dataesr/scientific-paragraphs-categorization huggingface dataset:
Ablation Overall, this ablation study shows that a compromise between performance on the training dataset and performance in generalization has to be made. Limitations. With our approach, large areas with no repetitive textures are prone to poor depth estimates. The feature matching performed by our cost volumes matching can indeed become unstable if large areas share exactly the same features. This can therefore lead to bad depth estimates. We mitigated this issue by using a multi-scale network and by including an SNCV at each of its levels, but these solutions do not make our network totally immune to this issue. Inference speed. Our network has 4. 5 million parameters and requires up to 500 MB of GPU memory to run with six levels. At inference time on Mid-Air, an NVidia Tesla V100 GPU needs 17 ms to process a single frame for a raw TensorFlow implementation.
Doing so gives networks the ability to use semantic cues directly to estimate depth. This ability is only valuable for instances where the set of features possibly encountered can be encoded by the network and associated to a specific depth. Our experiments show that reference methods perform well-better than M4Depth for some-on KITTI, the dataset with constrained and structured scenes. However, they fall behind in unstructured environments when the link between semantic cues and depth is weak, and in generalization when semantic cues are different from the reference. This tends to imply that baseline networks rely on the raw feature values to derive depth. All these observations lead us to believe that severing the direct link between the encoder and the decoder of the architecture while proposing relevant substitute data through the preprocessing unit is the key factor that allows M4Depth to perform so well overall in our experiments.
Conclusions In this paper, we address the challenging task of estimating depth from RGB image sequences acquired in unknown environments by a camera moving with six degrees of freedom. For this, we first define a notion of visual parallax for generic camera motion, which is central for our M4Depth method, and we then show how it can be used to estimate depth. Then, we present new cost volumes designed to boost the performance of the underlying deep neural network of our method. Three series of experiments were performed on synthetic datasets as well as on the KITTI dataset that features real data. They show that M4Depth is superior to the baseline both in unstructured environments and in generalization while also performing well on the standard KITTI benchmark, which shows its superiority for autonomous vehicles that would need to venture off road.
In addition to being motion-and feature-invariant, our method is lightweight and fast enough to be considered for real-time applications. Our further works on M4Depth will, among others, focus on the determination of its own uncertainty on depth estimates at inference time. Such an addition would provide a great advantage over other methods that do not offer this capability. To help the community to reproduce our results, we made the code of our method publicly available at https://github. com/michael-fonder/M4Depth. important factor to notice is that the performance in validation remains stable at the end of the training, which indicates that the network does not overfit the training set. In the paper, we have chosen six methods for our baseline, namely: Monodepth  [16, 60] , Monodepth2  [17, 61] , ST-CLSTM  [27, 62] , the method of Wang et al.
Data Availability Statement: All the datasets used in this work are public. The Mid-Air, Kitti, and TartanAir datasets can be respectively found at the following URLs: https://midair. ulg. ac. be/, https://www. cvlibs. net/datasets/kitti/, and https://theairlab. org/tartanair-dataset/. The code used to generate the results presented in this paper is publicly available on GitHub at https://github. com/michael-fonder/M4Depth. All the URLs mentioned here were accessed on 3 November 2022.
Spectral Synthesis Code: SINOPSIS This work is heavily based on results from the spectral synthesis code SImulatiNg OPtical Spectra wIth Stellar populations models (SINOPSIS;  Fritz et al. 2007 Fritz et al. , 2011 Fritz et al. , 2014 Fritz et al. , 2017)). SINOPSIS performs a nonparametric decomposition of galaxy spectra into a combination of stellar population models. This nonparametric approach allows the code to capture even very complex SFHs  (Conroy 2013; Leja et al. 2019 , but see also  Cid Fernandes et al. 2007). There are a few design choices that set SINOPSIS apart from other widely used nonparametric synthesis codes. First, unlike codes such as STARLIGHT (Cid Fernandes et al. 2005) and PENALIZED PIXEL-FITTING (PPXF; Cappellari 2017), SINOP-SIS does not perform full spectral fitting.
Instead, the fitting procedure is performed in a selection of continuum band fluxes and spectral indexes (equivalent widths and D n 4000), which focuses the fitting efforts on parts of the spectrum that are more information rich while also making the code less susceptible to problems regarding sky lines and other observational issues. Another distinct design choice in SINOPSIS is the inclusion of emission features in the fitting of stellar population parameters. The emission lines produced by each stellar population element up to the age of 20 Myr are modeled using CLOUDY  (Ferland et al. 2017 ), allowing us to get better constraints for young components. Finally, SINOPSIS allows for a full treatment of dust attenuation  (Charlot & Fall 2000). Each stellar population component can be fitted with an independent value of dust optical depth, although we do not take advantage of this feature in this work because our galaxies do not have young stars and this effect is not prominent.
In particular, including emission lines in the fit is very useful for modeling post-starburst galaxies. Because these galaxies do not have emission lines, this provides clear information to the code about the lack of current star formation and prevents solutions that might include small fractions of young stellar populations that are poorly constrained from the optical stellar continuum (see  Werle et al. 2019). The fitting of emission lines coupled with the versatility of a nonparametric method makes SINOPSIS the most suitable tool for the current work.
Conclusions In this work, we have presented results from the analysis of MUSE integral field spectroscopy of 21 post-starburst galaxies in the centers of eight clusters at z ∼ 0. 3-0. 4. We used the SINOPSIS spectral synthesis code to retrieve spatially resolved SFHs of all 21 objects, finding a clear enhancement in SFR prior to quenching in 16 cases. From these, we calculate the time since quenching (t Q ) and the fraction of stellar mass assembled in the past 1. 5 Gyr (μ 1. 5). We find that most galaxies in our sample have quenched their star formation from the outside in (seven objects) or show a side-to-side/irregular pattern (eight objects). Only three objects show an inside-out quenching pattern, all of which are at the high-mass end of our sample. For two of them, the quenching can be associated with an AGN.
RNA Sequencing and Pre-Processing RNA was extracted from bulk peripheral blood mononuclear cells using an RNeasy Micro Kit (Qiagen). Libraries were constructed using SMART-Seq (Clonetec). A paired-end 50-base-pair flow-cell lane Illumina HiSeq 2000 yielded an average of 240 million aligned reads per sample. To align RNA-seq reads from samples used in Selinexor and Fedratinib treatments, Illumina paired-end sequence data were analyzed with BWA/v0. 6 alignment software with option (-s) to disable Smith-Waterman alignment. Reads were mapped onto GRCh37-lite reference genome and exon-exon junction reference whose coordinates were defined based on transcript annotations in Ensembl/v59. Reads with mapping quality < 10 were discarded and duplicate reads were tagged using the Picard's MarkDuplicates program.
Characterization of scRNA-seq AML Populations For biological characterization of the re-annotated leukemic cell types, single-cell enrichment scores of hallmark genesets as well as custom genesets from Ng et al (LSC+ AML fractions)  23  and Xie et al (S1PR3 overexpression in LT-HSCs)  33  were calculated using AUCell 68. Cell cycle status was determined using the original annotations from van Galen et al  29  , in which cell cycle scoring and classification was performed. Shannon diversity of single-cell transcriptomes was calculated from raw count data using the python package "skbio" after down-sampling each cell to 1,000 UMIs.
Gene Expression Deconvolution Raw gene expression counts from 13653 cells belonging to any of seven leukemic populations (LSPC-Quiescent, LSPC-Primed, LSPC-Cycle, GMP-like, ProMono-like, Mono-like, cDC-like) or seven non-leukemic immune populations (T, CTL, NK, B, Plasma, and wild-type Monocyte and cDCs) were used as input for signature matrix generation with CIBERSORTx 71. Default settings were used with the exception of the minimum expression parameter which was set to 0. 25. Deconvolution was performed on TPM-normalized bulk RNA-seq data using S-mode batch correction and Absolute mode. Due to differences in S-mode batch correction performance between the CIBERSORTx web portal and the CIBERSORTx Docker image, we exclusively used the web portal for our analyses. For downstream analysis, the abundance of the seven leukemic populations were normalized to a sum of 1, wherein the score for each population represents the estimated proportion of all leukemic cells.
For bulk RNA-seq samples composed entirely of leukemic blasts (cell lines or sorted primary samples), a second signature matrix with seven leukemic populations and no immune populations was used. For deconvolution with DWLS  72  , a single-cell signature matrix was generated using MAST  73  for each cell type using default settings from the DWLS script. DWLS was then applied to TPMnormalized RNA-seq data using default settings. Deconvolution with Bisque 74 was applied to TPM-normalized RNA-seq data following package guidelines and using default settings. Deconvolution with MuSIC 75 was applied to TPM-normalized RNA-seq data as per tool guidelines. This was performed in two different ways: direct and recursive. Direct deconvolution involves calculating cell type abundance of each population directly.
Mutation Analysis Cytogenetic and driver mutation annotations from TCGA, BEAT-AML, and Leucegene were used to correlate hierarchy composition with genomic profiles. Mutation combinations between driver mutations were identified and all combinations present in at least 5 patients were retained and visualized along hierarchy axes PC1 and PC2 using the R package "ggridges". Due to missing variant allele frequency (VAF) information in an appreciable subset of mutation calls from genomic annotations, samples were considered mutated as long as the mutation was called. This analysis was repeated exclusively using mutation calls where VAF > 0. 25 to confirm that the observed trends remained the same.
Mobility Model This scenario is based on the actual data obtained during the TestFest in Holland. Using a sniffer, we captured the messages sent by all the surrounding vehicles in addition to PCAP files received from the other participants. Using this, we performed reverse engineering on the identity of each vehicle. Finally, we applied our solution to identify each vehicle and calculate its privacy level. These tests aim to test interoperability between the European partners. For all the test cases, vehicles have the same trajectory using one origin/destination pair. The test site corresponds to the start and arrival points.
Data Analysis In Figure  6 , we illustrate all the sniffed MAC addresses in their locations. All figures show the positions of each captured MAC address, and each of the five figures represents half a day of tests. We notice that Test 2 represented the peak of the participation of tester vehicles, as we received a more significant number for MAC address. In Table  1 , we detail one of the first captions tests. The table gives information about the first day of tests. All the information given in this table is based on the received messages. We calculated the distance traveled and the distance between origin (travel start point) and destination (travel endpoint). We also give the different StationIDs used during the travel and the type of messages sent. The IVI message is sent only by RSUs.
In Figure  7 , each box represents the variation of steps distance between all received messages from each MAC address in the first session of tests. This metric is very useful for our tracking algorithm. In order to apply our algorithm, we took the second set of data (Test 2) as a case study. Our algorithm analyzed all cases based on the different metrics and information in Table  1. We calculated their probabilities and their privacy entropy in order to estimate the identitie,s as seen in Section 4. This analysis gave place to the three clusters. All the explanations are based on the assumptions of the attacker model in Section 4. 1. Cluster 1: It is a trivial case for an attacker because even with changing the pseudonym certificate and the StationID, the attacker could quickly identify users using the same MAC address for all their journeys.
A Computational Approach for Drug Repurposing Computational methods, also known as in silico drug repurposing  [51] , are critical in predicting novel indications for current medicines, and they largely rely on data from databases such as DrugBank  [52] , ChemBank  [53] , Genecards  [54] , OMIM  [55] , and PubMed  [56]. As a result of the collection and systematic analysis of diverse data, including gene expression, chemical structure, genotype or proteomic data, or electronic health records (EHRs), innovative approaches for drug repositioning can be developed by emphasizing possible candidates for drug-disease connections  [57]. Here, we will discuss the most prevalent computational techniques as well as some examples of medication repurposing.
As a result, this computational technique employs the signature reversion principle (SRP), which allows for the comparison of transcriptomic profiles between drugs and diseases and helps in anticipating whether the repurposed drug may be effective on the disease of interest  [38, 77, 78]. Additionally, this concept uncovered novel repurposing prospects in a variety of therapeutic domains  [79] [80] [81] [82]  in addition to chemo-sensitizers in lymphoid malignancies  [83]. Furthermore, the expression-based profile concept requires publicly available gene expression databases. The Broad Institute's Connectivity Map (cMap) is derived from the outcomes of treating numerous cell types with over 1300 compounds  [84]. However, to make cMap more effective, this resource may be coupled with other public sources of gene expression data, including Gene Expression Omnibus and Array Express  [38].
However, this technique is limited due to the lack of 3D structures for some protein targets, lack of macromolecular target databases, a high rate of false positives, some predictability limitations, as well as the questioned docking algorithms utilized due to variances in software packages  [38, 76]. Nonetheless, recently, a new promising tool, "Al-phaFold", was developed, which counters this issue of structure prediction. It is a neural network-based computational model that can predict protein structures with atomic accuracy  [90]. AlphaFold can be used for molecular replacement and for interpreting cryogenic electron microscopy maps. Importantly, developing accurate protein structure prediction algorithms will accelerate the advancement of structural bioinformatics to keep pace with the genomics revolution.
However, obtaining and using EHR data remains challenging due to ethical and legislative hurdles that may limit data access, as well as the difficulty of extracting the unstructured data included in these databases  [96]. Text-mining tools, another form of data-based medication repurposing strategy, allows for prioritizing prospective research areas and expedite the disease-drug repurposing process among the extensively accumulating scientific literature  [98]. Thus, to discover possible drug-disease connections in the literature, several text mining approaches have been used. In this regard, Li and colleagues have developed disease-specific drug-protein connectivity maps for Alzheimer's disease through combining gene/protein and drug connection information based on protein interaction networks and literature mining.
Clinical Challenges in Prostate Cancer A major issue with low-risk PCa is that it is frequently overtreated, whereas high-risk PCa is frequently undertreated. Many individuals with high-risk illnesses are only offered palliative androgen deprivation therapy (ADT) rather than intense local therapy. According to the CaPSURE database, ADT is given to 41% of high-risk patients, whereas RP and RT are given to 24% and 28% of high-risk patients, respectively  [107, 108]. In contrast, local therapy, with either RP or RT, depending on the classification of high-risk illness, results in a 49-80% progression-free probability (PFP)  [109, 110]. Furthermore, as compared to observation or ADT alone, numerous randomized studies have indicated that active treatment improves survival in individuals with high-risk PCa  [111, 112].
2. Perform average error processing on UWB data to reduce its measurement error, connect it to the IMU pose node as a univariate hyperedge constraint and obtain more accurate positioning results by updating the sliding window. The balance of the article is organized as follows: Section 2 introduces the relevant work, Section 3 expounds the methods, Section 4 sets out the experimental results, and Section 5 summarizes and analyzes the article. Nitroxoline, a widely used antibiotic for treating urinary tract infections, inhibits endothelial cell proliferation for different solid cancer types  [158, 159]. It also mediates AMPK-dependent inhibition of the mTOR signaling pathway and cyclin D1-Rb-Cdc25A axis suggesting its potential role for therapeutic development against PCa  [160]. Nelfinavir, a human immunodeficiency virus (HIV) protease inhibitor authorized by the FDA, is typically used in therapies for HIV patients  [161].
Another anti-inflammatory agent, the glucocorticoid dexamethasone, inhibited ERG activity, an oncogenic transcription factor, of prostate tumor cells in an in-silico model  [142]. According to clinical studies, patients undergoing long-term NSAID treatment have a decreased chance of acquiring cancer  [182, 183]. Indomethacin, an NSAID used for rheumatic disease treatment  [184, 185] , is a notable antineoplastic agent. Mechanistically, it inhibits Cox-1/2-dependent angiogenesis  [186, 187]  as well as MAPK pathways  [188]. It also inhibits cancer cell growth via impairing PKC-p38-DRP1 axis-dependent mitochondrial dynamics or downregulating Wnt/β-catenin signaling  [189]. A clinical trial is currently ongoing (ClinicalTrials. gov; NCT02935205) to study the combined effect of hormone therapy using enzalutamide and indomethacin on PCa by lowering the amount of androgen the body makes and/or blocking the use of androgen by the tumor cells.
placebo Advanced PCa Low, Intermediate, or High Dose Anti-parasitic Suramin in Treating Patients with NCT00002723 III 1 January 1996 390 Completed Suramin Hormone-Refractory PCa Anti-parasitic Combination Chemotherapy with Suramin Plus Doxorubicin in Treating Patients with Advanced Solid Tumors NCT00003038 I 1 October 1997 20 Completed Doxorubicin + suramin.
dexamethasone + diethylstilbestrol + aspirin Steroid Combination of Docetaxel + Estramustine + Hydrocortisone Versus Docetaxel + Prednisone in Patients with Advanced PCa (PROSTATA) NCT00705822 III 1 August 2006 54 Terminated Docetaxel + prednisone vs. docetaxel + estramusine + hydrocortisone.
abiraterone + prednisone + AMG Steroid Cabozantinib Plus Docetaxel and Prednisone for Advanced PCa NCT01683994 I 1 September 2012 49 Completed Carbozantinib + docetaxel + prednisone Steroid Reducing Dexamethasone Around Docetaxel Infusion (REDEX) NCT02776436 I 1 January 2016 46 Active, not recruiting Dexamethasone GnRH agonist/antagonist Steroid Cognitive Effects of AR Directed Therapies for Advanced PCa NCT03016741 IV 1 March 2017 100 Recruiting + prednisone + abiraterone acetate vs. GnRH agonist/antagonist + enzalutamide.
LHRH agonists + calcium/vitamin D supplementation Study of Zoledronic Acid for Patients Bisphosphonate with Hormone-sensitive Bone NCT00242567 III 1 December 2005 522 Completed Zoledronic acid Metastases from PCa Calcium + Zoledronate in Preventing Osteoporosis zoledronic acid + and Bone Fractures in Patients with radiation therapy + Bisphosphonate Locally Advanced Nonmetastatic PCa NCT00329797 III 1 March 2006 109 Completed LHRH + vitamin D Undergoing Radiation Therapy and vs. calcium + Hormone Therapy radiation therapy + LHRH + vitamin D.
enzalutamide + leuprolide Study to Compare the Effects of Other Repeated Doses of an Investigational New Drug and a Placebo on Appetite in NCT04803305 I 1 May 2021 40 Recruiting PF-06946860 Advanced PCa and Anorexia Study of XL102 as Single-Agent and XL102 + fulvestrant Other Combination Therapy in Subjects with NCT04726332 I 1 February 2021 298 Recruiting + abiraterone + Solid Tumors prednisone Abbreviations.
Supply chain attacks on open-source projects aim at injecting and spreading malicious code such that it is executed by direct and indirect downstream users. Recent work systematized the knowledge about such attacks and proposed a taxonomy in the form of an attack tree. We propose a visualization tool called Risk Explorer for Software Supply Chains, which allows inspecting the taxonomy of attack vectors, their descriptions, references to real-world incidents and other literature, as well as information about associated safeguards. Being open-source itself, the community can easily reference new attacks, accommodate for entirely new attack vectors or reflect the development of new safeguards.
INTRODUCTION Open-Source Software (OSS) is extensively used across the whole technology stack. At the same time, more and more incidents Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). shows that attackers discovered open-source projects as a means for spreading malware to downstream users. Ladisa et al. [2]  propose a taxonomy in the form of an attack tree to systematically describe attack vectors at the disposal of attackers, independent of technologies or ecosystems.
RISK EXPLORER Our work proposes a companion tool to the taxonomy of OSS supply chain attacks  [2]. Being open-source, it facilitates keeping the said taxonomy relevant and up to date, similarly to other communitydriven efforts, e. g. , Common Weakness Enumeration (CWE)  2. The tool has been developed using React. js 3 , a front-end framework for single-page applications, and is hosted by a Node. js server. As such, it can be served via GitHub Pages, and the iterative exploration of the attack tree happens in the browser. The interactive visualization of the attack tree is the tool's main functionality and has been implemented using the collapsible tree from the d3. js library 4. Users can collapse and expand the different nodes of the attack tree to explore the attack surface of open-sourcebased software development.