Miracle-dz/UniFAR-FTUs huggingface dataset:
TARN-VIST: Topic Aware Reinforcement Network for Visual Storytelling

As a cross-modal task, visual storytelling aims to generate a story for an ordered image sequence automatically. Different from the image captioning task, visual storytelling requires not only modeling the relationships between objects in the image but also mining the connections between adjacent images. Recent approaches primarily utilize either end-to-end frameworks or multi-stage frameworks to generate relevant stories, but they usually overlook latent topic information. In this paper, in order to generate a more coherent and relevant story, we propose a novel method, Topic Aware Reinforcement Network for VIsual StoryTelling (TARN-VIST). In particular, we pre-extracted the topic information of stories from both visual and linguistic perspectives. Then we apply two topic-consistent reinforcement learning rewards to identify the discrepancy between the generated story and the human-labeled story so as to refine the whole generation process. Extensive experimental results on the VIST dataset and human evaluation demonstrate that our proposed model outperforms most of the competitive models across multiple evaluation metrics.
Chapter 7 Mental imagery and perception in hallucination-prone individuals Summary

College students screened for hallucination-proneness using the Launay-Slade Hallucination Scale (LSHS) were compared on measures of self-report vividness of imagery and on behavioral measures of imagery and perception (visual and auditory). Specifically, we tested the hypothesis whether hallucination-prone individuals would show smaller differences between imagery and perception performance, which may be indicative of increased sensory characteristics of mental images. We replicated earlier findings of higher self-report imagery ratings in the high hallucination-prone group. However, the two groups did not differ on five of six behavioral imagery-perception comparisons. Although vividness of mental images may be subjectively associated with mild hallucinatory experiences, we suggest that cognitive processes associated with reality discrimination rather than increased perceptual characteristics of mental images may play a role at the information processing level.
Generative Models for Evolutionary Clustering

This article studies evolutionary clustering, a recently emerged hot topic with many important applications, noticeably in dynamic social network analysis. In this article, based on the recent literature on nonparametric Bayesian models, we have developed two generative models: DPChain and HDP-HTM. DPChain is derived from the Dirichlet process mixture (DPM) model, with an exponential decaying component along with the time. HDP-HTM combines the hierarchical dirichlet process (HDP) with a hierarchical transition matrix (HTM) based on the proposed Infinite hierarchical Markov state model (iHMS). Both models substantially advance the literature on evolutionary clustering, in the sense that not only do they both perform better than those in the existing literature, but more importantly, they are capable of automatically learning the cluster numbers and explicitly addressing the corresponding issues. Extensive evaluations have demonstrated the effectiveness and the promise of these two solutions compared to the state-of-the-art literature.
Admission control policies for a multi-class QoS-aware service oriented architecture

In the service computing paradigm, a service broker can build new applications by composing network-accessible services offered by loosely coupled independent providers. In this paper, we address the problem of providing a service broker, which offers to prospective users a composite service with a range of different Quality of Service (QoS) classes, with a forward-looking admission control policy based on Markov Decision Processes (MDP). This mechanism allows the broker to decide whether to accept or reject a new potential user in such a way to maximize its gain while guaranteeing non-functional QoS requirements to its already admitted users. We model the broker using a continuous-time MDP and consider various techniques suitable to solve both infinite-horizon and finitehorizon MDPs. To assess the effectiveness of the MDP-based admission control for the service broker, we present simulation results where we compare the optimal decisions obtained by the analytical solution of the MDP with other admission control policies. To deal with large problem instances, we also propose a heuristic policy for the MDP solution.
Re-identification from histopathology images

In numerous studies, deep learning algorithms have proven their potential for the analysis of histopathology images, for example, for revealing the subtypes of tumors or the primary origin of metastases. These models require large datasets for training, which must be anonymized to prevent possible patient identity leaks. This study demonstrates that even relatively simple deep learning algorithms can re-identify patients in large histopathology datasets with substantial accuracy. We evaluated our algorithms on two TCIA datasets including lung squamous cell carcinoma (LSCC) and lung adenocarcinoma (LUAD). We also demonstrate the algorithm's performance on an in-house dataset of meningioma tissue. We predicted the source patient of a slide with F1 scores of 50.16 % and 52.30 % on the LSCC and LUAD datasets, respectively, and with 62.31 % on our meningioma dataset. Based on our findings, we formulated a risk assessment scheme to estimate the risk to the patient's privacy prior to publication.
NovelQA: A Benchmark for Long-Range Novel Question Answering

The rapid advancement of Large Language Models (LLMs) has introduced a new frontier in natural language processing, particularly in understanding and processing long-context information. However, the evaluation of these models' long-context abilities remains a challenge due to the limitations of current benchmarks. To address this gap, we introduce NovelQA, a benchmark specifically designed to test the capabilities of LLMs with extended texts. Constructed from English novels, NovelQA offers a unique blend of complexity, length, and narrative coherence, making it an ideal tool for assessing deep textual understanding in LLMs. This paper presents the design and construction of NovelQA, highlighting its manual annotation, and diverse question types. Our evaluation of Long-context LLMs on NovelQA reveals significant insights into the models' performance, particularly emphasizing the challenges they face with multi-hop reasoning, detail-oriented questions, and extremely long input with more than 100,000 tokens. The results underscore the necessity for further advancements in LLMs to improve their long-context comprehension and computational literary studies.
The α1K276E Startle Disease Mutation Reveals Multiple Intermediate States in the Gating of Glycine Receptors

Loss-of-function mutations in human glycine receptors cause hyperekplexia, a rare inherited disease associated with an exaggerated startle response. We have studied a human disease mutation in the M2–M3 loop of the glycine receptor α1 subunit (K276E) using direct fitting of mechanisms to single-channel recordings with the program HJCFIT. Whole-cell recordings from HEK293 cells showed the mutation reduced the receptor glycine sensitivity. In single-channel recordings, rat homomeric α1 K276E receptors were barely active, even at 200 mm glycine. Coexpression of the β subunit partially rescued channel function. Heteromeric mutant channels opened in brief bursts at 300 μm glycine (a concentration that is near-maximal for wild type) and reached a maximum one-channel open probability of about 45% at 100 mm glycine (compared to 96% for wild type). Distributions of apparent open times contained more than one component in high glycine and, therefore, could not be described by mechanisms with only one fully liganded open state. Fits to the data were much better with mechanisms in which opening can also occur from more than one fully liganded intermediate (e.g., “primed” models). Brief pulses of glycine (∼3 ms, 30 mm) applied to mutant channels in outside-out patches activated currents with a slower rise time (1.5 ms) than those of wild-type channels (0.2 ms) and a much faster decay. These features were predicted reasonably well by the mechanisms obtained from fitting single-channel data. Our results show that, by slowing and impairing channel gating, the K276E mutation facilitates the detection of closed reaction intermediates in the activation pathway of glycine channels.
VideoBadminton: A Video Dataset for Badminton Action Recognition

In the dynamic and evolving field of computer vision, action recognition has become a key focus, especially with the advent of sophisticated methodologies like Convolutional Neural Networks (CNNs), Convolutional 3D, Transformer, and spatial-temporal feature fusion. These technologies have shown promising results on well-established benchmarks but face unique challenges in real-world applications, particularly in sports analysis, where the precise decomposition of activities and the distinction of subtly different actions are crucial. Existing datasets like UCF101, HMDB51, and Kinetics have offered a diverse range of video data for various scenarios. However, there's an increasing need for fine-grained video datasets that capture detailed categorizations and nuances within broader action categories. In this paper, we introduce the VideoBadminton dataset derived from high-quality badminton footage. Through an exhaustive evaluation of leading methodologies on this dataset, this study aims to advance the field of action recognition, particularly in badminton sports. The introduction of VideoBadminton could not only serve for badminton action recognition but also provide a dataset for recognizing fine-grained actions. The insights gained from these evaluations are expected to catalyze further research in action comprehension, especially within sports contexts.
Computing Gaussian Mixture Models with EM Using Equivalence Constraints

Density estimation with Gaussian Mixture Models is a popular generative technique used also for clustering. We develop a framework to incorporate side information in the form of equivalence constraints into the model estimation procedure. Equivalence constraints are defined on pairs of data points, indicating whether the points arise from the same source (positive constraints) or from different sources (negative constraints). Such constraints can be gathered automatically in some learning problems, and are a natural form of supervision in others. For the estimation of model parameters we present a closed form EM procedure which handles positive constraints, and a Generalized EM procedure using a Markov net which handles negative constraints. Using publicly available data sets we demonstrate that such side information can lead to considerable improvement in clustering tasks, and that our algorithm is preferable to two other suggested methods using the same type of side information.
Generalization error of spectral algorithms

The asymptotically precise estimation of the generalization of kernel methods has recently received attention due to the parallels between neural networks and their associated kernels. However, prior works derive such estimates for training by kernel ridge regression (KRR), whereas neural networks are typically trained with gradient descent (GD). In the present work, we consider the training of kernels with a family of $\textit{spectral algorithms}$ specified by profile $h(\lambda)$, and including KRR and GD as special cases. Then, we derive the generalization error as a functional of learning profile $h(\lambda)$ for two data models: high-dimensional Gaussian and low-dimensional translation-invariant model. Under power-law assumptions on the spectrum of the kernel and target, we use our framework to (i) give full loss asymptotics for both noisy and noiseless observations (ii) show that the loss localizes on certain spectral scales, giving a new perspective on the KRR saturation phenomenon (iii) conjecture, and demonstrate for the considered data models, the universality of the loss w.r. t. non-spectral details of the problem, but only in case of noisy observation.
Impact of Regional Rangeland Cover Degradation on Increasing Dusty Days in West of Iran

Dust events of Iran mainly originate from Iraq, Syria, Saudi Arabia, Kuwait and inland territories which are influenced by droughts and Land Use/Land Cover (LU/LC) degradation in regional scale. The aim of this research was to investigate the impact of Regional Vegetation Cover Degradation (RVCD), particularly Regional Rangeland Cover Degradation (RRCD) on frequency of dusty days in western provinces of Iran (Khorramabad, Ahvaz, Hamadan and Kermanshah) since 2000. Therefore, the LU/LC and RRCD were evaluated with respect to time-series MODIS satellite images and NDVI Index. The trend of RRCD was predicted by Markov chain analysis for 2030, 2060 and 2100. The accuracy analysis of comparing the observed and predicted LU/LC classes to 2000 and 2016 indicated the absolute value of error around 5.49%. The findings showed that probability of changing from water body, high-cover and low-cover classes to non-cover class would probably be 55% and 62% during 2016-2030 and 2030-2060, respectively. Durability of non-cover class was 89% during 2000-2016. Thus, the area of non-cover class increased to 410 km2 in the study region. In general, it could be noted that increasing of RRCD and drought are the main causes of dusty days increasing from 2000 to 2060.
The importance of ultrasound in staging and gaining a pathological diagnosis in patients with lung cancer—a two year single centre experience

Background Initial studies on the use of ultrasound in the detection and sampling of supraclavicular lymph nodes in patients with suspected lung cancer show this to be a promising technique, giving both a cytological diagnosis and pathological N3 (pN3) stage. Leicester published its initial experience in 2005 and the aim of this study was to establish if this had been embedded into the diagnostic pathway, and further to examine the use of ultrasound in diagnosing and staging lung cancer by imaging other areas including pleural effusions, chest wall, bone and liver lesions. Methods All patients diagnosed with lung cancer, registered on the Leicester lung cancer database over a two year period between January 2007 and December 2008, had their imaging and pathology retrospectively reviewed; 996 primary lung cancer patients were identified (n=996). Of these, 318 patients underwent an ultrasound examination (n=318), consisting of ultrasound of the neck, pleural cavity, and metastatic lesions potentially amenable to ultrasound guided aspiration/biopsy. Results The overall malignant yield was 45% of patients scanned (95% CI 39.5% to 50.4%) and 81.3% of patients sampled (95% CI 75.5% to 87%). Of the 996 patients, 14.4% (n=143) had a positive ultrasound guided cytological diagnosis (95% CI 12.2% to 16.5%). Of all the pathological diagnoses (n=765), 18.7% were ultrasound guided (95% CI 15.9% to 21.5%). In particular, 32.2% of patients with CT detected neck or mediastinal nodes had a diagnosis and stage achieved by neck ultrasound. Conclusion The use of ultrasound gives a rapid and less invasive method of diagnosing and staging lung cancer and has become embedded into the diagnostic pathway. We advocate its increased use and availability in patients with lung cancer.
Foot Length, Chest Circumference, and Mid Upper Arm Circumference Are Good Predictors of Low Birth Weight and Prematurity in Ethnic Minority Newborns in Vietnam: A Hospital-Based Observational Study

Background The evaluation of tools to accurately identify low birth weight (LBW) and/or premature newborns in resource-limited countries is a research priority. We explored the use of foot length, chest circumference, and mid-upper arm circumference (MUAC) measured within 24 h as diagnostic tools for identifying newborns who are LBW, premature, or both; and compared measurements taken at birth with those taken at five days of age. Materials and Methods An observational study was undertaken in Hoa Binh Province General Hospital, Vietnam, in ethnic minority newborns. Birth weight, foot length, chest circumference, and MUAC were measured within 24 h of birth and in a subset of 200, were repeated on day five of life. Gestational age was estimated using the New Ballard Score. Receiver Operating Characteristic curves and optimal cut-points (the point with the highest sensitivity and specificity where the sensitivity was at least 0.8) were calculated, for predicting prematurity, LBW, and both. Measurements within 24 h and at five days of life were compared. Results 485 newborns were recruited. Chest circumference and MUAC measured within 24 h of birth were found to be highly predictive of LBW (both yielding area under the curve [AUC] of 0.98, 95% confidence interval [CI] 0.96–0.99), and performed marginally better than foot length (AUC 0.94, 95%CI 0.92–0.96). The optimal cut-points for measurements within 24 h of birth were ≤7.4cm for foot length; ≤30.4cm for chest circumference; and ≤ 9.0cm for MUAC. There was statistical evidence that anthropometric measurements taken within 24 h of birth were higher than measurements on day five (p<0.02 for all anthropometric measurements) but the magnitude of these differences was small (at most 2mm). Conclusions All measurements taken within 24 h of birth were good predictors of LBW, prematurity and both. Differences in measurements taken within 24 h and on day five were not clinically relevant. Further research will ensure that the application of these measures is reliable in community settings.
The Role of Context and Resilient Middleware in Next Generation Smart Grids

The emerging trends of volatile distributed energy resources and micro-grids are putting pressure on electrical power system infrastructure. This pressure is motivating the integration of digital technology and advanced power-industry practices to improve the management of distributed electricity generation, transmission, and distribution, thereby creating a web of systems. Unlike legacy power system infrastructure, however, this emerging next-generation smart grid should be context-aware and adaptive to enable the creation of applications needed to enhance grid robustness and efficiency. This paper describes key factors that are driving the architecture of smart grids and describes orchestration middleware needed to make the infrastructure resilient. We use an example of adaptive protection logic in smart grid substations as a use case to motivate the need for contextawareness and adaptivity.
Exploring Facial Expression Recognition through Semi-Supervised Pretraining and Temporal Modeling

Facial Expression Recognition (FER) plays a crucial role in computer vision and finds extensive applications across various fields. This paper aims to present our approach for the upcoming 6th Affective Behavior Analysis in-the-Wild (ABAW) competition, scheduled to be held at CVPR2024. In the facial expression recognition task, The limited size of the FER dataset poses a challenge to the expression recognition model's generalization ability, resulting in subpar recognition performance. To address this problem, we employ a semi-supervised learning technique to generate expression category pseudo-labels for unlabeled face data. At the same time, we uniformly sampled the labeled facial expression samples and implemented a debiased feedback learning strategy to address the problem of category imbalance in the dataset and the possible data bias in semi-supervised learning. Moreover, to further compensate for the limitation and bias of features obtained only from static images, we introduced a Temporal Encoder to learn and capture temporal relationships between neighbouring expression image features. In the 6th ABAW competition, our method achieved outstanding results on the official validation set, a result that fully confirms the effectiveness and competitiveness of our proposed method.
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite

Generative query rewrite generates reconstructed query rewrites using the conversation history while rely heavily on gold rewrite pairs that are expensive to obtain. Recently, few-shot learning is gaining increasing popularity for this task, whereas these methods are sensitive to the inherent noise due to limited data size. Besides, both attempts face performance degradation when there exists language style shift between training and testing cases. To this end, we study low-resource generative conversational query rewrite that is robust to both noise and language style shift. The core idea is to utilize massive unlabeled data to make further improvements via a contrastive co-training paradigm. Specifically, we co-train two dual models (namely Rewriter and Simplifier) such that each of them provides extra guidance through pseudo-labeling for enhancing the other in an iterative manner. We also leverage contrastive learning with data augmentation, which enables our model pay more attention on the truly valuable information than the noise. Extensive experiments demonstrate the superiority of our model under both few-shot and zero-shot scenarios. We also verify the better generalization ability of our model when encountering language style shift.
Shared Autonomy via Variable Impedance Control and Virtual Potential Fields for Encoding Human Demonstration

This article introduces a framework for complex human-robot collaboration tasks, such as the co-manufacturing of furniture. For these tasks, it is essential to encode tasks from human demonstration and reproduce these skills in a compliant and safe manner. Therefore, two key components are addressed in this work: motion generation and shared autonomy. We propose a motion generator based on a time-invariant potential field, capable of encoding wrench profiles, complex and closed-loop trajectories, and additionally incorporates obstacle avoidance. Additionally, the paper addresses shared autonomy (SA) which enables synergetic collaboration between human operators and robots by dynamically allocating authority. Variable impedance control (VIC) and force control are employed, where impedance and wrench are adapted based on the human-robot autonomy factor derived from interaction forces. System passivity is ensured by an energy-tank based task passivation strategy. The framework's efficacy is validated through simulations and an experimental study employing a Franka Emika Research 3 robot.
Vehicle single track modeling using physics guided neural differential equations

In this paper, we follow the physics guided modeling approach and integrate a neural differential equation network into the physical structure of a vehicle single track model. By relying on the kinematic relations of the single track ordinary differential equations (ODE), a small neural network and few training samples are sufficient to substantially improve the model accuracy compared with a pure physics based vehicle single track model. To be more precise, the sum of squared error is reduced by 68% in the considered scenario. In addition, it is demonstrated that the prediction capabilities of the physics guided neural ODE model are superior compared with a pure black box neural differential equation approach.