117 research outputs found
FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices
Deep neural networks show great potential as solutions to many sensing
application problems, but their excessive resource demand slows down execution
time, pausing a serious impediment to deployment on low-end devices. To address
this challenge, recent literature focused on compressing neural network size to
improve performance. We show that changing neural network size does not
proportionally affect performance attributes of interest, such as execution
time. Rather, extreme run-time nonlinearities exist over the network
configuration space. Hence, we propose a novel framework, called FastDeepIoT,
that uncovers the non-linear relation between neural network structure and
execution time, then exploits that understanding to find network configurations
that significantly improve the trade-off between execution time and accuracy on
mobile and embedded devices. FastDeepIoT makes two key contributions. First,
FastDeepIoT automatically learns an accurate and highly interpretable execution
time model for deep neural networks on the target device. This is done without
prior knowledge of either the hardware specifications or the detailed
implementation of the used deep learning library. Second, FastDeepIoT informs a
compression algorithm how to minimize execution time on the profiled device
without impacting accuracy. We evaluate FastDeepIoT using three different
sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus.
FastDeepIoT further reduces the neural network execution time by to
and energy consumption by to compared with the
state-of-the-art compression algorithms.Comment: Accepted by SenSys '1
The cortisol awakening response predicts response inhibition in the afternoon of the same day
The cortisol awakening response (CAR) is the rapid increase of cortisol levels 30–45 minutes after awakening in the morning. Numerous studies have indicated the relationship between the CAR and cognition. However, little is known about daily variation in the CAR and cognitive function in healthy adults. The aim of the present study was to investigate whether the CAR predicted the response inhibition function on the same day in both behaviour and the dynamic time course of brain processing. The saliva samples of 47 healthy men were collected at three time points: immediately on awakening, 30 minutes and 45 minutes post-awakening in the morning. Participants performed a Go/NoGo task while electroencephalograms (EEG) were recorded in the afternoon of the same day. The results showed that a greater CAR was associated with a stronger N2. In the sub-group of CAR responders (n = 33) the CAR was negatively related to the false alarm rate of NoGo-trials. Our findings suggested that the CAR was predictive of the function of response inhibition in both the earlier cognitive step (i.e., conflict monitoring) and the behavioural performance of response inhibition on the same day in healthy men
Tera-sample-per-second arbitrary waveform generation in the synthetic dimension
The synthetic dimension opens new horizons in quantum physics and topological
photonics by enabling new dimensions for field and particle manipulations. The
most appealing property of the photonic synthetic dimension is its ability to
emulate high-dimensional optical behavior in a unitary physical system. Here we
show that the photonic synthetic dimension can transform technical problems in
photonic systems between dimensionalities, providing unexpected solutions to
technical problems that are otherwise challenging. Specifically, we propose and
experimentally demonstrate a photonic Galton board (PGB) in the temporal
synthetic dimension, in which the temporal high-speed challenge is converted
into a spatial fiber-optic length matching problem, leading to the experimental
generation of tera-sample-per-second arbitrary waveforms. Limited by the speed
of the measurement equipment, waveforms with sampling rates of up to 341.53
GSa/s are recorded. Our proposed PGB operating in the temporal synthetic
dimension breaks the speed limit in a physical system, bringing arbitrary
waveform generation into the terahertz regime. The concept of dimension
conversion offers possible solutions to various physical dimension-related
problems, such as super-resolution imaging, high-resolution spectroscopy, time
measurement, etc
MUSER: A Multi-View Similar Case Retrieval Dataset
Similar case retrieval (SCR) is a representative legal AI application that
plays a pivotal role in promoting judicial fairness. However, existing SCR
datasets only focus on the fact description section when judging the similarity
between cases, ignoring other valuable sections (e.g., the court's opinion)
that can provide insightful reasoning process behind. Furthermore, the case
similarities are typically measured solely by the textual semantics of the fact
descriptions, which may fail to capture the full complexity of legal cases from
the perspective of legal knowledge. In this work, we present MUSER, a similar
case retrieval dataset based on multi-view similarity measurement and
comprehensive legal element with sentence-level legal element annotations.
Specifically, we select three perspectives (legal fact, dispute focus, and law
statutory) and build a comprehensive and structured label schema of legal
elements for each of them, to enable accurate and knowledgeable evaluation of
case similarities. The constructed dataset originates from Chinese civil cases
and contains 100 query cases and 4,024 candidate cases. We implement several
text classification algorithms for legal element prediction and various
retrieval methods for retrieving similar cases on MUSER. The experimental
results indicate that incorporating legal elements can benefit the performance
of SCR models, but further efforts are still required to address the remaining
challenges posed by MUSER. The source code and dataset are released at
https://github.com/THUlawtech/MUSER.Comment: Accepted by CIKM 2023 Resource Trac
Review on Applications of Lignin in Pavement Engineering: A Recent Survey
Lignin is the second-largest plant polymer on Earth after cellulose. About 98% of lignin produced in the papermaking and pulping industry is used for combustion heating or power generation. Less than 2% of lignin is used in more valuable fields, mainly in the formulation of dispersants, adhesives, and surfactants. Asphalt is one of the most important materials in pavement engineering. It is a dark brown complex mixture composed of hydrocarbons with different molecular weights and their non-metallic derivatives. Because the chemical structure of lignin is similar to that of asphalt, it is a carbon-based hydrocarbon material. More researchers studied the application of lignin in pavement engineering. In this paper, the structure, application, and extraction technology of lignin were summarized. This is a review article describing the different applications of lignin in pavement engineering and exploring the prospects of the application. There are three main types of pavement materials that can be used for lignin in pavement engineering, which are asphalt, asphalt mixture, and roadbed soil. In asphalt, lignin can be used as a modifier, extender, emulsifier, antioxidant, and coupling agent. In asphalt mixtures, lignin can be used as an additive. In road base soils, lignin can be used as a soil stabilizer. Furthermore, the article analyzed the application effects of lignin from the life cycle assessment. The conclusions suggest that lignin-modified asphalt exhibits more viscosity and hardness, and its high-temperature resistance and rutting resistance can be significantly improved compared with conventional asphalt. In addition, some lignin-modified asphalt binders exhibit reduced low-temperature crack resistance and fatigue resistance, which can be adjusted and selected according to the climate change in different regions. The performance of lignin as an asphalt mixture additive and asphalt extender has been proved to be feasible. Lignin can also produce good mechanical properties as well as environmental benefits as a soil stabilizer. In summary, lignin plays an important role in asphalt pavement and roadbed soil, and it is likely to be a development trend in the future due to its environmental friendliness and low cost. More research is needed to generalize the application of lignin in pavement engineering
An Experimental Evaluation of Datacenter Workloads On Low-Power Embedded Micro Servers
This paper presents a comprehensive evaluation of an ultra-low power cluster, built upon the Intel Edison based micro servers. The improved performance and high energy efficiency of micro servers have driven both academia and industry to explore the possibility of replacing conventional brawny servers with a larger swarm of embedded micro servers. Existing attempts mostly focus on mobile-class micro servers, whose capacities are similar to mobile phones. We, on the other hand, target on sensor-class micro servers, which are originally intended for uses in wearable technologies, sensor networks, and Internet-of-Things. Although sensor-class micro servers have much less capacity, they are touted for minimal power consumption (< 1 Watt), which opens new possibilities of achieving higher energy efficiency in datacenter workloads. Our systematic evaluation of the Edison cluster and comparisons to conventional brawny clusters involve careful workload choosing and laborious parameter tuning, which ensures maximum server utilization and thus fair comparisons. Results show that the Edison cluster achieves up to 3.5× improvement on work-done-per-joule for web service applications and data-intensive MapReduce jobs. In terms of scalability, the Edison cluster scales linearly on the throughput of web service workloads, and also shows satisfactory scalability for MapReduce workloads despite coordination overhead.This research was supported in part by NSF grant 13-20209.Ope
Text-driven Visual Synthesis with Latent Diffusion Prior
There has been tremendous progress in large-scale text-to-image synthesis
driven by diffusion models enabling versatile downstream applications such as
3D object synthesis from texts, image editing, and customized generation. We
present a generic approach using latent diffusion models as powerful image
priors for various visual synthesis tasks. Existing methods that utilize such
priors fail to use these models' full capabilities. To improve this, our core
ideas are 1) a feature matching loss between features from different layers of
the decoder to provide detailed guidance and 2) a KL divergence loss to
regularize the predicted latent features and stabilize the training. We
demonstrate the efficacy of our approach on three different applications,
text-to-3D, StyleGAN adaptation, and layered image editing. Extensive results
show our method compares favorably against baselines.Comment: Project website: https://latent-diffusion-prior.github.io
More Insight on Deep Learning-aided Cryptanalysis
In CRYPTO 2019, Gohr showed that well-trained neural networks could perform cryptanalytic distinguishing tasks superior to differential distribution table (DDT)-based distinguishers. This suggests that the differential-neural distinguisher (ND) may use additional information besides pure ciphertext differences. However, the explicit knowledge beyond differential distribution is still unclear. In this work, we provide explicit rules that can be used alongside DDTs to enhance the effectiveness of distinguishers compared to pure DDT-based distinguishers. These rules are based on strong correlations between bit values in right pairs of XOR-differential propagation through addition modulo . Interestingly, they can be closely linked to the earlier study of the multi-bit constraints and the recent study of the fixed-key differential probability. In contrast, combining these rules does not improve the NDs\u27 performance. This suggests that these rules or their equivalent form have already been exploited by NDs, highlighting the power of neural networks in cryptanalysis.
In addition, we find that to enhance the differential-neural distinguisher\u27s accuracy and the number of rounds, regulating the differential propagation is imperative. Introducing differences into the keys is typically believed to help eliminate differences in encryption states, resulting in stronger differential propagations. However, differential-neural attacks differ from traditional ones as they don\u27t specify output differences or follow a single differential trail. This questions the usefulness of introducing differences in a key in differential-neural attacks and the resistance of Speck against such attacks in the related-key setting. This work shows that the power of differential-neural cryptanalysis in the related-key setting can exceed that in the single-key setting by successfully conducting a 14-round key recovery attack on Speck32/64
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