MH-REACH-Mote: supporting multi-hop passive radio wake-up for wireless sensor network

A passive wake-up radio in a wireless sensor network (WSN) has the advantage of increasing network lifetime by using a wake-up radio receiver (WuRx) to eliminate unnecessary idle listening. A sensor node equipped with a WuRx can operate in an ultra-low-power sleep mode, waiting for a trigger signal sent by the wake-up radio transmitter (WuTx). The passive WuRx is entirely powered by the energy harvested from radio transmissions sent by the WuTx. Therefore, it has the advantage of not consuming any energy locally, which would drain the sensor node's battery. Even so, the high amount of energy required to wake up a passive WuRx by a WuTx makes it difficult to build a multi-hop passive wake-up sensor network. In this paper, we describe and discuss our implementation of a battery-powered sensor node with multi-hop wake-up capability using passive WuRxs, called MH-REACH-Mote (Multi-hop-Range EnhAnCing energy Harvester-Mote). The MH-REACH-Mote is kept in an ultra-low-power sleep mode until it receives a wake-up trigger signal. Upon receipt, it wakes up and transmits a new trigger signal to power other passive WuRxs. We evaluate the wake-up range and power consumption of an MH-REACH-Mote through a series of field tests. Results show that the MH-REACH-Mote enables multi-hop wake-up capabilities for passive WuRxs with a wake-up range of 9.4m while requiring a reasonable power consumption for WuTx functionality. We also simulate WSN data collection scenarios with MH-REACH-Motes and compare the results with those of active wake-up sensor nodes as well as a low power listening approach. The results show that the MH-REACH-Mote enables a longer overall lifetime than the other two approaches when data is collected infrequently.Peer ReviewedPostprint (author's final draft

IOPS: An Unified SpMM Accelerator Based on Inner-Outer-Hybrid Product

Sparse matrix multiplication (SpMM) is widely applied to numerous domains, such as graph processing, machine learning, and data analytics. However, inner product based SpMM induces redundant zero-element computing for mismatched nonzero operands, while outer product based approach lacks input reuse across Process Elements (PEs) and poor output locality for accumulating partial sum (psum) matrices. Besides, current works only focus on sparse-sparse matrix multiplication (SSMM) or sparse-dense matrix multiplication (SDMM), rarely performing efficiently for both. To address these problems, this paper proposes an unified SpMM accelerator, called IOPS, hybridizing inner with outer products. It reuses the input matrix among PEs with inner product dataflow, and removes zero-element calculations with outer product approach in each PE, which can efficiently process SSMM and SDMM. Moreover, an address mapping method is designed to accumulate the irregular sparse psum matrices, reducing the latency and DRAM access of psum accumulating. Furthermore, an adaptive partition strategy is proposed to tile the input matrices based on their sparsity ratios, effectively utilizing the storage of architecture and reducing DRAM access. Compared with the SSMM accelerator, SpArch, we achieve 1.7x~6.3x energy efficiency and 1.2x~4.4x resource efficiency, with 1.4x~2.1x DRAM access saving

Pharmacokinetic and pharmacodynamic modeling of erythropoiesis stimulating agents in rats

Erythropoiesis is a process by which red blood cells are produced in the bone marrow. Disruption of this process can lead to anemia. Erythropoiesis stimulating agents (ESAs), such as epoetin- and darbepoetin- , have been developed to treat anemia. CNTO 530 is a novel ESA that has a longer terminal half-life than either epoetin- or darbepoetin- . As these ESAs all activate erythropoietin receptor (EPO-R), we hypothesize that any di erences in the pharmacologic activity are solely dependent on their pharmacokinetic properties. To test this hypothesis, we proposed a new Pharmacokinetic/Pharmacodynamic (PK/PD) model to account for the pharmacological response. Rats received a single subcutaneous (s.c.) dose of the ESA and reticulocyte (RET) counts, red blood cell (RBC) counts and hemoglobin (HGB) levels were measured for up to 72 days (1728 hours) post-dosing. Various dosage levels were studied for each drug. A new indirect response model with multiple regulatory e ects was used to characterize the PD responses and a linear two-compartmental model was used to characterize the PK responses. All three agents caused a dose responsive increase in RET, RBC and HGB. Compared to epoetin- and darbepoetin- , CNTO 530 caused a longer-lived increase in these parameters. A single PK/PD model could represent all three agents. However, when comparing among the erythropoietic responses to doses that increased RBC, the coe cients of the model indicate that despite having a lower potency, CNTO 530 caused a more rapid mobilization of RET. The results of the PK/PD modeling suggest that CNTO 530 stimulates erythropoiesis in a similar fashion to epoetin- and darbepoetin- and that the PK properties of an ESA are the most important factor in determining e cacy. In addition, dose threshold is an important factor we need to consider in designing the PK/PD model. Understanding dose threshold of a drug aids in determining appropriate dose levels and, therefore, helps diminish side e ects of the compound and reduces treatment costs. Many studies have focused on non-quantitative analysis of drug dose threshold, which can be biased by various factors. Aiming for quantitative analysis of this parameter, we proposed two statistical methods to determine dose threshold of a drug and applied them to CNTO 530.Ph.D., Electrical Engineering -- Drexel University, 201

BandMap: Application Mapping with Bandwidth Allocation forCoarse-Grained Reconfigurable Array

This paper proposes an application mapping algorithm, BandMap, for coarse-grained reconfigurable array (CGRA), which allocates the bandwidth in PE array according to the transferring demands of data, especially the data with high spatial reuse, to reduce the routing PEs. To cover bandwidth allocation, BandMap maps the data flow graphs (DFGs), abstracted from applications, by solving the maximum independent set (MIS) on a mixture of tuple and quadruple resource occupation conflict graph. Compared to a state-of-art BusMap work, Bandmap can achieve reduced routing PEs with the same or even smaller initiation interval (II)

Sense: Model Hardware Co-design for Accelerating Sparse CNN on Systolic Array

Sparsity is an intrinsic property of convolutional neural network(CNN) and worth exploiting for CNN accelerators, but extra processing comes with hardware overhead, causing many architectures suffering from only minor profit. Meanwhile, systolic array has been increasingly competitive on CNNs acceleration for its high spatiotemporal locality and low hardware overhead. However, the irregularity of sparsity induces imbalanced workload under the rigid systolic dataflow, causing performance degradation. Thus, this paper proposed a systolicarray-based architecture, called Sense, for sparse CNN acceleration by model-hardware co-design, achieving large performance improvement. To balance input feature map(IFM) and weight loads across Processing Element(PE) array, we applied channel clustering to gather IFMs with approximate sparsity for array computation, and co-designed a load-balancing weight pruning method to keep the sparsity ratio of each kernel at a certain value with little accuracy loss, improving PE utilization and overall performance. Additionally, Adaptive Dataflow Configuration is applied to determine the computing strategy based on the storage ratio of IFMs and weights, lowering 1.17x-1.8x DRAM access compared with Swallow and further reducing system energy consumption. The whole design is implemented on ZynqZCU102 with 200MHz and performs at 471-, 34-, 53- and 191-image/s for AlexNet, VGG-16, ResNet-50 and GoogleNet respectively. Compared against sparse systolic-array-based accelerators, Swallow, FESA and SPOTS, Sense achieves 1x-2.25x, 1.95x-2.5x and 1.17x-2.37x performance improvement on these CNNs respectively with reasonable overhead.Comment: 14 pages, 29 figures, 6 tables, IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEM

Elastic scattering and total reaction cross sections of 6^{6}Li studied with a microscopic continuum discretized coupled channels model

We present a systematic study of $^{6}$Li elastic scattering and total reaction cross sections at incident energies around the Coulomb barrier within the continuum discretized coupled-channels (CDCC) framework, where $^{6}$Li is treated in an $\alpha$+$d$ two-body model. Collisions with $^{27}$Al, $^{64}$Zn, $^{138}$Ba and $^{208}$Pa are analyzed. The microscopic optical potentials (MOP) based on Skyrme nucleon-nucleon interaction for $\alpha$ and $d$ are adopted in CDCC calculations and satisfactory agreement with the experimental data is obtained without any adjustment on MOPs. For comparison, the $\alpha$ and $d$ global phenomenological optical potentials (GOP) are also used in CDCC analysis and a reduction no less than 50$\%$ on the surface imaginary part of deuteron GOP is required for describing the data. In all cases, the $^6$Li breakup effect is significant and provides repulsive correction to the folding model potential. The reduction on the GOP of deuteron reveals a strong suppression of the reaction probability of deuteron as a component of $^{6}$Li as compared with that of a free deuteron. A further investigation is made by taking the $d$ breakup process into account equivalently within the dynamic polarization potential approach and it shows that $d$ behaves like a tightly bound nucleus in $^{6}$Li induced reactions. We also compare the CDCC results with those calculated with a $^6$Li GOP and it shows that CDCC calculations provide a better reproduction for the elastic scattering angular distributions in the sub-barrier energy region and the total reaction cross sections at energies around the Coulomb barrier.Comment: 10 pages, 12 figure

497. Safety and tolerability of 2000mg intravenous sotrovimab dose in immunocompromised participants uninfected with SARS-CoV-2 in the PROTECT-V trial

Background: There remains a need for pre-exposure prophylaxis against SARS-CoV-2 infection in vulnerable patients in whom response to vaccination is often sub-optimal. The PROTECT-V platform trial is testing pre-exposure prophylactic interventions for COVID-19 in vulnerable patient populations: transplant recipients, individuals with oncological/haematological diagnoses, immune deficiency, autoimmune diseases requiring immunosuppression, and individuals receiving dialysis. Methods: Sotrovimab is a dual-action monoclonal antibody and the second agent to be added to the PROTECT-V platform (Clinicaltrials.gov: NCT04870333; EudraCT: 2020-004144-28). Although a single sotrovimab 500mg intravenous (IV) dose has been widely used for early treatment, data on a 2000mg IV dose are limited. Tolerability data in the first 143 participants randomized to this arm of the trial are presently available. Patients are randomized 1:1 sotrovimab to placebo. Data remain blinded. Results: Median age was 66 years (range 21 – 86) and 82 (57%) patients were female. 132 (96%) had received ≥3 doses of SARS-CoV-2 vaccine. Patient populations were: 82 (57%) autoimmune disease; 26 (18%) haematological/oncological diseases; 21 (15%) transplant recipients; 12 (8%) immunodeficiency; 2 (1%) on dialysis. Two (1%) participants experienced a mild infusion related reaction (IRR). The infusion was briefly interrupted, but completed. Thirty (21%) participants experienced at least one symptom in the 24 hours post-infusion, but none were severe or required hospital admission. The most common symptoms were dizziness (7 [5%]), headache (7 [5%]), rigors (5 [4%]) and fever (4 [3%]). No severe adverse events were reported within 72 hours of IMP infusion at the time of writing. The first 55 patients underwent routine hematological and biochemical blood test evaluation 72 hours post-infusion. Ten events from 9 participants exhibited worsening of laboratory parameters, meeting at least grade two DAIDS criteria or worse. None of these were clinically significant. Conclusion: A 2000mg IV dose of sotrovimab was tolerated well in this blinded analysis of immunocompromised participants, with no severe IRRs or significant change in haematological or biochemical markers up to 72 hours post infusion. Disclosures: Davinder Dosanjh, n/a, Astrazeneca: Honoraria|Astrazeneca: Employee|Boehringer Ingelheim: Advisor/Consultant|Boehringer Ingelheim: Honoraria|Gilead: Advisor/Consultant|GSK: Grant/Research Support|Synairgen: Advisor/Consultant Louise Crowley, n/a, GSK: Grant/Research Support Michael Chen-Xu, n/a, GSK: Grant/Research Support Rona M. Smith, MD MRCP, GSK: Grant/Research Support|Union Therapeutics: Grant/Research Suppor

TREA: Tree-Structure Reasoning Schema for Conversational Recommendation

Conversational recommender systems (CRS) aim to timely trace the dynamic interests of users through dialogues and generate relevant responses for item recommendations. Recently, various external knowledge bases (especially knowledge graphs) are incorporated into CRS to enhance the understanding of conversation contexts. However, recent reasoning-based models heavily rely on simplified structures such as linear structures or fixed-hierarchical structures for causality reasoning, hence they cannot fully figure out sophisticated relationships among utterances with external knowledge. To address this, we propose a novel Tree structure Reasoning schEmA named TREA. TREA constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities, and fully utilizes historical conversations to generate more reasonable and suitable responses for recommended results. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach.Comment: Accepted by ACL2023 main conferenc

Wave interference network with a wave function for traffic sign recognition

In this paper, we successfully combine convolution with a wave function to build an effective and efficient classifier for traffic signs, named the wave interference network (WiNet). In the WiNet, the feature map extracted by the convolutional filters is refined into many entities from an input image. Each entity is represented as a wave. We utilize Euler's formula to unfold the wave function. Based on the wave-like information representation, the model modulates the relationship between the entities and the fixed weights of convolution adaptively. Experiment results on the Chinese Traffic Sign Recognition Database (CTSRD) and the German Traffic Sign Recognition Benchmark (GTSRB) demonstrate that the performance of the presented model is better than some other models, such as ResMLP, ResNet50, PVT and ViT in the following aspects: 1) WiNet obtains the best accuracy rate with 99.80% on the CTSRD and recognizes all images exactly on the GTSRB; 2) WiNet gains better robustness on the dataset with different noises compared with other models; 3) WiNet has a good generalization on different datasets