Miracle-dz/UniFAR-FTUs huggingface dataset:
SwarmPRM: Probabilistic Roadmap Motion Planning for Large-Scale Swarm Robotic Systems

Large-scale swarm robotic systems consisting of numerous cooperative agents show considerable promise for performing autonomous tasks across various sectors. Nonetheless, traditional motion planning approaches often face a trade-off between scalability and solution quality due to the exponential growth of the joint state space of robots. In response, this work proposes SwarmPRM, a hierarchical, scalable, computationally efficient, and risk-aware sampling-based motion planning approach for large-scale swarm robots. SwarmPRM utilizes a Gaussian Mixture Model (GMM) to represent the swarm's macroscopic state and constructs a Probabilistic Roadmap in Gaussian space, referred to as the Gaussian roadmap, to generate a transport trajectory of GMM. This trajectory is then followed by each robot at the microscopic stage. To enhance trajectory safety, SwarmPRM incorporates the conditional value-at-risk (CVaR) in the collision checking process to impart the property of risk awareness to the constructed Gaussian roadmap. SwarmPRM then crafts a linear programming formulation to compute the optimal GMM transport trajectory within this roadmap. Extensive simulations demonstrate that SwarmPRM outperforms state-of-the-art methods in computational efficiency, scalability, and trajectory quality while offering the capability to adjust the risk tolerance of generated trajectories.
Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review

Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analysing behaviours through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most studies often assume uniform data distributions across datasets, contrasting with the varied nature of practical sensor data in human activities. Addressing data heterogeneity issues can improve performance, reduce computational costs, and aid in developing personalized, adaptive models with less annotated data. This review investigates how machine learning addresses data heterogeneity in HAR, by categorizing data heterogeneity types, applying corresponding suitable machine learning methods, summarizing available datasets, and discussing future challenges.
PCa-RadHop: A Transparent and Lightweight Feed-forward Method for Clinically Significant Prostate Cancer Segmentation

Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity. Also, DL models lack of feature interpretability and are perceived as ``black-boxes"in the medical field. PCa-RadHop pipeline is proposed in this work, aiming to provide a more transparent feature extraction process using a linear model. It adopts the recently introduced Green Learning (GL) paradigm, which offers a small model size and low complexity. PCa-RadHop consists of two stages: Stage-1 extracts data-driven radiomics features from the bi-parametric Magnetic Resonance Imaging (bp-MRI) input and predicts an initial heatmap. To reduce the false positive rate, a subsequent stage-2 is introduced to refine the predictions by including more contextual information and radiomics features from each already detected Region of Interest (ROI). Experiments on the largest publicly available dataset, PI-CAI, show a competitive performance standing of the proposed method among other deep DL models, achieving an area under the curve (AUC) of 0.807 among a cohort of 1,000 patients. Moreover, PCa-RadHop maintains orders of magnitude smaller model size and complexity.
Multi-radius Soler-Williams Augmented Plane Waves (SAPMR) and multi-radius Soler-Williams Linearized Augmented Plane Waves (SLAPWMR)

In this work we present a new basis set for electronic structures (Density Functional Theory (DFT)) calculations. It extends Soler Williams Linearized Augmented plane Wave (SLAPW) basis sets by allowing variable radii for the different angular momentum channels and wave vectors. This allows to match additional derivatives of the wave function at the Muffin Tin (MT) radius without having to resort to additional higher derivative terms. This should lead to low wave vector basis set and low linearization energy errors. We dub these basis sets SAPWMR and SLAPWMR. Similarly Local orbital (LO and lo) basis wave functions are suggested with a variable radius. Full Potential Hamiltonian calculations (FLAPW) are extended to FSLAPWMR full potential calculations in the appendix.
CLHA: A Simple yet Effective Contrastive Learning Framework for Human Alignment

Reinforcement learning from human feedback (RLHF) is a crucial technique in aligning large language models (LLMs) with human preferences, ensuring these LLMs behave in beneficial and comprehensible ways to users. However, a longstanding challenge in human alignment techniques based on reinforcement learning lies in their inherent complexity and difficulty in training. To address this challenge, we present a simple yet effective Contrastive Learning Framework for Human Alignment (CLHA) to align LLMs with human preferences directly. CLHA employs a novel rescoring strategy to evaluate the noise within the data by considering its inherent quality and dynamically adjusting the training process. Simultaneously, CLHA utilizes pairwise contrastive loss and adaptive supervised fine-tuning loss to adaptively modify the likelihood of generating responses, ensuring enhanced alignment with human preferences. Using advanced methods, CLHA surpasses other algorithms, showcasing superior performance in terms of reward model scores, automatic evaluations, and human assessments on the widely used ``Helpful and Harmless'' dataset.
Etiology‐dependent molecular mechanisms in human hepatocarcinogenesis

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and is characterized by aggressive tumor behavior coupled with poor prognosis. Various etiologies have been linked to HCC development, most prominently chronic hepatitis B and C virus infections as well as chronic alcohol consumption. In approximately 10% of HCCs, the etiology remains cryptic; however, recent epidemiological data suggest that most of these cryptogenic HCCs develop due to nonalcoholic steatohepatitis. To identify etiology‐dependent DNA copy number aberrations and genes relevant to hepatocarcinogenesis, we performed array‐based comparative genomic hybridization of 63 HCCs of well‐defined etiology and 4 HCC cell lines followed by gene expression profiling and functional analyses of candidate genes. For a 10‐megabase chromosome region on 8q24, we observed etiology‐dependent copy number gains and MYC overexpression in viral and alcohol‐related HCCs, resulting in up‐regulation of MYC target genes. Cryptogenic HCCs showed neither 8q24 gains, nor MYC overexpression, nor target gene activation, suggesting that tumors of this etiology develop by way of a distinct MYC‐independent pathomechanism. Furthermore, we detected several etiology‐independent small chromosome aberrations, including amplification of MDM4 on 1q32.1 and frequent gains of EEF1A2 on 20q13.33. Both genes were overexpressed in approximately half the HCCs examined, and gene silencing reduced cell viability as well as proliferation and increased apoptosis rates in HCC cell lines. Conclusion: Our findings suggest that MDM4 and EEF1A2 act as etiology‐independent oncogenes in a significant percentage of HCCs. (HEPATOLOGY 2008.)
CLOX: an executive clock drawing task

OBJECTIVE To describe a clock drawing task (CLOX) designed to elicit executive impairment and discriminate it from non-executive constructional failure. SUBJECTS 90 elderly subjects were studied (45 elderly and well persons from the independent living apartments of a continuing care retirement community and 45 patients with probable Alzheimer’s disease). The clock drawing performance of elderly patients was compared with that of 62 young adult controls. METHODS Subjects received the CLOX, an executive test (EXIT25), and the mini mental state examination (MMSE). The CLOX is divided into an unprompted task that is sensitive to executive control (CLOX1) and a copied version that is not (CLOX2). Between rater reliability (27 subjects) was high for both subtests. RESULTS In elderly subjects, CLOX subscores correlated strongly with cognitive severity (CLOX1:r=−0.83 v the EXIT25; CLOX2:r=0.85 v the MMSE). EXIT25 and MMSE scores predicted CLOX1 scores independently of age or education (F(4,82)=50.7, p<0.001;R 2 =0.71). The EXIT25 accounted for 68% of CLOX1 variance. Only the MMSE significantly contributed to CLOX2 scores (F(4,72)= 57.2, p<0.001;R 2 =0.74). CLOX subscales discriminated between patients with Alzheimer’s disease and elderly controls (83.1% of cases correctly classified; Wilkes’ lambda=0.48, p<0.001), and between Alzheimer’s disease subgroups with and without constructional impairment (91.9% of cases correctly classified; Wilkes’ lambda=0.31, p<0.001). CONCLUSIONS The CLOX is an internally consistent measure that is easy to administer and displays good inter-rater reliability. It is strongly associated with cognitive test scores. The pattern of CLOX failures may discriminate clinical dementia subgroups.
QGSJET-III model of high energy hadronic interactions: II. Particle production and extensive air shower characteristics

The hadronization procedure of the QGSJET-III Monte Carlo (MC) generator of high energy hadronic interactions is discussed. Selected results of the model, regarding production spectra of secondary particles, are presented in comparison to experimental data and to the corresponding predictions of the QGSJET-II-04 MC generator. The model is applied to calculations of basic characteristics of extensive air showers initiated by cosmic ray interactions in the atmosphere and the results are compared to predictions of other MC generators of cosmic ray interactions.
Mantle Degassing Through Continental Crust Triggered by Active Faults: The Case of the Baja California Peninsula, Mexico

In this work, we report new chemical and isotopic data (3He/4He, δ13CCO2, δ13CCH4, and δDCH4) from poorly or previously unstudied hydrothermal and magmatic gases that are emitted along the eastern coast of the Baja California Peninsula (BCP). High 3He/4He values (up to ~7 Ra) characterize the magmatic gases, while lower ratios (≤1.6 Ra) characterize hydrothermal springs. We infer that the mantle beneath the BCP could be Mid‐ocean‐ridge basalt (MORB)‐likes, as in the rift within the Gulf of California, or it may reflect contamination from C‐rich sediment during paleo‐subduction of the Farallon plate. During their ascent, through the crust, mantle/magmatic gases mix with CO2‐ and 4He‐rich fluids, thus forming CO2‐rich hydrothermal gases. These hydrothermal gases undergo partial dissolution of CO2 in shallow waters under different temperature and pH conditions, which further modifies their composition. Thermogenic and possibly abiogenic sources of methane are present only in magmatic gases from the BCP. Secondary methane oxidation (microbial/inorganic) processes are proposed for some hydrothermal gases, which are extremely enriched in heavy isotopes. Finally, we argue that the hydrothermal gases that are emitted from the BCP have variable percentages of mantle contribution, indicating the presence of lithospheric faults enhancing the rise of mantle fluids also in areas where volcanism is absent.
Learning to Detect Slip through Tactile Measures of the Contact Force Field and its Entropy

—Detection of slip during object grasping and manipulation plays a vital role in object handling. Existing solutions largely depend on visual information to devise a strategy for grasping. Nonetheless, in order to achieve proﬁciency akin to humans and achieve consistent grasping and manipulation of unfamiliar objects, the incorporation of artiﬁcial tactile sensing has become a necessity in robotic systems. In this work, we propose a novel physics-informed, data-driven method to detect slip continuously in real time. The GelSight Mini, an optical tactile sensor, is mounted on custom grippers to acquire tactile readings. Our work leverages the inhomogeneity of tactile sensor readings during slip events to develop distinctive features and formulates slip detection as a classiﬁcation problem. To evaluate our approach, we test multiple data-driven models on 10 common objects under different loading conditions, textures, and materials. Our results show that the best classiﬁcation al-gorithm achieves an average accuracy of 99%. We demonstrate the application of this work in a dynamic robotic manipulation task in which real-time slip detection and prevention algorithm is implemented.
Challenges Requiring the Combination of Machine Learning and Knowledge Engineering

The AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering brought together researchers and practitioners from machine learning and knowledge engineering. The goal was to explore how combining these two fields can help address future AI challenges. The symposium included a joint keynote presentation by AI pioneers, over 25 presentations by contributors and authors who shared their research findings, and two challenges for the community to tackle in a follow-up event. This paper reports on the symposium and focuses on the current trend of generative AI and large language models (LLMs) and its possible synergy with knowledge-based systems (KBS), as the keynote speakers and the symposium chair emphasized. The discussions highlighted the potential of combining KBS’s knowledge representation capabilities with LLMs’ language generation capabilities.
European Society of Gynaecological Oncology Guidelines for the Management of Patients With Vulvar Cancer

Objective The aim of this study was to develop clinically relevant and evidence-based guidelines as part of European Society of Gynaecological Oncology’s mission to improve the quality of care for women with gynecologic cancers across Europe. Methods The European Society of Gynaecological Oncology Council nominated an international development group made of practicing clinicians who provide care to patients with vulvar cancer and have demonstrated leadership and interest in the management of patients with vulvar cancer (18 experts across Europe). To ensure that the statements are evidence based, the current literature identified from a systematic search has been reviewed and critically appraised. In the absence of any clear scientific evidence, judgment was based on the professional experience and consensus of the development group (expert agreement). The guidelines are thus based on the best available evidence and expert agreement. Prior to publication, the guidelines were reviewed by 181 international reviewers including patient representatives independent from the development group. Results The guidelines cover diagnosis and referral, preoperative investigations, surgical management (local treatment, groin treatment including sentinel lymph node procedure, reconstructive surgery), radiation therapy, chemoradiation, systemic treatment, treatment of recurrent disease (vulvar recurrence, groin recurrence, distant metastases), and follow-up.
Computerized detection and segmentation of mitochondria on electron microscope images

Mitochondrial function plays an important role in the regulation of cellular life and death, including disease states. Disturbance in mitochondrial function and distribution can be accompanied by significant morphological alterations. Electron microscopy tomography (EMT) is a powerful technique to study the 3D structure of mitochondria, but the automatic detection and segmentation of mitochondria in EMT volumes has been challenging due to the presence of subcellular structures and imaging artifacts. Therefore, the interpretation, measurement and analysis of mitochondrial distribution and features have been time consuming, and development of specialized software tools is very important for high‐throughput analyses needed to expedite the myriad studies on cellular events. Typically, mitochondrial EMT volumes are segmented manually using special software tools. Automatic contour extraction on large images with multiple mitochondria and many other subcellular structures is still an unaddressed problem. The purpose of this work is to develop computer algorithms to detect and segment both fully and partially seen mitochondria on electron microscopy images. The detection method relies on mitochondria's approximately elliptical shape and double membrane boundary. Initial detection results are first refined using active contours. Then, our seed point selection method automatically selects reliable seed points along the contour, and segmentation is finalized by automatically incorporating a live‐wire graph search algorithm between these seed points. In our evaluations on four images containing multiple mitochondria, 52 ellipses are detected among which 42 are true and 10 are false detections. After false ellipses are eliminated manually, 14 out of 15 fully seen mitochondria and 4 out of 7 partially seen mitochondria are successfully detected. When compared with the segmentation of a trained reader, 91% Dice similarity coefficient was achieved with an average 4.9 nm boundary error.
Probabilistic ray identification: a new tool for ocean acoustic tomography

A crucial point in multipath ocean acoustic tomography (MOAT) is to associate the estimated time delays to acoustical paths in order to make a correct geophysical inversion. The authors propose a theoretic tool to solve the problem of ray identification in term of probabilistic data association and find the theory suitable to be used on a real data tomography experiment for which the classical tools were inefficient because of a large quantity of unresolved and unstable paths. It promises some useful results for future tomography experiment made in difficult acoustic conditions. <<ETX>>
The spatial relationship between exurban development and designated wilder

—Public lands provide recreational opportunities and preserve historic and ecological values. Increases in low-density residential development in the contiguous United States pose a threat not only along the boundaries of national parks and forests, but also around uniquely valuable Wilderness areas. Development within and around protected lands can affect land management and landscape ecology by fragmenting forest and wildlife habitat, diminishing air and water quality, and limiting recreational opportunities and access. Exurban and rural sprawl particularly affects wilderness areas because land development is inconsistent with the nature of wilderness and its associated values. This research uses U.S. Census and land ownership data to identify National Wilderness Preservation System (NWPS) units with exurban or urban housing densities and large amounts of surrounding private land. Identifying NWPS units within 10 miles of the Wilderness boundary that are most likely to experience housing density increases will assist in the management and protection of these valuable lands. 1.0 iNTRoduCTioN Public lands preserve historic and ecological values while also providing unparalleled recreational opportunities. However, increases in low-density residential development in the United States pose a threat along the boundaries of the nation’s public lands. This threat affects not only national parks and forests, but also uniquely valuable Wilderness areas that make up the National Wilderness Preservation System (NWPS) (Cordell et al. 2005, Stein et al. 2006). Wilderness areas are in a unique category of federal land protected through the 964 Wilderness Act, which expressly prohibits human modification of the landscape. Though Wilderness areas are managed by one of four agencies (Bureau of Land Management [BLM], Fish and Wildlife Service [FWS], Forest Service [FS], or National Park Service [NPS]), all Wilderness areas are part of the National Wilderness Preservation System (NWPS). These lands preserve inimitable research and recreational opportunities; provide sources of ecological and biological diversity; and offer oft-perceived aesthetic, existence, bequest, and intrinsic values (Cordell 2005, Noss 99 ). Wilderness areas are particularly affected by exurban and rural sprawl because land development clashes with the nature of wilderness and its associated values (Cordell et al. 2005). Development within and around protected lands can affect both the ecology and management of these ecosystems by increasing forest and wildlife habitat fragmentation and reducing air and water quality. Land ownership patterns may also affect recreational opportunities and access. Thus, housing density increases near Wilderness areas are incompatible with wilderness values and pose challenges to the management of these areas. Capitalization of “Wilderness” denotes federally designated units of the National Wilderness Preservation System in keeping with the current literature.
Non-negative lighting and specular object recognition

Recognition of specular objects is particularly difficult because their appearance is much more sensitive to lighting changes than that of Lambertian objects. We consider an approach in which we use a 3D model to deduce the lighting that best matches the model to the image. In this case, an important constraint is that incident lighting should be non-negative everywhere. In this paper, we propose a new method to enforce this constraint and explore its usefulness in specular object recognition, using the spherical harmonic representation of lighting. The method follows from a novel extension of Szego's eigenvalue distribution theorem to spherical harmonics, and uses semidefinite programming to perform a constrained optimization. The new method is faster as well as more accurate than previous methods. Experiments on both synthetic and real data indicate that the constraint can improve recognition of specular objects by better separating the correct and incorrect models
Taxonomic resolution in parasite community studies: are things getting worse?

SUMMARY Species identification is crucial for studies of parasite diversity, yet most surveys include taxa identified only to genus or family level. Using a large dataset comprising 950 surveys of helminth communities from 650 different species of fish, bird and mammal hosts, we investigated what determines the level of taxonomic resolution achieved. Identification of all helminths down to species level was achieved in only one-third of surveys, whereas all taxa were identified at least to genus level in two-thirds of surveys. The species richness of a parasite community and past study effort on the host species did not correlate with taxonomic resolution. However, the higher the proportion of parasite taxa occurring as larvae in a community, the lower the resolution achieved. Also, taxonomic resolution was better overall for communities in birds or mammals than for those in fish, and better for trematodes and acanthocephalans than for cestodes and nematodes. Perhaps the most intriguing result was a clear significant effect of year of publication on the taxonomic resolution achieved in parasite surveys: the proportion of helminth taxa identified at least to genus level has remained high until the year 2000, but has dropped in studies published since then. The loss of expertize in parasite taxonomy happening worldwide is one possible explanation. This downward trend needs to be reversed if we are to monitor new parasite occurrences in the face of environmental changes.