Miracle-dz/UniFAR-FTUs huggingface dataset:
Budget-aware Semi-Supervised Semantic and Instance Segmentation

Methods that move towards less supervised scenarios are key for image segmentation, as dense labels demand significant human intervention. Generally, the annotation burden is mitigated by labeling datasets with weaker forms of supervision, e.g. image-level labels or bounding boxes. Another option are semi-supervised settings, that commonly leverage a few strong annotations and a huge number of unlabeled/weakly-labeled data. In this paper, we revisit semi-supervised segmentation schemes and narrow down significantly the annotation budget (in terms of total labeling time of the training set) compared to previous approaches. With a very simple pipeline, we demonstrate that at low annotation budgets, semi-supervised methods outperform by a wide margin weakly-supervised ones for both semantic and instance segmentation. Our approach also outperforms previous semi-supervised works at a much reduced labeling cost. We present results for the Pascal VOC benchmark and unify weakly and semi-supervised approaches by considering the total annotation budget, thus allowing a fairer comparison between methods.
Accurate colorectal tumor segmentation for CT scans based on the label assignment generative adversarial network.

PURPOSE Colorectal tumor segmentation is an important step in the analysis and diagnosis of colorectal cancer. This task is a time consuming one since it is often performed manually by radiologists. This paper presents an automatic postprocessing module to refine the segmentation of deep networks. The label assignment generative adversarial network (LAGAN) is improved from the generative adversarial network (GAN) and assigns labels to the outputs of deep networks. We apply the LAGAN to segment colorectal tumors in computed tomography (CT) scans and explore the performances of different combinations of deep networks. MATERIAL AND METHODS A total of 223 patients with colorectal cancer (CRC) are enrolled in the study. The CT scans of the colorectal tumors are first segmented by FCN32 and Unet separately, which output probabilistic maps. Then, the probabilistic maps are labeled by the LAGAN and finally, the binary segmentation results are obtained. The LAGAN consists of a generating model and a discriminating model. The generating model utilizes the probabilistic maps from deep networks to imitate the distribution of the ground truths, and the discriminating model attempts to distinguish generations and ground truths. Through competitive training, the generating model of the LAGAN can realize label assignments for the probabilistic maps. RESULTS The LAGAN increases the DSC of FCN32 from 81.83% ± 0.35% to 90.82% ± 0.36%. In the Unet-based segmentation, the LAGAN increases the DSC from 86.67% ± 0.70% to 91.54% ± 0.53%. It takes approximately 10 ms to refine a single CT slice. CONCLUSIONS The results demonstrate that the LAGAN is a robust and flexible module, which can be used to refine the segmentation of diverse deep networks. Compared with other networks, the LAGAN can achieve desirable segmented accuracy for colorectal tumors.
Analysis of shear wave propagation derived from MR elastography in 3D thigh skeletal muscle using subject specific finite element model

The purpose of this study was to develop a subject specific finite element model derived from MRI images to numerically analyze the MRE (magnetic resonance elastography) shear wave propagation within skeletal thigh muscles. A sagittal T2 CUBE MRI sequence was performed on the 20-cm thigh segment of a healthy male subject. Skin, adipose tissue, femoral bone and 11 muscles were manually segmented in order to have 3D smoothed solid and meshed models. These tissues were modeled with different constitutive laws. A transient modal dynamics analysis was applied to simulate the shear wave propagation within the thigh tissues. The effects of MRE experimental parameters (frequency, force) and the muscle material properties (shear modulus: C10) were analyzed through the simulated shear wave displacement within the vastus medialis muscle. The results showed a plausible range of frequencies (from 90Hz to 120 Hz), which could be used for MRE muscle protocol. The wave amplitude increased with the level of the force, revealing the importance of the boundary condition. Moreover, different shear displacement patterns were obtained as a function of the muscle mechanical properties. The present study is the first to analyze the shear wave propagation in skeletal muscles using a 3D subject specific finite element model. This study could be of great value to assist the experimenters in the set-up of MRE protocols.
False fame effects emerge following an internal context shift and are abolished with contextual reinstatement

ABSTRACT Prior research has demonstrated that if you give people a list of nonfamous names and ask them to indicate if the names are famous, 24 hours later, more of these names will be incorrectly remembered as famous than without the delay. This is because while participants are no longer able to recall the specific circumstances in which they previously encountered the names, the names remain familiar and this sense of familiarity is falsely attributed to fame. The present study sought to determine whether a false fame effect would emerge if a daydreaming task, designed to shift participants’ internal context, was interpolated between a list of nonfamous names and a list of famous names. Results revealed that indeed, participants erroneously judged more nonfamous names as famous following an internal Context Change. This effect was abolished, however, when the initial context (in which the nonfamous list was studied) was reinstated. Therefore, a contextual-change account of the phenomenon may be appropriate.
Functional outcomes of the modified submandibular gland transfer procedure

Treatment for advanced head and neck cancers typically includes surgery followed by radiation therapy (RT). Radiation‐induced xerostomia is a common sequela of these treatments. The modified submandibular gland transfer (M‐SGT) procedure was developed to decrease xerostomia in the treatment of oral cavity cancer by sparing one submandibular gland (SMG) from radiation. This study's objectives were to: 1) elucidate the radiation‐sparing capacity of the M‐SGT, and 2) study the xerostomia‐reducing potential of the M‐SGT based on the University of Washington Quality‐of‐Life Questionnaire (UW‐QOL).
Biology at Different Temperatures, Thermal Requirements, and Ecological Zoning of Opogona sacchari (Lepidoptera: Tineidae)

Abstract The banana moth, Opogona sacchari (Bojer, 1856), is a polyphagous pest that causes serious damage to different crops around the world, particularly to bananas in southern Brazil.The insect is designated a quarantine pest in several countries including Argentina, the main consumer market for bananas produced in southern Brazil.To provide support for the management of O. sacchari, the present study investigated the biology and thermal requirements at eight temperatures (18, 22, 25, 28, 30, 32, 33, and 34 ± 1°C) and constructed a fertility life table at five temperatures (18, 22, 25, 28, and 30 ± 1°C). Above 30°C, the mortality of all life stages was 100%; the best temperature for development was 25°C. Based on this information, an ecological zoning of the pest was developed for Brazil.The lower temperature threshold was 8.6°C.The zoning indicated that the pest does not occur in regions with warmer climates, corresponding to the actual distribution of this pest in Brazil.
New gene variants alter type 2 diabetes risk predominantly through reduced beta-cell function

Purpose of reviewOver the past 18 months, the number of gene loci robustly associated with type 2 diabetes has risen from three to 18. In this study, we focus on explaining the genome-wide approach that has led to most of these discoveries and discuss some of the early insights the new gene loci have provided into the aetiology of type 2 diabetes. Recent findingsRecent genome-wide association studies have provided an important resource for furthering our understanding of type 2 diabetes disease mechanisms. Genes previously unsuspected of playing a role in diabetes are now implicated in the disease process. These include genes in cell cycling control (CDKN2A/2B, CDKAL1), transcription factors (TCF7L2, HHEX), and ion channels (SLC30A8). These variants are all associated with insulin-secretory defects in the general population and show little if any relationship to insulin resistance. Two common variants (near or in FTO and MC4R) alter diabetes risk through a primary effect on obesity. SummaryRecent genome-wide association studies show that there are now 18 gene loci associated with the risk of type 2 diabetes. Most of these T2D gene loci affect insulin secretion.
Solving the γ-ray radiative transfer equation for supernovae

We present a new relativistic radiative transfer code for γ-rays of energy less than 5 MeV in supernova (SN) ejecta. This code computes the opacities, the prompt emissivity (i.e. decay), and the scattering emissivity, and solves for the intensity in the co-moving frame. Because of the large expansion velocities of SN ejecta, we ignore redistribution effects associated with thermal motions. The energy deposition is calculated from the energy removed from the radiation field by scattering or photoelectric absorption. This new code yields comparable results to an independent Monte Carlo code. However, both yield non-trivial differences with the results from a pure absorption treatment of γ-ray transport. A synthetic observer’s frame spectrum is also produced from the co-moving frame intensity. At early times when the optical depth to γ-rays is large, the synthetic spectrum shows asymmetric line profiles with redshifted absorption as seen in SN 2014J. This new code is integrated within cmfgen and allows for an accurate and fast computation of the decay energy deposition in SN ejecta.
Variational Regret Bounds for Reinforcement Learning

We consider undiscounted reinforcement learning in Markov decision processes (MDPs) where both the reward functions and the state-transition probabilities may vary (gradually or abruptly) over time. For this problem setting, we propose an algorithm and provide performance guarantees for the regret evaluated against the optimal non-stationary policy. The upper bound on the regret is given in terms of the total variation in the MDP. This is the first variational regret bound for the general reinforcement learning setting.
Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science

Process mining, i.e., a sub-field of data science focusing on the analysis of event data generated during the execution of (business) processes, has seen a tremendous change over the past two decades. Starting off in the early 2000's, with limited to no tool support, nowadays, several software tools, i.e., both open-source, e.g., ProM and Apromore, and commercial, e.g., Disco, Celonis, ProcessGold, etc., exist. The commercial process mining tools provide limited support for implementing custom algorithms. Moreover, both commercial and open-source process mining tools are often only accessible through a graphical user interface, which hampers their usage in large-scale experimental settings. Initiatives such as RapidProM provide process mining support in the scientific workflow-based data science suite RapidMiner. However, these offer limited to no support for algorithmic customization. In the light of the aforementioned, in this paper, we present a novel process mining library, i.e. Process Mining for Python (PM4Py) that aims to bridge this gap, providing integration with state-of-the-art data science libraries, e.g., pandas, numpy, scipy and scikit-learn. We provide a global overview of the architecture and functionality of PM4Py, accompanied by some representative examples of its usage.
MicroRNA‐214 contributes to regulation of necroptosis via targeting ATF4 in diabetes‐associated periodontitis

Diabetes and periodontal diseases have a mutual promoting relationship that induces severe tissue damage and cell death. The potential roles of microRNAs (miRNAs) and the type of cell death involved in diabetes‐associated periodontitis are obscure. The gingival tissues of patients were obtained and MC3T3‐E1 cells were costimulated with high glucose and lipopolysaccharide (LPS). Osseous morphometric analysis was evaluated with micro‐CT, and histological characteristics were measured by hematoxylin/eosin and immunohistochemical staining. Cytokine secretion was confirmed by enzyme‐linked immunosorbent assay, and reactive oxygen species (ROS) was measured using a DCFH‐DA probe kit. Gene expression was measured by real‐time quantitative reverse transcription PCR (qRT‐PCR), and protein expression was assessed by Western blot and immunofluorescence analysis. The miR‐214 level, receptor‐interacting serine‐threonine protein (RIP) 1, RIP3, and phospho‐mixed lineage kinase domain‐like (p‐MLKL) protein expression were elevated in the inflamed gingival tissues of diabetes‐associated periodontitis patients, with activating transcription factor 4 (ATF4) expression showing the opposite effect. The high glucose (22 mM) could not induce significant increase of RIP1, RIP3, and p‐MLKL; however, the high glucose and LPS (500‐1000 ng/mL) cotreatment resulted in increase in the number of RIP1, RIP3, and p‐MLKL in MC3T3‐E1 cells. NAC (ROS inhibitor) inhibited RIP1, RIP3, and increased ATF4; however, necrostatin‐1 (Nec‐1) (RIP1 inhibitor) specifically inhibited the protein expression of RIP1 and RIP3 and had no influence on ATF4. The use of antagomir‐214 suppressed the expression of miR‐214, RIP1, RIP3, and p‐MLKL, but increased ATF4 protein level in glucose and LPS‐induced cells. ATF4 knockdown by ATF4 small interfering RNA offset the effect of antagomir‐214. RIP1‐ and RIP3‐dependent necroptosis was confirmed in the inflamed gingival tissues of diabetes‐associated periodontitis patients and high glucose‐ and LPS‐ cotreated cells. It was suggested that miR‐214‐targeted ATF4 participated in the regulation of necroptosis in vivo and in vitro.
Passage Ranking with Weak Supervsion

In this paper, we propose a \textit{weak supervision} framework for neural ranking tasks based on the data programming paradigm \citep{Ratner2016}, which enables us to leverage multiple weak supervision signals from different sources. Empirically, we consider two sources of weak supervision signals, unsupervised ranking functions and semantic feature similarities. We train a BERT-based passage-ranking model (which achieves new state-of-the-art performances on two benchmark datasets with full supervision) in our weak supervision framework. Without using ground-truth training labels, BERT-PR models outperform BM25 baseline by a large margin on all three datasets and even beat the previous state-of-the-art results with full supervision on two of the datasets.
MAP7 regulates organelle transport by recruiting kinesin-1 to microtubules

Microtubule-associated proteins (MAPs) regulate microtubule polymerization, dynamics, and organization. In addition, MAPs alter the motility of kinesin and dynein to control trafficking along microtubules. MAP7 (ensconsin, E-MAP-115) is a ubiquitous MAP that organizes the microtubule cytoskeleton in mitosis and neuronal branching. MAP7 also recruits kinesin-1 to microtubules. To understand how the activation of kinesin-1 by MAP7 regulates the motility of organelles transported by ensembles of kinesin and dynein, we isolated organelles and reconstituted their motility in vitro. In the absence of MAP7, isolated phagosomes exhibit approximately equal fractions of plus- and minus-end–directed motility along microtubules. MAP7 causes a pronounced shift in motility; phagosomes move toward the plus-end ∼80% of the time, and kinesin teams generate more force. To dissect MAP7-mediated regulation of kinesin-driven transport, we examined its effects on the motility and force generation of single and teams of full-length kinesin-1 motors. We find that MAP7 does not alter the force exerted by a single kinesin-1 motor, but instead increases its binding rate to the microtubule. For ensembles of kinesin, a greater number of kinesin motors are simultaneously engaged and generating force to preferentially target organelles toward the microtubule plus-end.
Connectivity-Aware UAV Path Planning with Aerial Coverage Maps

Cellular networks are promising to support effective wireless communications for unmanned aerial vehicles (UAVs), which will help to enable various long-range UAV applications. However, these networks are optimized for terrestrial users, and thus do not guarantee seamless aerial coverage. In this paper, we propose to overcome this difficulty by exploiting controllable mobility of UAVs, and investigate connectivity-aware UAV path planning. To explicitly impose communication requirements on UAV path planning, we introduce two new metrics to quantify the cellular connectivity quality of a UAV path. Moreover, aerial coverage maps are used to provide accurate locations of scattered coverage holes in the complicated propagation environment. We formulate the UAV path planning problem as finding the shortest path subject to connectivity constraints. Based on graph search methods, a novel connectivity-aware path planning algorithm with low complexity is proposed. The effectiveness and superiority of our proposed algorithm are demonstrated using the aerial coverage map of an urban section in Virginia, which is built by ray tracing. Simulation results also illustrate a tradeoff between the path length and connectivity quality of UAVs.
GMNN: Graph Markov Neural Networks

This paper studies semi-supervised object classification in relational data, which is a fundamental problem in relational data modeling. The problem has been extensively studied in the literature of both statistical relational learning (e.g. relational Markov networks) and graph neural networks (e.g. graph convolutional networks). Statistical relational learning methods can effectively model the dependency of object labels through conditional random fields for collective classification, whereas graph neural networks learn effective object representations for classification through end-to-end training. In this paper, we propose the Graph Markov Neural Network (GMNN) that combines the advantages of both worlds. A GMNN models the joint distribution of object labels with a conditional random field, which can be effectively trained with the variational EM algorithm. In the E-step, one graph neural network learns effective object representations for approximating the posterior distributions of object labels. In the M-step, another graph neural network is used to model the local label dependency. Experiments on object classification, link classification, and unsupervised node representation learning show that GMNN achieves state-of-the-art results.
Primary adrenal gland epithelioid sarcoma: A case report and literature review

Epithelioid sarcoma is a malignant mesenchymal neoplasm with morphologic and immunophenotypic epithelioid differentiation, which rarely arises in solid organs. We report a case of primary epithelioid sarcoma in the adrenal gland of a 31‐year‐old female. The patient initially presented with nausea and rectal bleeding, and subsequent imaging studies revealed a 4.4 cm left adrenal gland mass and left retroperitoneal lymphadenopathy. Clinical and radiological studies did not reveal tumor elsewhere in the patient. Histologic features were those of epithelioid sarcoma, proximal type with cohesive clusters of epithelioid tumor cells harboring frequent mitoses, and areas of necrosis. Immunohistochemical stains showed strong, diffuse expression of epithelial markers (pancytokeratin), and CD34 and Fli‐1. Partial and focal positive staining of CK7 was also noted. Nuclear expression of SMARCB1 (INI‐1) protein was lost. ERG was negative in this case. We believe that this is the second‐case report of a primary adrenal gland epithelioid sarcoma. Fli‐1 positivity was seen in our case, and ERG was negative as shown in some recent publications regarding epithelioid sarcomas.
Exploring the structure of misconceptions in the Force Concept Inventory with modified module analysis

Module Analysis for Multiple-Choice Responses (MAMCR) was applied to a large sample of Force Concept Inventory (FCI) pretest and post-test responses ($N_{pre}=4509$ and $N_{post}=4716$) to replicate the results of the original MAMCR study and to understand the origins of the gender differences reported in a previous study of this data set. When the results of MAMCR could not be replicated, a modification of the method was introduced, Modified Module Analysis (MMA). MMA was productive in understanding the structure of the incorrect answers in the FCI, identifying 9 groups of incorrect answers on the pretest and 11 groups on the post-test. These groups, in most cases, could be mapped on to common misconceptions used by the authors of the FCI to create distactors for the instrument. Of these incorrect answer groups, 6 of the pretest groups and 8 of the post-test groups were the same for men and women. Two of the male-only pretest groups disappeared with instruction while the third male-only pretest group was identified for both men and women post-instruction. Three of the groups identified for both men and women on the post-test were not present for either on the pretest. The rest of the identified incorrect answer groups did not represent misconceptions, but were rather related to the the blocked structure of some FCI items where multiple items are related to a common stem. The groups identified had little relation to the gender unfair items previously identified for this data set, and therefore, differences in the structure of student misconceptions between men and women cannot explain the gender differences reported for the FCI.
Joint haze image synthesis and dehazing with mmd-vae losses

Fog and haze are weathers with low visibility which are adversarial to the driving safety of intelligent vehicles equipped with optical sensors like cameras and LiDARs. Therefore image dehazing for perception enhancement and haze image synthesis for testing perception abilities are equivalently important in the development of such autonomous driving systems. From the view of image translation, these two problems are essentially dual with each other, which have the potentiality to be solved jointly. In this paper, we propose an unsupervised Image-to-Image Translation framework based on Variational Autoencoders (VAE) and Generative Adversarial Nets (GAN) to handle haze image synthesis and haze removal simultaneously. Since the KL divergence in the VAE objectives could not guarantee the optimal mapping under imbalanced and unpaired training samples with limited size, Maximum mean discrepancy (MMD) based VAE is utilized to ensure the translating consistency in both directions. The comprehensive analysis on both synthesis and dehazing performance of our method demonstrate the feasibility and practicability of the proposed method.