Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Demystifying Serverless Machine Learning Training

The appeal of serverless (FaaS) has triggered a growing interest on how to use it in data-intensive applications such as ETL, query processing, or machine learning (ML). Several systems exist for training large-scale ML models on top of serverless infrastructures (e.g., AWS Lambda) but with inconclusive results in terms of their performance and relative advantage over "serverful" infrastructures (IaaS). In this paper we present a systematic, comparative study of distributed ML training over FaaS and IaaS. We present a design space covering design choices such as optimization algorithms and synchronization protocols, and implement a platform, LambdaML, that enables a fair comparison between FaaS and IaaS. We present experimental results using LambdaML, and further develop an analytic model to capture cost/performance tradeoffs that must be considered when opting for a serverless infrastructure. Our results indicate that ML training pays off in serverless only for models with efficient (i.e., reduced) communication and that quickly converge. In general, FaaS can be much faster but it is never significantly cheaper than IaaS.
A Graph-Theoretic Condition for Delay Stability of Reaction Systems

Delay mass-action systems provide a model of chemical kinetics when past states influence the current dynamics. In this work, we provide a graph-theoretic condition for delay stability, i.e., linear stability independent of both rate constants and delay parameters. In particular, the result applies when the system has no delay, implying asymptotic stability for the ODE system. The graph-theoretic condition is about cycles in the directed species-reaction graph of the network, which encodes how different species in the system interact.
Posterior Contraction for Deep Gaussian Process Priors

We study posterior contraction rates for a class of deep Gaussian process priors applied to the nonparametric regression problem under a general composition assumption on the regression function. It is shown that the contraction rates can achieve the minimax convergence rate (up to $\log n$ factors), while being adaptive to the underlying structure and smoothness of the target function. The proposed framework extends the Bayesian nonparametric theory for Gaussian process priors.
Wafer-scale, epitaxial growth of single layer hexagonal boron nitride on Pt(111)

Single-layer hexagonal boron nitride is produced on 2 inch Pt(111)/sapphire wafers. The growth with borazine vapor deposition at process temperatures between 1000 and 1300 K is in situ investigated by photoelectron yield measurements. The growth kinetics is slower at higher temperatures and follows a tanh2 law which better fits for higher temperatures. The crystal-quality of hexagonal boron nitride (h-BN)/Pt(111) is inferred from scanning low energy electron diffraction (x-y LEED). The data indicate a strong dependence of the epitaxy on the growth temperature. The dominant structure is an aligned coincidence lattice with 10 h-BN on 9 Pt(1 × 1) unit cells and follows the substrate twinning at the millimeter scale.
Full operator preconditioning and the accuracy of solving linear systems

Unless special conditions apply, the attempt to solve ill-conditioned systems of linear equations with standard numerical methods leads to uncontrollably high numerical error and often slow convergence of an iterative solver. In many cases, such systems arise from the discretization of operator equations with a large number of discrete variables and the ill-conditioning is tackled by means of preconditioning. A key observation in this paper is the sometimes overlooked fact that while traditional preconditioning effectively accelerates convergence of iterative methods, it generally does not improve the accuracy of the solution. Nonetheless, it is sometimes possible to overcome this barrier: accuracy can be improved significantly if the equation is transformed before discretization, a process we refer to as full operator preconditioning (FOP). We highlight that this principle is already used in various areas, including second kind integral equations and Olver–Townsend’s spectral method. We formulate a sufficient condition under which high accuracy can be obtained by FOP. We illustrate this for a fourth order differential equation which is discretized using finite elements.
Fast-forwarding quantum evolution

We investigate the problem of fast-forwarding quantum evolution, whereby the dynamics of certain quantum systems can be simulated with gate complexity that is sublinear in the evolution time. We provide a definition of fast-forwarding that considers the model of quantum computation, the Hamiltonians that induce the evolution, and the properties of the initial states. Our definition accounts for any asymptotic complexity improvement of the general case and we use it to demonstrate fast-forwarding in several quantum systems. In particular, we show that some local spin systems whose Hamiltonians can be taken into block diagonal form using an efficient quantum circuit, such as those that are permutation-invariant, can be exponentially fast-forwarded. We also show that certain classes of positive semidefinite local spin systems, also known as frustration-free, can be polynomially fast-forwarded, provided the initial state is supported on a subspace of sufficiently low energies. Last, we show that all quadratic fermionic systems and number-conserving quadratic bosonic systems can be exponentially fast-forwarded in a model where quantum gates are exponentials of specific fermionic or bosonic operators, respectively. Our results extend the classes of physical Hamiltonians that were previously known to be fast-forwarded, while not necessarily requiring methods that diagonalize the Hamiltonians efficiently. We further develop a connection between fast-forwarding and precise energy measurements that also accounts for polynomial improvements.
An elementary property of the quadratic map over Q

It is shown that c = −29/16 is the unique rational number of smallest denominator, and the unique rational number of smallest numerator, for which the map f(x) = x+c has a rational cycle of period 3. Several arithmetic conditions on the set of all such rational numbers c are proved. In [3] the first author found the following parametrization of the quadratic maps over Q which have a rational cycle of period 3. Let f(x, y) = x2 + y and Φ3(x, y) = f3(x, y)− x f(x, y)− x = x + x + (3y + 1)x + (2y + 1)x + (3y + 3y + 1)x + (y + 2y + 1)x+ y + 2y + y + 1, (1) where f3 denotes the iterate of f with respect to the variable x. Then the curve Φ3(x, y) = 0 has the parametrization x = x(t) = t3 + t2 − t+ 7 4(t2 − 1) , y = y(t) = − t 6 − 2t5 + 11t4 + 20t3 + 23t2 − 18t+ 29 16(t2 − 1)2 ; (2) where t is the parameter given by t = 1 + 2(x + x+ y). See also [6] and the references in [4]. In particular, with t = 0 we have (see [3, eq. (2)]) Φ3 ( x,− 16 ) = ( x+ 7 4 )( x+ 1 4 )( x− 5 4 )( x + 1 4 x − 41 16 x+ 23 64 ) . 1 ar X iv :2 10 5. 07 43 5v 2 [ m at h. N T ] 2 1 Ju n 20 21 A quick inspection of [6, Table 2, p. 323], containing some rational numbers c with small denominators, for which the quadratic map fc(x) = x2 + c has a rational cycle with period 3, raises the following questions: • Is 16 the smallest such denominator and is c = − 16 the only such c with denominator 16? • For any possible denominator other than 16, is there more than one such c with that denominator? • Is 29 the smallest numerator of such a c and does 29 occur as the numerator only for c = − 16? We will prove that the answer to all of these questions is yes. Theorem 1. The value c = − 16 is the unique rational number with smallest denominator, for which the quadratic map fc(x) = x 2+c has a rational cycle with period 3. Proof. Let c = ab , with a, b ∈ Z and (a, b) = 1, and assume that c = y(t/s), with s, t ∈ Z and (s, t) = 1. Then we have the equation −(t−2ts+11ts+20ts+23ts−18ts+29s)b = 16as(t−s). (3) Let h(t) = t − 2t + 11t + 20t + 23t − 18t+ 29 = (t − 2t+ 13)(t − 1) + 16(t + 3t − t+ 1). (4) Putting (4) into (3) gives the equation −b((t+5s)(t−7s)+48s)(t−s)−16bs(t+3ts−ts+s) = 16as(t−s). This equation implies that 16 | b(t+ 5s)(t− 7s)(t − s). If s and t have opposite parity, this shows 16 | b. Otherwise, t ≡ s ≡ 1 (mod 2). In this case we have that t + 3ts− ts + s = (t+ 3s)(t − s) + 4s, which implies that −b((t+ 5s)(t− 7s) + 48s)(t − s)−16bs((t+ 3s)(t − s) + 4s) = 16as(t − s). (5)
Convex optimization for actionable \& plausible counterfactual explanations

Transparency is an essential requirement of machine learning based decision making systems that are deployed in real world. Often, transparency of a given system is achieved by providing explanations of the behavior and predictions of the given system. Counterfactual explanations are a prominent instance of particular intuitive explanations of decision making systems. While a lot of different methods for computing counterfactual explanations exist, only very few work (apart from work from the causality domain) considers feature dependencies as well as plausibility which might limit the set of possible counterfactual explanations. In this work we enhance our previous work on convex modeling for computing counterfactual explanations by a mechanism for ensuring actionability and plausibility of the resulting counterfactual explanations.
Hybrid Schrödinger-Feynman Simulation of Quantum Circuits With Decision Diagrams

Classical simulations of quantum computations are vital for the future development of this emerging technology. To this end, decision diagrams have been proposed as a complementary technique which frequently allows to tackle the inherent exponential complexity of these simulations. In the worst case, however, they still cannot escape this complexity. Additionally, while other techniques make use of all the available processing power, decision diagram-based simulation to date cannot exploit the many processing units of today’s systems. In this work, we show that both problems can be tackled together by employing a hybrid Schrödinger-Feynman scheme for the simulation. More precisely, we show that realizing such a scheme with decision diagrams is indeed possible, we discuss the resulting problems in its realization, and propose solutions how they can be handled. Experimental evaluations confirm that this significantly advances the state of the art in decision diagram-based simulation— allowing to simulate certain hard circuits within minutes that could not be simulated in a whole day thus far.
XAI Method Properties: A (Meta-)study

In the meantime, a wide variety of terminologies, motivations, approaches and evaluation criteria have been developed within the scope of research on explainable artiﬁcial intelligence (XAI). Many taxonomies can be found in the literature, each with a diﬀerent focus, but also showing many points of overlap. In this paper, we summarize the most cited and current taxonomies in a meta-analysis in order to highlight the essential aspects of the state-of-the-art in XAI. We also present and add terminologies as well as concepts from a large number of survey articles on the topic. Last but not least, we illustrate concepts from the higher-level taxonomy with more than 50 example methods, which we categorize accordingly, thus providing a wide-ranging overview of aspects of XAI and paving the way for use case-appropriate as well as context-speciﬁc subsequent research.
Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation

As users often express their preferences with binary behavior data~(implicit feedback), such as clicking items or buying products, implicit feedback based Collaborative Filtering~(CF) models predict the top ranked items a user might like by leveraging implicit user-item interaction data. For each user, the implicit feedback is divided into two sets: an observed item set with limited observed behaviors, and a large unobserved item set that is mixed with negative item behaviors and unknown behaviors. Given any user preference prediction model, researchers either designed ranking based optimization goals or relied on negative item mining techniques for better optimization. Despite the performance gain of these implicit feedback based models, the recommendation results are still far from satisfactory due to the sparsity of the observed item set for each user. To this end, in this paper, we explore the unique characteristics of the implicit feedback and propose Set2setRank framework for recommendation. The optimization criteria of Set2setRank are two folds: First, we design an item to an item set comparison that encourages each observed item from the sampled observed set is ranked higher than any unobserved item from the sampled unobserved set. Second, we model set level comparison that encourages a margin between the distance summarized from the observed item set and the most "hard'' unobserved item from the sampled negative set. Further, an adaptive sampling technique is designed to implement these two goals. We have to note that our proposed framework is model-agnostic and can be easily applied to most recommendation prediction approaches, and is time efficient in practice. Finally, extensive experiments on three real-world datasets demonstrate the superiority of our proposed approach.
An Enterprise Architecture Framework for E-learning

With a trend toward becoming more and more information and communication based, learning services and processes were also evolved. E-learning comprises all forms of electronically supported learning and teaching. The information and communication systems serve as a fundamental role to implement these learning processes. In the typical information-driven organizations, the E-learning is part of a much larger platform for applications and data that extends across the Internet and intranet/extranet. In this respect, E-learning has brought about an inevitable tendency to lunge towards organizing their information based activities in a comprehensive way. Building an Enterprise Architecture (EA) undoubtedly serves as a fundamental concept to accomplish this goal. In this paper, we propose an EA for E-learning information systems. The presented framework helps developers to design and justify completely integrated learning and teaching processes and information systems which results in improved pedagogical success rate.
TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction

The ability to simultaneously track and reconstruct multiple objects moving in the scene is of the utmost importance for robotic tasks such as autonomous navigation and interaction. Virtually all of the previous attempts to map multiple dynamic objects have evolved to store individual objects in separate reconstruction volumes and track the relative pose between them. While simple and intuitive, such formulation does not scale well with respect to the number of objects in the scene and introduces the need for an explicit occlusion handling strategy. In contrast, we propose a map representation that allows maintaining a single volume for the entire scene and all the objects therein. To this end, we introduce a novel multi-object TSDF formulation that can encode multiple object surfaces at any given location in the map. In a multiple dynamic object tracking and reconstruction scenario, our representation allows maintaining accurate reconstruction of surfaces even while they become temporarily occluded by other objects moving in their proximity. We evaluate the proposed TSDF++ formulation on a public synthetic dataset and demonstrate its ability to preserve reconstructions of occluded surfaces when compared to the standard TSDF map representation. Code is available at https://github.com/ethz-asl/tsdf-plusplus.
The effect of algorithmic bias and network structure on coexistence, consensus, and polarization of opinions

Individuals of modern societies share ideas and participate in collective processes within a pervasive, variable, and mostly hidden ecosystem of content filtering technologies that determine what information we see online. Despite the impact of these algorithms on daily life and society, little is known about their effect on information transfer and opinion formation. It is thus unclear to what extent algorithmic bias has a harmful influence on collective decision-making, such as a tendency to polarize debate. Here we introduce a general theoretical framework to systematically link models of opinion dynamics, social network structure, and content filtering. We showcase the flexibility of our framework by exploring a family of binary-state opinion dynamics models where information exchange lies in a spectrum from pairwise to group interactions. All models show an opinion polarization regime driven by algorithmic bias and modular network structure. The role of content filtering is, however, surprisingly nuanced; for pairwise interactions it leads to polarization, while for group interactions it promotes coexistence of opinions. This allows us to pinpoint which social interactions are robust against algorithmic bias, and which ones are susceptible to bias-enhanced opinion polarization. Our framework gives theoretical ground for the development of heuristics to tackle harmful effects of online bias, such as information bottlenecks, echo chambers, and opinion radicalization.
Neighbourhood-guided Feature Reconstruction for Occluded Person Re-Identification

Person images captured by surveillance cameras are often occluded by various obstacles, which lead to defective feature representation and harm person re-identification (Re-ID) performance. To tackle this challenge, we propose to reconstruct the feature representation of occluded parts by fully exploiting the information of its neighborhood in a gallery image set. Specifically, we first introduce a visible part-based feature by body mask for each person image. Then we identify its neighboring samples using the visible features and reconstruct the representation of the full body by an outlier-removable graph neural network with all the neighboring samples as input. Extensive experiments show that the proposed approach obtains significant improvements. In the large-scale Occluded-DukeMTMC benchmark, our approach achieves 64.2% mAP and 67.6% rank-1 accuracy which outperforms the state-of-the-art approaches by large margins, i.e.,20.4% and 12.5%, respectively, indicating the effectiveness of our method on occluded Re-ID problem.
Collaborative Variational Bandwidth Auto-encoder for Recommender Systems

Collaborative filtering has been widely adopted by modern recommender systems to discover user preferences based on their past behaviors. However, the observed interactions for different users are usually unbalanced, which leads to high uncertainty in the collaborative embeddings of users with sparse ratings, thereby severely degenerating the recommendation performance. Consequently, more efforts have been dedicated to the hybrid recommendation strategy where user/item features are utilized as auxiliary information to address the sparsity problem. However, since these features contain rich multimodal patterns and most of them are irrelevant to the recommendation purpose, excessive reliance on these features will make the model difficult to generalize. To address the above two challenges, we propose a collaborative variational bandwidth auto-encoder (vbae) for recommendation. vbae models both the collaborative and the user feature embeddings as Gaussian random variables inferred via deep neural networks to capture non-linear similarities between users based on their ratings and features. Furthermore, vbae establishes an information regulation mechanism by introducing a user-dependent channel variable where the bandwidth is determined by the information already contained in the observed ratings to dynamically control the amount of information allowed to be accessed from the corresponding user features. The user-dependent channel variable alleviates the uncertainty problem when the ratings are sparse while avoids unnecessary dependence of the model on noisy user features simultaneously. Codes and datasets are released at https://github.com/yaochenzhu/vbae.
Framing Causal Questions in Life Course Epidemiology

We describe the principles of counterfactual thinking in providing more precise definitions of causal effects and some of the implications of this work for the way in which causal questions in life course research are framed and evidence evaluated. Terminology is explained and examples of common life course analyses are discussed that focus on the timing of exposures, the mediation of their effects, observed and unobserved confounders, and measurement error. The examples are illustrated by analyses using singleton and twin cohort data. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
The interplay between morphological typology and script on a novel multi-layer Algerian dialect corpus

Recent years have seen a rise in interest for cross-lingual transfer between languages with similar typology, and between languages of various scripts. However, the interplay be-tween morphological typology and difference in script on cross-lingual transfer is a less studied problem. We explore this interplay on cross-lingual transfer for two supervised tasks, namely part-of-speech tagging and sentiment analysis. We introduce a newly annotated corpus of Algerian user-generated comments comprising parallel annotations of Algerian written in Latin, Arabic, and code-switched scripts, as well as annotations for sentiment and topic categories. We perform baseline experiments by ﬁne-tuning multi-lingual language models. We further explore the effect of script vs. morphological typology in cross-lingual transfer by ﬁne-tuning multi-lingual models on languages which are a) morphologically distinct, but use the same script, b) morphologically similar, but use a distinct script, or c) are morphologically similar and use the same script. We ﬁnd there is a delicate relationship between script and typology for part-of-speech, while sentiment analysis is less sensitive.
Computing Curie temperature of two-dimensional ferromagnets in the presence of exchange anisotropy

Sabyasachi Tiwari*, Joren Vanherck*, Maarten L. Van de Put, William G. Vandenberghe, and Bart Sorée 1 Department of Materials Science and Engineering, The University of Texas at Dallas, 800 W Campbell Rd., Richardson, Texas 75080, USA. Department of Materials Engineering, KU Leuven, Kasteelpark Arenberg 44, 3001 Leuven, Belgium. 3 Imec, Kapeldreef 75, 3001 Heverlee, Belgium. Department of Electrical Engineering, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. Department of Physics, Universiteit Antwerpen, Groenenborgerlaan 171, 2020 Antwerp, Belgium. and Email: bart.soree@imec.be
A new mixed finite-element method for the biharmonic problem

Fourth-order differential equations play an important role in many applications in science and engineering. In this paper, we present a three-field mixed finite-element formulation for fourth-order problems, with a focus on the effective treatment of the different boundary conditions that arise naturally in a variational formulation. Our formulation is based on introducing the gradient of the solution as an explicit variable, constrained using a Lagrange multiplier. The essential boundary conditions are enforced weakly, using Nitsche's method where required. As a result, the problem is rewritten as a saddle-point system, requiring analysis of the resulting finite-element discretization and the construction of optimal linear solvers. Here, we discuss the analysis of the well-posedness and accuracy of the finite-element formulation. Moreover, we develop monolithic multigrid solvers for the resulting linear systems. Two and three-dimensional numerical results are presented to demonstrate the accuracy of the discretization and efficiency of the multigrid solvers proposed.