Miracle-dz/UniFAR-FTUs huggingface dataset:
Recommendations and Results Organization in Netflix Search

Personalized recommendations on the Netflix Homepage are based on a user's viewing habits and the behavior of similar users. These recommendations, organized for efficient browsing, enable users to discover the next great video to watch and enjoy without additional input or an explicit expression of their intents or goals. The Netflix Search experience, on the other hand, allows users to take active control of discovering new videos by explicitly expressing their entertainment needs via search queries. In this talk, we discuss the importance of producing search results that go beyond traditional keyword-matches to effectively satisfy users' search needs in the Netflix entertainment setting. Motivated by users' various search intents, we highlight the necessity to improve Search by applying approaches that have historically powered the Homepage. Specifically, we discuss our approach to leverage recommendations in the context of Search and to effectively organize search results to provide a product experience that meaningfully adds value for our users.
Log2NS: Enhancing Deep Learning Based Analysis of Logs With Formal to Prevent Survivorship Bias

Analysis of large observational data sets generated by a reactive system is a common challenge in debugging system failures and determining their root cause. One of the major problems is that these observational data suffer from survivorship bias. Examples include analyzing traffic logs from networks, and simulation logs from circuit design. In such applications, users want to detect non-spurious correlations from observational data and obtain actionable insights about them. In this paper, we introduce log to Neuro-symbolic (Log2NS), a framework that combines probabilistic analysis from machine learning (ML) techniques on observational data with certainties derived from symbolic reasoning on an underlying formal model. We apply the proposed framework to network traffic debugging by employing the following steps. To detect patterns in network logs, we first generate global embedding vector representations of entities such as IP addresses, ports, and applications. Next, we represent large log flow entries as clusters that make it easier for the user to visualize and detect interesting scenarios that will be further analyzed. To generalize these patterns, Log2NS provides an ability to query from static logs and correlation engines for positive instances, as well as formal reasoning for negative and unseen instances. By combining the strengths of deep learning and symbolic methods, Log2NS provides a very powerful reasoning and debugging tool for log-based data. Empirical evaluations on a real internal data set demonstrate the capabilities of Log2NS.
Variational Autoencoders: A Harmonic Perspective

In this work we study Variational Autoencoders (VAEs) from the perspective of harmonic analysis. By viewing a VAE's latent space as a Gaussian Space, a variety of measure space, we derive a series of results that show that the encoder variance of a VAE controls the frequency content of the functions parameterised by the VAE encoder and decoder neural networks. In particular we demonstrate that larger encoder variances reduce the high frequency content of these functions. Our analysis allows us to show that increasing this variance effectively induces a soft Lipschitz constraint on the decoder network of a VAE, which is a core contributor to the adversarial robustness of VAEs. We further demonstrate that adding Gaussian noise to the input of a VAE allows us to more finely control the frequency content and the Lipschitz constant of the VAE encoder networks. To support our theoretical analysis we run experiments with VAEs with small fully-connected neural networks and with larger convolutional networks, demonstrating empirically that our theory holds for a variety of neural network architectures.
Vortex creation and control in the Kitaev spin liquid by local bond modulations

The Kitaev model realizes a quantum spin liquid where the spin excitations are fractionalized into itinerant Majorana fermions and localized Z2 vortices. Quantum entanglement between the fractional excitations can be utilized for decoherence-free topological quantum computation. Of particular interest is the anyonic statistics realized by braiding the vortex excitations under a magnetic field. Despite the promising potential, the practical methodology for creation and control of the vortex excitations remains elusive thus far. Here we theoretically propose how one can create and move the vortices in the Kitaev spin liquid. We find that the vortices are induced by a local modulation of the exchange interaction; especially, the local Dzyaloshinskii-Moriya (symmetric offdiagonal) interaction can create vortices most efficiently in the (anti)ferromagnetic Kitaev model, as it effectively flips the sign of the Kitaev interaction. We test this idea by performing the ab initio calculation for a candidate material α-RuCl3 through the manipulation of the ligand positions that breaks the inversion symmetry and induces the local Dzyaloshinskii-Moriya interaction. We also demonstrate a braiding of vortices by adiabatically and successively changing the local bond modulations.
A Multilingual Entity Linking System for Wikipedia with a Machine-in-the-Loop Approach

Hyperlinks constitute the backbone of the Web; they enable user navigation, information discovery, content ranking, and many other crucial services on the Internet. In particular, hyperlinks found within Wikipedia allow the readers to navigate from one page to another to expand their knowledge on a given subject of interest or to discover a new one. However, despite Wikipedia editors' efforts to add and maintain its content, the distribution of links remains sparse in many language editions. This paper introduces a machine-in-the-loop entity linking system that can comply with community guidelines for adding a link and aims at increasing link coverage in new pages and wiki-projects with low-resources. To tackle these challenges, we build a context and language agnostic entity linking model that combines data collected from millions of anchors found across wiki-projects, as well as billions of users' reading sessions. We develop an interactive recommendation interface that proposes candidate links to editors who can confirm, reject, or adapt the recommendation with the overall aim of providing a more accessible editing experience for newcomers through structured tasks. Our system's design choices were made in collaboration with members of several language communities. When the system is implemented as part of Wikipedia, its usage by volunteer editors will help us build a continuous evaluation dataset with active feedback. Our experimental results show that our link recommender can achieve a precision above 80% while ensuring a recall of at least 50% across 6 languages covering different sizes, continents, and families.
Large-Scale Data-Driven Airline Market Influence Maximization

We present a prediction-driven optimization framework to maximize the market influence in the US domestic air passenger transportation market by adjusting flight frequencies. At the lower level, our neural networks consider a wide variety of features, such as classical air carrier performance features and transportation network features, to predict the market influence. On top of the prediction models, we define a budget-constrained flight frequency optimization problem to maximize the market influence over 2,262 routes. This problem falls into the category of the non-linear optimization problem, which cannot be solved exactly by conventional methods. To this end, we present a novel adaptive gradient ascent (AGA) method. Our prediction models show two to eleven times better accuracy in terms of the median root-mean-square error (RMSE) over baselines. In addition, our AGA optimization method runs 690 times faster with a better optimization result (in one of our largest scale experiments) than a greedy algorithm.
Leveraging Mobile Phone Data for Migration Flows

Statistics on migration flows are often derived from census data, which suffer from intrinsic limitations, including costs and infrequent sampling. When censuses are used, there is typically a time gap - up to a few years - between the data collection process and the computation and publication of relevant statistics. This gap is a significant drawback for the analysis of a phenomenon that is continuously and rapidly changing. Alternative data sources, such as surveys and field observations, also suffer from reliability, costs, and scale limitations. The ubiquity of mobile phones enables an accurate and efficient collection of up-to-date data related to migration. Indeed, passively collected data by the mobile network infrastructure via aggregated, pseudonymized Call Detail Records (CDRs) is of great value to understand human migrations. Through the analysis of mobile phone data, we can shed light on the mobility patterns of migrants, detect spontaneous settlements and understand the daily habits, levels of integration, and human connections of such vulnerable social groups. This Chapter discusses the importance of leveraging mobile phone data as an alternative data source to gather precious and previously unavailable insights on various aspects of migration. Also, we highlight pending challenges that would need to be addressed before we can effectively benefit from the availability of mobile phone data to help make better decisions that would ultimately improve millions of people's lives.
PINGAN Omini-Sinitic at SemEval-2021 Task 4:Reading Comprehension of Abstract Meaning

This paper describes the winning system for subtask 2 and the second-placed system for subtask 1 in SemEval 2021 Task 4: ReadingComprehension of Abstract Meaning. We propose to use pre-trianed Electra discriminator to choose the best abstract word from five candidates. An upper attention and auto denoising mechanism is introduced to process the long sequences. The experiment results demonstrate that this contribution greatly facilitatesthe contextual language modeling in reading comprehension task. The ablation study is also conducted to show the validity of our proposed methods.
STRIDE along Spectrahedral Vertices for Solving Large-Scale Rank-One Semidefinite Relaxations

We consider solving high-order semideﬁnite programming (SDP) relaxations of nonconvex polynomial optimization problems (POPs) that admit rank-one optimal solutions. Existing approaches, which solve the SDP independently from the POP, either cannot scale to large problems or suffer from slow convergence due to the typical degeneracy of such SDPs. We propose a new algorithmic framework, called SpecTrahedral pRoximal gradIent Descent along vErtices ( STRIDE ), that blends fast local search on the nonconvex POP with global descent on the convex SDP. Speciﬁcally, STRIDE follows a globally convergent trajectory driven by a proximal gradient method (PGM) for solving the SDP, while simultaneously probing long, but safeguarded , rank-one “strides”, generated by fast nonlinear programming algorithms on the POP, to seek rapid descent. We prove STRIDE has global convergence. To solve the subproblem of projecting a given point onto the feasible set of the SDP, we reformulate the projection step as a continuously differentiable unconstrained optimization and apply a limited-memory BFGS method to achieve both scalability and accuracy. We conduct numerical experiments on solving second-order SDP relaxations arising from two important applications in machine learning and computer vision. STRIDE dominates a diverse set of ﬁve existing SDP solvers and is the only solver that can solve degenerate rank-one SDPs to high accuracy ( e.g., KKT residuals below 1e − 9 ), even in the presence of millions of equality constraints.
Applications of Epileptic Seizures Detection in Neuroimaging Modalities Using Deep Learning Techniques: Methods, Challenges, and Future Works

Epileptic seizures are a type of neurological disorder that affect many people worldwide. Specialist physicians and neurologists take advantage of structural and functional neuroimaging modalities to diagnose various types of epileptic seizures. Neuroimaging modalities assist specialist physicians considerably in analyzing brain tissue and the changes made in it. One method to accelerate the accurate and fast diagnosis of epileptic seizures is to employ computer aided diagnosis systems (CADS) based on artificial intelligence (AI) and functional ∗Corresponding author Email addresses: afshin.shoeibi@gmail.com (Afshin Shoeibi ), navidghassemi@mail.um. ac.ir (Navid Ghassemi), khodatars1marjane@gmail.com (Marjane Khodatars), mahbube.jafari@yahoo.com (Mahboobeh Jafari), parisamoridian@yahoo.com (Parisa Moridian), ralizadehsani@deakin.edu. au (Roohallah Alizadehsani), alikhadem@kntu.ac.ir (Ali Khadem), yinan.kong@mq.edu. au (Yinan Kong), assefzare@gmail.com (Assef Zare), gorriz@ugr.es (Juan Manuel Gorriz), javierrp@ugr.es (Javier Ramı́rez), Maryam.Panahiazar@ucsf.edu (Maryam Panahiazar), abbas.khosravi@deakin.edu. au (Abbas Khosravi), saeid.nahavandi@deakin.edu. au (Saeid Nahavandi) Preprint submitted to Elsevier June 1, 2021 ar X iv :2 10 5. 14 27 8v 1 [ cs . L G ] 2 9 M ay 2 02 1 and structural neuroimaging modalities. AI encompasses a variety of areas, and one of its branches is deep learning (DL). Not long ago, and before the rise of DL algorithms, feature extraction was an essential part of every conventional machine learning method, yet handcrafting features limit these models’ performances to the knowledge of system designers. DL methods resolved this issue entirely by automating the feature extraction and classification process; applications of these methods in many fields of medicine, such as the diagnosis of epileptic seizures, have made notable improvements. In this paper, a comprehensive overview of the types of DL methods exploited to diagnose epileptic seizures from various neuroimaging modalities has been studied. Additionally, rehabilitation systems and cloud computing in epileptic seizures diagnosis applications have been exactly investigated using various modalities.
Geophysical fluid models with simple energy backscatter: explicit flows and unbounded exponential growth

Motivated by numerical schemes for large-scale geophysical flow, we consider the rotating shallow water and Boussinesq equations on the whole space with horizontal kinetic energy backscatter source terms built from negative viscosity and stabilising hyperviscosity with constant parameters. We study the impact of this energy input through various explicit flows, which are simultaneously solving the nonlinear equations and the linear equations that arise upon dropping the transport nonlinearity, i.e. the linearisation in the zero state. These include barotropic, parallel and Kolmogorov flows as well as monochromatic inertia gravity waves. With focus on stable stratification, we find that the backscatter generates numerous solutions of this type that grow exponentially and unboundedly, also with vertical structure. This signifies the possibility of undesired energy concentration into specific modes due to the backscatter. Families of steady-state flows of this type arise as well and superposition principles in the nonlinear equations provide explicit sufficient conditions for instability of some of these. For certain steady barotropic flows of this type, we provide numerical evidence of eigenmodes whose growth rates are proportional to the amplitude factor of the flow. For all other arising steady solutions, we prove that this is not possible.
Predicting Chronic Homelessness: The Importance of Comparing Algorithms using Client Histories

Abstract This paper investigates how to best compare algorithms for predicting chronic homelessness for the purpose of identifying good candidates for housing programs. Predictive methods can rapidly refer potentially chronic shelter users to housing but also sometimes incorrectly identify individuals who will not become chronic (false positives). We use shelter access histories to demonstrate that these false positives are often still good candidates for housing. Using this approach, we compare a simple threshold method for predicting chronic homelessness to the more complex logistic regression and neural network algorithms. While traditional binary classification performance metrics show that the machine learning algorithms perform better than the threshold technique, an examination of the shelter access histories of the cohorts identified by the three algorithms show that they select groups with very similar characteristics. This has important implications for resource constrained not-for-profit organizations since the threshold technique can be implemented using much simpler information technology infrastructure than the machine learning algorithms.
Cloud Collectives: Towards Cloud-aware Collectives forML Workloads with Rank Reordering

ML workloads are becoming increasingly popular in the cloud. Good cloud training performance is contingent on efficient parameter exchange among VMs. We find that Collectives, the widely used distributed communication algorithms, cannot perform optimally out of the box due to the hierarchical topology of datacenter networks and multi-tenancy nature of the cloudenvironment.In this paper, we present Cloud Collectives , a prototype that accelerates collectives by reordering theranks of participating VMs such that the communication pattern dictated by the selected collectives operation best exploits the locality in the network.Collectives is non-intrusive, requires no code changes nor rebuild of an existing application, and runs without support from cloud providers. Our preliminary application of Cloud Collectives on allreduce operations in public clouds results in a speedup of up to 3.7x in multiple microbenchmarks and 1.3x in real-world workloads of distributed training of deep neural networks and gradient boosted decision trees using state-of-the-art frameworks.
Corn Yield Prediction With Ensemble CNN-DNN

We investigate the predictive performance of two novel CNN-DNN machine learning ensemble models in predicting county-level corn yields across the US Corn Belt (12 states). The developed data set is a combination of management, environment, and historical corn yields from 1980 to 2019. Two scenarios for ensemble creation are considered: homogenous and heterogenous ensembles. In homogenous ensembles, the base CNN-DNN models are all the same, but they are generated with a bagging procedure to ensure they exhibit a certain level of diversity. Heterogenous ensembles are created from different base CNN-DNN models which share the same architecture but have different hyperparameters. Three types of ensemble creation methods were used to create several ensembles for either of the scenarios: Basic Ensemble Method (BEM), Generalized Ensemble Method (GEM), and stacked generalized ensembles. Results indicated that both designed ensemble types (heterogenous and homogenous) outperform the ensembles created from five individual ML models (linear regression, LASSO, random forest, XGBoost, and LightGBM). Furthermore, by introducing improvements over the heterogenous ensembles, the homogenous ensembles provide the most accurate yield predictions across US Corn Belt states. This model could make 2019 yield predictions with a root mean square error of 866 kg/ha, equivalent to 8.5% relative root mean square and could successfully explain about 77% of the spatio-temporal variation in the corn grain yields. The significant predictive power of this model can be leveraged for designing a reliable tool for corn yield prediction which will in turn assist agronomic decision makers.
Rank-Based Norms, Capra-Conjugacies and the Rank Function

We consider the space of matrices, with given number of rows and of columns, equipped with the classic trace scalar product. With any matrix (source) norm, we associate a coupling, called Capra, between the space of matrices and itself. Then, we compute the Capra conjugate and biconjugate of the rank function. They are expressed in function of a sequence of rank-based norms, more precisely generalized r-rank and dual r-rank matrix norms associated with the matrix source norm. We deduce a lower bound of the rank function given by a variational formula which involves the generalized r-rank norms. In the case of the Frobenius norm, we show that the rank function is equal to the variational formula.
Towards a Rigorous Statistical Analysis of Empirical Password Datasets

A central challenge in password security is to characterize the attacker's guessing curve i.e., what is the probability that the attacker will crack a random user's password within the first G guesses. A key challenge is that the guessing curve depends on the attacker's guessing strategy and the distribution of user passwords both of which are unknown to us. In this work we aim to follow Kerckhoffs's principal and analyze the performance of an optimal attacker who knows the password distribution. Let λG denote the probability that such an attacker can crack a random user's password within G guesses. We develop several statistically rigorous techniques to upper and lower bound λG given N independent samples from the unknown password distribution ${\mathcal{P}}$. We show that our upper/lower bounds on λG hold with high confidence and we apply our techniques to analyze eight large password datasets. Our empirical analysis shows that even state-of-the-art password cracking models are often significantly less guess efficient than an attacker who can optimize its attack based on its (partial) knowledge of the password distribution. We also apply our statistical tools to re-examine different models of the password distribution i.e., the empirical password distribution and Zipf's Law. We find that the empirical distribution closely matches our upper/lower bounds on λG when the guessing number G is not too large i.e., G ≪ N. However, for larger values of G our empirical analysis rigorously demonstrates that the empirical distribution (resp. Zipf's Law) overestimates the attacker's success rate. We apply our statistical techniques to upper/lower bound the effectiveness of password throttling mechanisms (key-stretching) which are used to reduce the number of attacker guesses G. Finally, if we are willing to make an additional assumption about the way users respond to password restrictions, we can use our statistical techniques to evaluate the effectiveness of various password composition policies which restrict the passwords that users may select.