Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hierarchical Approach to Active Pose Estimation

Creating mobile robots which are able to find and manipulate objects in large environments is an active topic of research. These robots not only need to be capable of searching for specific objects but also to estimate their poses often relying on environment observations, which is even more difficult in the presence of occlusions. Therefore, to tackle this problem we propose a simple hierarchical approach to estimate the pose of a desired object. An Active Visual Search module operating with RGB images first obtains a rough estimation of the object 2D pose, followed by a more computationally expensive Active Pose Estimation module using point cloud data. We empirically show that processing image features to obtain a richer observation speeds up the search and pose estimation computations, in comparison to a binary decision that indicates whether the object is or not in the current image.
Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations

This work aims to learn how to perform complex robot manipulation tasks that are composed of several, consecutively executed low-level sub-tasks, given as input a few visual demonstrations of the tasks performed by a person. The sub-tasks consist of moving the robot's end-effector until it reaches a sub-goal region in the task space, performing an action, and triggering the next sub-task when a pre-condition is met. Most prior work in this domain has been concerned with learning only low-level tasks, such as hitting a ball or reaching an object and grasping it. This paper describes a new neural network-based framework for learning simultaneously low-level policies as well as high-level policies, such as deciding which object to pick next or where to place it relative to other objects in the scene. A key feature of the proposed approach is that the policies are learned directly from raw videos of task demonstrations, without any manual annotation or post-processing of the data. Empirical results on object manipulation tasks with a robotic arm show that the proposed network can efficiently learn from real visual demonstrations to perform the tasks, and outperforms popular imitation learning algorithms.
Semantic-Preserving Linguistic Steganography by Pivot Translation and Semantic-Aware Bins Coding

Linguistic steganography (LS) aims to embed secret information into a highly encoded text for covert communication. It can be roughly divided to two main categories, i.e., modification based LS (MLS) and generation based LS (GLS). MLS embeds secret data by slightly modifying a given text without impairing the meaning of the text, whereas GLS uses a well trained language model to directly generate a text carrying secret data. A common disadvantage for MLS methods is that the embedding payload is very small, whose return is well preserving the semantic quality of the text. In contrast, GLS enables the data hider to embed a large payload, which has to pay the high price of uncontrollable semantics. In this article, we propose a novel LS method to modify a given text by pivoting it between two different languages and embed secret data using a semantic-aware information encoding strategy. Our purpose is to alter the expression of the given text, enabling a large payload to be embedded while keeping the semantic information unchanged. Experiments have shown that the proposed work not only achieves a large embedding payload, but also shows superior performance in maintaining the semantic consistency and resisting linguistic steganalysis.
Structural Thermokinetic Modelling

To translate metabolic networks into dynamic models, the Structural Kinetic Modelling framework (SKM) assumes a given reference state and replaces the reaction elasticities in this state by random numbers. A new variant, called Structural Thermokinetic Modelling (STM), accounts for reversible reactions and thermodynamics. STM relies on a dependence schema in which some basic variables are sampled, fitted to data, or optimised, while all other variables can be easily computed. Correlated elasticities follow from enzyme saturation values and thermodynamic forces, which are physically independent. Probability distributions in the dependence schema define a model ensemble, which allows for probabilistic predictions even if data are scarce. STM highlights the importance of variabilities, dependencies, and covariances of biological variables. By varying network structure, fluxes, thermodynamic forces, regulation, or types of rate laws, the effects of these model features can be assessed. By choosing the basic variables, metabolic networks can be converted into kinetic models with consistent reversible rate laws. Metabolic control coefficients obtained from these models can tell us about metabolic dynamics, including responses and optimal adaptations to perturbations, enzyme synergies and metabolite correlations, as well as metabolic fluctuations arising from chemical noise. To showcase STM, I study metabolic control, metabolic fluctuations, and enzyme synergies, and how they are shaped by thermodynamic forces. Considering thermodynamics can improve predictions of flux control, enzyme synergies, correlated flux and metabolite variations, and the emergence and propagation of metabolic noise.
GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning

In this paper, we propose GIFAIR-FL, a framework that imposes group and individual fairness (GIFAIR) to federated learning (FL) settings. By adding a regularization term, our algorithm penalizes the spread in the loss of client groups to drive the optimizer to fair solutions. Our framework GIFAIR-FL can accommodate both global and personalized settings. Theoretically, we show convergence in nonconvex and strongly convex settings. Our convergence guarantees hold for both independent and identically distributed (i.i.d.) and non-i.i. d. data. To demonstrate the empirical performance of our algorithm, we apply our method to image classification and text prediction tasks. Compared with existing algorithms, our method shows improved fairness results while retaining superior or similar prediction accuracy.
Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers

We introduce AiD Regen, a novel system that generates 3D wound models combining 2D semantic segmentation with 3D reconstruction so that they can be printed via 3D bio-printers during the surgery to treat diabetic foot ulcers (DFUs). AiD Regen seamlessly binds the full pipeline, which includes RGB-D image capturing, semantic segmentation, boundary-guided point-cloud processing, 3D model reconstruction, and 3D printable G-code generation, into a single system that can be used out of the box. We developed a multi-stage data preprocessing method to handle small and unbalanced DFU image datasets. AiD Regen's human-in-the-loop machine learning interface enables clinicians to not only create 3D regenerative patches with just a few touch interactions but also customize and confirm wound boundaries. As evidenced by our experiments, our model outperforms prior wound segmentation models and our reconstruction algorithm is capable of generating 3D wound models with compelling accuracy. We further conducted a case study on a real DFU patient and demonstrated the effectiveness of AiD Regen in treating DFU wounds.
Data adaptive RKHS Tikhonov regularization for learning kernels in operators

We present DARTR: a Data Adaptive RKHS Tikhonov Regularization method for the linear inverse problem of nonparametric learning of function parameters in operators. A key ingredient is a system intrinsic data-adaptive (SIDA) RKHS, whose norm restricts the learning to take place in the function space of identifiability. DARTR utilizes this norm and selects the regularization parameter by the L-curve method. We illustrate its performance in examples including integral operators, nonlinear operators and nonlocal operators with discrete synthetic data. Numerical results show that DARTR leads to an accurate estimator robust to both numerical error due to discrete data and noise in data, and the estimator converges at a consistent rate as the data mesh refines under different levels of noises, outperforming two baseline regularizers using $l^2$ and $L^2$ norms.
Tensors and Algebras: An Algebraic Spacetime Interpretation for Tensor Models

The quest for a consistent theory for quantum gravity is one of the most challenging problems in theoretical high-energy physics. An often-used approach is to describe the gravitational degrees of freedom by the metric tensor or related variables, and finding a way to quantise this. In the canonical tensor model, the gravitational degrees of freedom are encoded in a tensorial quantity $P_{abc}$, and this quantity is subsequently quantised. This makes the quantisation much more straightforward mathematically, but the interpretation of this tensor as a spacetime is less evident. In this work we take a first step towards fully understanding the relationship to spacetime. By considering $P_{abc}$ as the generator of an algebra of functions, we first describe how we can recover the topology and the measure of a compact Riemannian manifold. Using the tensor rank decomposition, we then generalise this principle in order to have a well-defined notion of the topology and geometry for a large class of tensors $P_{abc}$. We provide some examples of the emergence of a topology and measure of both exact and perturbed Riemannian manifolds, and of a purely algebraically-defined space called the semi-local circle.
Image Search with Text Feedback by Additive Attention Compositional Learning

Effective image retrieval with text feedback stands to impact a range of real-world applications, such as e-commerce. Given a source image and text feedback that describes the desired modifications to that image, the goal is to retrieve the target images that resemble the source yet satisfy the given modifications by composing a multi-modal (image-text) query. We propose a novel solution to this problem, Additive Attention Compositional Learning (AACL), that uses a multi-modal transformer-based architecture and effectively models the image-text contexts. Specifically, we propose a novel image-text composition module based on additive attention that can be seamlessly plugged into deep neural networks. We also introduce a new challenging benchmark derived from the Shopping100k dataset. AACL is evaluated on three large-scale datasets (FashionIQ, Fashion200k, and Shopping100k), each with strong baselines. Extensive experiments show that AACL achieves new state-of-the-art results on all three datasets.
Analyzing General-Purpose Deep-Learning Detection and Segmentation Models with Images from a Lidar as a Camera Sensor

Over the last decade, robotic perception algorithms have significantly benefited from the rapid advances in deep learning (DL). Indeed, a significant amount of the autonomy stack of different commercial and research platforms relies on DL for situational awareness, especially vision sensors. This work explores the potential of general-purpose DL perception algorithms, specifically detection and segmentation neural networks, for processing image-like outputs of advanced lidar sensors. Rather than processing the three-dimensional point cloud data, this is, to the best of our knowledge, the first work to focus on low-resolution images with 360\textdegree field of view obtained with lidar sensors by encoding either depth, reflectivity, or near-infrared light in the image pixels. We show that with adequate preprocessing, general-purpose DL models can process these images, opening the door to their usage in environmental conditions where vision sensors present inherent limitations. We provide both a qualitative and quantitative analysis of the performance of a variety of neural network architectures. We believe that using DL models built for visual cameras offers significant advantages due to the much wider availability and maturity compared to point cloud-based perception.
A New 27 Class Sign Language Dataset Collected from 173 Individuals

After the interviews, it has been comprehended that speech-impaired individuals who use sign languages have difficulty communicating with other people who do not know sign language. Due to the communication problems, the sense of independence of speech-impaired individuals could be damaged and lead them to socialize less with society. To contribute to the development of technologies, that can reduce the communication problems of speech-impaired persons, a new dataset was presented with this paper. The dataset was created by processing American Sign Language-based photographs collected from 173 volunteers, published as "27 Class Sign Language Dataset” on the Kaggle Datasets web page. Dataset is available at https://www.kaggle. com/ardamavi/27-class-sign-language-dataset upon publication of this paper.
Enhancing Door Detection for Autonomous Mobile Robots with Environment-Specific Data Collection

—Door detection represents a fundamental capability for autonomous mobile robots employed in tasks involving indoor navigation. Recognizing the presence of a door and its status (open or closed) can induce a remarkable impact on the navigation performance, especially for dynamic settings where doors can enable or disable passages, hence changing the actual topology of the map. In this work, we address the problem of building a door detector module for an autonomous mobile robot deployed in a long–term scenario, namely operating in the same environment for a long time, thus observing the same set of doors from different points of view. First, we show how the mainstream approach for door detection, based on object recognition, falls short in considering the constrained perception setup typical of a mobile robot. Hence, we devise a method to build a dataset of images taken from a robot’s perspective and we exploit it to obtain a door detector based on an established deep–learning object–recognition method. We then exploit the long–term assumption of our scenario to qualify the model on the robot working environment via ﬁne–tuning with additional images acquired in the deployment environment. Our experimental analysis shows how this method can achieve good performance and highlights a trade–off between costs and beneﬁts of the ﬁne–tuning approach.
Kinetic and kinematic evaluation of kneeling work

Kneeling work is considered an occupational hazard that may ultimately result in lower extremity diseases such as osteoarthritis. Previous studies indicate that there is a relationship between exposure to knee-stressing work activities and the development of knee osteoarthritis. This relationship is understudied and remains unclear. This study aims at understanding the kinetics and kinematics of hip and knee joints and trunk during simulated kneeling work with and without the use of kneepads. Ten participants were recruited and consented to perform a series of kneeling tasks that simulated a tile setting task. Five wooden platforms were placed in five locations surrounding two force plates. Participants were asked to reach five locations and randomly place a ceramic tile on the wooden platforms. Prior to performing the tasks, 36 markers were placed on anatomical land marks. The simulation was recorded using ARENA by Natural PointTM, with 18 cameras. The data recorded from the force plates and motion capture were combined and analyzed using MatlabTM and OpenSimTM to calculate joint angles and joint moments. The outputs from OpenSimTM were normalized and then analyzed using JMPTM. Dunnett’s control method was used to compare the means of the independent variables. Results indicated that hip and knee kinematics and kinetics are influenced by reach location and kneepad used. Lumbar extension moment is observed to remain the same with respect to reach location and kneepad used while lumbar bending and rotation moments were altered. These results strengthen the need to study kneeling work and
Tuberculosis Presenting as Acute Sepsis and Secondary Hemophagocytic Lymphohistiocytosis

We report the case of a 61-year-old man admitted to our emergency department with fever. At admission, he was hypotensive and tachycardic. In the initial investigation, elevation of inflammatory parameters, acute kidney injury (Kidney Disease Improving Global Outcomes (KDIGO) 3), hyperbilirubinemia, and hepatic cytocholestasis were evident. Empirical antibiotic therapy was started, after sepsis was assumed without an identifiable cause. His condition took an unfavorable clinical course, with respiratory failure, hepatosplenomegaly, pancytopenia, hyperferritinemia and hypofibrinogenemia. Microbial culture studies and a general immunological study were negative and lymphoproliferative disease was therefore excluded. Bone marrow aspirate revealed hemophagocytosis without granulomas. A diagnosis of hemophagocytic lymphohistiocytosis was assumed and pulse methylprednisolone therapy initiated. As this resulted in only a transient improvement, immunoglobulin and rituximab were initiated as a second-line therapy. The patient sadly had an unfavorable outcome despite all measures undertaken. In the postmortem study, Mycobacterium tuberculosis complex was isolated in the bone marrow aspirate, which led to the postmortem diagnosis of disseminated tuberculosis and angioinvasive pulmonary aspergillosis. The clinical presentation of disseminated tuberculosis is non-specific and hemophagocytic lymphohistiocytosis is one of its rare presentations. The mortality rate of hemophagocytic lymphohistiocytosis is high and increases with delayed diagnosis of the underlying condition and respective treatment. LEARNING POINTS Hemophagocytic lymphohistiocytosis should be considered in patients presenting with fever, lymphadenopathy, splenomegaly, cytopenias, hyperferritinaemia and hypertriglyceridemia. Despite its rarity, tuberculosis should be considered as an etiology of hemophagocytic lymphohistiocytosis and, if suspected, antituberculosis therapy should be initiated early, even in the absence of a definite diagnosis. Immunosuppressant therapy increases the risk of opportunistic infections, which establishes the need for prophylactic antibiotic, antifungal, and antiviral drugs.
Microdeletions and vertical transmission of the Y-chromosome azoospermia factor region

Spermatogenesis is regulated by several Y chromosome-specific genes located in a specific region of the long arm of the Y chromosome, the azoospermia factor region (AZF). AZF microdeletions are the main structural chromosomal abnormalities that cause male infertility. Assisted reproductive technology (ART) has been used to overcome natural fertilization barriers, allowing infertile couples to have children. However, these techniques increase the risk of vertical transmission of genetic defects. Despite widespread awareness of AZF microdeletions, the occurrence of de novo deletions and overexpression, as well as the expansion of AZF microdeletion vertical transmission, remains unknown. This review summarizes the mechanism of AZF microdeletion and the function of the candidate genes in the AZF region and their corresponding clinical phenotypes. Moreover, vertical transmission cases of AZF microdeletions, the impact of vertical inheritance on male fertility, and the prospective direction of research in this field are also outlined.
Medicinal Plants in Cancer Treatment: Contribution of Nuclear Factor-Kappa B (NF-kB) Inhibitors.

Nuclear factor-kappa B (NF-κB) is one of the principal inducible proteins that is a predominant transcription factor known to control the gene expression in mammals and plays a pivotal role in regulating cell signalling in the body under certain physiological and pathological conditions. In cancer cells, such as colon, breast, pancreatic, ovarian, melanoma, and lymphoma, the NF-κB pathway has been reported to be active. In cellular proliferation, promoting angiogenesis, invasion, metastasis of tumour cells and blocking apoptosis, the constitutive activity of NF-κB signalling has been reported. Therefore, immense attention has been given to developing drugs targeting NF-κB signalling pathways to treat many types of tumours. They are a desirable therapeutic target for drugs, and many studies concentrated on recognizing compounds. They may be able to reverse or standstill the growth and spread of tumours that selectively interfere with this pathway. Recently, numerous substances derived from plants have been evaluated as possible inhibitors of the NF-κB pathway. These include various compounds, such as flavonoids, lignans, diterpenes, sesquiterpenes, polyphenols, etc. A study supported by folk medicine demonstrated that plant-derived compounds could suppress NF-κB signalling. Taking this into account, the present review revealed the anticancer potential of naturally occurring compounds which have been verified both by inhibiting the NF-κB signalling and suppressing growth and spread of cancer and highlighting their mechanism of NF-κB inhibition.
Predictive Model of Early Death of Resectable Pancreatic Ductal Adenocarcinoma After Curative Resection: A SEER-Based Study

Objective This study aims to determine the factors that predict early death and establish a predictive model for early death by analyzing clinical characteristics of patients with resectable pancreatic ductal adenocarcinoma (R-PDAC) who die early after radical surgery. Materials and Methods This was a retrospective study of patients who underwent radical surgical resection for R-PDAC in the Surveillance, Epidemiology, and End Results (SEER) database. Patients with overall survival ≤ 12 months were assigned as early death group and above 1 year as the late death group. Univariate and multivariate logistic regression was conducted to identify factors significantly associated with early death. An early death predictive model was constructed based on the identified independent risk factors. Results A total of 9695 patients were analyzed, and the total incidence of early death was 30.72%. Multivariable analysis showed that factors significantly associated with early death included age at diagnosis, race, marital status, tumor location, tumor size, tumor grade, number of positive lymph nodes, number of examined lymph nodes, positive lymph node ratio, chemotherapy, and radiotherapy. The predictive model showed good discrimination with a C-index of 0.722 (95% confidence interval: 0.711-0.733) and convincing calibration. Conclusions We developed a predictive model that may be easily applied to patients with R-PDAC after radical resection to predict the chance of death within 1 year. For patients with high risk of early death, neoadjuvant therapy should be considered. Even after radical resection, more aggressive adjuvant chemotherapy (with or without combined radiotherapy) must be used to minimize the chance of early death.