Breaking the challenge of signal integrity using time-domain spoof surface plasmon polaritons

In modern integrated circuits and wireless communication systems/devices, three key features need to be solved simultaneously to reach higher performance and more compact size: signal integrity, interference suppression, and miniaturization. However, the above-mentioned requests are almost contradictory using the traditional techniques. To overcome this challenge, here we propose time-domain spoof surface plasmon polaritons (SPPs) as the carrier of signals. By designing a special plasmonic waveguide constructed by printing two narrow corrugated metallic strips on the top and bottom surfaces of a dielectric substrate with mirror symmetry, we show that spoof SPPs are supported from very low frequency to the cutoff frequency with strong subwavelength effects, which can be converted to the time-domain SPPs. When two such plasmonic waveguides are tightly packed with deep-subwavelength separation, which commonly happens in the integrated circuits and wireless communications due to limited space, we demonstrate theoretically and experimentally that SPP signals on such two plasmonic waveguides have better propagation performance and much less mutual coupling than the conventional signals on two traditional microstrip lines with the same size and separation. Hence the proposed method can achieve significant interference suppression in very compact space, providing a potential solution to break the challenge of signal integrity

How to Train Your Dragon: Tamed Warping Network for Semantic Video Segmentation

Real-time semantic segmentation on high-resolution videos is challenging due to the strict requirements of speed. Recent approaches have utilized the inter-frame continuity to reduce redundant computation by warping the feature maps across adjacent frames, greatly speeding up the inference phase. However, their accuracy drops significantly owing to the imprecise motion estimation and error accumulation. In this paper, we propose to introduce a simple and effective correction stage right after the warping stage to form a framework named Tamed Warping Network (TWNet), aiming to improve the accuracy and robustness of warping-based models. The experimental results on the Cityscapes dataset show that with the correction, the accuracy (mIoU) significantly increases from 67.3% to 71.6%, and the speed edges down from 65.5 FPS to 61.8 FPS. For non-rigid categories such as "human" and "object", the improvements of IoU are even higher than 18 percentage points

Thermal Characterization of Low-Dimensional Materials by Resistance Thermometers

The design, fabrication, and use of a hotspot-producing and temperature-sensing\ua0resistance thermometer for evaluating the thermal properties of low-dimensional materials are\ua0described in this paper. The materials that are characterized include one-dimensional (1D) carbon\ua0nanotubes, and two-dimensional (2D) graphene and boron nitride films. The excellent thermal performance of these materials shows great potential for cooling electronic devices and systems\ua0such as in three-dimensional (3D) integrated chip-stacks, power amplifiers, and light-emitting\ua0diodes. The thermometers are designed to be serpentine-shaped platinum resistors serving both as\ua0hotspots and temperature sensors. By using these thermometers, the thermal performance of the\ua0abovementioned emerging low-dimensional materials was evaluated with high accuracy

Waveform Design for Communication-Assisted Sensing in 6G Perceptive Networks

The integrated sensing and communication (ISAC) technique has the potential to achieve coordination gain by exploiting the mutual assistance between sensing and communication (S&C) functions. While the sensing-assisted communications (SAC) technology has been extensively studied for high-mobility scenarios, the communication-assisted sensing (CAS) counterpart remains widely unexplored. This paper presents a waveform design framework for CAS in 6G perceptive networks, aiming to attain an optimal sensing quality of service (QoS) at the user after the target's parameters successively ``pass-through'' the S$\&$C channels. In particular, a pair of transmission schemes, namely, separated S&C and dual-functional waveform designs, are proposed to optimize the sensing QoS under the constraints of the rate-distortion and power budget. The first scheme reveals a power allocation trade-off, while the latter presents a water-filling trade-off. Numerical results demonstrate the effectiveness of the proposed algorithms, where the dual-functional scheme exhibits approximately 12% performance gain compared to its separated waveform design counterpart

Prognostic value of HMGN family expression in acute myeloid leukemia

Aim: The objective of this work was to investigate the prognostic role of the HMGN family in acute myeloid leukemia (AML). Methods: A total of 155 AML patients with HMGN1-5 expression data from the Cancer Genome Atlas database were enrolled in this study. Results: In the chemotherapy-only group, patients with high HMGN2 expression had significantly longer event-free survival (EFS) and overall survival (OS) than those with low expression (all p < 0.05), whereas high HMGN5 expressers had shorter EFS and OS than the low expressers (all p < 0.05). Multivariate analysis identified that high HMGN2 expression was an independent favorable prognostic factor for patients who only received chemotherapy (all p < 0.05). HMGN family expression had no impact on EFS and OS in AML patients receiving allogeneic hematopoietic stem cell transplantation. Conclusion: High HMGN2/5 expression is a potential prognostic indicator for AML

Overexpression of PDK2 and PDK3 reflects poor prognosis in acute myeloid leukemia

Acute myeloid leukemia (AML) is a hematological malignancy characterized by the proliferation of immature myeloid cells, with impaired differentiation and maturation. Pyruvate dehydrogenase kinase (PDK) is a pyruvate dehydrogenase complex (PDC) phosphatase inhibitor that enhances cell glycolysis and facilitates tumor cell proliferation. Inhibition of its activity can induce apoptosis of tumor cells. Currently, little is known about the role of PDKs in AML. Therefore, we screened The Cancer Genome Atlas (TCGA) database for de novo AML patients with complete clinical information and PDK family expression data, and 84 patients were included for the study. These patients did not undergo allogeneic hematopoietic stem cell transplantation (allo-HSCT). Univariate analysis showed that high expression of PDK2 was associated with shorter EFS (P = 0.047), and high expression of PDK3 was associated with shorter OS (P = 0.026). In multivariate analysis, high expression of PDK3 was an independent risk factor for EFS and OS (P 0.05). Our results indicated that high expressions of PDK2 and PDK3, especially the latter, were poor prognostic factors of AML, and the effect could be overcome by allo-HSCT

Probabilistic Constellation Shaping for OFDM-Based ISAC Signaling

Integrated Sensing and Communications (ISAC) has garnered significant attention as a promising technology for the upcoming sixth-generation wireless communication systems (6G). In pursuit of this goal, a common strategy is that a unified waveform, such as Orthogonal Frequency Division Multiplexing (OFDM), should serve dual-functional roles by enabling simultaneous sensing and communications (S&C) operations. However, the sensing performance of an OFDM communication signal is substantially affected by the randomness of the data symbols mapped from bit streams. Therefore, achieving a balance between preserving communication capability (i.e., the randomness) while improving sensing performance remains a challenging task. To cope with this issue, in this paper we analyze the ambiguity function of the OFDM communication signal modulated by random data. Subsequently, a probabilistic constellation shaping (PCS) method is proposed to devise the probability distributions of constellation points, which is able to strike a scalable S&C tradeoff of the random transmitted signal. Finally, the superiority of the proposed PCS method over conventional uniformly distributed constellations is validated through numerical simulations

Upregulation of Glutamic-Oxaloacetic Transaminase 1 Predicts Poor Prognosis in Acute Myeloid Leukemia

One of the key features of acute myeloid leukemia (AML), a group of very aggressive myeloid malignancies, is their strikingly heterogenous outcomes. Accurate biomarkers are needed to improve prognostic assessment. Glutamate oxaloacetate transaminase 1 (GOT1) is essential for cell proliferation and apoptosis by regulating cell's metabolic dependency on glucose. It is unclear whether the expression level of GOT1 has clinical implications in AML. Therefore, we analyzed the data of 155 AML patients with GOT1 expression information from The Cancer Genome Atlas (TCGA) database. Among them, 84 patients were treated with chemotherapy alone, while 71 received allogeneic hematopoietic stem cell transplantation (allo-HSCT). In both treatment groups, high GOT1 expression was associated with shorter event-free survival (EFS) and overall survival (OS) (all P = 60 years, white blood cell count >= 15 x 10(9)/L, bone marrow blasts >= 70%, and DNMT3A, RUNX1 or TP53 mutations (all P <0.05); but in the allo-HSCT group, the only independent risk factor for survival was high GOT1 expression (P <0.05 for both EFS and OS). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the genes related to GOT1 expression were mainly concentrated in "hematopoietic cell lineage" and "leukocyte transendothelial migration" signaling pathways. Collectively, GOT1 expression may be a useful prognostic indicator in AML, especially in patients who have undergone allo-HSCT

Brainformers: Trading Simplicity for Efficiency

Transformers are central to recent successes in natural language processing and computer vision. Transformers have a mostly uniform backbone where layers alternate between feed-forward and self-attention in order to build a deep network. Here we investigate this design choice and find that more complex blocks that have different permutations of layer primitives can be more efficient. Using this insight, we develop a complex block, named Brainformer, that consists of a diverse sets of layers such as sparsely gated feed-forward layers, dense feed-forward layers, attention layers, and various forms of layer normalization and activation functions. Brainformer consistently outperforms the state-of-the-art dense and sparse Transformers, in terms of both quality and efficiency. A Brainformer model with 8 billion activated parameters per token demonstrates 2x faster training convergence and 5x faster step time compared to its GLaM counterpart. In downstream task evaluation, Brainformer also demonstrates a 3% higher SuperGLUE score with fine-tuning compared to GLaM with a similar number of activated parameters. Finally, Brainformer largely outperforms a Primer dense model derived with NAS with similar computation per token on fewshot evaluations