Miracle-dz/UniFAR-FTUs huggingface dataset:
Oxygen Content and Bias Influence on Amorphous InGaZnO TFT-Based Temperature Sensor Performance

A temperature (<inline-formula> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula>) sensor based on an amorphous InGaZnO (a-IGZO) thin-film transistor (TFT) is demonstrated and its performance is analyzed quantitatively. The performance analysis includes the influences of the gate-to-source bias (<inline-formula> <tex-math notation="LaTeX">${V}_{{GS}}$ </tex-math></inline-formula>) and oxygen content in IGZO on the mobility (<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>), threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{T}$ </tex-math></inline-formula>), and drain-to-source current (<inline-formula> <tex-math notation="LaTeX">${I}_{\text {DS}}$ </tex-math></inline-formula>) of TFTs. The linearity and sensitivity of the sensor are found to improve with decreasing oxygen content and increasing <inline-formula> <tex-math notation="LaTeX">${V}_{\text {GS}}$ </tex-math></inline-formula>, because <inline-formula> <tex-math notation="LaTeX">$\partial {I}_{\text {DS}}/\partial {T}$ </tex-math></inline-formula> is quantitatively dominated by <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> and (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {GS}} -{V}_{\text {T}}$ </tex-math></inline-formula>) rather than by <inline-formula> <tex-math notation="LaTeX">$\partial \mu /\partial {T}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$- \partial {V}_{\text {T}}/\partial {T}$ </tex-math></inline-formula>. This indicates that the IGZO TFT has great potential as a temperature sensor in terms of immunity in device/process variation. A sensitivity of 125 nA/K with 1.55 K resolution over a temperature range of 303–373 K was successfully demonstrated in low oxygen and <inline-formula> <tex-math notation="LaTeX">${V}_{\text {GS}}= {9}$ </tex-math></inline-formula> V conditions.
An Assurance Framework for Independent Co-assurance of Safety and Security

Integrated safety and security assurance for complex systems is difficult for many technical and socio-technical reasons, such as mismatched processes, inadequate information, differing use of language and philosophies, etc. Many co-assurance techniques rely on disregarding some of these challenges to present a unified methodology. Even with this simplification, no methodology has been widely adopted, primarily because this approach is unrealistic when met with the complexity of real-world system development. This paper presents an alternate approach by providing a Safety-Security Assurance Framework (SSAF) based on a core set of assurance principles. This is done so that safety and security can be co-assured independently, as opposed to a unified co-assurance, which has been shown to have significant drawbacks. This also allows for separate processes and expertise from practitioners in each domain. In this structure, the focus is shifted from simplified unification to integration through exchanging the correct information at the right time using synchronization activities.
Reliable State Machines: A Framework for Programming Reliable Cloud Services

Building reliable applications for the cloud is challenging because of unpredictable failures during a program's execution. This paper presents a programming framework called Reliable State Machines (RSMs), that offers fault-tolerance by construction. Using our framework, a programmer can build an application as several (possibly distributed) RSMs that communicate with each other via messages, much in the style of actor-based programming. Each RSM is additionally fault-tolerant by design and offers the illusion of being "always-alive". An RSM is guaranteed to process each input request exactly once, as one would expect in a failure-free environment. The RSM runtime automatically takes care of persisting state and rehydrating it on a failover. We present the core syntax and semantics of RSMs, along with a formal proof of failure-transparency. We provide an implementation of the RSM framework and runtime on the . NET platform for deploying services to Microsoft Azure. We carried out an extensive performance evaluation on micro-benchmarks to show that one can build high-throughput applications with RSMs. We also present a case study where we rewrote a significant part of a production cloud service using RSMs. The resulting service has simpler code and exhibits production-grade performance.
Transfer Learning for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) is a technique to recover source appliances from only the recorded mains in a household. NILM is unidentifiable and thus a challenge problem because the inferred power value of an appliance given only the mains could not be unique. To mitigate the unidentifiable problem, various methods incorporating domain knowledge into NILM have been proposed and shown effective experimentally. Recently, among these methods, deep neural networks are shown performing best. Arguably, the recently proposed sequence-to-point (seq2point) learning is promising for NILM. However, the results were only carried out on the same data domain. It is not clear if the method could be generalised or transferred to different domains, e.g., the test data were drawn from a different country comparing to the training data. We address this issue in the paper, and two transfer learning schemes are proposed, i.e., appliance transfer learning (ATL) and cross-domain transfer learning (CTL). For ATL, our results show that the latent features learnt by a ‘complex’ appliance, e.g., washing machine, can be transferred to a ‘simple’ appliance, e.g., kettle. For CTL, our conclusion is that the seq2point learning is transferable. Precisely, when the training and test data are in a similar domain, seq2point learning can be directly applied to the test data without fine tuning; when the training and test data are in different domains, seq2point learning needs fine tuning before applying to the test data. Interestingly, we show that only the fully connected layers need fine tuning for transfer learning. Source code can be found at https://github.com/MingjunZhong/transferNILM.
SCAR: Strong Consistency using Asynchronous Replication with Minimal Coordination

Data replication is crucial in modern distributed systems as a means to provide high availability. Many techniques have been proposed to utilize replicas to improve a system's performance, often requiring expensive coordination or sacrificing consistency. In this paper, we present SCAR, a new distributed and replicated in-memory database that allows serializable transactions to read from backup replicas with minimal coordination. SCAR works by assigning logical timestamps to database records so that a transaction can safely read from a backup replica without coordinating with the primary replica, because the records cannot be changed up to a certain logical time. In addition, we propose two optimization techniques, timestamp synchronization and parallel locking and validation, to further reduce coordination. We show that SCAR outperforms systems with conventional concurrency control algorithms and replication strategies by up to a factor of 2 on three popular benchmarks. We also demonstrate that SCAR achieves higher throughput by running under reduced isolation levels and detects concurrency anomalies in real time.
Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery

To autonomously navigate and plan interactions in real-world environments, robots require the ability to robustly perceive and map complex, unstructured surrounding scenes. Besides building an internal representation of the observed scene geometry, the key insight toward a truly functional understanding of the environment is the usage of higher level entities during mapping, such as individual object instances. This work presents an approach to incrementally build volumetric object-centric maps during online scanning with a localized RGB-D camera. First, a per-frame segmentation scheme combines an unsupervised geometric approach with instance-aware semantic predictions to detect both recognized scene elements as well as previously unseen objects. Next, a data association step tracks the predicted instances across the different frames. Finally, a map integration strategy fuses information about their 3D shape, location, and, if available, semantic class into a global volume. Evaluation on a publicly available dataset shows that the proposed approach for building instance-level semantic maps is competitive with state-of-the-art methods, while additionally able to discover objects of unseen categories. The system is further evaluated within a real-world robotic mapping setup, for which qualitative results highlight the online nature of the method. Code is available at https://github.com/ethz-asl/voxblox-plusplus.
Empowering Elasticsearch with Exact and Fast $r$-Neighbor Search in Hamming Space

A growing interest has been witnessed recently in building nearest neighbor search solutions within Elasticsearch--one of the most popular full-text search engines. In this paper, we focus specifically on Hamming space nearest neighbor search using Elasticsearch. By combining three techniques: bit operation, substring filtering and data preprocessing with permutation, we develop a novel approach called FENSHSES (Fast Exact Neighbor Search in Hamming Space on Elasticsearch), which achieves dramatic speed-ups over the existing term match baseline. This will empower Elasticsearch with the capability of fast information retrieval even when documents (e.g., texts, images and sounds) are represented with binary codes--a common practice in nowadays semantic representation learning.
Parallel Index-based Stream Join on a Multicore CPU

Indexing sliding window content to enhance the performance of streaming queries can be greatly improved by utilizing the computational capabilities of a multicore processor. Conventional indexing data structures optimized for frequent search queries on a prestored dataset do not meet the demands of indexing highly dynamic data as in streaming environments. In this paper, we introduce an index data structure, called the partitioned in-memory merge tree, to address the challenges that arise when indexing highly dynamic data, which are common in streaming settings. Utilizing the specific pattern of streaming data and the distribution of queries, we propose a low-cost and effective concurrency control mechanism to meet the demands of high-rate update queries. To complement the index, we design an algorithm to realize a parallel index-based stream join that exploits the computational power of multicore processors. Our experiments using an octa-core processor show that our parallel stream join achieves up to 5.5 times higher throughput than a single-threaded approach.
Beyond Frequency

Consumer behavior plays a very important role in economics and targeted marketing. However, understanding economic consumer behavior is quite challenging, such as finding credible and reliable information on product profitability. Different from frequent pattern mining, utility-oriented mining integrates utility theory and data mining. Utility mining is a useful tool for understanding economic consumer behavior. Traditional algorithms for mining high-utility patterns (HUPs) applies a single/uniform minimum utility threshold (minutil) to obtain the set of HUPs, but in some real-life circumstances, some specific products may bring lower utilities compared with others, but their profit may offer some vital information. If minutil is set high, the patterns with low minutil are missed; if minutil is set low, the number of patterns becomes unmanageable. In this article, an efficient one-phase utility-oriented pattern mining algorithm, called HIMU, is proposed for mining HUPs with varied item-specific minimum utility. A novel tree structure called a multiple item utility set-enumeration tree (MIU-tree) and the global sorted and the conditional downward closure properties are introduced in HIMU. In addition, we extended the compact utility-list structure to keep the necessary information, and thus this one-phase HIMU model greatly reduces the computational costs and memory requirements. Moreover, two pruning strategies are then extended to enhance the performance. We conducted extensive experiments in several synthetic and real-world datasets; the results indicate that the designed one-phase HIMU algorithm can address the “rare item problem” and has better performance than the state-of-the-art algorithms in terms of runtime, memory usage, and scalability. Furthermore, the enhanced algorithms outperform the non-optimized HIMU approach.
GraphFlow: A New Graph Convolutional Network Based on Parallel Flows

In view of the huge success of convolution neural networks (CNN) for image classification and object recognition, there have been attempts to generalize the method to general graph-structured data. One major direction is based on spectral graph theory and graph signal processing. In this paper, we study the problem from a completely different perspective, by introducing parallel flow decomposition of graphs. The essential idea is to decompose a graph into families of non-intersecting one dimensional (1D) paths, after which, we may apply a 1D CNN along each family of paths. We demonstrate that the our method, which we call GraphFlow, is able to transfer CNN architectures to general graphs. To show the effectiveness of our approach, we test our method on the classical MNIST dataset, synthetic datasets on network information propagation and a news article classification dataset.
Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly

Precise robotic manipulation skills are desirable in many industrial settings, reinforcement learning (RL) methods hold the promise of acquiring these skills autonomously. In this paper, we explicitly consider incorporating operational space force/torque information into reinforcement learning; this is motivated by humans heuristically mapping perceived forces to control actions, which results in completing high-precision tasks in a fairly easy manner. Our approach combines RL with force/torque information by incorporating a proper operational space force controller; where we also exploit different ablations on processing this information. Moreover, we propose a neural network architecture that generalizes to reasonable variations of the environment. We evaluate our method on the open-source Siemens Robot Learning Challenge, which requires precise and delicate force-controlled behavior to assemble a tight-fit gear wheel set.
A multimodal movie review corpus for fine-grained opinion mining

In this paper, we introduce a set of opinion annotations for the POM movie review dataset, composed of 1000 videos. The annotation campaign is motivated by the development of a hierarchical opinion prediction framework allowing one to predict the different components of the opinions (e.g. polarity and aspect) and to identify the corresponding textual spans. The resulting annotations have been gathered at two granularity levels: a coarse one (opinionated span) and a finer one (span of opinion components). We introduce specific categories in order to make the annotation of opinions easier for movie reviews. For example, some categories allow the discovery of user recommendation and preference in movie reviews. We provide a quantitative analysis of the annotations and report the inter-annotator agreement under the different levels of granularity. We provide thus the first set of ground-truth annotations which can be used for the task of fine-grained multimodal opinion prediction. We provide an analysis of the data gathered through an inter-annotator study and show that a linear structured predictor learns meaningful features even for the prediction of scarce labels. Both the annotations and the baseline system will be made publicly available.
Global Vectors for Node Representations

Most network embedding algorithms consist in measuring co-occur-rences of nodes via random walks then learning the embeddings using Skip-Gram with Negative Sampling. While it has proven to be a relevant choice, there are alternatives, such as GloVe, which has not been investigated yet for network embedding. Even though SGNS better handles non co-occurrence than GloVe, it has a worse time-complexity. In this paper, we propose a matrix factorization approach for network embedding, inspired by GloVe, that better handles non co-occurrence with a competitive time-complexity. We also show how to extend this model to deal with networks where nodes are documents, by simultaneously learning word, node and document representations. Quantitative evaluations show that our model achieves state-of-the-art performance, while not being so sensitive to the choice of hyper-parameters. Qualitatively speaking, we show how our model helps exploring a network of documents by generating complementary network-oriented and content-oriented keywords.
Vine Robots: Design, Teleoperation, and Deployment for Navigation and Exploration

Researchers are developing a new class of continuum robots characterized by tip extension, significant length change, and directional control. In this article, we call these vine robots because of their similarity to plants in their growth-trailing behavior. Due to their growth-based movement, vine robots are well suited for navigation and exploration in cluttered environments. Until now, however, they have not been deployed outside the lab. There are three features that are key for successful deployment in the field. First is portability. Second is the ability to be guided over long enough distances to be useful for navigation. Third is intuitive human-in-theloop teleoperation, which enables movement in unknown and dynamic environments. We present a vine robot system that is teleoperated using a custom-designed flexible joystick and camera system, can extend long enough for use in navigation tasks, and is portable for use in the field. We report on the deployment of this system in two scenarios: completion of a soft robot navigation competition and exploration of an archaeological site. The competition course required movement over uneven terrain, past unstable obstacles, and through a small aperture. The archeological site required movement over rocks and through horizontal and vertical turns. The robot tip successfully moved past the obstacles and through the tunnels, demonstrating the capability of vine robots to achieve navigation and exploration tasks in the field.
Learning to Generate Questions by LearningWhat not to Generate

Automatic question generation is an important technique that can improve the training of question answering, help chatbots to start or continue a conversation with humans, and provide assessment materials for educational purposes. Existing neural question generation models are not sufficient mainly due to their inability to properly model the process of how each word in the question is selected, i.e., whether repeating the given passage or being generated from a vocabulary. In this paper, we propose our Clue Guided Copy Network for Question Generation (CGC-QG), which is a sequence-to-sequence generative model with copying mechanism, yet employing a variety of novel components and techniques to boost the performance of question generation. In CGC-QG, we design a multi-task labeling strategy to identify whether a question word should be copied from the input passage or be generated instead, guiding the model to learn the accurate boundaries between copying and generation. Furthermore, our input passage encoder takes as input, among a diverse range of other features, the prediction made by a clue word predictor, which helps identify whether each word in the input passage is a potential clue to be copied into the target question. The clue word predictor is designed based on a novel application of Graph Convolutional Networks onto a syntactic dependency tree representation of each passage, thus being able to predict clue words only based on their context in the passage and their relative positions to the answer in the tree. We jointly train the clue prediction as well as question generation with multi-task learning and a number of practical strategies to reduce the complexity. Extensive evaluations show that our model significantly improves the performance of question generation and out-performs all previous state-of-the-art neural question generation models by a substantial margin.
Data Augmentation for Drum Transcription with Convolutional Neural Networks

A recurrent issue in deep learning is the scarcity of data, in particular precisely annotated data. Few publicly available databases are correctly annotated and generating correct labels is very time consuming. The present article investigates into data augmentation strategies for Neural Networks training, particularly for tasks related to drum transcription. These tasks need very precise annotations. This article investigates state-of the-art sound transformation algorithms for remixing noise and sinusoidal parts, remixing attacks, transposing with and without time compensation and compares them to basic regularization methods such as using dropout and additive Gaussian noise. And it shows how a drum transcription algorithm based on CNN benefits from the proposed data augmentation strategy.
Unifying Ensemble Methods for Q-learning via Social Choice Theory

Ensemble methods have been widely applied in Reinforcement Learning (RL) in order to enhance stability, increase convergence speed, and improve exploration. These methods typically work by employing an aggregation mechanism over actions of different RL algorithms. We show that a variety of these methods can be unified by drawing parallels from committee voting rules in Social Choice Theory. We map the problem of designing an action aggregation mechanism in an ensemble method to a voting problem which, under different voting rules, yield popular ensemble-based RL algorithms like Majority Voting Q-learning or Bootstrapped Q-learning. Our unification framework, in turn, allows us to design new ensemble-RL algorithms with better performance. For instance, we map two diversity-centered committee voting rules, namely Single Non-Transferable Voting Rule and Chamberlin-Courant Rule, into new RL algorithms that demonstrate excellent exploratory behavior in our experiments.
Should All Temporal Difference Learning Use Emphasis?

Emphatic Temporal Difference (ETD) learning has recently been proposed as a convergent off-policy learning method. ETD was proposed mainly to address convergence issues of conventional Temporal Difference (TD) learning under off-policy training but it is different from conventional TD learning even under on-policy training. A simple counterexample provided back in 2017 pointed to a potential class of problems where ETD converges but TD diverges. In this paper, we empirically show that ETD converges on a few other well-known on-policy experiments whereas TD either diverges or performs poorly. We also show that ETD outperforms TD on the mountain car prediction problem. Our results, together with a similar pattern observed under off-policy training in prior works, suggest that ETD might be a good substitute over conventional TD.