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The rechargeable Li ion batteries are approaching their energy density limitation, while the prosperous growth of electric vehicle market is demanding cheaper and more sustainable batteries with higher energy density.To meet this goal, new battery material is needed to replace the current battery cathode, namely the LiCoO2 and LiNixMnyCo1-x-yO2 (NMC), which both contains the increasingly expensive transition metal, cobalt. One way to limit the cobalt usage is to increase the nickel substitution, as Ni is cheaper and more abundant compared to Co. Additionally, high Ni NMC delivers more capacity than their low Ni counterparts. However, transition metal substituent introduced an unexpected problem, i.e., the 1st cycle capacity loss. With electrochemical characterization and synchrotron X-ray diffraction, we have identified the sluggish Li intercalation at the end of discharge is the root-cause of this problem, which provided guidance for future improvement on these materials. In addition to optimizing the NMC cathode material, designing new cathode chemistry is another promising approach. Sulfur is a good cathode candidate for next generation energy storage system, due to its high capacity (~1675 mAh cm-2, 8 times as high as NMC), low price, and abundance in earth’s crust. However, elemental sulfur cathode suffers from its insulating nature and polysulfide dissolution problem. Sulfurized polyacrylonitrile (SPAN) is a sulfur based conductive polymer, which prevents sulfur dissolution by forming covalent bonding with sulfur and provides electron pathway by the chemical backbone. Although SPAN typically shows extraordinary stable cycling performance due to its unique structure and high specific capacity (~700 mAh cm-2), the Li-SPAN batteries reported in literature are yet to satisfy the industry demand due to its low areal capacity and incompatibility with ether electrolyte, which is commonly used in Li metal batteries. We discovered that LiNO3 as an electrolyte additive

The rechargeable Li ion batteries are approaching their energy density limitation, while the prosperous growth of electric vehicle market is demanding cheaper and more sustainable batteries with higher energy density.To meet this goal, new battery material is needed to replace the current battery cathode, namely the LiCoO2 and LiNixMnyCo1-x-yO2 (NMC), which both contains the increasingly expensive transition metal, cobalt. One way to limit the cobalt usage is to increase the nickel substitution, as Ni is cheaper and more abundant compared to Co. Additionally, high Ni NMC delivers more capacity than their low Ni counterparts. However, transition metal substituent introduced an unexpected problem, i.e., the 1st cycle capacity loss. With electrochemical characterization and synchrotron X-ray diffraction, we have identified the sluggish Li intercalation at the end of discharge is the root-cause of this problem, which provided guidance for future improvement on these materials. In addition to optimizing the NMC cathode material, designing new cathode chemistry is another promising approach. Sulfur is a good cathode candidate for next generation energy storage system, due to its high capacity (~1675 mAh cm-2, 8 times as high as NMC), low price, and abundance in earth’s crust. However, elemental sulfur cathode suffers from its insulating nature and polysulfide dissolution problem. Sulfurized polyacrylonitrile (SPAN) is a sulfur based conductive polymer, which prevents sulfur dissolution by forming covalent bonding with sulfur and provides electron pathway by the chemical backbone. Although SPAN typically shows extraordinary stable cycling performance due to its unique structure and high specific capacity (~700 mAh cm-2), the Li-SPAN batteries reported in literature are yet to satisfy the industry demand due to its low areal capacity and incompatibility with ether electrolyte, which is commonly used in Li metal batteries. We discovered that LiNO3 as an electrolyte additive

3D hosts are promising to extend the cycle life of lithium metal anodes but have rarely been implemented with lean electrolytes thus impacting the practical cell energy density. To overcome this challenge, a 3D host that is lightweight and easy to fabricate with optimum pore size that enables full utilization of its pore volume, essential for lean electrolyte operations, is reported. The host is fabricated by casting a VGCF (vapor-grown carbon fiber)-based slurry loaded with a sparingly soluble rubidium nitrate salt as an additive. The network of fibers generates uniform pores of ≈3 µm in diameter with a porosity of 80%, while the nitrate additive enhances lithiophilicity. This 3D host delivers an average coulombic efficiency of 99.36% at 1 mA cm-2 and 1 mAh cm-2 for over 860 cycles in half-cell tests. Full cells containing an anode with 1.35-fold excess lithium paired with LiNi0.8 Mn0.1 Co0.1 O2 (NMC811) cathodes exhibit capacity retention of 80% over 176 cycles at C/2 under a lean electrolyte condition of 3 g Ah-1 . This work provides a facile and scalable method to advance 3D lithium hosts closer to practical lithium-metal batteries.

Surface plasmons in graphene provide a compelling strategy for advanced photonic technologies thanks to their tight confinement, fast response and tunability. Recent advances in the field of all-optical generation of graphene's plasmons in planar waveguides offer a promising method for high-speed signal processing in nanoscale integrated optoelectronic devices. Here, we use two counter propagating frequency combs with temporally synchronized pulses to demonstrate deterministic all-optical generation and electrical control of multiple plasmon polaritons, excited via difference frequency generation (DFG). Electrical tuning of a hybrid graphene-fibre device offers a precise control over the DFG phase-matching, leading to tunable responses of the graphene's plasmons at different frequencies across a broadband (0 ~ 50 THz) and provides a powerful tool for high-speed logic operations. Our results offer insights for plasmonics on hybrid photonic devices based on layered materials and pave the way to high-speed integrated optoelectronic computing circuits.

3D hosts are promising to extend the cycle life of lithium metal anodes but have rarely been implemented with lean electrolytes thus impacting the practical cell energy density. To overcome this challenge, a 3D host that is lightweight and easy to fabricate with optimum pore size that enables full utilization of its pore volume, essential for lean electrolyte operations, is reported. The host is fabricated by casting a VGCF (vapor-grown carbon fiber)-based slurry loaded with a sparingly soluble rubidium nitrate salt as an additive. The network of fibers generates uniform pores of ≈3 µm in diameter with a porosity of 80%, while the nitrate additive enhances lithiophilicity. This 3D host delivers an average coulombic efficiency of 99.36% at 1 mA cm-2 and 1 mAh cm-2 for over 860 cycles in half-cell tests. Full cells containing an anode with 1.35-fold excess lithium paired with LiNi0.8 Mn0.1 Co0.1 O2 (NMC811) cathodes exhibit capacity retention of 80% over 176 cycles at C/2 under a lean electrolyte condition of 3 g Ah-1 . This work provides a facile and scalable method to advance 3D lithium hosts closer to practical lithium-metal batteries.

Surface plasmons in graphene provide a compelling strategy for advanced photonic technologies thanks to their tight confinement, fast response and tunability. Recent advances in the field of all-optical generation of graphene's plasmons in planar waveguides offer a promising method for high-speed signal processing in nanoscale integrated optoelectronic devices. Here, we use two counter propagating frequency combs with temporally synchronized pulses to demonstrate deterministic all-optical generation and electrical control of multiple plasmon polaritons, excited via difference frequency generation (DFG). Electrical tuning of a hybrid graphene-fibre device offers a precise control over the DFG phase-matching, leading to tunable responses of the graphene's plasmons at different frequencies across a broadband (0 ~ 50 THz) and provides a powerful tool for high-speed logic operations. Our results offer insights for plasmonics on hybrid photonic devices based on layered materials and pave the way to high-speed integrated optoelectronic computing circuits.

Neuromorphic computing is an emerging research field that aims to develop new intelligent systems by integrating theories and technologies from multi-disciplines such as neuroscience and deep learning. Currently, there have been various software frameworks developed for the related fields, but there is a lack of an efficient framework dedicated for spike-based computing models and algorithms. In this work, we present a Python based spiking neural network (SNN) simulation and training framework, aka SPAIC that aims to support brain-inspired model and algorithm researches integrated with features from both deep learning and neuroscience. To integrate different methodologies from the two overwhelming disciplines, and balance between flexibility and efficiency, SPAIC is designed with neuroscience-style frontend and deep learning backend structure. We provide a wide range of examples including neural circuits Simulation, deep SNN learning and neuromorphic applications, demonstrating the concise coding style and wide usability of our framework. The SPAIC is a dedicated spike-based artificial intelligence computing platform, which will significantly facilitate the design, prototype and validation of new models, theories and applications. Being user-friendly, flexible and high-performance, it will help accelerate the rapid growth and wide applicability of neuromorphic computing research., Comment: This Paper has been submitted to IEEE computational intelligence magazine

BackgroundUveal melanoma (UVM) is a rare cancer that shows sex difference in incidence and survival, with little previous report for the underlying mechanism.MethodsThis study used the SEER data (1974-2016) for an age-dependent analysis on sex difference in UVM, and further used the TCGA-UVM genomics dataset for analyzing the differential gene expression profiles in tumors from men and women.ResultsOur results demonstrate a sex difference in older age (≥40 years) but not in younger patients, with men exhibiting a higher incidence rate than women. However, younger women have shown a continuous increasing trend since 1974. Examining the 11 major oncogenes and tumor suppressors in UVM revealed that EIF1AX showed a significant sex difference in mRNA accumulation and copy number variation, with female tumors expressing higher levels of EIF1AX and exhibiting more variations in copy numbers. EIF1AX mRNA levels were significantly inversely correlated with EIF1AX copy numbers in female tumors only, but not in male tumors. Differential gene expression analysis at the whole genomic level identified a set of 92 protein-coding and 16 RNA-coding genes which exhibited differential expression in men and women (fold of change cutoff at 1.7, adjusted p value < 0.05, FDR < 0.05). Network analysis showed significant difference in immune response and in disulfide bond formation, with EGR1/EGR2 and PDIA2 genes as regulators for immune response and disulfide bond formation, respectively. The melanocortin pathway which is linked to both melanin synthesis and obesity seems to be altered with unclear significance, as the sex difference in POMC, DCT/TYRP2, and MRAP2 was observed but with no clear direction.ConclusionThis study reveals possible mechanisms for the sex difference in tumorigenesis of UVM which has potentials for better understanding and prevention of UVM.

BackgroundUveal melanoma (UVM) is a rare cancer that shows sex difference in incidence and survival, with little previous report for the underlying mechanism.MethodsThis study used the SEER data (1974-2016) for an age-dependent analysis on sex difference in UVM, and further used the TCGA-UVM genomics dataset for analyzing the differential gene expression profiles in tumors from men and women.ResultsOur results demonstrate a sex difference in older age (≥40 years) but not in younger patients, with men exhibiting a higher incidence rate than women. However, younger women have shown a continuous increasing trend since 1974. Examining the 11 major oncogenes and tumor suppressors in UVM revealed that EIF1AX showed a significant sex difference in mRNA accumulation and copy number variation, with female tumors expressing higher levels of EIF1AX and exhibiting more variations in copy numbers. EIF1AX mRNA levels were significantly inversely correlated with EIF1AX copy numbers in female tumors only, but not in male tumors. Differential gene expression analysis at the whole genomic level identified a set of 92 protein-coding and 16 RNA-coding genes which exhibited differential expression in men and women (fold of change cutoff at 1.7, adjusted p value < 0.05, FDR < 0.05). Network analysis showed significant difference in immune response and in disulfide bond formation, with EGR1/EGR2 and PDIA2 genes as regulators for immune response and disulfide bond formation, respectively. The melanocortin pathway which is linked to both melanin synthesis and obesity seems to be altered with unclear significance, as the sex difference in POMC, DCT/TYRP2, and MRAP2 was observed but with no clear direction.ConclusionThis study reveals possible mechanisms for the sex difference in tumorigenesis of UVM which has potentials for better understanding and prevention of UVM.

Lithium metal batteries (LMBs) hold the promise to pushing cell level energy densities beyond 300 Wh kg-1 while operating at ultra-low temperatures (< -30°C). Batteries capable of both charging and discharging at these temperature extremes are highly desirable due to their inherent reduction of external warming requirements. Here we demonstrate that the local solvation structure of the electrolyte defines the charge-transfer behavior at ultra-low temperature, which is crucial for achieving high Li metal coulombic efficiency (CE) and avoiding dendritic growth. These insights were applied to Li metal full cells, where a high-loading 3.5 mAh cm-2 sulfurized polyacrylonitrile (SPAN) cathode was paired with a one-fold excess Li metal anode. The cell retained 84 % and 76 % of its room temperature capacity when cycled at -40 and -60 °C, respectively, which presented stable performance over 50 cycles. This work provides design criteria for ultra-low temperature LMB electrolytes, and represents a defining step for the performance of low-temperature batteries.

Lithium metal batteries (LMBs) hold the promise to pushing cell level energy densities beyond 300 Wh kg-1 while operating at ultra-low temperatures (< -30°C). Batteries capable of both charging and discharging at these temperature extremes are highly desirable due to their inherent reduction of external warming requirements. Here we demonstrate that the local solvation structure of the electrolyte defines the charge-transfer behavior at ultra-low temperature, which is crucial for achieving high Li metal coulombic efficiency (CE) and avoiding dendritic growth. These insights were applied to Li metal full cells, where a high-loading 3.5 mAh cm-2 sulfurized polyacrylonitrile (SPAN) cathode was paired with a one-fold excess Li metal anode. The cell retained 84 % and 76 % of its room temperature capacity when cycled at -40 and -60 °C, respectively, which presented stable performance over 50 cycles. This work provides design criteria for ultra-low temperature LMB electrolytes, and represents a defining step for the performance of low-temperature batteries.

BackgroundUveal melanoma (UVM) is a rare cancer that shows sex difference in incidence and survival, with little previous report for the underlying mechanism.MethodsThis study used the SEER data (1974-2016) for an age-dependent analysis on sex difference in UVM, and further used the TCGA-UVM genomics dataset for analyzing the differential gene expression profiles in tumors from men and women.ResultsOur results demonstrate a sex difference in older age (≥40 years) but not in younger patients, with men exhibiting a higher incidence rate than women. However, younger women have shown a continuous increasing trend since 1974. Examining the 11 major oncogenes and tumor suppressors in UVM revealed that EIF1AX showed a significant sex difference in mRNA accumulation and copy number variation, with female tumors expressing higher levels of EIF1AX and exhibiting more variations in copy numbers. EIF1AX mRNA levels were significantly inversely correlated with EIF1AX copy numbers in female tumors only, but not in male tumors. Differential gene expression analysis at the whole genomic level identified a set of 92 protein-coding and 16 RNA-coding genes which exhibited differential expression in men and women (fold of change cutoff at 1.7, adjusted p value < 0.05, FDR < 0.05). Network analysis showed significant difference in immune response and in disulfide bond formation, with EGR1/EGR2 and PDIA2 genes as regulators for immune response and disulfide bond formation, respectively. The melanocortin pathway which is linked to both melanin synthesis and obesity seems to be altered with unclear significance, as the sex difference in POMC, DCT/TYRP2, and MRAP2 was observed but with no clear direction.ConclusionThis study reveals possible mechanisms for the sex difference in tumorigenesis of UVM which has potentials for better understanding and prevention of UVM.

This paper proposes a voltage-to-frequency converter (VFC) design using unipolar metal-oxide thin film transistor (TFT) technology. The proposed VFC has an integrator and Schmitt trigger based structure. This structure has the advantages of constant power consumption, full-swing output, and low circuit complexity compared to the early designs. To verify the proposed design, SmartSpice simulation based on a Rensselaer Polytechnic Institute (RPI) model whose parameters are turned to fix the measured characteristics of our indium tin oxide-(ITO-) stabilized ZnO TFTs is carried out. The ITO-stabilized ZnO TFT has a single-gate staggered structure. Its typical field-effect mobility, threshold voltage, on/off current ratio, and subthreshold-slope are 14.5 cm2/V.s, 0.5 V, 1.2× 108, and 77 mV/decade, respectively. Simulation results show that the proposed VFC has maximum linearity error less than 1.8%, tuning sensitivity about 1 kHz/V, and power consumption less than 130 μW even under device variations. These performances are competitive compared to the state-of-the-arts. When configured to an analog-to-digital converter (ADC), 6 bit resolution and 14 S/s sampling rate can be realized. These results indicate that the VFC can find potential applications in flexible large-area low-voltage sensor interfaces for quasi-static signals. CCBY

The global uptake of Cloud computing has attracted increased interest within both academia and industry resulting in the formation of large-scale and complex distributed systems. This has led to increased failure occurrence within computing systems that induce substantial negative impact upon system performance and task reliability perceived by users. Such systems also consume vast quantities of power, resulting in significant operational costs perceived by providers. Virtualization – a commonly deployed technology within Cloud datacenters – can enable flexible scheduling of virtual machines to maximize system reliability and energy-efficiency. However, existing work address these two objectives separately, providing limited understanding towards studying the explicit trade-offs towards dependable and energy-efficient compute infrastructure. In this paper, we propose two failure-aware energy-efficient scheduling algorithms that exploit the holistic operational characteristics of the Cloud datacenter comprising the cooling unit, computing infrastructure and server failures. By comprehensively modeling the power and failure profiles of a Cloud datacenter, we propose workload scheduling algorithms Ella-W and Ella-B, capable of reducing cooling and compute energy while minimizing the impact of system failures. A novel and overall metric is proposed that combines energy efficiency and reliability to specify the performance of various algorithms. We evaluate our algorithms against Random, MaxUtil, TASA, MTTE and OBFIT under various system conditions of failure prediction accuracy and workload intensity. Evaluation results demonstrate that Ella-W can reduce energy usage by 29.5% and improve task completion rate by 3.6%, while Ella-B reduces energy usage by 32.7% with no degradation to task completion rate.

Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy efficiency and reduced operation cost. Energy consumption of datacenters consists of computing energy and cooling energy. However, due to the complexity of energy and thermal modeling of realistic Cloud datacenter operation, traditional approaches are unable to provide a comprehensive in-depth solution for virtual machine scheduling which encompasses both computing and cooling energy. This paper addresses this challenge by presenting an elaborate thermal model that analyzes the temperature distribution of airflow and server CPU. We propose GRANITE – a holistic virtual machine scheduling algorithm capable of minimizing total datacenter energy consumption. The algorithm is evaluated against other existing workload scheduling algorithms MaxUtil, TASA, IQR and Random using real Cloud workload characteristics extracted from Google datacenter tracelog. Results demonstrate that GRANITE consumes 4.3% - 43.6% less total energy in comparison to the state-of-the-art, and reduces the probability of critical temperature violation by 99.2% with 0.17% SLA violation rate as the performance penalty.

The global uptake of Cloud computing has attracted increased interest within both academia and industry resulting in the formation of large-scale and complex distributed systems. This has led to increased failure occurrence within computing systems that induce substantial negative impact upon system performance and task reliability perceived by users. Such systems also consume vast quantities of power, resulting in significant operational costs perceived by providers. Virtualization – a commonly deployed technology within Cloud datacenters – can enable flexible scheduling of virtual machines to maximize system reliability and energy-efficiency. However, existing work address these two objectives separately, providing limited understanding towards studying the explicit trade-offs towards dependable and energy-efficient compute infrastructure. In this paper, we propose two failure-aware energy-efficient scheduling algorithms that exploit the holistic operational characteristics of the Cloud datacenter comprising the cooling unit, computing infrastructure and server failures. By comprehensively modeling the power and failure profiles of a Cloud datacenter, we propose workload scheduling algorithms Ella-W and Ella-B, capable of reducing cooling and compute energy while minimizing the impact of system failures. A novel and overall metric is proposed that combines energy efficiency and reliability to specify the performance of various algorithms. We evaluate our algorithms against Random, MaxUtil, TASA, MTTE and OBFIT under various system conditions of failure prediction accuracy and workload intensity. Evaluation results demonstrate that Ella-W can reduce energy usage by 29.5% and improve task completion rate by 3.6%, while Ella-B reduces energy usage by 32.7% with no degradation to task completion rate.

Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy efficiency and reduced operation cost. Energy consumption of datacenters consists of computing energy and cooling energy. However, due to the complexity of energy and thermal modeling of realistic Cloud datacenter operation, traditional approaches are unable to provide a comprehensive in-depth solution for virtual machine scheduling which encompasses both computing and cooling energy. This paper addresses this challenge by presenting an elaborate thermal model that analyzes the temperature distribution of airflow and server CPU. We propose GRANITE – a holistic virtual machine scheduling algorithm capable of minimizing total datacenter energy consumption. The algorithm is evaluated against other existing workload scheduling algorithms MaxUtil, TASA, IQR and Random using real Cloud workload characteristics extracted from Google datacenter tracelog. Results demonstrate that GRANITE consumes 4.3% - 43.6% less total energy in comparison to the state-of-the-art, and reduces the probability of critical temperature violation by 99.2% with 0.17% SLA violation rate as the performance penalty.

Spectral graph theory has been widely applied in unsupervised and semi-supervised learning. In this paper, we find for the first time, to our knowledge, that it also plays a concrete role in supervised classification. It turns out that two classifiers are inherently related to the theory: linear regression for classification (LRC) and normalized radial basis function network (nRBFN), corresponding to linear and nonlinear kernel respectively. The spectral graph theory provides us with a new insight into a fundamental aspect of classification: the tradeoff between fitting error and overfitting risk. With the theory, ideal working conditions for LRC and nRBFN are presented, which ensure not only zero fitting error but also low overfitting risk. For quantitative analysis, two concepts, the fitting error and the spectral risk (indicating overfitting), have been defined. Their bounds for nRBFN and LRC are derived. A special result shows that the spectral risk of nRBFN is lower bounded by the number of classes and upper bounded by the size of radial basis. When the conditions are not met exactly, the classifiers will pursue the minimum fitting error, running into the risk of overfitting. It turns out that $\ell_2$-norm regularization can be applied to control overfitting. Its effect is explored under the spectral context. It is found that the two terms in the $\ell_2$-regularized objective are one-one correspondent to the fitting error and the spectral risk, revealing a tradeoff between the two quantities. Concerning practical performance, we devise a basis selection strategy to address the main problem hindering the applications of (n)RBFN. With the strategy, nRBFN is easy to implement yet flexible. Experiments on 14 benchmark data sets show the performance of nRBFN is comparable to that of SVM, whereas the parameter tuning of nRBFN is much easier, leading to reduction of model selection time.

With the rapid proliferation of Internet of Things and intelligent edge devices, there is an increasing need for implementing machine learning algorithms, including deep learning, on resource-constrained mobile embedded devices with limited memory and computation power. Typical large Convolutional Neural Networks (CNNs) need large amounts of memory and computational power, and cannot be deployed on embedded devices efficiently. We present Two-Bit Networks (TBNs) for model compression of CNNs with edge weights constrained to (-2, -1, 1, 2), which can be encoded with two bits. Our approach can reduce the memory usage and improve computational efficiency significantly while achieving good performance in terms of classification accuracy, thus representing a reasonable tradeoff between model size and performance.

Bike sharing is booming globally as a green transportation mode, but the occurrence of over-demand stations that have no bikes or docks available greatly affects user experiences. Directly predicting individual over-demand stations to carry out preventive measures is difficult, since the bike usage pattern of a station is highly dynamic and context dependent. In addition, the fact that bike usage pattern is affected not only by common contextual factors (e.g., time and weather) but also by opportunistic contextual factors (e.g., social and traffic events) poses a great challenge. To address these issues, we propose a dynamic cluster-based framework for over-demand prediction. Depending on the context, we construct a weighted correlation network to model the relationship among bike stations, and dynamically group neighboring stations with similar bike usage patterns into clusters. We then adopt Monte Carlo simulation to predict the over-demand probability of each cluster. Evaluation results using real-world data from New York City and Washington, D.C. show that our framework accurately predicts over-demand clusters and outperforms the baseline methods significantly. © 2016 ACM.

Container ports are generally measured and compared using performance indicators such as container throughput and facility productivity. Being able to measure the performance of container ports quantitatively is of great importance for researchers to design models for port operation and container logistics. Instead of relying on the manually collected statistical information from different port authorities and shipping companies, we propose to leverage the pervasive ship GPS traces and maritime open data to derive port performance indicators, including ship traffic, container throughput, berth utilization, and terminal productivity. These performance indicators are found to be directly related to the number of container ships arriving at the terminals and the number of containers handled at each ship. Therefore, we propose a framework that takes the ships' container-handling events at terminals as the basis for port performance measurement. With the inferred port performance indicators, we further compare the strengths and weaknesses of different container ports at the terminal level, port level, and region level, which can potentially benefit terminal productivity improvement, liner schedule optimization, and regional economic development planning. In order to evaluate the proposed framework, we conduct extensive studies on large-scale real-world GPS traces of container ships collected from major container ports worldwide through the year, as well as various maritime open data sources concerning ships and ports. Evaluation results confirm that the proposed framework not only can accurately estimate various port performance indicators but also effectively produces port comparison results such as port performance ranking and port region comparison.

Bike sharing is booming globally as a green transportation mode, but the occurrence of over-demand stations that have no bikes or docks available greatly affects user experiences. Directly predicting individual over-demand stations to carry out preventive measures is difficult, since the bike usage pattern of a station is highly dynamic and context dependent. In addition, the fact that bike usage pattern is affected not only by common contextual factors (e.g., time and weather) but also by opportunistic contextual factors (e.g., social and traffic events) poses a great challenge. To address these issues, we propose a dynamic cluster-based framework for over-demand prediction. Depending on the context, we construct a weighted correlation network to model the relationship among bike stations, and dynamically group neighboring stations with similar bike usage patterns into clusters. We then adopt Monte Carlo simulation to predict the over-demand probability of each cluster. Evaluation results using real-world data from New York City and Washington, D.C. show that our framework accurately predicts over-demand clusters and outperforms the baseline methods significantly. © 2016 ACM.

Container ports are generally measured and compared using performance indicators such as container throughput and facility productivity. Being able to measure the performance of container ports quantitatively is of great importance for researchers to design models for port operation and container logistics. Instead of relying on the manually collected statistical information from different port authorities and shipping companies, we propose to leverage the pervasive ship GPS traces and maritime open data to derive port performance indicators, including ship traffic, container throughput, berth utilization, and terminal productivity. These performance indicators are found to be directly related to the number of container ships arriving at the terminals and the number of containers handled at each ship. Therefore, we propose a framework that takes the ships' container-handling events at terminals as the basis for port performance measurement. With the inferred port performance indicators, we further compare the strengths and weaknesses of different container ports at the terminal level, port level, and region level, which can potentially benefit terminal productivity improvement, liner schedule optimization, and regional economic development planning. In order to evaluate the proposed framework, we conduct extensive studies on large-scale real-world GPS traces of container ships collected from major container ports worldwide through the year, as well as various maritime open data sources concerning ships and ports. Evaluation results confirm that the proposed framework not only can accurately estimate various port performance indicators but also effectively produces port comparison results such as port performance ranking and port region comparison.

The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.e., ratbots, exhibit superior learning abilities in a maze learning task, even when their vision and whisker sensation were blocked. We anticipate that our study will encourage other researchers to investigate combinations of various rule operations and other artificial intelligence algorithms with the learning and memory processes of organic brains to develop more powerful cyborg intelligence systems. Our results potentially have profound implications for a variety of applications in intelligent systems and neural rehabilitation.

Temperature prediction can enhance datacenter thermal management towards minimizing cooling power draw. Traditional approaches achieve this through analyzing task-temperature profiles or resistor-capacitor circuit models to predict CPU temperature. However, they are unable to capture task resource heterogeneity within multi-tenant environments and make predictions under dynamic scenarios such as virtual machine migration, which is one of the main characteristics of Cloud computing. This paper proposes virtual machine level temperature prediction in Cloud datacenters. Experiments show that the mean squared error of stable CPU temperature prediction is within 1.10, and dynamic CPU temperature prediction can achieve 1.60 in most scenarios.

Temperature prediction can enhance datacenter thermal management towards minimizing cooling power draw. Traditional approaches achieve this through analyzing task-temperature profiles or resistor-capacitor circuit models to predict CPU temperature. However, they are unable to capture task resource heterogeneity within multi-tenant environments and make predictions under dynamic scenarios such as virtual machine migration, which is one of the main characteristics of Cloud computing. This paper proposes virtual machine level temperature prediction in Cloud datacenters. Experiments show that the mean squared error of stable CPU temperature prediction is within 1.10, and dynamic CPU temperature prediction can achieve 1.60 in most scenarios.

Bike sharing is booming globally as a green transportation mode, but the occurrence of over-demand stations that have no bikes or docks available greatly affects user experiences. Directly predicting individual over-demand stations to carry out preventive measures is difficult, since the bike usage pattern of a station is highly dynamic and context dependent. In addition, the fact that bike usage pattern is affected not only by common contextual factors (e.g., time and weather) but also by opportunistic contextual factors (e.g., social and traffic events) poses a great challenge. To address these issues, we propose a dynamic cluster-based framework for over-demand prediction. Depending on the context, we construct a weighted correlation network to model the relationship among bike stations, and dynamically group neighboring stations with similar bike usage patterns into clusters. We then adopt Monte Carlo simulation to predict the over-demand probability of each cluster. Evaluation results using real-world data from New York City and Washington, D.C. show that our framework accurately predicts over-demand clusters and outperforms the baseline methods significantly. © 2016 ACM.

Container ports are generally measured and compared using performance indicators such as container throughput and facility productivity. Being able to measure the performance of container ports quantitatively is of great importance for researchers to design models for port operation and container logistics. Instead of relying on the manually collected statistical information from different port authorities and shipping companies, we propose to leverage the pervasive ship GPS traces and maritime open data to derive port performance indicators, including ship traffic, container throughput, berth utilization, and terminal productivity. These performance indicators are found to be directly related to the number of container ships arriving at the terminals and the number of containers handled at each ship. Therefore, we propose a framework that takes the ships' container-handling events at terminals as the basis for port performance measurement. With the inferred port performance indicators, we further compare the strengths and weaknesses of different container ports at the terminal level, port level, and region level, which can potentially benefit terminal productivity improvement, liner schedule optimization, and regional economic development planning. In order to evaluate the proposed framework, we conduct extensive studies on large-scale real-world GPS traces of container ships collected from major container ports worldwide through the year, as well as various maritime open data sources concerning ships and ports. Evaluation results confirm that the proposed framework not only can accurately estimate various port performance indicators but also effectively produces port comparison results such as port performance ranking and port region comparison.

Service mashups are applications created by combining single-functional services (or APIs) dispersed over the web. With the development of cloud computing and web technologies, service mashups are becoming more and more widely used and a large number of mashup platforms have been produced. However, due to the proliferation of services on the web, how to select component services to create mashups has become a challenging issue. Most developers pay more attention to the QoS (quality of service) and cost of services. Beside service selection, mashup deployment is another pivotal process, as the platform can significantly affect the quality of mashups. In this paper, we focus on creating service mashups from the perspective of developers. A genetic algorithm-based method, GA4MC (genetic algorithm for mashup creation), is proposed to select component services and deployment platforms in order to create service mashups with optimal cost performance. A series of experiments are conducted to evaluate the performance of GA4MC. The results show that the GA4MC method can achieve mashups whose cost performance is extremely close to the optimal . Moreover, the execution time of GA4MC is in a low order of magnitude and the algorithm performs good scalability as the experimental scale increases.

Planning an itinerary before traveling to a city is one of the most important travel preparation activities. In this paper, we propose a novel framework called TripPlanner, leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints to achieve personalized, interactive, and traffic-aware trip planning. First, we construct a dynamic point-of-interest network model by extracting relevant information from crowdsourced LBSN and taxi GPS traces. Then, we propose a two-phase approach for personalized trip planning. In the route search phase, TripPlanner works interactively with users to generate candidate routes with specified venues. In the route augmentation phase, TripPlanner applies heuristic algorithms to add user's preferred venues iteratively to the candidate routes, with the objective of maximizing the route score while satisfying both the venue visiting time and total travel time constraints. To validate the efficiency and effectiveness of the proposed approach, extensive empirical studies were performed on two real-world data sets from the city of San Francisco, which contain more than 391 900 passenger delivery trips generated by 536 taxis in a month and 110 214 check-ins left by 15 680 Foursquare users in six months.

This article proposes a state-space model with Cauchy observation noise (SSMC) to detect seizure onset in a long-term EEG monitoring system. Facing the challenge of high false detection rates (FDRs) in many existing methods caused by impulsive EOG/EMG artifacts, the SSMC uses a nonlinear state-space model to encode the gradual changes of epileptic seizure signals and reject abrupt changes brought by artifacts. The Cauchy distribution is proposed to model impulsive observation noises because this heavy-tailed distribution is better at capturing abrupt changes in noise than Gaussian, thus reducing false alarms. Experiments are carried out on a dataset collected from an EEG-monitoring brain-machine interface system that contains 10 patients and 367 hours of EEG data. The authors' method achieves a high sensitivity of 100 percent with a low FDR of 0.08 per hour and a median time delay of 8.10 seconds, demonstrating the method's effectiveness. © 2015 IEEE.

Planning an itinerary before traveling to a city is one of the most important travel preparation activities. In this paper, we propose a novel framework called TripPlanner, leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints to achieve personalized, interactive, and traffic-aware trip planning. First, we construct a dynamic point-of-interest network model by extracting relevant information from crowdsourced LBSN and taxi GPS traces. Then, we propose a two-phase approach for personalized trip planning. In the route search phase, TripPlanner works interactively with users to generate candidate routes with specified venues. In the route augmentation phase, TripPlanner applies heuristic algorithms to add user's preferred venues iteratively to the candidate routes, with the objective of maximizing the route score while satisfying both the venue visiting time and total travel time constraints. To validate the efficiency and effectiveness of the proposed approach, extensive empirical studies were performed on two real-world data sets from the city of San Francisco, which contain more than 391 900 passenger delivery trips generated by 536 taxis in a month and 110 214 check-ins left by 15 680 Foursquare users in six months.

This article proposes a state-space model with Cauchy observation noise (SSMC) to detect seizure onset in a long-term EEG monitoring system. Facing the challenge of high false detection rates (FDRs) in many existing methods caused by impulsive EOG/EMG artifacts, the SSMC uses a nonlinear state-space model to encode the gradual changes of epileptic seizure signals and reject abrupt changes brought by artifacts. The Cauchy distribution is proposed to model impulsive observation noises because this heavy-tailed distribution is better at capturing abrupt changes in noise than Gaussian, thus reducing false alarms. Experiments are carried out on a dataset collected from an EEG-monitoring brain-machine interface system that contains 10 patients and 367 hours of EEG data. The authors' method achieves a high sensitivity of 100 percent with a low FDR of 0.08 per hour and a median time delay of 8.10 seconds, demonstrating the method's effectiveness. © 2015 IEEE.

Planning an itinerary before traveling to a city is one of the most important travel preparation activities. In this paper, we propose a novel framework called TripPlanner, leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints to achieve personalized, interactive, and traffic-aware trip planning. First, we construct a dynamic point-of-interest network model by extracting relevant information from crowdsourced LBSN and taxi GPS traces. Then, we propose a two-phase approach for personalized trip planning. In the route search phase, TripPlanner works interactively with users to generate candidate routes with specified venues. In the route augmentation phase, TripPlanner applies heuristic algorithms to add user's preferred venues iteratively to the candidate routes, with the objective of maximizing the route score while satisfying both the venue visiting time and total travel time constraints. To validate the efficiency and effectiveness of the proposed approach, extensive empirical studies were performed on two real-world data sets from the city of San Francisco, which contain more than 391 900 passenger delivery trips generated by 536 taxis in a month and 110 214 check-ins left by 15 680 Foursquare users in six months.

This article proposes a state-space model with Cauchy observation noise (SSMC) to detect seizure onset in a long-term EEG monitoring system. Facing the challenge of high false detection rates (FDRs) in many existing methods caused by impulsive EOG/EMG artifacts, the SSMC uses a nonlinear state-space model to encode the gradual changes of epileptic seizure signals and reject abrupt changes brought by artifacts. The Cauchy distribution is proposed to model impulsive observation noises because this heavy-tailed distribution is better at capturing abrupt changes in noise than Gaussian, thus reducing false alarms. Experiments are carried out on a dataset collected from an EEG-monitoring brain-machine interface system that contains 10 patients and 367 hours of EEG data. The authors' method achieves a high sensitivity of 100 percent with a low FDR of 0.08 per hour and a median time delay of 8.10 seconds, demonstrating the method's effectiveness. © 2015 IEEE.

Motor brain-machine interface (BMI) has great potentials in neural motor prostheses and has received increasing attention during the past decades in the neural engineering field. It requires an approach to decode neural activities that represents desired movements. Much of the progress in decoding algorithms has been driven by the availability of neural data, e.g. spike trains, in some research groups having animal laboratories and capable of performing surgery and building BMI systems. However, researchers in the neural signal processing field often face a dilemma of lacking neural data. To continue the innovation in decoding algorithms, this paper introduces a public neural dataset, the ZJU Neural Decoding Dataset (ZJUNDD). We give the detailed paradigm of the BMI system on monkey, including the experimental setup and the collection of 96-channel motor cortical activities. The dataset contains spike rates of neurons obtained by a consistent spike sorting method. To improve the data quality and reduce outliers, the spike data are carefully selected according to the quality of hand movements of the monkey. A standard protocol is provided for the assessment of decoding algorithms on the dataset, including the partition of training and testing sets, and the evaluation metrics. We also build an online evaluation system in order to enable a fair comparison between decoding approaches. Further, we benchmark several existing algorithms, which provides a basic performance of the methods. To the best of our knowledge, this is the first public dataset of spike trains for the decoding research of motor cortical activities. © 2014 IEEE.

Motor brain-machine interface (BMI) has great potentials in neural motor prostheses and has received increasing attention during the past decades in the neural engineering field. It requires an approach to decode neural activities that represents desired movements. Much of the progress in decoding algorithms has been driven by the availability of neural data, e.g. spike trains, in some research groups having animal laboratories and capable of performing surgery and building BMI systems. However, researchers in the neural signal processing field often face a dilemma of lacking neural data. To continue the innovation in decoding algorithms, this paper introduces a public neural dataset, the ZJU Neural Decoding Dataset (ZJUNDD). We give the detailed paradigm of the BMI system on monkey, including the experimental setup and the collection of 96-channel motor cortical activities. The dataset contains spike rates of neurons obtained by a consistent spike sorting method. To improve the data quality and reduce outliers, the spike data are carefully selected according to the quality of hand movements of the monkey. A standard protocol is provided for the assessment of decoding algorithms on the dataset, including the partition of training and testing sets, and the evaluation metrics. We also build an online evaluation system in order to enable a fair comparison between decoding approaches. Further, we benchmark several existing algorithms, which provides a basic performance of the methods. To the best of our knowledge, this is the first public dataset of spike trains for the decoding research of motor cortical activities. © 2014 IEEE.

Motor brain-machine interface (BMI) has great potentials in neural motor prostheses and has received increasing attention during the past decades in the neural engineering field. It requires an approach to decode neural activities that represents desired movements. Much of the progress in decoding algorithms has been driven by the availability of neural data, e.g. spike trains, in some research groups having animal laboratories and capable of performing surgery and building BMI systems. However, researchers in the neural signal processing field often face a dilemma of lacking neural data. To continue the innovation in decoding algorithms, this paper introduces a public neural dataset, the ZJU Neural Decoding Dataset (ZJUNDD). We give the detailed paradigm of the BMI system on monkey, including the experimental setup and the collection of 96-channel motor cortical activities. The dataset contains spike rates of neurons obtained by a consistent spike sorting method. To improve the data quality and reduce outliers, the spike data are carefully selected according to the quality of hand movements of the monkey. A standard protocol is provided for the assessment of decoding algorithms on the dataset, including the partition of training and testing sets, and the evaluation metrics. We also build an online evaluation system in order to enable a fair comparison between decoding approaches. Further, we benchmark several existing algorithms, which provides a basic performance of the methods. To the best of our knowledge, this is the first public dataset of spike trains for the decoding research of motor cortical activities. © 2014 IEEE.

Globalization and the world wide web has resulted in academia and science being an international and multicultural community forged by researchers and scientists with different ethnicities. How ethnicity shapes the evolution of membership, status and interactions of the scientific community, however, is not well understood. This is due to the difficulty of ethnicity identification at the large scale. We use name ethnicity classification as an indicator of ethnicity. Based on automatic name ethnicity classification of 1.7+ million authors gathered from Web, the name ethnicity of computer science scholars is investigated by population size, publication contribution and collaboration strength. By showing the evolution of name ethnicity from 1936 to 2010, we discover that ethnicity diversity has increased significantly over time and that different research communities in certain publication venues have different ethnicity compositions. We notice a clear rise in the number of Asian name ethnicities in papers. Their fraction of publication contribution increases from approximately 10% to near 50% from 1970 to 2010. We also find that name ethnicity acts as a homophily factor on coauthor networks, shaping the formation of coauthorship as well as evolution of research communities., Comment: 11 pages, 13 figures

Dimensionality reduction, cluster analysis, and sparse representation are basic components in machine learning. However, their relationships have not yet been fully investigated. In this paper, we find that the spectral graph theory underlies a series of these elementary methods and can unify them into a complete framework. The methods include PCA, K-means, Laplacian eigenmap (LE), ratio cut (Rcut), and a new sparse representation method developed by us, called spectral sparse representation (SSR). Further, extended relations to conventional over-complete sparse representations (e.g., method of optimal directions, KSVD), manifold learning (e.g., kernel PCA, multidimensional scaling, Isomap, locally linear embedding), and subspace clustering (e.g., sparse subspace clustering, low-rank representation) are incorporated. We show that, under an ideal condition from the spectral graph theory, PCA, K-means, LE, and Rcut are unified together. And when the condition is relaxed, the unification evolves to SSR, which lies in the intermediate between PCA/LE and K-mean/Rcut. An efficient algorithm, NSCrt, is developed to solve the sparse codes of SSR. SSR combines merits of both sides: its sparse codes reduce dimensionality of data meanwhile revealing cluster structure. For its inherent relation to cluster analysis, the codes of SSR can be directly used for clustering. Scut, a clustering approach derived from SSR reaches the state-of-the-art performance in the spectral clustering family. The one-shot solution obtained by Scut is comparable to the optimal result of K-means that are run many times. Experiments on various data sets demonstrate the properties and strengths of SSR, NSCrt, and Scut.

Sparse principal component analysis (sparse PCA) aims at finding a sparse basis to improve the interpretability over the dense basis of PCA, meanwhile the sparse basis should cover the data subspace as much as possible. In contrast to most of existing work which deal with the problem by adding some sparsity penalties on various objectives of PCA, in this paper, we propose a new method SPCArt, whose motivation is to find a rotation matrix and a sparse basis such that the sparse basis approximates the basis of PCA after the rotation. The algorithm of SPCArt consists of three alternating steps: rotate PCA basis, truncate small entries, and update the rotation matrix. Its performance bounds are also given. SPCArt is efficient, with each iteration scaling linearly with the data dimension. It is easy to choose parameters in SPCArt, due to its explicit physical explanations. Besides, we give a unified view to several existing sparse PCA methods and discuss the connection with SPCArt. Some ideas in SPCArt are extended to GPower, a popular sparse PCA algorithm, to overcome its drawback. Experimental results demonstrate that SPCArt achieves the state-of-the-art performance. It also achieves a good tradeoff among various criteria, including sparsity, explained variance, orthogonality, balance of sparsity among loadings, and computational speed.