Miracle-dz/UniFAR-FTUs huggingface dataset:
Secure Virtual Mobile Small Cells: A Stepping Stone Toward 6G

As fifth generation research reaches its twilight, the research community must go beyond 5G and look toward the 2030 connectivity landscape, namely 6G. In this context, this work takes a step toward the 6G vision by proposing a next generation communication platform, which aims to extend the rigid coverage area of fixed deployment networks by considering virtual mobile small cells (MSCs) that are created on demand. Relying on emerging computing paradigms such as network function virtualization and software defined networking, these cells can harness radio and networking capability, locally reducing protocol signaling latency and overhead. These MSCs constitute an intelligent pool of networking resources that can collaborate to form a wireless network of MSCs providing a communication platform for localized, ubiquitous, and reliable connectivity. The technology enablers for implementing the MSC concept are also addressed in terms of virtualization, lightweight wireless security, and energy-efficient RF. The benefits of the MSC architecture toward reliable and efficient cell offloading are demonstrated as a use case.
Trading Data with Personalized Differential Privacy and Partial Arbitrage Freeness

—There is a growing trend regarding perceiving personal data as a commodity. Existing studies have built frameworks and theories about how to determine an arbitrage-free price of a given query according to the privacy loss quantiﬁed by differential privacy. However, those previous works have assumed that data buyers can purchase query answers with the arbitrary privacy loss of data owners, which may not be valid under strict privacy regulations such as GDPR and the increasing privacy concerns of data owners. In this paper, we study how to empower data owners with the control of privacy loss in regard to data trading. First, we propose a modularized framework for trading personal data that enables each data owner to bound her personalized privacy loss from data trading. Second, since bounded privacy losses indicate bounded utilities of query answers, we propose a reasonable relaxation of arbitrage freeness named partial arbitrage freeness , i.e., the guarantee of arbitrage-free pricing only for a limited range of utilities, which provides more possibilities for our market design. Third, to avoid arbitrage behaviors, we propose a general method for ensuring arbitrage freeness under personalized differential privacy. Fourth, to make full use of data owners’ personalized privacy loss bounds, we propose online privacy budget allocation techniques to dynamically allocate privacy losses for queries under arbitrage freeness. Finally, we conduct experiments to verify the effectiveness of our proposed trading protocols.
Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning

Transformer-based language model approaches to automated story generation currently provide state-of-the-art results. However, they still suffer from plot incoherence when generating narratives over time, and critically lack basic commonsense reasoning. Furthermore, existing methods generally focus only on single-character stories, or fail to track characters at all. To improve the coherence of generated narratives and to expand the scope of character-centric narrative generation, we introduce Commonsense-inference Augmented neural StoryTelling (CAST), a framework for introducing commonsense reasoning into the generation process with the option to model the interaction between multiple characters. We find that our CAST method produces significantly more coherent, on-topic, enjoyable and fluent stories than existing models in both the single-character and two-character settings in three storytelling domains.
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning

Finding the optimal hyperparameters of a model can be cast as a bilevel optimization problem, typically solved using zero-order techniques. In this work we study first-order methods when the inner optimization problem is convex but non-smooth. We show that the forward-mode differentiation of proximal gradient descent and proximal coordinate descent yield sequences of Jacobians converging toward the exact Jacobian. Using implicit differentiation, we show it is possible to leverage the non-smoothness of the inner problem to speed up the computation. Finally, we provide a bound on the error made on the hypergradient when the inner optimization problem is solved approximately. Results on regression and classification problems reveal computational benefits for hyperparameter optimization, especially when multiple hyperparameters are required.
Phase-Dependence of Near-Resonant Two-Photon Excitation

Measurements of the phase of two-photon matrix elements are presented for near-resonant twocolor ionization of helium. A tunable, narrow-bandwidth, near-infrared (NIR) laser source is used for extreme ultra-violet (XUV) high-harmonic generation (HHG). The 15th harmonic of the laser is used within (1+1’) XUV+NIR two-photon ionization, and tuned in and out of resonance with members of the 1snp P1 (n = 3, 4, 5) Rydberg series. Rapid changes in the phase of the two-photon matrix elements around resonances and at the mid-way point between two resonances are observed, encoding the relative importance of resonant and near-resonant two-color ionization. Similar effects are observed for (1+2’) XUV+NIR three-photon ionization. The experimental results are compared to a perturbative model and numerical solution of the time-dependent Schrödinger equation (TDSE) in the single active electron (SAE) approximation.
Sketches image analysis: Web image search engine usingLSH index and DNN InceptionV3

The adoption of an appropriate approximate similarity search method is an essential prereq-uisite for developing a fast and efficient CBIR system, especially when dealing with large amount ofdata. In this study we implement a web image search engine on top of a Locality Sensitive Hashing(LSH) Index to allow fast similarity search on deep features. Specifically, we exploit transfer learningfor deep features extraction from images. Firstly, we adopt InceptionV3 pretrained on ImageNet asfeatures extractor, secondly, we try out several CNNs built on top of InceptionV3 as convolutionalbase fine-tuned on our dataset. In both of the previous cases we index the features extracted within ourLSH index implementation so as to compare the retrieval performances with and without fine-tuning.In our approach we try out two different LSH implementations: the first one working with real numberfeature vectors and the second one with the binary transposed version of those vectors. Interestingly,we obtain the best performances when using the binary LSH, reaching almost the same result, in termsof mean average precision, obtained by performing sequential scan of the features, thus avoiding thebias introduced by the LSH index. Lastly, we carry out a performance analysis class by class in terms ofrecall againstmAPhighlighting, as expected, a strong positive correlation between the two.
Automatic Learning to Detect Concept Drift

Many methods have been proposed to detect concept drift, i.e., the change in the distribution of streaming data, due to concept drift causes a decrease in the prediction accuracy of algorithms. However, the most of current detection methods are based on the assessment of the degree of change in the data distribution, cannot identify the type of concept drift. In this paper, we propose Active Drift Detection with Meta learning (Meta-ADD), a novel framework that learns to classify concept drift by tracking the changed pattern of error rates. Specifically, in the training phase, we extract meta-features based on the error rates of various concept drift, after which a meta-detector is developed via a prototypical neural network by representing various concept drift classes as corresponding prototypes. In the detection phase, the learned meta-detector is fine-tuned to adapt to the corresponding data stream via stream-based active learning. Hence, Meta-ADD uses machine learning to learn to detect concept drifts and identify their types automatically, which can directly support drift understand. The experiment results verify the effectiveness of Meta-ADD.
Subgame-perfect equilibrium strategies in state-constrained recursive stochastic control problems

We study time-inconsistent recursive stochastic control problems. Since, for this class of problems, classical optimal controls may fail to exist or to be relevant in practice, we focus on sub-game perfect equilibrium policies. The approach followed in our work relies on the stochastic maximum principle and, indeed, we adapt the classical spike variation technique to obtain a characterisation of equilibrium strategies in terms of a generalised Hamiltonian function of second-order defined through a flow of pairs of BSDEs. We deal then time-inconsistent recursive stochastic control problems under a state constraint, defined by means of an additional recursive utility, by adapting the Ekeland variational principle to this more tricky situation. The theoretical results are applied to the financial field on finite horizon investment-consumption policies with non-exponential actualisation. The possibility that the constraint is a risk constraint is covered.
Discourse Relation Embeddings: Representing the Relations between Discourse Segments in Social Media

Discourse relations are typically modeled as a discrete class that characterizes the relation between segments of text (e.g. causal explanations, expansions). However, such predefined discrete classes limit the universe of potential relationships and their nuanced differences. Adding higher-level semantic structure to contextual word embeddings, we propose representing discourse relations as points in high dimensional continuous space. However, unlike words, discourse relations often have no surface form (relations are in between two segments, often with no word or phrase in that gap) which presents a challenge for existing embedding techniques. We present a novel method for automatically creating discourse relation embeddings (DiscRE), addressing the embedding challenge through a weakly supervised, multitask approach to learn diverse and nuanced relations in social media. Results show DiscRE representations obtain the best performance on Twitter discourse relation classification (macro F1=0.76), social media causality prediction (from F1=0.79 to 0.81), and perform beyond modern sentence and word transformers at traditional discourse relation classification, capturing novel nuanced relations (e.g. relations at the intersection of causal explanations and counterfactuals).
Quantifying and Maximizing the Benefits of Back-End Noise Adaption on Attention-Based Speech Recognition Models

This work analyzes how attention-based Bidirectional Long Short-Term Memory (BLSTM) models adapt to noise-augmented speech. We identify crucial components for noise adaptation in BLSTM models by freezing model components during fine-tuning. We first freeze larger model subnetworks and then pursue a fine-grained freezing approach in the encoder after identifying its importance for noise adaptation. The first encoder layer is shown to be crucial for noise adaptation, and the weights are shown to be more important than the other layers. Appreciable accuracy benefits are identified when fine-tuning on a target noisy environment from a model pretrained with noisy speech relative to fine-tuning from a model pretrained with only clean speech when tested on the target noisy environment. For this analysis, we produce our own dataset augmentation tool and it is open-sourced to encourage future efforts in exploring noise adaptation in ASR.
Orienting point clouds with dipole propagation

Establishing a consistent normal orientation for point clouds is a notoriously difficult problem in geometry processing, requiring attention to both local and global shape characteristics. The normal direction of a point is a function of the local surface neighborhood; yet, point clouds do not disclose the full underlying surface structure. Even assuming known geodesic proximity, calculating a consistent normal orientation requires the global context. In this work, we introduce a novel approach for establishing a globally consistent normal orientation for point clouds. Our solution separates the local and global components into two different sub-problems. In the local phase, we train a neural network to learn a coherent normal direction per patch (i.e., consistently oriented normals within a single patch). In the global phase, we propagate the orientation across all coherent patches using a dipole propagation. Our dipole propagation decides to orient each patch using the electric field defined by all previously orientated patches. This gives rise to a global propagation that is stable, as well as being robust to nearby surfaces, holes, sharp features and noise.
Fusing multimodal neuroimaging data with a variational autoencoder

Neuroimaging studies often collect multimodal data. These modalities contain both shared and mutually exclusive information about the brain. This work aims to find a scalable and interpretable method to fuse the information of multiple neuroimaging modalities into a lower-dimensional latent space using a variational autoencoder (VAE). To assess whether the encoder-decoder pair retains meaningful information, this work evaluates the representations using a schizophrenia classification task. The linear classifier, trained on the representations obtained through dimensionality reduction, achieves an area under the curve of the receiver operating characteristic (ROC-AUC) of 0.8609. Thus, training on a multimodal dataset with functional brain networks and a structural magnetic resonance imaging (sMRI) scan, leads to dimensionality reduction that retains meaningful information. The proposed dimensionality reduction outperforms both early and late fusion principal component analysis on the classification task.Clinical relevance — This work examines the interplay between neuroimaging modalities and their relation to mental disorders. This allows for more complex and rigorous analysis of multimodal neuroimaging data throughout clinical settings.
Training Quantized Neural Networks to Global Optimality via Semidefinite Programming

Neural networks (NNs) have been extremely successful across many tasks in machine learning. Quantization of NN weights has become an important topic due to its impact on their energy efficiency, inference time and deployment on hardware. Although post-training quantization is well-studied, training optimal quantized NNs involves combinatorial non-convex optimization problems which appear intractable. In this work, we introduce a convex optimization strategy to train quantized NNs with polynomial activations. Our method leverages hidden convexity in twolayer neural networks from the recent literature, semidefinite lifting, and Grothendieck’s identity. Surprisingly, we show that certain quantized NN problems can be solved to global optimality provably in polynomial time in all relevant parameters via tight semidefinite relaxations. We present numerical examples to illustrate the effectiveness of our method.
Separating layered treewidth and row treewidth

Layered treewidth and row treewidth are recently introduced graph parameters that have been key ingredients in the solution of several well-known open problems. It follows from the definitions that the layered treewidth of a graph is at most its row treewidth plus 1. Moreover, a minor-closed class has bounded layered treewidth if and only if it has bounded row treewidth. However, it has been open whether row treewidth is bounded by a function of layered treewidth. This paper answers this question in the negative. In particular, for every integer $k$ we describe a graph with layered treewidth 1 and row treewidth $k$. We also prove an analogous result for layered pathwidth and row pathwidth.
Operator splitting for adaptive radiation therapy with nonlinear health dynamics

ABSTRACT We present an optimization-based approach to radiation treatment planning over time. Our approach formulates treatment planning as an optimal control problem with nonlinear patient health dynamics derived from the standard linear-quadratic cell survival model. As the formulation is nonconvex, we propose a method for obtaining an approximate solution by solving a sequence of convex optimization problems. This method is fast, efficient, and robust to model error, adapting readily to changes in the patient's health between treatment sessions. Moreover, we show that it can be combined with the operator splitting method ADMM to produce an algorithm that is highly scalable and can handle large clinical cases. We introduce an open-source Python implementation of our algorithm, AdaRad, and demonstrate its performance on several examples.
Searches for supersymmetry in CMS

The results from the CMS search for supersymmetric particles based on Run-2 data recorded at a center-of-mass energy of 13 TeV are summarized. Strong and weak production of SUSY scenarios are considered. Results presented include the searches for squarks and gluinos, direct production of charginos, neutralinos and sleptons. These searches involve final state objects comprising jets, missing transverse momentum, electrons or muons, taus or photons, as well as long-lived particles. The data in these searches are found to be consistent with Standard Model predictions and no significant excess is observed. Upper limits have been set on the masses of supersymmetric particles from a variety of search channels.
Quantifying the Tradeoff Between Cybersecurity and Location Privacy

When it comes to location-based services (LBS), user privacy protection can be in conflict with security of both users and trips. While LBS providers could adopt privacy preservation mechanisms to obfuscate customer data, the accuracy of vehicle location data and trajectories is crucial for detecting anomalies, especially when machine learning methods are adopted by LBS. This paper aims to tackle this dilemma by evaluating the tradeoff between location privacy and security in LBS. In particular, we investigate the impact of applying location data privacy-preservation techniques on the performance of two detectors, namely a Density-based spatial clustering of applications with noise (DBSCAN), and a Recurrent Neural Network (RNN). The experimental results suggest that, by applying privacy on location data, DBSCAN is more sensitive to Laplace noise than RNN, although they achieve similar detection accuracy on the trip data without privacy preservation. Further experiments reveal that DBSCAN is not scalable to large size datasets containing millions of trips, because of the large number of computations needed for clustering trips. On the other hand, DBSCAN only requires less than 10 percent of the data used by RNN to achieve similar performance when applied to vehicle data without obfuscation, demonstrating that clustering-based methods can be easily applied to small datasets. Based on the results, we recommend usage scenarios of the two types of trajectory anomaly detectors when applying privacy preservation, by taking into account customers' need for privacy, the size of the available vehicle trip data, and real-time constraints of the LBS application.
Solo: Enforcing Differential Privacy Without Fancy Types

All current approaches for statically enforcing diﬀerential privacy in higher order languages make use of either linear or relational reﬁnement types. A barrier to adoption for these approaches is the lack of support for expressing these “fancy types” in mainstream programming languages. For example, no mainstream language supports relational reﬁnement types, and although Rust and modern versions of Haskell both employ some linear typing techniques, they are inadequate for embedding enforcement of diﬀerential privacy, which requires “full” linear types a la Girard/Reynolds. We propose a new type system that enforces diﬀerential privacy, avoids the use of linear and relational reﬁnement types, and can be easily embedded in mainstream richly typed programming languages such as Scala, OCaml and Haskell. We demonstrate such an embedding in Haskell, demonstrate its expressiveness on case studies, and prove that our type-based enforcement of diﬀerential privacy is sound.
Tangent Vector Variational Quantum Eigensolver: A Robust Variational Quantum Eigensolver against the inaccuracy of derivative

Observing rapid developments of both the number of qubits and quantum volume, especially with recent advances in ion-trap quantum computers, it is no doubt that Fault-Tolerant-Quantum-Computer (FTQC) will be realized in the near future. Since FTQC requires 10,000 physical qubits for every 100 logical ones, it will be used as the first large-scale Noisy-Intermediate-Scale-Quantum (NISQ) . The Variational Quantum Eigensolver (VQE) method will be used until large-scale FTQC with more than 100 logical qubits are realized. Therefore, the VQE method must be improved with respect to both accuracy and time to solution using large resource of the near FTQC . In this paper, we propose Tangent-Vector VQE (TVVQE) method to manage these issues. The method optimizes the norm of tangent vector of trial energy. We demonstrate the calculation of energy levels on Hydrogen molecule, Hubbard model, and Lithium Hydride molecule and reveal that TVVQE has a potential to calculate ground and excited energy levels more accurately than other VQE methods.
WiFi Fingerprint Clustering for Urban Mobility Analysis

In this paper, we present an unsupervised learning approach to identify the user points of interest (POI) by exploiting WiFi measurements from smartphone application data. Due to the lack of GPS positioning accuracy in indoor, sheltered, and high rise building environments, we rely on widely available WiFi access points (AP) in contemporary urban areas to accurately identify POI and mobility patterns, by comparing the similarity in the WiFi measurements. We propose a system architecture to scan the surrounding WiFi AP, and perform unsupervised learning to demonstrate that it is possible to identify three major insights, namely the indoor POI within a building, neighborhood activity, and micro mobility of the users. Our results show that it is possible to identify the aforementioned insights, with the fusion of WiFi and GPS, which are not possible to identify by only using GPS.
End-to-End Learning for Uplink MU-SIMO Joint Transmitter and Non-Coherent Receiver Design in Fading Channels

In this paper, a novel end-to-end learning approach, namely JTRD-Net, is proposed for uplink multiuser single-input multiple-output (MU-SIMO) joint transmitter and non-coherent receiver design (JTRD) in fading channels. The basic idea lies in the use of artificial neural networks (ANNs) to replace traditional communication modules at both transmitter and receiver sides. More specifically, the transmitter side is modeled as a group of parallel linear layers, which are responsible for multiuser waveform design; and the non-coherent receiver is formed by a deep feed-forward neural network (DFNN) so as to provide multiuser detection (MUD) capabilities. The entire JTRD-Net can be trained from end to end to adapt to channel statistics through deep learning. After training, JTRD-Net can work efficiently in a non-coherent manner without requiring any levels of channel state information (CSI). In addition to the network architecture, a novel weight-initialization method, namely symmetrical-interval initialization, is proposed for JTRD-Net. It is shown that the symmetrical-interval initialization outperforms the conventional method (e.g. Xavier initialization) in terms of well-balanced convergence-rate among users. Simulation results show that the proposed JTRD-Net approach takes significant advantages in terms of reliability and scalability over baseline schemes on both i.i. d. complex Gaussian channels and spatially-correlated channels.