dataesr/scientific-paragraphs-categorization huggingface dataset:
Compared with refractory patients from SCHOLAR-1, these data indicate that patients with relapsed disease may have potentially better outcomes than those with refractory disease. In addition to real-world data, SCHOLAR-1. Risk ratio by subgroup for ORR adjusted for stabilized inverse probability of treatment weights. The index date was defined as the day the patient received the new therapy. ORR was defined as the percentage of patients who achieved a best overall response of partial response or complete response as assessed by the investigator (treating physician for real-world patients). Multiple imputation procedures created 25 data sets. Estimates were then obtained using Rubin's rules to combine the individual estimates from each data set. Subgroups for bridging therapy (i. e. , anticancer therapy for disease control) were applied to the LTAC only to compare with the real-world patients.
Stabilized weights used for all analyses were those of the overall ACC and LTAC. Bulky disease was defined as the presence of individual masses !10 cm in diameter. ACC: analytic comparator cohort; CI: confidence interval; liso-cel: lisocabtagene maraleucel; LTAC: liso-cel-treated analysis cohort; ORR: overall response rate. also included clinical trial data where response endpoints were centrally reviewed, potentially impacting outcomes. In studies comparing CAR T-cell therapies with SCHOLAR-1 using matching and adjusted indirect treatment comparisons, the CAR T-cell therapies lisocel and axicabtagene ciloleucel were found to significantly increase the odds of CR and reduce the risk of death compared with conventional regimens for R/R disease  [9, 14]. Similarly, in a retrospective observational study of 215 patients with R/R LBCL from Memorial Sloan Kettering (MSK) Cancer Center, response rates and survival outcomes for patients treated with commercial CAR T-cell therapies (both axicabtagene ciloleucel and tisagenlecleucel) were superior to those in the historical population treated with alternate therapies.
Given the retrospective and nonrandomized nature of this study, there were some inherent limitations. Despite extensive efforts to select real-world patients similar to patients in TRANSCEND, any 'unmeasured' confounders could not be controlled and balanced between the 2 groups. As part of the stringent inclusion criteria, real-world patients treated with investigational agents in any line of therapy were excluded from all but the post hoc analysis of the primary endpoint. This was important as these investigational treatments are not considered standard of care; however, it diverges from a true real-world data set where many patients are treated on clinical trials due to the poor outcomes with conventional therapies. Of note, ORR analyses including patients who received investigational therapy were consistent with results excluding these patients.
The treatment landscape in R/R DLBCL has continued to evolve since the data cutoff date for these analyses (December 2019), potentially limiting the generalizability of these results. Potential bias could also occur because of the variable timing and availability of real-world clinical assessments versus scheduled investigator clinical trial assessments in TRANSCEND. As real-world data collection included all clinical and imaging assessments available in the electronic health records, not only those associated with progression events, the analysis was afforded considerable data density to reflect a patient's longitudinal disease journey. Further, the endpoint analyses were blinded such that the analysts did not have visibility to the response data when conducting the PS matching. This likely obviated non-differential misclassification of study outcomes.
Figure2. Cohort construction for all analyses. From a total of 1450 RW patients, 606 RW patients met the eligibility criteria for inclusion in the ICC. Additional eligibility criteria similar to those of TRANSCEND were then applied, resulting in the selection of 381 RW patients for the QCC. All 381 patients in the QCC were selected for the ACC by propensity score balancing. For comparison, the LTAC consisted of 257 liso-cel-treated patients from the LBCL cohort of TRANSCEND. Propensity scores were obtained for each imputed data set (LTAC and ACC), with 25 imputations used based on a moderate amount of missing information ( 15%) [16, 17]. The primary analysis cohort and 2 sensitivity analysis cohorts contributed to the totality of evidence of the effectiveness of liso-cel compared with standard of care in RW settings.
4 4 Figure 4. Risk ratio by subgroup for ORR adjusted for stabilized inverse probability of treatment weights. The index date was defined as the day the patient received the new therapy. ORR was defined as the percentage of patients who achieved a best overall response of partial response or complete response as assessed by the investigator (treating physician for real-world patients). Multiple imputation procedures created 25 data sets. Estimates were then obtained using Rubin's rules to combine the individual estimates from each data set. Subgroups for bridging therapy (i. e. , anticancer therapy for disease control) were applied to the LTAC only to compare with the real-world patients. Stabilized weights used for all analyses were those of the overall ACC and LTAC. Bulky disease was defined as the presence of individual masses !10 cm in diameter.
One of the major focuses in the remote sensing community is the rapid processing of deep neural networks (DNNs) on very high resolution (VHR) aerial images. Few studies have investigated the acceleration of training and prediction by optimizing the architecture of the DNN system rather than designing a lightweight DNN. Parallel processing using multiple graphics processing units (GPUs) increases VHR image processing performance. It drives extremely large and frequent data transfers (input/output(I/O)) from random access memory (RAM) to GPU memory. As a result, the system bus congestion causes the system to hang, resulting in long latency in training/predicting. In this paper, we evaluate the causes of long latency and propose a space-to-speed (S2S) DNN system to overcome the aforementioned challenges.
A three-level memory system aiming to reduce data transfer during system operation is presented. Distribution optimization with parallel processing was used to accelerate the training. Training optimizations on VHR images (such as hot-zone searching and image/ground truth queues for data saving) were used to train the VHR images efficiently. Inference optimization was performed to speed up prediction in the release mode. To verify the efficiency of the proposed system, we used aerial image labeling from the Institut National de Recherche en Informatique et en Automatique (INRIA) and benchmarks from the Massachusetts Institute of Technology Aerial Imagery for Roof Segmentation (MITAIRS) to test the system performance and accuracy. Without the loss of accuracy, the S2S system improved prediction speed on the testing dataset by eight GPUs in a normal setting in both the INRIA dataset (from 534 to 72 s) and the MITAIRS dataset (818 to 120 s).
With the prediction in half-float (using float-16 data), an 8-GPU parallel processing increased the speed to 38 s in the INRIA dataset and 83 s in the MITAIRS dataset. In a pressure test, our proposed system operated on 18,000 images with a size of 5000 × 5000 from 18. 2 to 1. 8 h with the prediction in full-float (using float-32 data) and 43 min with the prediction in half-float, increasing the speed by a factor of 9. 78 and 25. 3, respectively, when compared to system runs without optimization.
In general, the above methods can reduce the number of nodes and output map size, resulting in an inevitable loss of accuracy. Aside from DNN architecture limitations, another major drawback of VHR image processing is the long latency period caused by data transferals, as well as the brute-force strategy in training and prediction. In this paper, semantic segmentation was selected for a comprehensive evaluation. In particular, building/roof segmentation was employed because several datasets  (Chen et al. , 2019; Maggiori et al. , 2017)  in VHR images have been open-sourced, allowing for the rapid training and testing of our model. Building segmentation focuses on optimizing the DNN architecture  (Bischke et al. , 2019; Kurdi and Awrangjeb, 2020; Shi et al. , 2020). Previously, very limited effort was put into optimizing the training and interference architecture.
Unlike RAM, data stored in read-only memory (ROM) cannot be electronically modified after the memory device is manufactured. The south bridge region includes peripheral component interconnect express (PCIE) facilities, GPUs, and disks, which are lowspeed transferring regions. The steps for DNN prediction (shown in Fig. 1B ) are as follows: (1) The CPU orders the disk to transfer the allocated data to the memory. (2) The disk transfers data via the system bus to the memory at the allocated address. (3) The memory transfers the decoded data to the GPU via the system bus for processing. (4) The GPU transfers the results to RAM via the system bus. (5) The results are encoded and saved in the disk via the system bus. We found that the data were transferred four times via the system bus in a single step.
The five major causes of slow processing speed are as follows: (1) Inputting high-resolution images into a DNN significantly increases the memory usage of a GPU with additional nodes in the DNN; (2) Frequent data transfer through the ROM, RAM, and GPU/CPU requires a long loading time (over 1 GB/s in multiple GPU parallel processing), thus reducing speed; (3) Parallel synchronization of multi-core processing results in asynchronous outputs and hangs during processing; (4) During model training and VHR image prediction, very few optimizations are conducted. The current method operates in a sequence mode without multi-threading, wasting computing resources on the desktop and resulting in long latency during model operation; (5) Previous methods had issues with imbalanced background noise and long pre-processing times (e.
g. , splitting VHR images to save small blocks), or the combining of small blocks to form a complete VHR image was insufficient. Because of the time spent on encoding/decoding VHR images, the frequent I/O on original images and output labels, and pre-processing on oversized VHR images due to their large size, these protocols are difficult to deploy for this task. The proposed S2S system includes the following optimizations. First, the maximum GPU memory usage rate is compared to the input resolution to determine the most reasonable input size to ensure a large receptive field. Second, a three-level memory architecture is proposed to increase operating RAM capacity and create a high-speed RAM disk path, reducing the frequency of data transfer via the system bus and thus decreasing processing latency.
Third, distributed GPU allocation technology with accumulating loss reduction is proposed to resolve the system hang caused by synchronous lock in different GPU steps during training. The training/prediction optimization is used to maximally allocate computing resources during DNN operation. In addition to architecture optimizations, we enhanced the method for region proposal during training and inference. Common methods include image splitting or randomization. These typically encounter issues with imbalance selection on samples because the background labels occupy the largest part on the VHR image, but common methods make it difficult to maintain the balance inputting on different samples. The sample imbalance on VHR is solved by hotzone search during training. Finally, optimizations, such as reducing data type complexity, combining blocks as a concat from the rectified linear unit (ReLU) bias and convolution, and decreasing unnecessary operations and outputs, are conducted otherwise, queue inputting and VHR sampling on RAM are proposed to ensure the prediction is in the release mode to increase its speed on VHR image.
To demonstrate the efficacy of our proposed S2S architecture, we conducted the following experiments: (1) The GPU memory and image input size relationship was evaluated to select the most reasonable VHR image input size. (  2   4 ) Finally, a pressure test was performed to compare the proposed system with the standard system and to evaluate its performance for long-term prediction. The results showed that our proposed methods could maintain the same accuracy while increasing the speed by more than 10 times when using multiple GPUs. When using eight GPUs, the proposed system only required 1. 8 h to process 18,000 VHR images with a size of 5000 × 5000, which was 10 times faster than it would be without the S2S system (18. 2 h). When using the float-16 data type, the speed increased to 43 min, which was approximately 20 times faster than that without the S2S system.
The results illustrated that the system could overcome the data transfer limitation in the face of data traffic jams on the system bus. The major contributions of this paper can be summarized as follows: (1) The S2S system promises to reduce the number of data transfers via the system from four to two times in every step of training and prediction, releasing system bus bandwidth resources and enabling maximum speed processing using multi-GPU processing of VHR images. (2) Training optimizations are proposed to speed up training by using multi-CPU resources to coordinate the GPU for optimally allocating the computing resources. To avoid imbalance training, the hot-zone searching proposal is used during training to separate the foreground region from the substantial background on the VHR images.
Low latency of data transfer Based on semantic segmentation of VHR satellite images, frequent and long data transfers from the ROM disk to the RAM are required. However, because satellite images are generally very data intensive, frequent decoding of such images and processing of the CPU are time-consuming. The major limitations have been discussed in the Introduction. To solve these limitations, we optimized the system architecture of the DNN using lower frequency data transfers via the system bus at a higher transfer speed. The semantic segmentation of VHR satellite images necessitates frequent and lengthy data transfers from the ROM disk to the RAM. However, because satellite images are typically highly data-intensive, frequent decoding and CPU processing are time-consuming. The major limitations were discussed in the Introduction.
To overcome these limitations, we optimized the system architecture of the DNN using lower frequency data transfers via the system bus at a higher transfer speed. As shown in Fig. 2 , our proposed S2S system divides RAM into three parts: transfer memory, which exchanges temporary data in the system, pinned memory, which pre-allocates the address for DNN training and prediction in different threads, and the RAM disk, which serves as a high-speed data-saving region. The architectural concept is adapted to accelerate processing by employing task-specific tools. Sequence reading/writing with a disk is significantly faster than random I/O therefore, the original data of the VHR images are pre-transferred to the RAM disk. The CPU reads images directly from the RAM disk, decoding them and writing them back onto the RAM disk simultaneously.
In contrast, our method copies the entire dataset from the disk to the RAM disk before operation. The system then imports data from the RAM disk to the pinned memory, transfers the data to the GPUs, redelivers the obtained results to the pinned memory, and saves them back on the RAM disk. Finally, the complete data transfer from the RAM disk to the disk is repeated. The proposed memory system employs the existing operational mechanism, as well as the sequence I/O importing/exporting large-sized data to the RAM disk, which is a major driver of random I/O for further processing. The adequate I/O ability ensures that the GPUs can be optimally utilized. In contrast to the original mechanism, the S2S system transfers data only twice via the system bus (from pinned memory to GPUs then back to pinned memory) during training and testing in each step, significantly releasing the bandwidth during operation.
Multi-GPU training Multi-GPU parallel processing has recently emerged as a key technology for processing DNNs with large batch sizes to reduce training and testing times. Based on the semantic segmentation of satellite images (VHR), the parallel processing of large batch sizes can reduce the processing time for prediction using VHR images. As shown in Fig. 4 , the system reads the image and label data from the disk to the RAM during each step of the training. The images are then exported to the GPUs via forward transfer, and the optimizer calculates the information loss caused by this step. Next, the loss values in different GPUs are combined to update the model with the summarized loss (as shown in Fig. 5 ). In the ordinary training model, the GPU results are reduced in comparison to the other GPUs and then backward transferred with the loss calculated based on the average value from the other GPUs.
Training optimizations on VHR images In addition to data transfer, VHR image training optimizations are limited. As shown in Fig. 8A , there are two major trends. Some methods split the images into small blocks before saving the data to a disk for future training. This method is ROM-intensive because the small blocks require more space than one large image. It also encounters the issue of sample imbalance during training. Most images in VHR image processing have a lot of background but only a few building labels. Splitting uses all pixels for training, which results in over-fitting to the background because the background occupies the most space. Another method, similar to the one described in Fig. 8B , was suggested. For every step, the image is loaded onto the RAM disk from the ROM disk, and then a random proposal is used for the cropped image samples.
Implementation and training details To run the proposed system in an experiment, we used a desktop computer with an AMD Ryzen 9 5950X CPU (16score, 32 tensors), 128-GB DDR 4 Memory (four paths), and eight Titan RTX. We allocated 32 GB of RAM disk space. The platform was used for both the training and testing of the models, including those in the system. To evaluate the system, we employed the INRIA Air building segmentation dataset of  (Maggiori et al. , 2017) , which contains 540 VHR images with a resolution is 5000 × 5000, of which covers 810 km 2 (405 km 2 for training and 405 km 2 for testing) in 0. 3m/pixel, and MIT Roof Segmentation dataset  (Chen et al. , 2019)  covering 457 km 2 of orthorectified aerial images with over 220,000 buildings in 0. 75 m/pixel. Based on the assessment presented in the Appendix (Image Size versus Memory in Training and Testing), the memory usage in the GPUs approaches the maximum value (24 GB) when using 1024 × 1024 resolution; therefore, we selected 1024 × 1024 as the input resolution.
8 h to 13. 5 h for MITAIRS. Moreover, the TO contributes to the DP, S2S, and distribution  deployment with a speed improvement by one-fifth as TO assigns the computer resources more reasonably to reduce the hang time on training. It also has 5% accuracy in INRIA and 8% accuracy in MIT because more reasonable region proposal causes DNN coverage to be faster than that without optimizations. To prove that more training epochs do not influence the final accuracy, we also employ a training ''S2S + Distribution + 50 epochs''. The results show that the accuracy remains at 68. 05% and 86. 38% in INRIA and MITAIRS datasets, respectively, with only minor improvements. The system bus congestion was the main cause of the improved performance of the S2S system. Because multiple GPUs could occupy the system bus in the system I/O, the bandwidth on the desktop was consumed, and frequent write/reads using the ROM disk resulted in long latency.
Furthermore, we assessed the accuracies of the float-32, float-16, and Int-8 data types. The speed of predicting  In Tables  3  and 4 , the PO indicates that the DNN is used for prediction. The results show that the accuracy does not change when using different disks. The original version, as well as the optimized float-32 data type, achieve a mean intersection over union (MIoU) accuracy of 72. 5%. When using the float-16 format, the MIoU accuracy decreases by 0. 2% in the INRIA dataset, and by 95. 50% in the MITAIRS dataset with a 1% accuracy loss. MIoU accuracy decreases to 67. 9% and 89. 20% when Int-8 is used. The ROM disks are unable to accelerate multi-GPU parallel processing when inference optimization settings are enabled or disabled. The speed cannot increase with the increase in GPUs.
The program uses hybrid hard disk (HHD) and runs in 1142 s and 1095 s in INRIA and MIT in the original parallel processing settings, respectively, and in 1090 s/ using the inference optimizations. The SSD (Sata3, NVME, and PCIE) achieves comparable speeds. The speed (eight GPUs) is approximately 540 s for INRIA and 830 s for MITAIRS, with inference optimizations of approximately 490 and 820 s for float-32, 450 and 790 s for float-16, and 440 and 853 s for Int-8, respectively. However, the speed with eight GPUs (540 s) is similar to that with 2/4 GPUs (580/520 s in INRIA and 1000/860 s in MITAIRS). The results show that system bus congestion occurs during operation and limits prediction speed as the number of GPU increases. In contrast, RAM disks with different path numbers have demonstrated that even with a single path, the influence of system bus content is significantly less than that of ROM disks.
When using the RAM disk, the speed using four GPUs increases to approximately 240 s in INRIA and 292 in MIT; when using eight GPU, it increases to approximately 110/207 s. When using inference optimizations and float-16, it accelerates to 74/122 and 38/83 s, respectively. This is significantly faster than that of the ROM disk. Due to the reduced transfer volume on the system bus, the S2S system can ensure high-speed I/O even with several GPUs in the bandwidth content when the south bridge is busy. The results show that the S2S system can effectively enhance the multi-GPU parallel processing of the DNN on the VHR images. With prediction optimization, the VHR image prediction speed on increased significantly (3. 2 times quicker with limited accuracy loss). Figs. 14 and 15 depict the visualization of INRIA and MITAIRS datasets.
Pressure test Next, we compare long-term prediction using the S2S system and the ROM disk. We run the INRIA dataset test 1000 times, with 18,000 VHR images with a size of 5000 × 5000 to determine the speed. Notably, the S2S system performed in approximately 1. 8 h with the float-32 data type and 43 min with the float-16 data type, which is over 10 and 20 times faster than the HDD and SSD, respectively. The results show that the S2S system is stable over long-term data transfer. Because the INRIA dataset has a resolution of 0. 3 m/pixel, the pressure test shows that it can process a 40,500 km 2 area in just 43 min using float-16. The same region is processed in days (15. 9 h using a PCIE disk) using conventional methods the processing time was drastically reduced in the present study. This would change the urban information update from a weekly to hourly scale.