Miracle-dz/UniFAR-FTUs huggingface dataset:
Imitation-Regularized Offline Learning

We study the problem of offline learning in automated decision systems under the contextual bandits model. We are given logged historical data consisting of contexts, (randomized) actions, and (nonnegative) rewards. A common goal is to evaluate what would happen if different actions were taken in the same contexts, so as to optimize the action policies accordingly. The typical approach to this problem, inverse probability weighted estimation (IPWE) [Bottou et al., 2013], requires logged action probabilities, which may be missing in practice due to engineering complications. Even when available, small action probabilities cause large uncertainty in IPWE, rendering the corresponding results insignificant. To solve both problems, we show how one can use policy improvement (PIL) objectives, regularized by policy imitation (IML). We motivate and analyze PIL as an extension to Clipped-IPWE, by showing that both are lower-bound surrogates to the vanilla IPWE. We also formally connect IML to IPWE variance estimation [Swaminathan and Joachims 2015] and natural policy gradients. Without probability logging, our PIL-IML interpretations justify and improve, by reward-weighting, the state-of-art cross-entropy (CE) loss that predicts the action items among all action candidates available in the same contexts. With probability logging, our main theoretical contribution connects IML-underfitting to the existence of either confounding variables or model misspecification. We show the value and accuracy of our insights by simulations based on Simpson's paradox, standard UCI multiclass-to-bandit conversions and on the Criteo counterfactual analysis challenge dataset.
XNet: GAN Latent Space Constraints

Recent GAN-based architectures have been able to deliver impressive performance on the general task of image-to-image translation. In particular, it was shown that a wide variety of image translation operators may be learned from two image sets, containing images from two different domains, without establishing an explicit pairing between the images. This was made possible by introducing clever regularizers to overcome the under-constrained nature of the unpaired translation problem. In this work, we introduce a novel architecture for unpaired image translation, and explore several new regularizers enabled by it. Specifically, our architecture comprises a pair of GANs, as well as a pair of translators between their respective latent spaces. These cross-translators enable us to impose several regularizing constraints on the learnt image translation operator, collectively referred to as latent cross-consistency. Our results show that our proposed architecture and latent cross-consistency constraints are able to outperform the existing state-of-the-art on a wide variety of image translation tasks.
Generative Adversarial Networks for Real-time Stability of Inverter-based Systems

In islanded systems with droop-controlled sources, the droop coefficients need to be tuned in real-time using supervisory control to maintain asymptotic stability. In contrast to offline tuning methods, online domain-of-stability estimation yields non-conservative droop gains in real-time, ensuring good power sharing performance as the operating point varies. The challenge in the conventional online domain-of-stability estimation process is its unscalability and high computational complexity. In this paper, an efficient alternative using conditional Generative Adversarial Networks (cGANs) is described. We demonstrate that the notion of power system stability can be learned by such deep neural networks, and that they can offer a scalable alternative to conventional domain-of-stability estimation methods in islanded distribution systems. The implementation of cGANs-based stability assessment is described for an LV distribution test case and its advantages demonstrated.
AI Pipeline - bringing AI to you. End-to-end integration of data, algorithms and deployment tools

Next generation of embedded Information and Communication Technology (ICT) systems are interconnected collaborative intelligent systems able to perform autonomous tasks. Training and deployment of such systems on Edge devices however require a fine-grained integration of data and tools to achieve high accuracy and overcome functional and non-functional requirements. In this work, we present a modular AI pipeline as an integrating framework to bring data, algorithms and deployment tools together. By these means, we are able to interconnect the different entities or stages of particular systems and provide an end-to-end development of AI products. We demonstrate the effectiveness of the AI pipeline by solving an Automatic Speech Recognition challenge and we show that all the steps leading to an end-to-end development for Key-word Spotting tasks: importing, partitioning and pre-processing of speech data, training of different neural network architectures and their deployment on heterogeneous embedded platforms.
ReNeg and Backseat Driver: Learning from Demonstration with Continuous Human Feedback

In autonomous vehicle (AV) control, allowing mistakes can be quite dangerous and costly in the real world. For this reason we investigate methods of training an AV without allowing the agent to explore and instead having a human explorer collect the data. Supervised learning has been explored for AV control, but it encounters the issue of the covariate shift. That is, training data collected from an optimal demonstration consists only of the states induced by the optimal control policy, but at runtime, the trained agent may encounter a vastly different state distribution with little relevant training data. To mitigate this issue, we have our human explorer make sub-optimal decisions. In order to have our agent not replicate these sub-optimal decisions, supervised learning requires that we either erase these actions, or replace these action with the correct action. Erasing is wasteful and replacing is difficult, since it is not easy to know the correct action without driving. We propose an alternate framework that includes continuous scalar feedback for each action, marking which actions we should replicate, which we should avoid, and how sure we are. Our framework learns continuous control from sub-optimal demonstration and evaluative feedback collected before training. We find that a human demonstrator can explore sub-optimal states in a safe manner, while still getting enough gradation to benefit learning. The collection method for data and feedback we call "Backseat Driver. " We call the more general learning framework ReNeg, since it learns a regression from states to actions given negative as well as positive examples. We empirically validate several models in the ReNeg framework, testing on lane-following with limited data. We find that the best solution is a generalization of mean-squared error and outperforms supervised learning on the positive examples alone.
Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

The vehicular edge computing system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states, and computing workloads. These uncertainties bring extra challenges to task offloading. In this paper, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning based task offloading (ALTO) algorithm based on the multi-armed bandit theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with input-awareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to $30\%$ compared with the existing upper confidence bound based learning algorithm.
Coded Federated Computing in Wireless Networks with Straggling Devices and Imperfect CSI

Distributed computing platforms typically assume the availability of reliable and dedicated connections among the processors. This work considers an alternative scenario, relevant for wireless data centers and federated learning, in which the distributed processors, operating on generally distinct coded data, are connected via shared wireless channels accessed via full-duplex transmission. The study accounts for both wireless and computing impairments, including interference, imperfect Channel State Information, and straggling processors, and it assumes a Map-Shuffle-Reduce coded computing paradigm. The total latency of the system, obtained as the sum of computing and communication delays, is studied for different shuffling strategies revealing the interplay between distributed computing, coding, and cooperative or coordinated transmission.
AlSub: Fully Parallel and Modular Subdivision

In recent years, mesh subdivision---the process of forging smooth free-form surfaces from coarse polygonal meshes---has become an indispensable production instrument. Although subdivision performance is crucial during simulation, animation and rendering, state-of-the-art approaches still rely on serial implementations for complex parts of the subdivision process. Therefore, they often fail to harness the power of modern parallel devices, like the graphics processing unit (GPU), for large parts of the algorithm and must resort to time-consuming serial preprocessing. In this paper, we show that a complete parallelization of the subdivision process for modern architectures is possible. Building on sparse matrix linear algebra, we show how to structure the complete subdivision process into a sequence of algebra operations. By restructuring and grouping these operations, we adapt the process for different use cases, such as regular subdivision of dynamic meshes, uniform subdivision for immutable topology, and feature-adaptive subdivision for efficient rendering of animated models. As the same machinery is used for all use cases, identical subdivision results are achieved in all parts of the production pipeline. As a second contribution, we show how these linear algebra formulations can effectively be translated into efficient GPU kernels. Applying our strategies to $\sqrt{3}$, Loop and Catmull-Clark subdivision shows significant speedups of our approach compared to state-of-the-art solutions, while we completely avoid serial preprocessing.
Entwicklung und Anwendung synthetischer RNA-Schalter zur Regulation der Genexpression

Der genetische Werkzeugkasten des relativ jungen Forschungsgebietes der Synthetischen Biologie wird standig durch neue Bauteile erweitert, wobei in den letzten Jahren verstarkt die Anwendung RNA-basierter Regulationselemente in den Fokus geruckt ist. In dieser Arbeit wird die Entwicklung und Anwendbarkeit dieser sogenannten synthetischen Riboswitche zur konditionalen Genexpression fur verschiedene Organismen beschrieben. Im ersten Teil wird die in vitro-Selektion (SELEX) von RNA-Aptameren gezeigt, die als sensorische Domanen zum Aufbau dieser Riboswitche genutzt werden konnen. Als Zielsubstanzen dienten dabei Azobenzole. Das sind photochrome Molekule, die eine reversible Konformationsanderung durch Licht unterschiedlicher Wellenlangenbereiche erfahren. Die Selektionen hatten dabei das Ziel der Anreicherung photoschaltbarer Aptamer-Liganden-komplexe, wobei die jeweiligen trans-Isomere bevorzugt werden sollten. Zunachst wurde eine in vitro-Selektion gegen das Azobenzol TH72 unter Verwendung paramagnetischer streptavidinbeschichteter Partikel durchgefuhrt. Hier wurde eine Anreicherung bindender RNA-Spezies erzielt, welche jedoch nicht imstande waren, das freie Zielmolekul zu erkennen, sondern eine Abhangigkeit zur Selektionsmatrix aufwiesen. Durch eine SELEX mittels saulenbasierter Affinitatschromatographie gegen die mit einer Chloramphenicoleinheit substituierten Azobenzole TH-CA bzw. TH-CA-Amino wurden indes Aptamere identifiziert, welche freie Zielmolekule binden konnten und sich durch Dissoziationskonstanten im niedrigen mikromolaren Bereich auszeichneten (Kd = 0,8 – 3,6 µM). Fur die Aptamere 42 und 42_V1 wurden daruber hinaus lichtabhangige Bindeverhalten gezeigt, wobei die cis-Isomere der Azobenzole nicht oder nur sehr schwach gebunden wurden. Ein erster Konstruktionsversuch azobenzolabhangiger Riboswitche im Hefetestsystem war noch nicht zielfuhrend. Im zweiten Teil dieser Arbeit wurde der Einsatz synthetischer theophyllinabhangiger Riboswitche zur konditionalen Genexpression in Streptomyces coelicolor gepruft. Die von Justin P. Gallivan entwickelten RNA-Schalter liesen sich mit allen drei getesteten Promotoren galP2, ermEp1 und SF14 kombinieren. Hierdurch konnten insbesondere die Riboswitche A und E* als robuste Schaltelemente identifiziert werden, welche eine 30- bis 260-fache Aktivierung der s-Glucuronidaseexpression zuliesen. Mit Riboswitch E* lies sich daruber hinaus zeigen, dass die Genexpression dosisabhangig und zu bestimmten Zeitpunkten der Kultivierung kontrolliert werden konnte. Das Riboswitchsystem zeichnete sich somit als leicht zu implementierende, modular kombinierbare und zudem robuste Methode zur konditionalen Genexpression aus und konnte auch fur diverse native und heterologe Proteine in anderen Streptomyces-Arten zur Anwendung kommen.
Variable-sized input, character-level recurrent neural networks in lead generation: predicting close rates from raw user inputs

Predicting lead close rates is one of the most problematic tasks in the lead generation industry. In most cases, the only available data on the prospect is the self-reported information inputted by the user on the lead form and a few other data points publicly available through social media and search engine usage. All the major market niches for lead generation [1], such as insurance, health & medical and real estate, deal with life-altering decision making that no amount of data will be ever be able to describe or predict. This paper illustrates how character-level, deep long short-term memory networks can be applied to raw user inputs to help predict close rates. The output of the model is then used as an additional, highly predictive feature to significantly boost performance of lead scoring models.
GridSim: A Vehicle Kinematics Engine for Deep Neuroevolutionary Control in Autonomous Driving

Current state of the art solutions in the control of an autonomous vehicle mainly use supervised end-to-end learning, or decoupled perception, planning and action pipelines. Another possible solution is deep reinforcement learning, but such a method requires that the agent interacts with its surroundings in a simulated environment. In this paper we introduce GridSim, which is an autonomous driving simulator engine running a car-like robot architecture to generate occupancy grids from simulated sensors. We use GridSim to study the performance of two deep learning approaches, deep reinforcement learning and driving behavioral learning through genetic algorithms. The deep network encodes the desired behavior in a two elements fitness function describing a maximum travel distance and a maximum forward speed, bounded to a specific interval. The algorithms are evaluated on simulated highways, curved roads and inner-city scenarios, all including different driving limitations.
On the Optimality of Interference Decoding Schemes for K-User Gaussian Interference Channels †

The sum capacity of the general K-user Gaussian Interference Channel (GIC) is known only when the channel coefficients are such that treating interference as noise (TIN) is optimal. The Han-Kobayashi (HK) scheme achieves the best known achievable rate region for the K-user interference channel (IC). Simple HK schemes are HK schemes with Gaussian signaling, no time sharing, and no private-common power splitting. The class of simple HK (S-HK) schemes includes the TIN scheme and schemes that involve various levels of interference decoding and cancellation at each receiver. We derive conditions under which simple HK schemes achieve sum capacity for general K-user Gaussian ICs. These results generalize existing sum capacity results for the TIN scheme to the class of simple HK schemes.
Hybrid Beamformer Design for High Dynamic Range Ambient Backscatter Receivers

In bi-static Ambient Backscatter Communications (AmBC) systems, the receiver needs to operate at a large dynamic range because the direct path from the ambient source to the receiver can be several orders of magnitude stronger than the scattered path modulated by the AmBC device. In this paper, we propose a novel analog-digital hybrid null-steering beamformer which allows the backscatter receiver to detect and decode the weak AmBC-modulated signal buried in the strong direct path signals and the noise, without requiring the instantaneous channel state information. The analog cancellation of the strong signal components allows the receiver automatic gain control to adjust to the level of the weak AmBC signals. This hence allows common analog-to-digital converters to be used for sampling the signal. After cancelling the strong components, the ambient source signal appears as zero mean fast fading from the AmBC system point of view. We use the direct path signal component to track the phase of the unknown ambient signal. In order to avoid channel estimation, we propose AmBC to use orthogonal channelization codes. The results show that the design allows the AmBC receiver to detect the backscatter binary phase shift keying signals without decoding the ambient signals and requiring knowledge of the instantaneous channel state information.
A review of single-source unsupervised domain adaptation

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the questions: when and how a classifier can learn from a source domain and generalize to a target domain. As for when, we review conditions that allow for cross-domain generalization error bounds. As for how, we present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods focus on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods focus on alternative estimators, such as robust, minimax or Bayesian. Our categorization highlights recurring ideas and raises a number of questions important to further research.
The Influence of IM Use on Job Satisfaction in Cross-organizational Projects

Instant messaging (IM) tools have been used by more and more companies for cross–organizational projects. They face two choices: dedicated systems in form of enterprise IM and public platforms like WeChat. This study conducts a comparative analysis of WeChat and enterprise IM. It examines the impact of IM usage on job satisfaction in cross-organizational projects, and uses IM Motivation as a moderator. Research shows that for WeChat, hedonic motivation plays a significant negative moderation role in the impact of instant messaging usage on job satisfaction. For enterprise IM, utilitarian motivation plays a significant positive moderation role in the impact of IM usage on job satisfaction.
An Example of Machine Learning Applied in Additive Manufacturing

Artificial Intelligence has become ubiquitous in many application sectors nowadays. One field, machine learning, has proved to be promising for industries as it can solve complex problems thanks to various methods and algorithms. The present article is an example of machine learning with EBM, a recent additive manufacturing technology. Several algorithms are tested onto the manufacturing of a part in order to check which results can extrapolate at the best the deformation risks vs the quality of the part to build. In this example, a few parameters of support structures are identified and are varied to observe the algorithms validity.
Brain Teleoperation of a Mobile Robot Using Deep Learning Technique

This paper proposes a brain-teleoperation control strategy that combines the deep learning technique (DLT) to realize the control and navigation of a mobile robot in unknown environments. The support vector machine (SVM) algorithm is utilized to recognize the human electroencephalograph (EEG) signals in the brain-computer interface (BCI) system which is based on steady state visually evoked potentials (SSVEP). In this way the intentions of human can be distinguished and control commands are generated for mobile robot. The DLT is used to recognize the type of environmental obstacles and environmental features by analysing the images that describe the environment. And then according to the classification of obstacle, various potential fields are built for the specific obstacles. By utilizing bottles as the features of environment, a whole map of the surroundings can be built through a sequential simultaneous localization and mapping (SLAM) algorithm. The main contribution of this paper is that the relationship between the potential field strength and classification of EEG signals is built up through the combination of multiple artificial potential fields with the brain signals, which produces the motion commands and designs a trajectory free of obstacles in un-structure environments. Three volunteer subjects are invited to test the entire system, and all operators can successfully complete experiments of manipulating the robot in corridor environments.
Race Classification from Face using Deep Convolutional Neural Networks

As a basic and key attribute of human beings, race plays an indispensable role in face analysis. Traditional machine learning methods all tackle the problem of race classification in combination with two separate steps: extracting artificially designed features and training a proper classifier with these features. Some convolutional neural networks have also been proposed to deal with this problem, but get unsatisfactory accuracies. In this paper, we propose an improved deep convolutional neural network based on an existing network. The network uses a branch structure to merge networks of different depths, such that it can see multi-scale features (features in the low layers are more global and general than those in the high layers). To train this network, we collect a private race database using the available search engines on the Internet, which is larger and more balanced than publicly available databases. Experimental results show that the proposed network can not only extract features and classify them simultaneously compared with traditional methods, but also to achieve state-of-the-art accuracy of almost 99% on both public and self-made databases. Finally, it is necessary to highlight the importance of the advanced face detection and face alignment for the final result.
Torque Tracking Impedance Control for a 3DOF Lower Limb Rehabilitation Robot*

This paper presents a torque tracking impedance control strategy for a lower limb rehabilitation robot (LLRR) with three joints, hip, knee and ankle. The strategy allows the human-robot system to track a desired interaction torque. To track the desired torque, the robot reference trajectory and actuator torque are designed properly based on the torque tracking impedance control. A constant torque tracking experiment performed on a healthy male subject has shown that the maximum normalized root mean square deviation (NRMSD) of each joint is less than 32%. It indicates that the control strategy is feasible and the LLRR is capable of providing stroke patients with active rehabilitation training.