Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering

The pre-training-fine-tuning paradigm based on layout-aware multimodal pre-trained models has achieved significant progress on document image question answering. However, domain pre-training and task fine-tuning for additional visual, layout, and task modules prevent them from directly utilizing off-the-shelf instruction-tuning language foundation models, which have recently shown promising potential in zero-shot learning. Contrary to aligning language models to the domain of document image question answering, we align document image question answering to off-the-shell instruction-tuning language foundation models to utilize their zero-shot capability. Specifically, we propose layout and task aware instruction prompt called LATIN-Prompt, which consists of layout-aware document content and task-aware descriptions. The former recovers the layout information among text segments from OCR tools by appropriate spaces and line breaks. The latter ensures that the model generates answers that meet the requirements, especially format requirements, through a detailed description of task. Experimental results on three benchmarks show that LATIN-Prompt can improve the zero-shot performance of instruction-tuning language foundation models on document image question answering and help them achieve comparable levels to SOTAs based on the pre-training-fine-tuning paradigm. Quantitative analysis and qualitative analysis demonstrate the effectiveness of LATIN-Prompt. We provide the code in supplementary and will release the code to facilitate future research.Comment: Code is available at https://github.com/WenjinW/LATIN-Promp

Algorithms for local sensor synchronization

In a wireless sensor network (WSN), each sensor monitors environmental parameters, and reports its readings to a base station, possibly through other nodes. A sensor works in cycles, in each of which it stays active for a fixed duration, and then sleeps until the next cycle. The frequency of such cycles determines the portion of time that a sensor is active, and is the dominant factor on its battery life. The majority of existing work assumes globally synchronized WSN where all sensors have the same frequency. This leads to waste of battery power for applications that entail different accuracy of measurements, or environments where sensor readings have large variability. To overcome this problem, we propose LS, a query processing framework for locally synchronized WSN. We consider that each sensor n(i) has a distinct sampling frequency f(i), which is determined by the application or environment requirements. The complication of LS is that ni has to wake up with a network frequency F(i)>= f(i), in order to forward messages of other sensors. Our goal is to minimize the sum of F(i) without delaying packet transmissions. Specifically, given a routing tree, we first present a dynamic programming algorithm that computes the optimal network frequency of each sensor; then, we develop a heuristic for finding the best tree topology, if this is not fixed in advance

Exploiting Constructive Interference for Scalable Flooding in Wireless Networks

Abstract—Exploiting constructive interference in wireless networks is an emerging trend for it allows multiple senders transmit an identical packet simultaneously. Constructive interference based flooding can realize millisecond network flooding latency and sub-microsecond time synchronization accuracy, require no network state information and adapt to topology changes. However, constructive interference has a precondition to function, namely, the maximum temporal displacement ∆ of concurrent packet transmissions should be less than a given hardware constrained threshold. We disclose that constructive interference based flooding suffers the scalability problem. The packet reception performances of intermediate nodes degrade significantly as the density or the size of the network increases. We theoretically show that constructive interference based flooding has a packet reception ratio (PRR) lower bound (95.4%) in the grid topology. For a general topology, we propose the spine constructive interference based flooding (SCIF) protocol. With little overhead, SCIF floods the entire network much more reliably than Glossy [1] in high density or large-scale networks. Extensive simulations illustrate that the PRR of SCIF keeps stable above 96 % as the network size grows from 400 to 4000 while the PRR of Glossy is only 26 % when the size of the network is 4000. We also propose to use waveform analysis to explain the root cause of constructive interference, which is mainly examined in simulations and experiments. We further derive the closed-form PRR formula and define interference gain factor (IGF) to quantitatively measure constructive interference. I

exploiting constructive interference for scalable flooding in wireless networks

Exploiting constructive interference in wireless networks is an emerging trend for it allows multiple senders transmit an identical packet simultaneously. Constructive interference based flooding can realize millisecond network flooding latency and sub-microsecond time synchronization accuracy, require no network state information and adapt to topology changes. However, constructive interference has a precondition to function, namely, the maximum temporal displacement Δ of concurrent packet transmissions should be less than a given hardware constrained threshold. We disclose that constructive interference based flooding suffers the scalability problem. The packet reception performances of intermediate nodes degrade significantly as the density or the size of the network increases. We theoretically show that constructive interference based flooding has a packet reception ratio (PRR) lower bound (95:4%) in the grid topology. For a general topology, we propose the spine constructive interference based flooding (SCIF) protocol. With little overhead, SCIF floods the entire network much more reliably than Glossy [1] in high density or large-scale networks. Extensive simulations illustrate that the PRR of SCIF keeps stable above 96% as the network size grows from 400 to 4000 while the PRR of Glossy is only 26% when the size of the network is 4000. We also propose to use waveform analysis to explain the root cause of constructive interference, which is mainly examined in simulations and experiments. We further derive the closed-form PRR formula and define interference gain factor (IGF) to quantitatively measure constructive interference. © 2012 IEEE.Exploiting constructive interference in wireless networks is an emerging trend for it allows multiple senders transmit an identical packet simultaneously. Constructive interference based flooding can realize millisecond network flooding latency and sub-microsecond time synchronization accuracy, require no network state information and adapt to topology changes. However, constructive interference has a precondition to function, namely, the maximum temporal displacement Δ of concurrent packet transmissions should be less than a given hardware constrained threshold. We disclose that constructive interference based flooding suffers the scalability problem. The packet reception performances of intermediate nodes degrade significantly as the density or the size of the network increases. We theoretically show that constructive interference based flooding has a packet reception ratio (PRR) lower bound (95:4%) in the grid topology. For a general topology, we propose the spine constructive interference based flooding (SCIF) protocol. With little overhead, SCIF floods the entire network much more reliably than Glossy [1] in high density or large-scale networks. Extensive simulations illustrate that the PRR of SCIF keeps stable above 96% as the network size grows from 400 to 4000 while the PRR of Glossy is only 26% when the size of the network is 4000. We also propose to use waveform analysis to explain the root cause of constructive interference, which is mainly examined in simulations and experiments. We further derive the closed-form PRR formula and define interference gain factor (IGF) to quantitatively measure constructive interference. © 2012 IEEE