Split Time Series into Patches: Rethinking Long-term Series Forecasting with Dateformer

Time is one of the most significant characteristics of time-series, yet has received insufficient attention. Prior time-series forecasting research has mainly focused on mapping a past subseries (lookback window) to a future series (forecast window), and time of series often just play an auxiliary role even completely ignored in most cases. Due to the point-wise processing within these windows, extrapolating series to longer-term future is tough in the pattern. To overcome this barrier, we propose a brand-new time-series forecasting framework named Dateformer who turns attention to modeling time instead of following the above practice. Specifically, time-series are first split into patches by day to supervise the learning of dynamic date-representations with Date Encoder Representations from Transformers (DERT). These representations are then fed into a simple decoder to produce a coarser (or global) prediction, and used to help the model seek valuable information from the lookback window to learn a refined (or local) prediction. Dateformer obtains the final result by summing the above two parts. Our empirical studies on seven benchmarks show that the time-modeling method is more efficient for long-term series forecasting compared with sequence modeling methods. Dateformer yields state-of-the-art accuracy with a 40% remarkable relative improvement, and broadens the maximum credible forecasting range to a half-yearly level

Attentive Mask CLIP

Image token removal is an efficient augmentation strategy for reducing the cost of computing image features. However, this efficient augmentation strategy has been found to adversely affect the accuracy of CLIP-based training. We hypothesize that removing a large portion of image tokens may improperly discard the semantic content associated with a given text description, thus constituting an incorrect pairing target in CLIP training. To address this issue, we propose an attentive token removal approach for CLIP training, which retains tokens with a high semantic correlation to the text description. The correlation scores are computed in an online fashion using the EMA version of the visual encoder. Our experiments show that the proposed attentive masking approach performs better than the previous method of random token removal for CLIP training. The approach also makes it efficient to apply multiple augmentation views to the image, as well as introducing instance contrastive learning tasks between these views into the CLIP framework. Compared to other CLIP improvements that combine different pre-training targets such as SLIP and MaskCLIP, our method is not only more effective, but also much more efficient. Specifically, using ViT-B and YFCC-15M dataset, our approach achieves $43.9\%$ top-1 accuracy on ImageNet-1K zero-shot classification, as well as $62.7/42.1$ and $38.0/23.2$ I2T/T2I retrieval accuracy on Flickr30K and MS COCO, which are $+1.1\%$, $+5.5/+0.9$, and $+4.4/+1.3$ higher than the SLIP method, while being $2.30\times$ faster. An efficient version of our approach running $1.16\times$ faster than the plain CLIP model achieves significant gains of $+5.3\%$, $+11.3/+8.0$, and $+9.5/+4.9$ on these benchmarks

Ultrastrong Terahertz Emission from InN Nanopyramids on Single Crystal ZnO Substrates

The creation of high efficiency and room temperature terahertz (THz) emitters has long been expected in both scientific and industrial communities. Despite the recent progress in THz source such as quantum cascade lasers, high efficiency THz emitters capable of operating at room temperature are still elusive. Indium nitride (InN), a narrow bandgap semiconductor, has emerged as a promising THz emitter due to its unique electronic properties. However, the efficiency of InN THz emitters reported up to now is still far from theoretically predicted because of inadequately engineered electrical conduction and radiative coupling. In this study, the authors report a novel, high performance THz emitting structure consisting of nanoengineered InN micro/nanopyramid arrays on a single crystal zinc oxide (ZnO) substrate. With improved electronic conduction from Zn diffusion induced doping and enhanced radiation coupling benefiting from uniquely structured geometry, the InN nanopyramids yielded THz emission intensity is close to an order of magnitude stronger than that of p-type indium arsenide (InAs). These findings prove that InN is a promising THz material and of wide importance in material science, optical engineering sectors, etc

Bortezomib-based therapy for newly diagnosed mantle-cell lymphoma

Background: the proteasome inhibitor bortezomib was initially approved for the treatment of relapsed mantle-cell lymphoma. We investigated whether substituting bortezomib for vincristine in frontline therapy with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) could improve outcomes in patients with newly diagnosed mantle-cell lymphoma. Methods: in this phase 3 trial, we randomly assigned 487 adults with newly diagnosed mantle-cell lymphoma who were ineligible or not considered for stem-cell transplantation to receive six to eight 21-day cycles of R-CHOP intravenously on day 1 (with prednisone administered orally on days 1 to 5) or VR-CAP (R-CHOP regimen, but replacing vincristine with bortezomib at a dose of 1.3 mg per square meter of body-surface area on days 1, 4, 8, and 11). The primary end point was progression-free survival. Results: after a median follow-up of 40 months, median progression-free survival (according to independent radiologic review) was 14.4 months in the R-CHOP group versus 24.7 months in the VR-CAP group (hazard ratio favoring the VR-CAP group, 0.63; P<0.001), a relative improvement of 59%. On the basis of investigator assessment, the median durations of progression-free survival were 16.1 months and 30.7 months, respectively (hazard ratio, 0.51; P<0.001), a relative improvement of 96%. Secondary end points were consistently improved in the VR-CAP group, including the complete response rate (42% vs. 53%), the median duration of complete response (18.0 months vs. 42.1 months), the median treatment-free interval (20.5 months vs. 40.6 months), and the 4-year overall survival rate (54% vs. 64%). Rates of neutropenia and thrombocytopenia were higher in the VR-CAP group. Conclusions: VR-CAP was more effective than R-CHOP in patients with newly diagnosed mantle-cell lymphoma but at the cost of increased hematologic toxicity. (Funded by Janssen Research and Development and Millennium Pharmaceuticals; LYM-3002 ClinicalTrials.gov number, NCT00722137)

Primary gastric non-Hodgkin's lymphoma in Chinese patients: clinical characteristics and prognostic factors

<p>Abstract</p> <p>Background</p> <p>Optimal management and outcome of primary gastric lymphoma (PGL) have not been well defined in the rituximab era. This study aimed to analyze the clinical characteristics, prognostic factors, and roles of different treatment modalities in Chinese patients with PGL.</p> <p>Methods</p> <p>The clinicopathological features of 83 Chinese patients with PGL were retrospectively reviewed. Staging was performed according to the Lugano staging system for gastrointestinal non-Hodgkin's lymphoma.</p> <p>Results</p> <p>The predominant pathologic subtype among Chinese patients with PGL in our study was diffuse large B cell lymphoma (DLBCL), followed by mucosa-associated lymphoid tissue (MALT) lymphoma. Among the 57 patients with gastric DLBCL, 20 patients (35.1%) were classified as the germinal center B cell-like (GCB) subtype and 37 patients (64.9%) as the non-GCB subtype. The 83 patients had a five-year overall survival (OS) and event-free survival (EFS) of 52% and 59%, respectively. Cox regression analysis showed that stage-modified international prognostic index (IPI) and performance status (PS) were independent predictors of survival. In the 67 B-cell lymphoma patients who received chemotherapy, 36 patients treated with rituximab (at least 3 cycles) had a mean OS of 72 months (95% CI 62-81) versus 62 months (95% CI 47-76) for patients without rituximab treatment (P = 0.021).</p> <p>Conclusion</p> <p>The proportion of Chinese gastric DLBCL cases with non-GCB subtype was higher than the GCB subtype. Stage-modified IPI and PS were effective prognostic factors in Chinese patients with PGL. Our data suggested that primary gastric B-cell lymphoma might have an improved outcome with rituximab in addition to chemotherapy. More studies are necessary, preferentially large prospective randomized clinical trials to obtain more information on the impact of the rituximab in the primary gastric B-cell lymphoma.</p

Immunogenic Cell Death (ICD)-Related Gene Signature Could Predict the Prognosis of Patients with Diffuse Large B-Cell Lymphoma

Background: Diffuse large B-cell lymphoma (DLBCL) is the most prevalent type of lymphoma that is potentially curable by chemotherapy. Immunogenic cell death (ICD) is regarded as an essential process for the clearance of residual tumor cells. However, the impact of ICD on DLBCL remains unknown. Here, we tried to explore the prognostic role of ICD in DLBCL. Methods: A gene expression microarray of DLBCL was downloaded from the Gene Expression Omnibus (GEO). The genes involved in ICD were obtained via literature reviews. Then, based on univariate, multivariate, and LASSO Cox regression analysis, the ICD-related gene signature was identified. The effect of the ICD-related gene signature on DLBCL was explored. The chi-square test was used to compare complete response rate (CRR) and recurrence rate between high- and low-risk groups. Results: The signature based on 12 ICD-related genes could independently predict the overall survival of DLBCL. Furthermore, high risk was linked to lower CRR and higher recurrence rate. Then, a nomogram based on the ICD-related gene signature was established. The area under the curve of the prediction model reached 0.820 in the training set and 0.780 in the validation set. Conclusions: This study suggested that the ICD-related gene signature could be a novel prognostic indicator for DLCBL

Individual Tree Detection in Coal Mine Afforestation Area Based on Improved Faster RCNN in UAV RGB Images

Forests are the most important part of terrestrial ecosystems. In the context of China’s industrialization and urbanization, mining activities have caused huge damage to the forest ecology. In the Ulan Mulun River Basin (Ordos, China), afforestation is standard method for reclamation of coal mine degraded land. In order to understand, manage and utilize forests, it is necessary to collect local mining area’s tree information. This paper proposed an improved Faster R-CNN model to identify individual trees. There were three major improved parts in this model. First, the model applied supervised multi-policy data augmentation (DA) to address the unmanned aerial vehicle (UAV) sample label size imbalance phenomenon. Second, we proposed Dense Enhance Feature Pyramid Network (DE-FPN) to improve the detection accuracy of small sample. Third, we modified the state-of-the-art Alpha Intersection over Union (Alpha-IoU) loss function. In the regression stage, this part effectively improved the bounding box accuracy. Compared with the original model, the improved model had the faster effect and higher accuracy. The result shows that the data augmentation strategy increased AP by 1.26%, DE-FPN increased AP by 2.82%, and the improved Alpha-IoU increased AP by 2.60%. Compared with popular target detection algorithms, our improved Faster R-CNN algorithm had the highest accuracy for tree detection in mining areas. AP was 89.89%. It also had a good generalization, and it can accurately identify trees in a complex background. Our algorithm detected correct trees accounted for 91.61%. In the surrounding area of coal mines, the higher the stand density is, the smaller the remote sensing index value is. Remote sensing indices included Green Leaf Index (GLI), Red Green Blue Vegetation Index (RGBVI), Visible Atmospheric Resistance Index (VARI), and Normalized Green Red Difference Index (NGRDI). In the drone zone, the western area of Bulianta Coal Mine (Area A) had the highest stand density, which was 203.95 trees ha−1. GLI mean value was 0.09, RGBVI mean value was 0.17, VARI mean value was 0.04, and NGRDI mean value was 0.04. The southern area of Bulianta Coal Mine (Area D) was 105.09 trees ha−1 of stand density. Four remote sensing indices were all the highest. GLI mean value was 0.15, RGBVI mean value was 0.43, VARI mean value was 0.12, and NGRDI mean value was 0.09. This study provided a sustainable development theoretical guidance for the Ulan Mulun River Basin. It is crucial information for local ecological environment and economic development