Showing papers by "NEC published in 2021"

A silicon photonic–electronic neural network for fibre nonlinearity compensation

Abstract: In optical communication systems, fibre nonlinearity is the major obstacle in increasing the transmission capacity. Typically, digital signal processing techniques and hardware are used to deal with optical communication signals, but increasing speed and computational complexity create challenges for such approaches. Highly parallel, ultrafast neural networks using photonic devices have the potential to ease the requirements placed on digital signal processing circuits by processing the optical signals in the analogue domain. Here we report a silicon photonic–electronic neural network for solving fibre nonlinearity compensation in submarine optical-fibre transmission systems. Our approach uses a photonic neural network based on wavelength-division multiplexing built on a silicon photonic platform compatible with complementary metal–oxide–semiconductor technology. We show that the platform can be used to compensate for optical fibre nonlinearities and improve the quality factor of the signal in a 10,080 km submarine fibre communication system. The Q-factor improvement is comparable to that of a software-based neural network implemented on a workstation assisted with a 32-bit graphic processing unit. A neural network platform that incorporates photonic components can be used to predict optical fibre nonlinearities and improve the signal quality of submarine fibre communications.

A CMOS Dual-Polarized Phased-Array Beamformer Utilizing Cross-Polarization Leakage Cancellation for 5G MIMO Systems

Abstract: This article introduces a power-efficient and low-cost CMOS 28-GHz phased-array beamformer supporting fifth-generation (5G) dual-polarized multiple-in-multiple-out (MIMO) (DP-MIMO) operation. To improve the cross-polarization (cross-pol.) isolation degraded by the antennas and propagation, a power-efficient analog-assisted cross-pol. leakage cancellation technique is implemented. After the high-accuracy cancellation, more than 41.3-dB cross-pol. isolation is maintained along with the transmitter array to the receiver array. The element-beamformer in this work adopts the compact neutralized bi-directional architecture featuring a minimized manufacturing cost. The proposed beamformer achieves 22% per path TX-mode efficiency and a 4.9-dB RX-mode noise figure. The required on-chip area for the beamformer is only 0.48 mm2. In over-the-air measurement, a 64-element dual-polarized phased-array module achieves 52.2-dBm saturated effective isotropic radiated power (EIRP). The 5G standard-compliant OFDMA-mode modulated signals of up to 256-QAM could be supported by the 64-element modules. With the help of the cross-pol. leakage cancellation technique, the proposed array module realizes improved DP-MIMO EVMs even under severe polarization coupling and rotation conditions. The measured DP-MIMO EVMs are 3.4% in both 64-QAM and 256-QAM. The consumed power per beamformer path is 186 mW in the TX mode and 88 mW in the RX mode.

Thermally altered subsurface material of asteroid (162173) Ryugu

Abstract: Analyses of meteorites and theoretical models indicate that some carbonaceous near-Earth asteroids may have been thermally altered due to radiative heating during close approaches to the Sun1–3. However, the lack of direct measurements on the subsurface doesn’t allow us to distinguish thermal alteration due to radiative heating from parent-body processes. In April 2019, the Hayabusa2 mission successfully completed an artificial impact experiment on the carbonaceous near-Earth asteroid (162173) Ryugu4,5, which provided an opportunity to investigate exposed subsurface material and test potential effects of radiative heating. Here we report observations of Ryugu’s subsurface material by the Near-Infrared Spectrometer (NIRS3) on the Hayabusa2 spacecraft. Reflectance spectra of excavated material exhibit a hydroxyl (OH) absorption feature that is slightly stronger and peak-shifted compared with that observed for the surface, indicating that space weathering and/or radiative heating have caused subtle spectral changes in the uppermost surface. The strength and shape of the OH feature suggests that the subsurface material experienced heating above 300 °C, similar to the surface. In contrast, thermophysical modelling indicates that radiative heating cannot increase the temperature above 200 °C at the estimated excavation depth of 1 m, even at the smallest heliocentric distance possible for Ryugu. This supports the hypothesis that primary thermal alteration occurred on Ryugu’s parent body. Hayabusa2 created an artificial crater on Ryugu to analyse the subsurficial material of the asteroid. Results show that the subsurface is more hydrated than the surface. It experienced alteration processes that can be traced back to Ryugu’s parent body.

Design, Acceptance and Capacity of Subsea Open Cables

Abstract: This article will discuss the collaboratively formed cross-industry open cables concept for characterizing optical performance of undersea cables with the intent of assessing and understanding their capacity potential. The article proposes definitions of two critical nonlinear and linear performance metrics for open cables: GSNR (Gaussian or generalized signal to noise ratio) and SNRASE (Signal to noise ratio amplified spontaneous emission), including effects such as GAWBS (guided acoustic wave Brillouin scattering) and signal droop. Measurement methodologies for these metrics are proposed, with considerations for limitations and impact of the test conditions and characteristics of the transponders used. Expanded definitions are offered to enable variable symbol rate transponders to be used for measurement, with considerations for scaling of SNR values. Considerations for using these metrics for capacity assessment and applying these techniques to concatenated multi-segment systems are introduced. Recommendations on key parameters for system specification, system characterization, and proposals for SNR-based performance budgeting tables are also discussed as foundational elements to enabling accurate estimation of the capacity potential of a subsea open cable.

Anomalously porous boulders on (162173) Ryugu as primordial materials from its parent body

Abstract: Planetesimals—the initial stage of the planetary formation process—are considered to be initially very porous aggregates of dusts1,2, and subsequent thermal and compaction processes reduce their porosity3. The Hayabusa2 spacecraft found that boulders on the surface of asteroid (162173) Ryugu have an average porosity of 30–50% (refs. 4–6), higher than meteorites but lower than cometary nuclei7, which are considered to be remnants of the original planetesimals8. Here, using high-resolution thermal and optical imaging of Ryugu’s surface, we discovered, on the floor of fresh small craters ( 70%, which is as high as in cometary bodies. The artificial crater formed by Hayabusa2’s impact experiment9 is similar to these craters in size but does not have such high-porosity boulders. Thus, we argue that the observed high porosity is intrinsic and not created by subsequent impact comminution and/or cracking. We propose that these boulders are the least processed material on Ryugu and represent remnants of porous planetesimals that did not undergo a high degree of heating and compaction3. Our multi-instrumental analysis suggests that fragments of the highly porous boulders are mixed within the surface regolith globally, implying that they might be captured within collected samples by touch-down operations10,11. The Hayabusa2 spacecraft found dark boulders with very high porosity (>70%, as high as cometary nuclei) at the bottom of small craters on Ryugu. Such boulders are probably the most pristine parts of the planetesimals that formed Ryugu’s parent body and might have been captured by Hayabusa2 sampling.

Secure Graph Analysis at Scale

Abstract: We present a highly-scalable secure computation of graph algorithms, which hides all information about the topology of the graph or other input values associated with nodes or edges. The setting is where all nodes and edges of the graph are secret-shared between multiple servers, and a secure computation protocol is run between these servers. While the method is general, we demonstrate it in a 3-server setting with an honest majority, with either semi-honest security or full security. A major technical contribution of our work is replacing the usage of secure sort protocols with secure shuffles, which are much more efficient. Full security against malicious behavior is achieved by adding an efficient verification for the shuffle operation, and computing circuits using fully secure protocols. We demonstrate the applicability of this technology by implementing two major algorithms: computing breadth-first search (BFS), which is also useful for contact tracing on private contact graphs, and computing maximal independent set (MIS). We implement both algorithms, with both semi-honest and full security, and run them within seconds on graphs of millions of elements.

Are These from the Same Place? Seeing the Unseen in Cross-View Image Geo-Localization

Abstract: In an era where digital maps act as gateways to exploring the world, the availability of large scale geo-tagged imagery has inspired a number of visual navigation techniques. One promising approach to visual navigation is cross-view image geo-localization. Here, the images whose location needs to be determined are matched against a database of geo-tagged aerial imagery. The methods based on this approach sought to resolve view point changes. But scenes also vary temporally, during which new landmarks might appear or existing ones might disappear. One cannot guarantee storage of aerial imagery across all time instants and hence a technique robust to temporal variation in scenes becomes of paramount importance. In this paper, we address the temporal gap between scenes by proposing a two step approach. First, we propose a semantically driven data augmentation technique that gives Siamese networks the ability to hallucinate unseen objects. Then we present the augmented samples to a multi-scale attentive embedding network to perform matching tasks. Experiments on standard benchmarks demonstrate the integration of the proposed approach with existing frameworks improves top-1 image recall rate on the CVUSA data-set from 89.84 % to 93.09 %, and from 81.03 % to 87.21 % on the CVACT data-set.

IEEE P7001: A Proposed Standard on Transparency.

Abstract: This paper describes IEEE P7001, a new draft standard on transparency of autonomous systems. In the paper, we outline the development and structure of the draft standard. We present the rationale for transparency as a measurable, testable property. We outline five stakeholder groups: users, the general public and bystanders, safety certification agencies, incident/accident investigators and lawyers/expert witnesses, and explain the thinking behind the normative definitions of “levels” of transparency for each stakeholder group in P7001. The paper illustrates the application of P7001 through worked examples of both specification and assessment of fictional autonomous systems.

Quantum Behavior of a Heavy Impurity Strongly Coupled to a Bose Gas.

Abstract: We investigate the problem of an infinitely heavy impurity interacting with a dilute Bose gas at zero temperature. When the impurity-boson interactions are short-ranged, we show that boson-boson interactions induce a quantum blockade effect, where a single boson can effectively block or screen the impurity potential. Since this behavior depends on the quantum granular nature of the Bose gas, it cannot be captured within a standard classical-field description. Using a combination of exact quantum Monte Carlo methods and a truncated basis approach, we show how the quantum correlations between bosons lead to universal few-body bound states and a logarithmically slow dependence of the polaron ground-state energy on the boson-boson scattering length. Moreover, we expose the link between the polaron energy and the spatial structure of the quantum correlations, spanning the infrared to ultraviolet physics.

Variational optimization of the quantum annealing schedule for the Lechner-Hauke-Zoller scheme

Abstract: The annealing schedule is optimized for a parameter in the Lechner-Hauke-Zoller (LHZ) scheme for quantum annealing designed for the all-to-all-interacting Ising model representing generic combinatorial optimization problems. We adapt the variational approach proposed by Matsuura et al. (arXiv:2003.09913) to the annealing schedule of a term representing a constraint for variables intrinsic to the LHZ scheme with the annealing schedule of other terms kept intact. Numerical results for a simple ferromagnetic model and the spin-glass problem show that nonmonotonic annealing schedules optimize the performance measured by the residual energy and the final ground-state fidelity. This improvement does not accompany a notable increase in the instantaneous energy gap, which suggests the importance of a dynamical viewpoint in addition to static analyses in the study of practically relevant diabatic processes in quantum annealing.

Xi-Vector Embedding for Speaker Recognition

Abstract: We present a Bayesian formulation for deep speaker embedding, wherein the xi-vector is the Bayesian counterpart of the x-vector, taking into account the uncertainty estimate. On the technology front, we offer a simple and straightforward extension to the now widely used x-vector. It consists of an auxiliary neural net predicting the frame-wise uncertainty of the input sequence. We show that the proposed extension leads to substantial improvement across all operating points, with a significant reduction in error rates and detection cost. On the theoretical front, our proposal integrates the Bayesian formulation of linear Gaussian model to speaker-embedding neural networks via the pooling layer. In one sense, our proposal integrates the Bayesian formulation of the i-vector to that of the x-vector. Hence, we refer to the embedding as the xi-vector, which is pronounced as /zai/ vector. Experimental results on the SITW evaluation set show a consistent improvement of over 17.5% in equal-error-rate and 10.9% in minimum detection cost.

Vacuum-Field Catalysis: Accelerated Reactions by Vibrational Ultra Strong Coupling

Abstract: In conventional catalysis, the reactants interact with specific sites of the catalyst in such a way that the reaction barrier is lowered by changing the reaction path, causing the reaction rate to be accelerated. Here we take a radically differentapproach to catalysis by ultra-strongly coupling the vibrations of the reactants to the infrared vacuum electromagnetic field. To demonstrate the possibility of suchvacuum-field catalysis (or cavity catalysis), we have studied hydrolysis reactions under the vibrational ultra strong coupling (V-USC) of the OH stretching mode of water to a Fabry-Perot microfluidic cavity mode. This results in a giant Rabi splitting energy (92 meV), indicating the system is in the V-USC regime. We have found that V-USC water enhances the hydrolysis reaction rate of cyanate ions by102-fold and that of ammonia borane by 104-fold. This catalytic ability is found to depend upon the coupling ratio of the vibrational light-matter interaction. Given the vital importance of water for life and human activities, we expect that our finding not only offers an unconventional way of controlling chemical reactions by vacuum-field catalysis but also brings a fresh perspective to science and technology.

Relaxing the Conditional Independence Assumption of CTC-based ASR by Conditioning on Intermediate Predictions

Abstract: This paper proposes a method to relax the conditional independence assumption of connectionist temporal classification (CTC)-based automatic speech recognition (ASR) models. We train a CTC-based ASR model with auxiliary CTC losses in intermediate layers in addition to the original CTC loss in the last layer. During both training and inference, each generated prediction in the intermediate layers is summed to the input of the next layer to condition the prediction of the last layer on those intermediate predictions. Our method is easy to implement and retains the merits of CTC-based ASR: a simple model architecture and fast decoding speed. We conduct experiments on three different ASR corpora. Our proposed method improves a standard CTC model significantly (e.g., more than 20 % relative word error rate reduction on the WSJ corpus) with a little computational overhead. Moreover, for the TEDLIUM2 corpus and the AISHELL-1 corpus, it achieves a comparable performance to a strong autoregressive model with beam search, but the decoding speed is at least 30 times faster.

Transmitter and receiver impairment monitoring using adaptive multi-layer linear and widely linear filter coefficients controlled by stochastic gradient descent.

Abstract: We propose a monitoring method for individual impairments in a transmitter (Tx) and receiver (Rx) by using filter coefficients of multi-layer strictly linear (SL) and widely linear (WL) filters to compensate for relevant impairments where the filter coefficients are adaptively controlled by stochastic gradient descent with back propagation from the last layer outputs. Considering the order of impairments occurring in a Tx or Rx of coherent optical transmission systems and their non-commutativity, we derive a model relating in-phase (I) and quadrature (Q) skew, IQ gain imbalance, and IQ phase deviation in a Tx or Rx to the WL filter responses in our multi-layer filter architecture. We evaluated the proposed method through simulations using polarization-division multiplexed (PDM)-quadrature phase shift keying and a transmission experiment of 32-Gbaud PDM 64-quadrature amplitude modulation over a 100-km single-mode fiber span. The results indicate that both Tx and Rx impairments could be individually monitored by using the filter coefficients of adaptively controlled multi-layer SL and WL filters precisely and simultaneously, decoupled by chromatic dispersion and frequency offset, even when multiple impairments existed.

Overcoming Challenges of Distributed Fiber-Optic Sensing for Highway Traffic Monitoring:

Abstract: This work presents a wide-area highway monitoring system based on distributed fiber-optic sensing (DFOS) as a cost-effective way of gathering traffic information at numerous sensing points along a ...

Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series.

Abstract: Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging applications. However, most existing methods process MTS's individually, and do not leverage the dynamic distributions underlying the MTS's, leading to sub-optimal results when the sparsity is high. To address this challenge, we propose a novel generative model, which tracks the transition of latent clusters, instead of isolated feature representations, to achieve robust modeling. It is characterized by a newly designed dynamic Gaussian mixture distribution, which captures the dynamics of clustering structures, and is used for emitting timeseries. The generative model is parameterized by neural networks. A structured inference network is also designed for enabling inductive analysis. A gating mechanism is further introduced to dynamically tune the Gaussian mixture distributions. Extensive experimental results on a variety of real-life datasets demonstrate the effectiveness of our method.

Impact of Sound Duration and Inactive Frames on Sound Event Detection Performance

Abstract: In many methods of sound event detection (SED), a segmented time frame is regarded as one data sample to model training. The durations of sound events greatly depend on the sound event class, e.g., the sound event "fan" has a long duration, whereas the sound event "mouse clicking" is instantaneous. Thus, the difference in the duration between sound event classes results in a serious data imbalance in SED. Moreover, most sound events tend to occur occasionally; therefore, there are many more inactive time frames of sound events than active frames. This also causes a severe data imbalance between active and inactive frames. In this paper, we investigate the impact of sound duration and inactive frames on SED performance by introducing four loss functions, such as simple reweighting loss, inverse frequency loss, asymmetric focal loss, and focal batch Tversky loss. Then, we provide insights into how we tackle this imbalance problem.

DuroNet: A Dual-robust Enhanced Spatial-temporal Learning Network for Urban Crime Prediction

Abstract: Urban crime is an ongoing problem in metropolitan development and attracts general concern from the international community. As an effective means of defending urban safety, crime prediction plays a crucial role in patrol force allocation and public safety. However, urban crime data is a macro result of crime patterns overlapped by various irrelevant factors that cause inhomogeneous noises—local outliers and irregular waves. These noises might obstruct the learning process of crime prediction models and result in a deviation of performance. To tackle the problem, we propose a novel paradigm of Dual-robust Enhanced Spatial-temporal Learning Network (DuroNet), an encoder-decoder architecture that possesses an adaptive robustness for reducing the effect of outliers and waves. The robustness is mainly reflected on two aspects. One is a locality enhanced module that employs local temporal context information to smooth the deviation of outliers and dynamic spatial information to assist in understanding normal points. The other is a self-attention-based pattern representation module to weaken the effect of irregular waves by learning attentive weights. Finally, extensive experiments are conducted on two real-world crime datasets before and after adding Gaussian noises. The results demonstrate the superior performance of our DuroNet over the state-of-the-art methods.

Machine learning autonomous identification of magnetic alloys beyond the Slater-Pauling limit

Abstract: Discovery of new magnets with high magnetization has always been important in human history because it has given birth to powerful motors and memory devices. Currently, the binary alloy Fe3Co1 exhibits the largest magnetization of any stable alloys explained by the Slater-Pauling rule. A multi-element system is expected to include alloys with magnetization beyond that of Fe3Co1, but it has been difficult to identify appropriate elements and compositions because of combinatorial explosion. In this work, we identified an alloy with magnetization beyond that of Fe3Co1 by using an autonomous materials search system combining machine learning and ab-initio calculation. After an autonomous and automated exploration in the large material space of multi-element alloys for six weeks, the system unexpectedly indicated that Ir and Pt impurities would enhance the magnetization of FeCo alloys, despite both impurity elements having small magnetic moments. To confirm this experimentally, we synthesized FexCoyIr1-x-y and FexCoyPt1-x-y alloys and found that some of them have magnetization beyond that of Fe3Co1. Finding materials with large magnetization is highly desirable for technological applications. Here, a machine learning autonomous search and automated combinatorial synthesis reveal that multi-element alloys with Ir and Pt impurities have a magnetization exceeding the Slater-Pauling limit of Fe75Co25.

Voting-based Approaches For Differentially Private Federated Learning

Abstract: While federated learning (FL) enables distributed agents to collaboratively train a centralized model without sharing data with each other, it fails to protect users against inference attacks that mine private information from the centralized model. Thus, facilitating federated learning methods with differential privacy (DPFL) becomes attractive. Existing algorithms based on privately aggregating clipped gradients require many rounds of communication, which may not converge, and cannot scale up to large-capacity models due to explicit dimension-dependence in its added noise. In this paper, we adopt the knowledge transfer model of private learning pioneered by Papernot et al. (2017; 2018) and extend their algorithm PATE, as well as the recent alternative PrivateKNN (Zhu et al., 2020) to the federated learning setting. The key difference is that our method privately aggregates the labels from the agents in a voting scheme, instead of aggregating the gradients, hence avoiding the dimension dependence and achieving significant savings in communication cost. Theoretically, we show that when the margins of the voting scores are large, the agents enjoy exponentially higher accuracy and stronger (data-dependent) differential privacy guarantees on both agent-level and instance-level. Extensive experiments show that our approach significantly improves the privacy-utility trade-off over the current state-of-the-art in DPFL.

Heuristic Approach for Countermeasure Selection Using Attack Graphs

Abstract: Selecting the optimal set of countermeasures to secure a network is a challenging task, since it involves various considerations and trade-offs, such as prioritizing the risks to mitigate given the mitigation costs. Previously suggested approaches are based on limited and largely manual risk assessment procedures, provide recommendations for a specific event, or don’t consider the organization’s constraints (e.g., limited budget). In this paper, we present an improved attack graph-based risk assessment process and apply heuristic search to select an optimal countermeasure plan for a given network and budget. The risk assessment process represents the risk in the system in such a way that incorporates the quantitative risk factors and relevant countermeasures; this allows us to assess the risk in the system under different countermeasure plans during the search, without the need to regenerate the attack graph. We also provide a detailed description of countermeasure modeling and discuss how the countermeasures can be automatically matched to the security issues discovered in the network.

What is Next when Sequential Prediction Meets Implicitly Hard Interaction

Abstract: Hard interaction learning between source sequences and their next targets is challenging, which exists in a myriad of sequential prediction tasks. During the training process, most existing methods focus on explicitly hard interactions caused by wrong responses. However, a model might conduct correct responses by capturing a subset of learnable patterns, which results in implicitly hard interactions with some unlearned patterns. As such, its generalization performance is weakened. The problem gets more serious in sequential prediction due to the interference of substantial similar candidate targets. To this end, we propose a Hardness Aware Interaction Learning framework (HAIL) that mainly consists of two base sequential learning networks and mutual exclusivity distillation (MED). The base networks are initialized differently to learn distinctive view patterns, thus gaining different training experiences. The experiences in the form of the unlikelihood of correct responses are drawn from each other by MED, which provides mutual exclusivity knowledge to figure out implicitly hard interactions. Moreover, we deduce that the unlikelihood essentially introduces additional gradients to push the pattern learning of correct responses. Our framework can be easily extended to more peer base networks. Evaluation is conducted on four datasets covering cyber and physical spaces. The experimental results demonstrate that our framework outperforms several state-of-the-art methods in terms of top-k based metrics.

Continuous Optimization-Based Task and Motion Planning with Signal Temporal Logic Specifications for Sequential Manipulation

Abstract: We propose a new optimization-based task and motion planning (TAMP) with signal temporal logic (STL) specifications for robotic sequential manipulation such as pick-and-place tasks. Given a high-level task specification, the TAMP problem is to plan a trajectory that satisfies the specification. This is, however, a challenging problem due to the difficulty of combining continuous motion planning and discrete task specifications. The optimization-based TAMP with temporal logic specifications is a promising method, but existing works use mixed integer problems (MIP) and do not scale well. To address this issue, in our approach, a new hybrid system model without discrete variables is introduced and combined with smooth approximation methods for STL. This allows the TAMP to be formulated as a nonlinear programming problem whose computational cost is significantly less than that of MIP. Furthermore, it is also possible to deal with nonlinear dynamics and geometric constraints represented by nonlinear functions. The effectiveness of the proposed method is demonstrated with both numerical experiments and a real robot.

Multi-subspace Implicit Alignment for Cross-modal Retrieval on Cooking Recipes and Food Images

Abstract: Cross-modal retrieval technology can help people quickly achieve mutual information between cooking recipes and food images. Both the embeddings of the image and the recipe consist of multiple representation subspaces. We argue that multiple aspects in the recipe are related to multiple regions in the food image. It is challenging to improve the cross-modal retrieval quality by making full use of the implicit connection between multiple subspaces of recipes and images. In this paper, we propose a multi-subspace implicit alignment cross-modal retrieval framework of recipes and images. Our framework learns multi-subspace information about cooking recipes and food images with multi-head attention networks; the implicit alignment at the subspace level promotes narrowing the semantic gap between recipe embeddings and food image embeddings; triple loss and adversarial loss are combined to help our framework for cross-modal learning. The experimental results show that our framework significantly outperforms to state-of-the-art methods in terms of MedR and R@K on Recipe 1M.

Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach

Abstract: Finding joinable tables in data lakes is key procedure in many applications such as data integration, data augmentation, data analysis, and data market. Traditional approaches that find equi-joinable tables are unable to deal with misspellings and different formats, nor do they capture any semantic joins. In this paper, we propose PEXESO, a framework for joinable table discovery in data lakes. We target the case when textual values are embedded as high-dimensional vectors and columns are joined upon similarity predicates on high-dimensional vectors, hence to address the limitations of equi-join approaches and identify more meaningful results. To efficiently find joinable tables with similarity, we propose a block-and-verify method that utilizes pivot-based filtering. A partitioning technique is developed to cope with the case when the data lake is large and cannot fit in main memory. An experimental evaluation on real datasets shows that our solution identifies substantially more tables than equi-joins and outperforms other similarity-based options, and the join results are useful in data enrichment for machine learning tasks. The experiments also demonstrate the efficiency of the proposed method.

Field Trial of Abnormal Activity Detection and Threat Level Assessment with Fiber Optic Sensing for Telecom Infrastructure Protection

Abstract: We report the field trial results of monitoring abnormal activities near deployed cable with fiber-optic-sensing technology for cable protection. Detection and position determination of abnormal events and evaluating the threat to the cable is realized.

Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology.

Abstract: Tremendous advances in artificial intelligence (AI) in medical image analysis have been achieved in recent years. The integration of AI is expected to cause a revolution in various areas of medicine, including gastrointestinal (GI) pathology. Currently, deep learning algorithms have shown promising benefits in areas of diagnostic histopathology, such as tumor identification, classification, prognosis prediction, and biomarker/genetic alteration prediction. While AI cannot substitute pathologists, carefully constructed AI applications may increase workforce productivity and diagnostic accuracy in pathology practice. Regardless of these promising advances, unlike the areas of radiology or cardiology imaging, no histopathology-based AI application has been approved by a regulatory authority or for public reimbursement. Thus, implying that there are still some obstacles to be overcome before AI applications can be safely and effectively implemented in real-life pathology practice. The challenges have been identified at different stages of the development process, such as needs identification, data curation, model development, validation, regulation, modification of daily workflow, and cost-effectiveness balance. The aim of this review is to present challenges in the process of AI development, validation, and regulation that should be overcome for its implementation in real-life GI pathology practice.