Denominators of the Weierstrass Coefficients of the Canonical Lifting

Given an ordinary elliptic curve $E/k:y_{0}^{2}=x_{0}^{3}+a_{0}x_{0}+b_{0}$ with characteristic $p \geq 5$, the canonical lifting $\mathbf{E}$ over the ring of Witt vectors is given by $\mathbf{E}/W(k): y^{2}=x^{3}+ax+b$, where $a = (a_0, A_1, A_2, \ldots)$ and $b = (b_0, B_1, B_2, \ldots)$ are functions of $a_0$ and $b_0$. Finotti has proved that these functions $A_i$ and $B_i$ can be taken to be rational functions on $a_0$ and $b_0$ and raised questions about their denominators. In this dissertation we will find all the possible factors of the denominators, and give an upper bound for each factor, in the case when these functions are obtained from formulas for the $j$-invariant of the canonical lifting. We will also find an isomorphic canonical lifting that is universal up to the second coordinates. In addition, we will show some computations done with Magma

OpenDriver: an open-road driver state detection dataset

In modern society, road safety relies heavily on the psychological and physiological state of drivers. Negative factors such as fatigue, drowsiness, and stress can impair drivers' reaction time and decision making abilities, leading to an increased incidence of traffic accidents. Among the numerous studies for impaired driving detection, wearable physiological measurement is a real-time approach to monitoring a driver's state. However, currently, there are few driver physiological datasets in open road scenarios and the existing datasets suffer from issues such as poor signal quality, small sample sizes, and short data collection periods. Therefore, in this paper, a large-scale multimodal driving dataset for driver impairment detection and biometric data recognition is designed and described. The dataset contains two modalities of driving signals: six-axis inertial signals and electrocardiogram (ECG) signals, which were recorded while over one hundred drivers were following the same route through open roads during several months. Both the ECG signal sensor and the six-axis inertial signal sensor are installed on a specially designed steering wheel cover, allowing for data collection without disturbing the driver. Additionally, electrodermal activity (EDA) signals were also recorded during the driving process and will be integrated into the presented dataset soon. Future work can build upon this dataset to advance the field of driver impairment detection. New methods can be explored for integrating other types of biometric signals, such as eye tracking, to further enhance the understanding of driver states. The insights gained from this dataset can also inform the development of new driver assistance systems, promoting safer driving practices and reducing the risk of traffic accidents. The OpenDriver dataset will be publicly available soon

Cell Therapy Must Be Regulated as Medicine

Background The current standard of care for relapsed and refractory acute lymphoblastic leukemia (ALL) is combination chemotherapy. Case presentation We report a case of highly refractory ALL who was treated with blinatumomab. The ALL in this patient relapsed within a month after completion of hyperCVAD regimen and was refractory to high dose mitoxantrone/cytarabine and CLAG regimens. Conclusion This highly refractory pre-B Ph− ALL was induced to complete remission after one course of single agent blinatumomab

Unleashing the Imagination of Text: A Novel Framework for Text-to-image Person Retrieval via Exploring the Power of Words

The goal of Text-to-image person retrieval is to retrieve person images from a large gallery that match the given textual descriptions. The main challenge of this task lies in the significant differences in information representation between the visual and textual modalities. The textual modality conveys abstract and precise information through vocabulary and grammatical structures, while the visual modality conveys concrete and intuitive information through images. To fully leverage the expressive power of textual representations, it is essential to accurately map abstract textual descriptions to specific images. To address this issue, we propose a novel framework to Unleash the Imagination of Text (UIT) in text-to-image person retrieval, aiming to fully explore the power of words in sentences. Specifically, the framework employs the pre-trained full CLIP model as a dual encoder for the images and texts , taking advantage of prior cross-modal alignment knowledge. The Text-guided Image Restoration auxiliary task is proposed with the aim of implicitly mapping abstract textual entities to specific image regions, facilitating alignment between textual and visual embeddings. Additionally, we introduce a cross-modal triplet loss tailored for handling hard samples, enhancing the model's ability to distinguish minor differences. To focus the model on the key components within sentences, we propose a novel text data augmentation technique. Our proposed methods achieve state-of-the-art results on three popular benchmark datasets, and the source code will be made publicly available shortly

Recent Updates in Cancer Immunotherapy: A Comprehensive Review and Perspective of the 2018 China Cancer Immunotherapy Workshop in Beijing

The immune system is the hard-wired host defense mechanism against pathogens as well as cancer. Five years ago, we pondered the question if the era of cancer immunotherapy was upon us (Li et al., Exp Hem Oncol 2013). Exciting progresses have been made at all fronts since then, including (1) sweeping approval of six agents by the US Food and Drug Administration (FDA) to block the PD-1/PD-L1 pathway for treatment of 13 cancer types; (2) a paradigm shifting indication of PD-1 and CTLA4 blockers for the management of a broad class of cancers with DNA mismatch repair defect, the first-ever tissue agnostic approval of cancer drugs; (3) real world practice of adoptive T cell therapy with two CD19-directed chimeric antigen receptor T cell products (CAR-T) for relapsed and/or refractory B cell malignancies including acute lymphoid leukemia and diffuse large B cell lymphoma, signaling the birth of a field now known as synthetic immunology; (4) the award of 2018 Nobel Prize in Physiology and Medicine from the Nobel Committee to Tasuku Honjo and James Allison for their discovery of cancer medicine by inhibition of negative immune regulation ( www.nobelprize.org/prizes/medicine/2018 ); and (5) the emerging new concept of normalizing rather than amplifying anti-tumor immunity for guiding the next wave of revolution in the field of immuno-oncology (IO) (Sanmamed and Chen, Cell 2018).This article will highlight the significant developments of immune-oncology as of October 2018. The US FDA approved indications of all seven immune checkpoint blockers, and two CD19-directed CAR-T products are tabulated for easy references. We organized our discussion into the following sections: introduction, cell therapy, emerging immunotherapeutic strategies, expediting oncology drug development in an era of breakthrough therapies, new concepts in cancer immunology and immunotherapy, and concluding remarks. Many of these topics were covered by the 2018 China Cancer Immunotherapy Workshop in Beijing, the fourth annual conference co-organized by the Chinese American Hematologist and Oncologist Network (CAHON), China FDA (CFDA; now known as China National Medical Product Administration (NMPA)), and the Tsinghua University. We significantly expanded our discussion of important IO developments beyond what were covered in the conference, and proposed a new Three Rs conceptual framework for cancer immunotherapy, which is to reverse tolerance, rejuvenate the immune system, and restore immune homeostasis. We conclude that the future of immuno-oncology as a distinct discipline of cancer medicine has arrived

REAL TIME PEDESTRIAN DETECTION-BASED FASTER HOG/DPM AND DEEP LEARNING APPROACH

International audienceThe work presented aims to show the feasibility of scientific and technological concepts in embedded vision dedicated to the extraction of image characteristics allowing the detection and the recognition/localization of objects. Object and pedestrian detection are carried out by two methods: 1. Classical image processing approach, which are improved with Histogram Oriented Gradient (HOG) and Deformable Part Model (DPM) based detection and pattern recognition. We present how we have improved the HOG/DPM approach to make pedestrian detection as a real time task by reducing calculation time. The developed approach allows us not only a pedestrian detection but also calculates the distance between pedestrians and vehicle. 2. Pedestrian detection based Artificial Intelligence (AI) approaches such as Deep Learning (DL). This work has first been validated on a closed circuit and subsequently under real traffic conditions through mobile platforms (mobile robot, drone and vehicles). Several tests have been carried out in the city center of Rouen in order to validate the platform developed