows that enabling design correspondence tuning and customization is one way to support interactive, flexible style transfer.
A survey on the semantics of sequential patterns with negation

A sequential pattern with negation, or negative sequential pattern, takes the form of a sequential pattern for which the negation symbol may be used in front of some of the pattern's itemsets. Intuitively, such a pattern occurs in a sequence if negated itemsets are absent in the sequence. Recent work has shown that different semantics can be attributed to these pattern forms, and that state-of-the-art algorithms do not extract the same sets of patterns. This raises the important question of the interpretability of sequential pattern with negation. In this study, our focus is on exploring how potential users perceive negation in sequential patterns. Our aim is to determine whether specific semantics are more"intuitive"than others and whether these align with the semantics employed by one or more state-of-the-art algorithms. To achieve this, we designed a questionnaire to reveal the semantics' intuition of each user. This article presents both the design of the questionnaire and an in-depth analysis of the 124 responses obtained. The outcomes indicate that two of the semantics are predominantly intuitive; however, neither of them aligns with the semantics of the primary state-of-the-art algorithms. As a result, we provide recommendations to account for this disparity in the conclusions drawn.
LLM Guided Inductive Inference for Solving Compositional Problems

While large language models (LLMs) have demonstrated impressive performance in question-answering tasks, their performance is limited when the questions require knowledge that is not included in the model's training data and can only be acquired through direct observation or interaction with the real world. Existing methods decompose reasoning tasks through the use of modules invoked sequentially, limiting their ability to answer deep reasoning tasks. We introduce a method, Recursion based extensible LLM (REBEL), which handles open-world, deep reasoning tasks by employing automated reasoning techniques like dynamic planning and forward-chaining strategies. REBEL allows LLMs to reason via recursive problem decomposition and utilization of external tools. The tools that REBEL uses are specified only by natural language description. We further demonstrate REBEL capabilities on a set of problems that require a deeply nested use of external tools in a compositional and conversational setting.