Capacity-CRB Tradeoff in OFDM Integrated Sensing and Communication Systems

Integrated sensing and communication (ISAC) has emerged as a key technology for future communication systems. In this paper, we provide a general framework to reveal the fundamental tradeoff between sensing and communication in OFDM systems, where a unified ISAC waveform is exploited to perform both tasks. In particular, we define the Capacity-Bayesian Cramer Rao Bound (BCRB) region in the asymptotically case when the number of subcarriers is large. Specifically, we show that the asymptotically optimal input distribution that achieves the Pareto boundary point of the Capacity-BCRB region is Gaussian and the entire Pareto boundary can be obtained by solving a convex power allocation problem. Moreover, we characterize the structure of the sensing-optimal power allocation in the asymptotically case. Finally, numerical simulations are conducted to verify the theoretical analysis and provide useful insights

Gradual Network for Single Image De-raining

Most advances in single image de-raining meet a key challenge, which is removing rain streaks with different scales and shapes while preserving image details. Existing single image de-raining approaches treat rain-streak removal as a process of pixel-wise regression directly. However, they are lacking in mining the balance between over-de-raining (e.g. removing texture details in rain-free regions) and under-de-raining (e.g. leaving rain streaks). In this paper, we firstly propose a coarse-to-fine network called Gradual Network (GraNet) consisting of coarse stage and fine stage for delving into single image de-raining with different granularities. Specifically, to reveal coarse-grained rain-streak characteristics (e.g. long and thick rain streaks/raindrops), we propose a coarse stage by utilizing local-global spatial dependencies via a local-global subnetwork composed of region-aware blocks. Taking the residual result (the coarse de-rained result) between the rainy image sample (i.e. the input data) and the output of coarse stage (i.e. the learnt rain mask) as input, the fine stage continues to de-rain by removing the fine-grained rain streaks (e.g. light rain streaks and water mist) to get a rain-free and well-reconstructed output image via a unified contextual merging sub-network with dense blocks and a merging block. Solid and comprehensive experiments on synthetic and real data demonstrate that our GraNet can significantly outperform the state-of-the-art methods by removing rain streaks with various densities, scales and shapes while keeping the image details of rain-free regions well-preserved.Comment: In Proceedings of the 27th ACM International Conference on Multimedia (MM 2019

Influence of isoniazid on T lymphocytes, cytokines, and macrophages in rats

T lymphocytes, cytokines, and macrophages play important roles in the clearance of Mycobacterium tuberculosis (Mtb) by the immune system. This study aimed to investigate the effects of isoniazid on the functions of both innate and adaptive immune cells. Healthy rats were randomly divided into experimental and control groups. Each group was randomly divided into three subgroups and named according to the duration of drug feeding, 1, 3, and 3 months followed by drug withdrawal for 1 month. The experimental groups were fed with isoniazid (12 mg/mL) and the control groups with normal saline. The percentage of CD4+ and CD8+ T lymphocytes, level of interleukin (IL)-12 and interferon (IFN)-γ, and function of macrophages were determined at these three time points. Isoniazid significantly increased the percentage of CD4+ T lymphocytes and the CD4+/CD8+ T lymphocyte cell ratio (P < 0.05). It transiently (<1 month) enhanced the functions of rat macrophages significantly (P < 0.05). In summary, isoniazid could increase the percentage of CD4+ T lymphocytes, CD4+/CD8+ T lymphocyte cell ratio, and enhance macrophage function in healthy rats

Constraints on the primordial gravitational waves with variable sound speed from current CMB data

We make a comprehensive investigation of the observational effect of the inflation consistency relation. We focus on the general single-field inflation model with the consistency relation $r=-8c_s n_t$, and investigate the observational constraints of sound speed $c_s$ by using the Seven-Year WMAP data, the BICEP tensor power spectrum data, and the constraints on $f_{\rm NL}^{\rm equil.}$ and $f_{\rm NL}^{\rm orth.}$ from the Five-Year WMAP observations. We find that the constraints on the tensor-to-scalar ratio $r$ is much tighter if $c_s$ is small, since a large tilt $n_t$ is strongly constrained by the observations. We obtain $r<0.37, 0.27$ and 0.09 ($dn_s/d\ln k=0$) for $c_s$=1, 0.1 and 0.01 models at 95.4% confidence level. When taking smaller values of $c_s$, the positive correlation between $r$ and $n_s$ also leads to slightly tighter constraint on the upper bound of $n_s$, while the running of scalar spectral index $dn_s/d\ln k$ is generally unaffected. For the sound speed $c_s$, it is not well constrained if only the CMB power spectrum data is used, while the constraints are obtainable by taking $f_{\rm NL}^{\rm equil.}$ and $f_{\rm NL}^{\rm orth.}$ priors into account. With the constraining data of $f_{\rm NL}^{\rm equil.}$ and $f_{\rm NL}^{\rm orth.}$, we find that, $c_s\lesssim 0.01$ region is excluded at 99.7% CL, and the $c_s=1$ case (the single-field slow-roll inflation) is slightly disfavored at 68.3% CL. In addition, the inclusion of $f_{\rm NL}^{\rm equil.}$ and $f_{\rm NL}^{\rm orth.}$ into the analysis can improve the constraints on $r$ and $n_s$. We further discuss the implications of our constraints on the test of inflation models.Comment: 15 pages, 7 figures, updated versio

Propagation Path Loss Models in Forest Scenario at 605 MHz

When signals propagate through forest areas, they will be affected by environmental factors such as vegetation. Different types of environments have different influences on signal attenuation. This paper analyzes the existing classical propagation path loss models and the model with excess loss caused by forest areas and then proposes a new short-range wireless channel propagation model, which can be applied to different types of forest environments. We conducted continuous-wave measurements at a center frequency of 605 MHz on predetermined routes in distinct types of forest areas and recorded the reference signal received power. Then, we use various path loss models to fit the measured data based on different vegetation types and distributions. Simulation results show that the proposed model has substantially smaller fitting errors with reasonable computational complexity, as compared with representative traditional counterparts

The components of tumor microenvironment as biomarker for immunotherapy in metastatic renal cell carcinoma

Substantial improvement in prognosis among metastatic renal cell carcinoma (mRCC) patients has been achieved, owing to the rapid development and utilization of immunotherapy. In particular, immune checkpoint inhibitors (ICIs) have been considered the backbone of systemic therapy for patients with mRCC alongside multi-targeted tyrosine kinase inhibitors (TKIs) in the latest clinical practice guidelines. However, controversies and challenges in optimal individualized treatment regarding immunotherapy remains still About 2/3 of the patients presented non-response or acquired resistance to ICIs. Besides, immune-related toxicities, namely immune-related adverse events, are still elusive and life-threatening. Thus, reliable biomarkers to predict immunotherapeutic outcomes for mRCC patients are needed urgently. Tumor microenvironment (TME), consisting of immune cells, vasculature, signaling molecules, and extracellular matrix and regulates tumor immune surveillance and immunological evasion through complex interplay, plays a critical role in tumor immune escape and consequently manipulates the efficacy of immunotherapy. Various studied have identified the different TME components are significantly associated with the outcome of mRCC patients receiving immunotherapy, making them potential valuable biomarkers in therapeutic guidance. The present review aims to summarize the latest evidence on the associations between the components of TME including immune cells, cytokines and extracellular matrix, and the therapeutic responses among mRCC patients with ICI-based treatment. We further discuss the feasibility and limitation of these components as biomarkers