There is an urgent need to control global warming caused by humans to achieve a sustainable future.CO_(2) levels are rising steadily,and while countries worldwide are actively moving toward the sustainability goals pr...There is an urgent need to control global warming caused by humans to achieve a sustainable future.CO_(2) levels are rising steadily,and while countries worldwide are actively moving toward the sustainability goals proposed during the Paris Agreement in 2015,we are still a long way to go from achieving a sustainable mode of global operation.The increased popularity of cryptocurrencies since the introduction of Bitcoin in 2009 has been accompanied by an increasing trend in greenhouse gas emissions and high electrical energy consumption.Popular energy tracking studies(e.g.,Digiconomist and the Cambridge Bitcoin Energy Consumption Index(CBECI))have estimated energy consumption ranges from 29.96 TWh to 135.12 TWh and 26.41 TWh to 176.98 TWh,respectively for Bitcoin as of July 2021,which are equivalent to the energy consumption of countries such as Sweden and Thailand.The latest estimate by Digiconomist on carbon footprints shows a 64.18 MtCO_(2) emission by Bitcoin as of July 2021,close to the emissions by Greece and Oman.This review compiles estimates made by various studies from 2018 to 2021.We compare the energy consumption and carbon footprints of these cryptocurrencies with countries around the world and centralized transaction methods such as Visa.We identify the problems associated with cryptocurrencies and propose solutions that can help reduce their energy consumption and carbon footprints.Finally,we present case studies on cryptocurrency networks,namely,Ethereum 2.0 and Pi Network,with a discussion on how they can solve some of the challenges we have identified.展开更多
The rapid advancement of deep learning and the emergence of largescale neural models,such as bidirectional encoder representations from transformers(BERT),generative pre-trained transformer(GPT),and large language mod...The rapid advancement of deep learning and the emergence of largescale neural models,such as bidirectional encoder representations from transformers(BERT),generative pre-trained transformer(GPT),and large language model Meta AI(LLaMa),have brought significant computational and energy challenges.Neuromorphic computing presents a biologically inspired approach to addressing these issues,leveraging event-driven processing and in-memory computation for enhanced energy efficiency.This survey explores the intersection of neuromorphic computing and large-scale deep learning models,focusing on neuromorphic models,learning methods,and hardware.We highlight transferable techniques from deep learning to neuromorphic computing and examine the memoryrelated scalability limitations of current neuromorphic systems.Furthermore,we identify potential directions to enable neuromorphic systems to meet the growing demands of modern AI workloads.展开更多
Chirality,defined by Lord Kelvin,refers to the geometric symmetry property of an object that cannot be superposed onto its mirror image using rotations and translations.The material’s chirality can be probed with lig...Chirality,defined by Lord Kelvin,refers to the geometric symmetry property of an object that cannot be superposed onto its mirror image using rotations and translations.The material’s chirality can be probed with light as the optical activity:optical rotary dispersion(ORD)and circular dichroism(CD).It is still challenging to yield extremely sensitive ORD and CD for very weak chirality and measure both simultaneously.Cavity ringdown polarimetry has been reported to improve ORD detection sensitivity with the absence of equally important CD signature,at the price of high cavity finesse near 400,frequency-locking sophistication,and large magnetic field.Here,we report a unique recipe to demonstrate the simultaneous measurement of ORD and the CD by separately observing the chiral eigenmode spectra from a bowtie optical cavity with a finesse about 30,without resorting to frequency locking or magnetic field.We obtain a sensitivity of2.7×10^(−3)deg/√Hz for ORD,8.1×10^(−6)/√Hz for CD,and a spectral resolution of 0.04 pm within a millisecond-scale measurement.We present a cost-effective yet ultrasensitive account for chiral chromatography,the conformational dynamics and chiroptical analysis of biological samples which particularly exhibit weak and narrow spectral signals.展开更多
Optimizing autonomous vehicle movements through roadway intersections is a challenging problem.It has been demonstrated in the literature that traditional traffic control,such as traffic signal and stop sign control a...Optimizing autonomous vehicle movements through roadway intersections is a challenging problem.It has been demonstrated in the literature that traditional traffic control,such as traffic signal and stop sign control are not optimal especially for heavy traffic demand levels.Alternatively,centralized autonomous vehicle control strategies are costly and not scalable given that the ability of a central controller to track and schedule the movement of hundreds of vehicles in real-time is questionable.Consequently,in this paper a fully distributed algorithm is proposed where vehicles in the vicinity of an intersection continuously cooperate with each other to develop a schedule that allows them to safely proceed through the intersection while incurring minimum delay.Unlike other distributed approaches described in the literature,the wireless communication constraints are considered in the design of the control algorithm.Specifically,the proposed algorithm requires vehicles heading to an intersection to communicate only with neighboring vehicles,while the lead vehicles on each approach lane share information to develop a complete intersection utilization schedule.The scheduling rotates between vehicles to identify higher traffic volumes and favor vehicles coming from heavier lanes to minimize the overall intersection delay.The simulated experiments show significant reductions in the average delay using the proposed approach compared to other methods reported in the literature and reduction in the maximum delay experienced by a vehicle especially in cases of heavy traffic demand levels.展开更多
基金supported by the SERB ASEAN project CRD/2020/000369 received by Dr.Vinay Chamolasupported by a 2021-2022 Fulbright U.S.scholar grant award administered by the U.S.
文摘There is an urgent need to control global warming caused by humans to achieve a sustainable future.CO_(2) levels are rising steadily,and while countries worldwide are actively moving toward the sustainability goals proposed during the Paris Agreement in 2015,we are still a long way to go from achieving a sustainable mode of global operation.The increased popularity of cryptocurrencies since the introduction of Bitcoin in 2009 has been accompanied by an increasing trend in greenhouse gas emissions and high electrical energy consumption.Popular energy tracking studies(e.g.,Digiconomist and the Cambridge Bitcoin Energy Consumption Index(CBECI))have estimated energy consumption ranges from 29.96 TWh to 135.12 TWh and 26.41 TWh to 176.98 TWh,respectively for Bitcoin as of July 2021,which are equivalent to the energy consumption of countries such as Sweden and Thailand.The latest estimate by Digiconomist on carbon footprints shows a 64.18 MtCO_(2) emission by Bitcoin as of July 2021,close to the emissions by Greece and Oman.This review compiles estimates made by various studies from 2018 to 2021.We compare the energy consumption and carbon footprints of these cryptocurrencies with countries around the world and centralized transaction methods such as Visa.We identify the problems associated with cryptocurrencies and propose solutions that can help reduce their energy consumption and carbon footprints.Finally,we present case studies on cryptocurrency networks,namely,Ethereum 2.0 and Pi Network,with a discussion on how they can solve some of the challenges we have identified.
文摘The rapid advancement of deep learning and the emergence of largescale neural models,such as bidirectional encoder representations from transformers(BERT),generative pre-trained transformer(GPT),and large language model Meta AI(LLaMa),have brought significant computational and energy challenges.Neuromorphic computing presents a biologically inspired approach to addressing these issues,leveraging event-driven processing and in-memory computation for enhanced energy efficiency.This survey explores the intersection of neuromorphic computing and large-scale deep learning models,focusing on neuromorphic models,learning methods,and hardware.We highlight transferable techniques from deep learning to neuromorphic computing and examine the memoryrelated scalability limitations of current neuromorphic systems.Furthermore,we identify potential directions to enable neuromorphic systems to meet the growing demands of modern AI workloads.
基金supported by the National Key R&D Program of China(Grants No.2022YFA1405000,No.2019YFA0308700)the National Natural Science Foundation of China(Grants No.92365107,No.12305020,and No.11890704)+5 种基金the Program for Innovative Talents and Teams in Jiangsu(Grant No.JSSCTD202138)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0301400)China Postdoctoral Science Foundation(Grant No.2023M731613)Jiangsu Funding Program for Excellent Postdoctoral Talent(Grant No.2023ZB708)C.-W.Q.is supported by the Competitive Research Program Award(NRF-CRP22-2019-0006&NRF-CRP26-2021-0004)from the NRF,Prime Minister’s Office,Singaporeby a grant(A-0005947-16-00)from A*STAR MTC IRG(M22K2c0088 with A-8001322-00-00).
文摘Chirality,defined by Lord Kelvin,refers to the geometric symmetry property of an object that cannot be superposed onto its mirror image using rotations and translations.The material’s chirality can be probed with light as the optical activity:optical rotary dispersion(ORD)and circular dichroism(CD).It is still challenging to yield extremely sensitive ORD and CD for very weak chirality and measure both simultaneously.Cavity ringdown polarimetry has been reported to improve ORD detection sensitivity with the absence of equally important CD signature,at the price of high cavity finesse near 400,frequency-locking sophistication,and large magnetic field.Here,we report a unique recipe to demonstrate the simultaneous measurement of ORD and the CD by separately observing the chiral eigenmode spectra from a bowtie optical cavity with a finesse about 30,without resorting to frequency locking or magnetic field.We obtain a sensitivity of2.7×10^(−3)deg/√Hz for ORD,8.1×10^(−6)/√Hz for CD,and a spectral resolution of 0.04 pm within a millisecond-scale measurement.We present a cost-effective yet ultrasensitive account for chiral chromatography,the conformational dynamics and chiroptical analysis of biological samples which particularly exhibit weak and narrow spectral signals.
基金This research effort was funded by the Connected Vehicle Initiative University Transportation Center(CVI-UTC).
文摘Optimizing autonomous vehicle movements through roadway intersections is a challenging problem.It has been demonstrated in the literature that traditional traffic control,such as traffic signal and stop sign control are not optimal especially for heavy traffic demand levels.Alternatively,centralized autonomous vehicle control strategies are costly and not scalable given that the ability of a central controller to track and schedule the movement of hundreds of vehicles in real-time is questionable.Consequently,in this paper a fully distributed algorithm is proposed where vehicles in the vicinity of an intersection continuously cooperate with each other to develop a schedule that allows them to safely proceed through the intersection while incurring minimum delay.Unlike other distributed approaches described in the literature,the wireless communication constraints are considered in the design of the control algorithm.Specifically,the proposed algorithm requires vehicles heading to an intersection to communicate only with neighboring vehicles,while the lead vehicles on each approach lane share information to develop a complete intersection utilization schedule.The scheduling rotates between vehicles to identify higher traffic volumes and favor vehicles coming from heavier lanes to minimize the overall intersection delay.The simulated experiments show significant reductions in the average delay using the proposed approach compared to other methods reported in the literature and reduction in the maximum delay experienced by a vehicle especially in cases of heavy traffic demand levels.
