Query: Multi-class active learning for image classification

Relevant passages:
Probability Estimates for Multi-class Classification by Pairwise Coupling <=SEP=> Pairwise coupling is a popular multi-class classification method that combines together all pairwise comparisons for each pair of classes. This paper presents two approaches for obtaining class probabilities. Both methods can be reduced to linear systems and are easy to implement. We show conceptually and experimentally that the proposed approaches are more stable than two existing popular methods: voting and [3].

Support Vector Machine Active Learning with Applications to Text Classification <=SEP=> Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using pool-based active learning. Instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels for some number of them. We introduce a new algorithm for performing active learning with support vector machines, i.e., an algorithm for choosing which instances to request next. We provide a theoretical motivation for the algorithm using the notion of a version space. We present experimental results showing that employing our active learning method can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.

LIBSVM: a library for support vector machines <=SEP=> 

A Bayesian hierarchical model for learning natural scene categories <=SEP=> We propose a novel approach to learn and recognize natural scene categories. Unlike previous work, it does not require experts to annotate the training set. We represent the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning. Each region is represented as part of a "theme". In previous work, such themes were learnt from hand-annotations of experts, while our method learns the theme distributions as well as the codewords distribution over the themes without supervision. We report satisfactory categorization performances on a large set of 13 categories of complex scenes.

Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope <=SEP=> In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very low dimensional representation of the scene, that we term the Spatial Envelope. We propose a set of perceptual dimensions (naturalness, openness, roughness, expansion, ruggedness) that represent the dominant spatial structure of a scene. Then, we show that these dimensions may be reliably estimated using spectral and coarsely localized information. The model generates a multidimensional space in which scenes sharing membership in semantic categories (e.g., streets, highways, coasts) are projected closed together. The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.

When two choices are not enough: Balancing at scale in Distributed Stream Processing <=SEP=> Carefully balancing load in distributed stream processing systems has a fundamental impact on execution latency and throughput. Load balancing is challenging because real-world workloads are skewed: some tuples in the stream are associated to keys which are significantly more frequent than others. Skew is remarkably more problematic in large deployments: having more workers implies fewer keys per worker, so it becomes harder to “average out” the cost of hot keys with cold keys. We propose a novel load balancing technique that uses a heavy hitter algorithm to efficiently identify the hottest keys in the stream. These hot keys are assigned to d ≥ 2 choices to ensure a balanced load, where d is tuned automatically to minimize the memory and computation cost of operator replication. The technique works online and does not require the use of routing tables. Our extensive evaluation shows that our technique can balance real-world workloads on large deployments, and improve throughput and latency by 150% and 60% respectively over the previous state-of-the-art when deployed on Apache Storm.

Smart Configuration of Smart Environments <=SEP=> One of the central research challenges in the Internet of Things and Ubiquitous Computing domains is how users can be enabled to “program” their personal and industrial smart environments by combining services that are provided by devices around them. We present a service composition system that enables the goal-driven configuration of smart environments for end users by combining semantic metadata and reasoning with a visual modeling tool. In contrast to process-driven approaches where service mashups are statically defined, we make use of embedded semantic API descriptions to dynamically create mashups that fulfill the user's goal. The main advantage of our system is its high degree of flexibility, as service mashups can adapt to dynamic environments and are fault-tolerant with respect to individual services becoming unavailable. To support users in expressing their goals, we integrated a visual programming tool with our system that allows to model the desired state of a smart environment graphically, thereby hiding the technicalities of the underlying semantics. Possible applications of the presented system include the management of smart homes to increase individual well-being, and reconfigurations of smart environments, for instance in the industrial automation or healthcare domains.

Smart Locks: Lessons for Securing Commodity Internet of Things Devices <=SEP=> We examine the security of home smart locks: cyber-physical devices that replace traditional door locks with deadbolts that can be electronically controlled by mobile devices or the lock manufacturer's remote servers. We present two categories of attacks against smart locks and analyze the security of five commercially-available locks with respect to these attacks. Our security analysis reveals that flaws in the design, implementation, and interaction models of existing locks can be exploited by several classes of adversaries, allowing them to learn private information about users and gain unauthorized home access. To guide future development of smart locks and similar Internet of Things devices, we propose several defenses that mitigate the attacks we present. One of these defenses is a novel approach to securely and usably communicate a user's intended actions to smart locks, which we prototype and evaluate. Ultimately, our work takes a first step towards illuminating security challenges in the system design and novel functionality introduced by emerging IoT systems.

A hybrid machine learning approach to automatic plant phenotyping for smart agriculture <=SEP=> Recently, a new ICT approach to agriculture called “Smart Agriculture” has been received great attention to support farmers' decision-making for good final yield on various kinds of field conditions. For this purpose, this paper presents two image sensing methods that enable an automatic observation to capture flowers and seedpods of soybeans in real fields. The developed image sensing methods are considered as sensors in an agricultural cyber-physical system in which big data on the growth status of agricultural plants and environmental information (e.g., weather, temperature, humidity, solar radiation, soil condition, etc.) are analyzed to mine useful rules for appropriate cultivation. The proposed image sensing methods are constructed by combining several image processing and machine learning techniques. The flower detection is realized based on a coarse-to-fine approach where candidate areas of flowers are first detected by SLIC and hue information, and the acceptance of flowers is decided by CNN. In the seedpod detection, candidates of seedpod regions are first detected by the Viola-Jones object detection method, and we also use CNN to make a final decision on the acceptance of detected seedpods. The performance of the proposed image sensing methods is evaluated for a data set of soybean images that were taken from a crowd of soybeans in real agricultural fields in Hokkaido, Japan.

Low temperature Cu-Cu direct bonding for 3D-IC by using fine crystal layer <=SEP=> In this paper, we report a method of low temperature solid diffusion bonding. To investigate bondability of solid diffusion, we examined the effect of bump metals and bump planarization methods. Cu and Au bump were used for bump metals and CMP and ultra-precision cutting were used for bump planarization methods. We found that fine crystal layer could be formed on only cut Cu and Au bumps, and especially cut Cu bumps had a thick fine crystal layer on the surface. The layer on cut Cu bump was found to be easily to recrystallize at low temperature condition of 150 degree C. Moreover, the bonding interface of cut Cu bump disappeared at 200 degree C for 30 min, which means solid diffusion across the interface was realized with the contribution of fine crystal layer. In addition, for Cu-Cu direct bonding, formic acid treatment before bonding is effective because formic acid can react at low temperature without destroying fine crystal layer. That led to achieve high bonding strength between cut Cu bumps.

Hybrid excited claw pole electric machine <=SEP=> The paper presents the concept and results of simulation and experimental research of a claw pole generator with hybrid excitation. Hybrid excitation is performed with a conventional coil located between two parts of the claw-shape rotor and additional permanent magnets which are placed on claw poles. Within the research first a simulation and next constructed experimental model has been developed on the basis of the mass-produced vehicle alternator. Experimental researches have shown that - at a suitable rotational speed - it is possible to self-excite of the generator without any additional source of electrical power.

A 92GHz bandwidth SiGe BiCMOS HBT TIA with less than 6dB noise figure <=SEP=> A low-noise, broadband amplifier with resistive degeneration and transimpedance feedback is reported with 200 mVpp input linearity and less than 6 dB noise figure up to 88 GHz. The measured gain of 13 dB, noise figure, linearity, and group delay variation of ±1.5 ps are in excellent agreement with simulation. Eye diagram measurements were conducted up to 120 Gb/s and a dynamic range larger than 36 dB was obtained from eye diagram measurements at 40 Gb/s. The chip, which includes a 50Ω output buffer, occupies 0.138 mm2 and consumes 21 mA from a 2.3V supply.

Video Question Answering via Hierarchical Spatio-Temporal Attention Networks <=SEP=> Open-ended video question answering is a challenging problem in visual information retrieval, which automatically generates the natural language answer from the referenced video content according to the question. However, the existing visual question answering works only focus on the static image, which may be ineffectively applied to video question answering due to the lack of modeling the temporal dynamics of video contents. In this paper, we consider the problem of open-ended video question answering from the viewpoint of spatio-temporal attentional encoderdecoder learning framework. We propose the hierarchical spatio-temporal attention network for learning the joint representation of the dynamic video contents according to the given question. We then develop the spatio-temporal attentional encoder-decoder learning method with multi-step reasoning process for open-ended video question answering. We construct a large-scale video question answering dataset. The extensive experiments show the effectiveness of our method.

Persistence Diagrams with Linear Machine Learning Models <=SEP=> Persistence diagrams have been widely recognized as a compact descriptor for characterizing multiscale topological features in data. When many datasets are available, statistical features embedded in those persistence diagrams can be extracted by applying machine learnings. In particular, the ability for explicitly analyzing the inverse in the original data space from those statistical features of persistence diagrams is significantly important for practical applications. In this paper, we propose a unified method for the inverse analysis by combining linear machine learning models with persistence images. The method is applied to point clouds and cubical sets, showing the ability of the statistical inverse analysis and its advantages. This work is partially supported by JSPS KAKENHI Grant Number JP 16K17638, JST CREST Mathematics15656429, JST “Materials research by Information Integration” Initiative (MI2I) project of the Support Program for Starting Up Innovation Hub, Structural Materials for Innovation Strategic Innovation Promotion Program D72, and New Energy and Industrial Technology Development Organization (NEDO). Ippei Obayashi Advanced Institute for Materials Research (WPI-AIMR), Tohoku University. 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577 Japan Tel.: +81-22-217-6320 Fax: +81-22-217-5129 E-mail: ippei.obayashi.d8@tohoku.ac.jp Y. Hiraoka Advanced Institute for Materials Research (WPI-AIMR), Tohoku University. Center for Materials research by Information Integration (CMI2), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS). E-mail: hiraoka@tohoku.ac.jp

The neural correlates of trustworthiness evaluations of faces and brands: Implications for behavioral and consumer neuroscience. <=SEP=> When we buy a product of a brand, we trust the brand to provide good quality and reliability. Therefore, trust plays a major role in consumer behavior. It is unclear, however, how trust in brands is processed in the brain and whether it is processed differently from interpersonal trust. In this study, we used fMRI to investigate the neural correlates of interpersonal and brand trust by comparing the brain activation patterns during explicit trustworthiness judgments of faces and brands. Our results showed that while there were several brain areas known to be linked to trustworthiness evaluations, such as the amygdalae, more active in trustworthiness judgments when compared to a control task (familiarity judgment) for faces, no such difference was found for brands. Complementary ROI analysis revealed that the activation of both amygdalae was strongest for faces in the trustworthiness judgments. The direct comparison of the brain activation patterns during the trustworthiness evaluations between faces and brands in this analysis showed that trustworthiness judgments of faces activated the orbitofrontal cortex, another region that was previously linked to interpersonal trust, more strongly than trustworthiness judgments of brands. Further, trustworthiness ratings of faces, but not brands, correlated with activation in the orbitofrontal cortex. Our results indicate that the amygdalae, as well as the orbitofrontal cortex, play a prominent role in interpersonal trust (faces), but not in trust for brands. It is possible that this difference is due to brands being processed as cultural objects rather than as having human-like personality characteristics.

SeVR+: Secure and privacy-aware cloud-assisted video reporting service for 5G vehicular networks <=SEP=> Recently, Eiza et al. proposed a secure and privacy-aware scheme for video reporting service in 5G enabled Vehicular Ad hoc Networks (VANET). They employ heterogeneous network and cloud platform to obtain more availability with low latency platform for an urgent accident video reporting service. In their study, for the first time the security issues of 5G enabled vehicular networks have been addressed. Eiza et al. claimed that their scheme guarantees user's privacy, confidentiality, non-repudiation, message integrity and availability for participant vehicles. In this paper, we show that Eiza et al. scheme is vulnerable to replay, message fabrication and DoS attacks. Regarding the sensibility of video reporting services in VANET, then, we propose an efficient protocol to overcome security weaknesses of Eiza et.al. scheme and show that the proposed protocol resists against commonplace attacks in VANET with acceptable communication and computation overhead.

`Putting the Face to the Voice' Matching Identity across Modality <=SEP=> Speech perception provides compelling examples of a strong link between auditory and visual modalities. This link originates in the mechanics of speech production, which, in shaping the vocal tract, determine the movement of the face as well as the sound of the voice. In this paper, we present evidence that equivalent information about identity is available cross-modally from both the face and voice. Using a delayed matching to sample task, XAB, we show that people can match the video of an unfamiliar face, X, to an unfamiliar voice, A or B, and vice versa, but only when stimuli are moving and are played forward. The critical role of time-varying information is underlined by the ability to match faces to voices containing only the coarse spatial and temporal information provided by sine wave speech [5]. The effect of varying sentence content across modalities was small, showing that identity-specific information is not closely tied to particular utterances. We conclude that the physical constraints linking faces to voices result in bimodally available dynamic information, not only about what is being said, but also about who is saying it.

Control and path planning of a walk-assist robot using differential flatness <=SEP=> With the growth of elderly population in our society, technology will play an important role in providing functional mobility to humans. In this paper, we propose a robot walking helper with both passive and active control modes of guidance. From the perspective of human safety, the passive mode adopts the braking control law on the wheels to differentially steer the vehicle. The active mode can guide the user efficiently when the passive control with user-applied force is not adequate for guidance. The theory of differential flatness is used to plan the trajectory of control gains within the proposed scheme of the controller. Since the user input force is not known a-priori, the theory of model predictive control is used to periodically compute the trajectory of these control gains. The simulation results show that the walking assist robot, along with the structure of this proposed control scheme, can guide the user to a goal effectively.

Electrically Large Zero-Phase-Shift Line Grid-Array UHF Near-Field RFID Reader Antenna <=SEP=> A grid-array antenna using a zero-phase-shift (ZPS) line is proposed to enlarge the interrogation zone of a reader antenna for near-field ultra-high-frequency (UHF) radio frequency identification (RFID) system. The proposed grid-array antenna is composed of a number of grid cells and a double-sided parallel-strip line feeding network. Each grid cell, namely segmented loop constructed by the ZPS line, has uniform and single-direction flowing current along the line. By configuration of the cells sharing a common side with its adjacent grid cells which carry reverse-direction flowing current, a grid-array antenna is formed to generate a strong and uniform magnetic-field distribution over a large interrogation zone even when the perimeter of the interrogation zone reaches up to 3λ (where λ is the operating wavelength in free space) or larger. As an example, a grid-array antenna with 1 × 2 segmented ZPS line loop cells implemented onto a piece of FR4 printed board (PCB) is designed and prototyped. The results show that the grid-array antenna achieves the impedance matching over the frequency range from 790 to 1040 MHz and produces strong and uniform magnetic-field distribution over an interrogation zone of 308 mm × 150 mm.

101companies: A Community Project on Software Technologies and Software Languages <=SEP=> 101companies is a community project in computer science (or software science) with the objective of developing a free, structured, wiki-accessible knowledge resource including an open-source repository for different stakeholders with interests in software technologies, software languages, and technological spaces; notably: teachers and learners in software engineering or software languages as well as software developers, software technologists, and ontologists. The present paper introduces the 101companies Project. In fact, the present paper is effectively a call for contributions to the project and a call for applications of the project in research and education.

An approach to Korean license plate recognition based on vertical edge matching <=SEP=> License plate recognition (LPR) has many applications in traffic monitoring systems. In this paper, a vertical edge matching based algorithm to recognize Korean license plate from input gray-scale image is proposed. The algorithm is able to recognize license plates in normal shape, as well as plates that are out of shape due to the angle of view. The proposed algorithm is fast enough, the recognition unit of a LPR system can be implemented only in software so that the cost of the system is reduced.

Electromagnetic simulation of 3D stacked ICs: Full model vs. S-parameter cascaded based model <=SEP=> Three-dimensional electromagnetic simulation models are often simplified and/or segmented in order to reduce the simulation time and memory requirements without sacrificing the accuracy of the results. This paper investigates the difference between full model and S-parameter cascaded based model of 3D stacked ICs with the presence of Through Silicon Vias. It is found that the simulation of the full model is required for accurate results, however, a divide and conquers (segmentation) approach can be used for preliminary post layout analysis. Modeling guidelines are discussed and details on the proper choice of ports, boundary conditions, and solver technology are highlighted. A de-embedding methodology is finally explored to improve the accuracy of the cascaded/segmented results.

Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach <=SEP=> In this paper, we propose a machine-learning solution to problems consisting of many similar prediction tasks. Each of the individual tasks has a high risk of overrtting. We combine two types of knowledge transfer between tasks to reduce this risk: multi-task learning and hierarchical Bayesian modeling. Multi-task learning is based on the assumption that there exist features typical to the task at hand. To nd these features, we train a huge two-layered neural network. Each task has its own output, but shares the weights from the input to the hidden units with all other tasks. In this way a relatively large set of possible explanatory variables (the network inputs) is reduced to a smaller and easier to handle set of features (the hidden units). Given this set of features and after an appropriate scale transformation, we assume that the tasks are exchangeable. This assumption allows for a hierarchical Bayesian analysis in which the hyperparameters can be estimated from the data. EEectively, these hyperpa-rameters act as regularizers and prevent over-tting. We describe how to make the system robust against nonstationarities in the time series and give directions for further improvement. We illustrate our ideas on a database regarding the prediction of newspaper sales.

True Random Number Generator Embedded in Reconfigurable Hardware <=SEP=> This paper presents a new True Random Number Generator (TRNG) based on an analog Phase-Locked Loop (PLL) implemented in a digital Altera Field Programmable Logic Device (FPLD). Starting with an analysis of the one available on chip source of randomness the PLL synthesized low jitter clock signal, a new simple and reliable method of true randomness extraction is proposed. Basic assumptions about statistical properties of jitter signal are confirmed by testing of mean value of the TRNG output signal. The quality of generated true random numbers is confirmed by passing standard NIST statistical tests. The described TRNG is tailored for embedded System-On-a-ProgrammableChip (SOPC) cryptographic applications and can provide a good quality true random bit-stream with throughput of several tens of kilobits per second. The possibility of including the proposed TRNG into a SOPC design significantly increases the system security of embedded cryptographic hardware.

Reliability-aware design to suppress aging <=SEP=> Due to aging, circuit reliability has become extraordinary challenging. Reliability-aware circuit design flows do virtually not exist and even research is in its infancy. In this paper, we propose to bring aging awareness to EDA tool flows based on so-called degradation-aware cell libraries. These libraries include detailed delay information of gates/cells under the impact that aging has on both threshold voltage (Vth) and carrier mobility (μ) of transistors. This is unlike state of the art which considers Vth only. We show how ignoring μ degradation leads to underestimating guard-bands by 19% on average. Our investigation revealed that the impact of aging is strongly dependent on the operating conditions of gates (i.e. input signal slew and output load capacitance), and not solely on the duty cycle of transistors. Neglecting this fact results in employing insufficient guard-bands and thus not sustaining reliability during lifetime.
 We demonstrate that degradation-aware libraries and tool flows are indispensable for not only accurately estimating guardbands, but also efficiently containing them. By considering aging degradations during logic synthesis, significantly more resilient circuits can be obtained. We further quantify the impact of aging on the degradation of image processing circuits. This goes far beyond investigating aging with respect to path delays solely. We show that in a standard design without any guardbanding, aging leads to unacceptable image quality after just one year. By contrast, if the synthesis tool is provided with the degradation-aware cell library, high image quality is sustained for 10 years (even under worst-case aging and without a guard-band). Hence, using our approach, aging can be effectively suppressed.

Hexahedral Meshing With Varying Element Sizes <=SEP=> Hexahedral (or Hex-) meshes are preferred in a number of scientific and engineering simulations and analyses due to their desired numerical properties. Recent state-of-the-art techniques can generate high quality hex-meshes. However, they typically produce hex-meshes with uniform element sizes and thus may fail to preserve small scale features on the boundary surface. In this work, we present a new framework that enables users to generate hex-meshes with varying element sizes so that small features will be filled with smaller and denser elements, while the transition from smaller elements to larger ones is smooth, compared to the octree-based approach. This is achieved by first detecting regions of interest (ROI) of small scale features. These ROIs are then magnified using the as-rigid-as-possible (ARAP) deformation with either an automatically determined or a user-specified scale factor. A hex-mesh is then generated from the deformed mesh using existing approaches that produce hex-meshes with uniform-sized elements. This initial hex-mesh is then mapped back to the original volume before magnification to adjust the element sizes in those ROIs. We have applied this framework to a variety of man-made and natural models to demonstrate its effectiveness.

An Efficient Participant’s Selection Algorithm for Crowdsensing <=SEP=> With the advancement of mobile technology the use of Smartphone is greatly increased. Everyone has the mobile phones and it becomes the necessity of life. Today, smart devices are flooding the internet data at every time and in any form that cause the mobile crowdsensing (MCS). One of the key challenges in mobile crowd sensing system is how to effectively identify and select the well-suited participants in recruitments from a large user pool. This research work presents the concept of crowdsensing along with the selection process of participants from a large user pool. MCS provides the efficient selection process for participants that how well suited participant’s selects/recruit from a large user pool. For this, the proposed selection algorithm plays our role in which the recruitment of participants takes place with the availability status from the large user pool. At the end, the graphical result presented with the suitable location of the participants and their time slot. Keywords—Mobile crowdsensing (MCS); Mobile Sensing Platform (MSP]); crowd sensing; participant; user pool; crowdsourcing

Active-feedback frequency-compensation technique for low-power multistage amplifiers <=SEP=> An active-feedback frequency-compensation (AFFC) technique for low-power operational amplifiers is presented in this paper. With an active-feedback mechanism, a high-speed block separates the low-frequency high-gain path and high-frequency signal path such that high gain and wide bandwidth can be achieved simultaneously in the AFFC amplifier. The gain stage in the active-feedback network also reduces the size of the compensation capacitors such that the overall chip area of the amplifier becomes smaller and the slew rate is improved. Furthermore, the presence of a left-half-plane zero in the proposed AFFC topology improves the stability and settling behavior of the amplifier. Three-stage amplifiers based on AFFC and nested-Miller compensation (NMC) techniques have been implemented by a commercial 0.8m CMOS process. When driving a 120-pF capacitive load, the AFFC amplifier achieves over 100-dB dc gain, 4.5-MHz gain-bandwidth product (GBW) , 65 phase margin, and 1.5-V/ s average slew rate, while only dissipating 400W power at a 2-V supply. Compared to a three-stage NMC amplifier, the proposed AFFC amplifier provides improvement in both the GBW and slew rate by 11 times and reduces the chip area by 2.3 times without significant increase in the power consumption.

Effective Question Answering Techniques and their Evaluation Metrics <=SEP=> Question Answering (QA) is a focused way of information retrieval. Question Answering system tries to get back the accurate answers to questions posed in natural language provided a set of documents. Basically question answering system (QA) has three elements i. e. question classification, information retrieval (IR), and answer extraction. These elements play a major role in Question Answering. In Question classification, the questions are classified depending upon the type of its entity. Information retrieval component is used to determine success by retrieving relevant answer for different questions posted by the intelligent question answering system. Answer extraction module is growing topics in the QA in which ranking and validating a candidate&apos;s answer is the major job. This paper offers a concise discussion regarding different Question Answering types. In addition we describe different evaluation metrics used to evaluate the performance of different question answering systems. We also discuss the recent question answering systems developed and their corresponding techniques.

A 500mA analog-assisted digital-LDO-based on-chip distributed power delivery grid with cooperative regulation and IR-drop reduction in 65nm CMOS <=SEP=> With the die area of modern processors growing larger and larger, the IR drop across the power supply rail due to its parasitic resistance becomes considerable. There is an urgent demand for local power regulation to reduce the IR drop and to enhance transient response. A distributed power-delivery grid (DPDG) is an attractive solution for large area power-supply applications. The dual-loop distributed micro-regulator in [1] achieves a tight regulation and fast response, but suffers from large ripple and high power consumption due to the comparator-based regulator. Digital low-dropout regulators (DLDOs) [2] can be used as local micro-regulators to implement a DPDG, due to their low-voltage operation and process scalability. Adaptive control [3], asynchronous 3D pipeline control [4], analog-assisted tri-loop control [5], and event-driven PI control [6] are proposed to enhance the transient response speed. However, digital LDOs suffer from the intrinsic limitations of large output ripple and narrow current range. This paper presents an on-chip DPDg with cooperative regulation based on an analog-assisted digital LDO (AADLDO), which inherits the merits of low output ripple and sub-LSB current supply ability from the analog control, and the advantage of low supply voltage operation and adaptive fast response from the digital control.