Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Purchase Behavior: Modeling and Optimization

We study the problem of modeling purchase of multiple items and utilizing it to display optimized recommendations, which is a central problem for online e-commerce platforms. Rich personalized modeling of users and fast computation of optimal products to display given these models can lead to significantly higher revenues and simultaneously enhance the end user experience. We present a parsimonious multi-purchase family of choice models called the BundleMVL-K family, and develop a binary search based iterative strategy that efficiently computes optimized recommendations for this model. This is one of the first attempts at operationalizing multi-purchase class of choice models. We characterize structural properties of the optimal solution, which allow one to decide if a product is part of the optimal assortment in constant time, reducing the size of the instance that needs to be solved computationally. We also establish the hardness of computing optimal recommendation sets. We show one of the first quantitative links between modeling multiple purchase behavior and revenue gains. The efficacy of our modeling and optimization techniques compared to competing solutions is shown using several real world datasets on multiple metrics such as model fitness, expected revenue gains and run-time reductions. The benefit of taking multiple purchases into account is observed to be $6-8\%$ in relative terms for the Ta Feng and UCI shopping datasets when compared to the MNL model for instances with $\sim 1500$ products. Additionally, across $8$ real world datasets, the test log-likelihood fits of our models are on average $17\%$ better in relative terms. The simplicity of our models and the iterative nature of our optimization technique allows practitioners meet stringent computational constraints while increasing their revenues in practical recommendation applications at scale.
Morphology of Banded Anisotropic Structures

The study of the formation of bands of drops along the direction of the vorticity in a concentrated emulsion is presented. That is, the observed anisotropic structures are characterized by regions of high concentration of drops interspaced with regions of about equal width where hardly any particles are present. These particle distributions are a clear departure to what has previously reported for this emulsion. These patterns are induced by the effect of a simple flow in circular parallel plate geometry (CSS450-Linkam), but only after a prolonged time after the inception of the flow. That is, the observation of bands in emulsions only appears after various other phenomena occur in the emulsion. That is, the separation of the flow cell plates is 100 \mu m, and the observation of bands occurs within the interval of shear rates of 3.0 to 5.0 s^{-1}, but only after a long evolution of the initial structure with a series of flows of 0.75 to 2.25 s^{-1}.
Direct inner-shell photoionization of Xe atoms embedded in helium nanodroplets

We present the first measurements of photoelectron spectra of atomic clusters embedded in superfluid helium (He) nanodroplets. Owing to the large absorption cross section of xenon (Xe) around 100 eV photon energy (4d inner-shell ionization), direct dopant photoionization exceeds charge transfer ionization via the ionized He droplets. Despite the predominant creation of Xe2+ and Xe3+ by subsequent Auger decay of free Xe atoms, for Xe embedded in He droplets only singly charged Xek+, k = 1, 2, 3 fragments are observed. Broad Xe+ ion kinetic-energy distributions indicate Coulomb explosion of the ions due to electron transfer to the primary Auger ions from surrounding neutral atoms. The electron spectra correlated with Xe ions emitted from the He nanodroplets contain a low-energy feature and nearly unshifted Xe photolines. These results pave the way to extreme ultraviolet and x-ray photoelectron spectroscopy of clusters and molecular complexes embedded in He nanodroplets.
Geodesic-HOF: 3D Reconstruction Without Cutting Corners

Single-view 3D object reconstruction is a challenging fundamental problem in machine perception, largely due to the morphological diversity of objects in the natural world. In particular, high curvature regions are not always represented accurately by methods trained with common set-based loss functions such as Chamfer Distance, resulting in reconstructions short-circuiting the surface or "cutting corners. " To address this issue, we propose an approach to 3D reconstruction that embeds points on the surface of an object into a higher-dimensional space that captures both the original 3D surface as well as geodesic distances between points on the surface of the object. The precise specification of these additional "lifted" coordinates ultimately yields useful surface information without requiring excessive additional computation during either training or testing, in comparison with existing approaches. Our experiments show that taking advantage of these learned lifted coordinates yields better performance for estimating surface normals and generating surfaces than using point cloud reconstructions alone. Further, we find that this learned geodesic embedding space provides useful information for applications such as unsupervised object decomposition.
Resurrecting Submodularity for Neural Text Generation

Submodularity is a desirable property for a variety of objectives in content selection where the current neural encoder-decoder framework is deficient. We propose diminishing attentions, a class of novel attention mechanisms that exploit the properties of submodular functions. The resulting attention module offers an architecturally simple yet empirically effective method to improve the coverage of neural text generation. We run on three directed text generation tasks with different levels of recovering rate, across two modalities, three neural model architectures and two training strategy variations. The results and analyses demonstrate that our method generalizes well across these settings, produces texts of good quality, outperforms comparable baselines and achieves state-of-the-art performance.
Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization

Localizing persons and recognizing their actions from videos is a challenging task towards high-level video understanding. Recent advances have been achieved by modeling direct pairwise relations between entities. In this paper, we take one step further, not only model direct relations between pairs but also take into account indirect higher-order relations established upon multiple elements. We propose to explicitly model the Actor-Context-Actor Relation, which is the relation between two actors based on their interactions with the context. To this end, we design an Actor-Context-Actor Relation Network (ACAR-Net) which builds upon a novel High-order Relation Reasoning Operator and an Actor-Context Feature Bank to enable indirect relation reasoning for spatio-temporal action localization. Experiments on AVA and UCF101-24 datasets show the advantages of modeling actor-context-actor relations, and visualization of attention maps further verifies that our model is capable of finding relevant higher-order relations to support action detection. Notably, our method ranks first in the AVA-Kinetics action localization task of ActivityNet Challenge 2020, outperforming other entries by a significant margin (+6.71 mAP). The code is available online.1
Nonconvex Optimization Tools for Large-Scale Matrix and Tensor Decomposition with Structured Factors

The proposed article aims at offering a comprehensive tutorial for the computational aspects of structured matrix and tensor factorization. Unlike existing tutorials that mainly focus on {\it algorithmic procedures} for a small set of problems, e.g., nonnegativity or sparsity-constrained factorization, we take a {\it top-down} approach: we start with general optimization theory (e.g., inexact and accelerated block coordinate descent, stochastic optimization, and Gauss-Newton methods) that covers a wide range of factorization problems with diverse constraints and regularization terms of engineering interest. Then, we go `under the hood' to showcase specific algorithm design under these introduced principles. We pay a particular attention to recent algorithmic developments in structured tensor and matrix factorization (e.g., random sketching and adaptive step size based stochastic optimization and structure-exploiting second-order algorithms), which are the state of the art---yet much less touched upon in the literature compared to {\it block coordinate descent} (BCD)-based methods. We expect that the article to have an educational values in the field of structured factorization and hope to stimulate more research in this important and exciting direction.
Explicit Best Arm Identification in Linear Bandits Using No-Regret Learners

We study the problem of best arm identification in linearly parameterised multi-armed bandits. Given a set of feature vectors $\mathcal{X}\subset\mathbb{R}^d,$ a confidence parameter $\delta$ and an unknown vector $\theta^*,$ the goal is to identify $\arg\max_{x\in\mathcal{X}}x^T\theta^*$, with probability at least $1-\delta,$ using noisy measurements of the form $x^T\theta^*. $ For this fixed confidence ($\delta$-PAC) setting, we propose an explicitly implementable and provably order-optimal sample-complexity algorithm to solve this problem. Previous approaches rely on access to minimax optimization oracles. The algorithm, which we call the \textit{Phased Elimination Linear Exploration Game} (PELEG), maintains a high-probability confidence ellipsoid containing $\theta^*$ in each round and uses it to eliminate suboptimal arms in phases. PELEG achieves fast shrinkage of this confidence ellipsoid along the most confusing (i.e., close to, but not optimal) directions by interpreting the problem as a two player zero-sum game, and sequentially converging to its saddle point using low-regret learners to compute players' strategies in each round. We analyze the sample complexity of PELEG and show that it matches, up to order, an instance-dependent lower bound on sample complexity in the linear bandit setting. We also provide numerical results for the proposed algorithm consistent with its theoretical guarantees.
Derivative-free global minimization for a class of multiple minima problems

We prove that the finite-difference based derivative-free descent (FD-DFD) methods have a capability to find the global minima for a class of multiple minima problems. Our main result shows that, for a class of multiple minima objectives that is extended from strongly convex functions with Lipschitz-continuous gradients, the iterates of FD-DFD converge to the global minimizer $x_*$ with the linear convergence $\|x_{k+1}-x_*\|_2^2\leqslant\rho^k \|x_1-x_*\|_2^2$ for a fixed $0 0$. Numerical experiments in various dimensions from $5$ to $500$ demonstrate the benefits of the FD-DFD method.
Lethe: A Tunable Delete-Aware LSM Engine (Updated Version)

Data-intensive applications fueled the evolution of log structured merge (LSM) based key-value engines that employ the out-of-place paradigm to support high ingestion rates with low read/write interference. These benefits, however, come at the cost of treating deletes as a second-class citizen. A delete inserts a tombstone that invalidates older instances of the deleted key. State-of-the-art LSM engines do not provide guarantees as to how fast a tombstone will propagate to persist the deletion. Further, LSM engines only support deletion on the sort key. To delete on another attribute (e.g., timestamp), the entire tree is read and re-written. We highlight that fast persistent deletion without affecting read performance is key to support: (i) streaming systems operating on a window of data, (ii) privacy with latency guarantees on the right-to-be-forgotten, and (iii) en masse cloud deployment of data systems that makes storage a precious resource. To address these challenges, in this paper, we build a new key-value storage engine, Lethe, that uses a very small amount of additional metadata, a set of new delete-aware compaction policies, and a new physical data layout that weaves the sort and the delete key order. We show that Lethe supports any user-defined threshold for the delete persistence latency offering higher read throughput ($1.17-1.4\times$) and lower space amplification ($2.1-9.8\times$), with a modest increase in write amplification (between $4\%$ and $25\%$). In addition, Lethe supports efficient range deletes on a secondary delete key by dropping entire data pages without sacrificing read performance nor employing a costly full tree merge.
Defect Resonances of Truncated Crystal Structures

Defects in the atomic structure of crystalline materials may spawn electronic bound states, known as \emph{defect states}, which decay rapidly away from the defect. Simplified models of defect states typically assume the defect is surrounded on all sides by an infinite perfectly crystalline material. In reality the surrounding structure must be finite, and in certain contexts the structure can be small enough that edge effects are significant. In this work we investigate these edge effects and prove the following result. Suppose that a one-dimensional infinite crystalline material hosting a positive energy defect state is truncated a distance $M$ from the defect. Then, for sufficiently large $M$, there exists a resonance \emph{exponentially close} (in $M$) to the bound state eigenvalue. It follows that the truncated structure hosts a metastable state with an exponentially long lifetime. Our methods allow both the resonance frequency and associated resonant state to be computed to all orders in $e^{-M}$. We expect this result to be of particular interest in the context of photonic crystals, where defect states are used for wave-guiding and structures are relatively small. Finally, under a mild additional assumption we prove that if the defect state has negative energy then the truncated structure hosts a bound state with exponentially-close energy.
Equivariant Systems Theory and Observer Design

A wide range of system models in modern robotics and avionics applications admit natural symmetries. Such systems are termed equivariant and the structure provided by the symmetry is a powerful tool in the design of observers. Significant progress has been made in the last ten years in the design of filters and observers for attitude and pose estimation, tracking of homographies, and velocity aided attitude estimation, by exploiting their inherent Lie-group state-space structure. However, little work has been done for systems on homogeneous spaces, that is systems on manifolds on which a Lie-group acts rather than systems on the Lie-group itself. Recent research in robotic vision has discovered symmetries and equivariant structure on homogeneous spaces for a host of problems including the key problems of visual odometry and visual simultaneous localisation and mapping. These discoveries motivate a deeper look at the structure of equivariant systems on homogeneous spaces. This paper provides a comprehensive development of the foundation theory required to undertake observer and filter design for such systems.
A Geologically Robust Procedure for Observing Rocky Exoplanets to Ensure that Detection of Atmospheric Oxygen Is a Modern Earth-like Biosignature

In the next decades, the astrobiological community will debate whether the first observations of oxygen in an exoplanet’s atmosphere signify life, so it is critical to establish procedures now for collection and interpretation of such data. We present a step-by-step observational strategy for using oxygen as a robust biosignature to prioritize exoplanet targets and design future observations. It is premised on avoiding planets lacking subaerial weathering of continents, which would imply geochemical cycles drastically different from modern Earth’s, precluding use of oxygen as a biosignature. The strategy starts with the most readily obtained data: orbital semimajor axis and stellar luminosity to ensure residence in the habitable zone and stellar X-ray/ultraviolet flux to ensure an exoplanet can retain a secondary (outgassed) atmosphere. Next, high-precision mass and radius information should be combined with high-precision stellar abundance data to constrain the exoplanet’s water content; those incompatible with <0.1 wt% H2O can be deprioritized. Then, reflectance photometry or low-resolution transmission spectroscopy should confirm an optically thin atmosphere. Subsequent long-duration, high-resolution transmission spectroscopy should search for oxygen and ensure that water vapor and CO2 are present only at low (∼102–104 ppm levels). Assuming oxygen is found, attribution to life requires the most difficult step, acquisition of a detailed, multispectral light curve of the exoplanet, to ensure both surface land and water. Exoplanets failing some of these steps might be habitable, even have observable biogenic oxygen, but should be deprioritized because oxygen could not be attributed unambiguously to life, and life therefore would not be detectable on such planets. Finally, we show how to use this scheme for the solar system, the 55 Cnc system, and the TRAPPIST-1 system, in which only the Earth and TRAPPIST-1e successfully pass through our procedure.
Propargylimine in the laboratory and in space: millimetre-wave spectroscopy and its first detection in the ISM

Context. Small imines containing up to three carbon atoms are present in the interstellar medium (ISM). As alkynyl compounds are abundant in this medium, propargylimine (2-propyn-1-imine, HC ≡C−CH =NH) thus represents a promising candidate for a new interstellar detection. Aims. The goal of the present work is to perform a comprehensive laboratory investigation of the rotational spectrum of propargylimine in its ground vibrational state in order to obtain a highly precise set of rest frequencies and to search for it in space. Methods. The rotational spectra of E and Z geometrical isomers of propargylimine have been recorded in the laboratory in the 83–500 GHz frequency interval. The measurements have been performed using a source-modulation millimetre-wave spectrometer equipped with a pyrolysis system for the production of unstable species. High-level ab initio calculations were performed to assist the analysis and to obtain reliable estimates for an extended set of spectroscopic quantities. We searched for propargylimine at 3 mm and 2 mm in the spectral survey of the quiescent giant molecular cloud G+0.693-0.027 located in the central molecular zone, close to the Galactic centre. Results. About 1000 rotational transitions have been recorded for the E- and Z-propargylimine, in the laboratory. These new data have enabled the determination of a very accurate set of spectroscopic parameters including rotational, quartic, and sextic centrifugal distortion constants. The improved spectral data allowed us to perform a successful search for this new imine in the G+0.693-0.027 molecular cloud. Eighteen lines of Z-propargylimine were detected at level >2. 5σ, resulting in a column-density estimate of N = (0.24 ± 0.02) × 1014 cm−2. An upper limit was retrieved for the higher energy E isomer, which was not detected in the data. The fractional abundance (with respect to H2) derived for Z-propargylimine is 1.8 × 10−10. We discuss the possible formation routes by comparing the derived abundance with those measured in the source for possible chemical precursors.
Neutron-Capture Element Abundances in Planetary Nebulae

Nebular spectroscopy is a valuable tool for assessing the production of heavy elements by slow neutron(n)-capture nucleosynthesis (the s-process). Several transitions of n-capture elements have been identified in planetary nebulae (PNe) in the last few years, with the aid of sensitive, high-resolution, near-infrared spectrometers. Combined with optical spectroscopy, the newly discovered near-infrared lines enable more accurate abundance determinations than previously possible, and provide access to elements that had not previously been studied in PNe or their progenitors. Neutron-capture elements have also been detected in PNe in the Sagittarius Dwarf galaxy and in the Magellanic Clouds. In this brief review, I discuss developments in observational studies of s-process enrichments in PNe, with an emphasis on the last five years, and note some open questions and preliminary trends.
Differentially Private Decentralized Learning

Decentralized learning has received great attention for its high efficiency and performance. In such systems, every participant constantly exchanges parameters with each other to train a shared model, which can put him at the risk of data privacy leakage. Differential Privacy (DP) has been adopted to enhance the Stochastic Gradient Descent (SGD) algorithm. However, these approaches mainly focus on single-party learning, or centralized learning in the synchronous mode. In this paper, we design a novel DP-SGD algorithm for decentralized learning systems. The key contribution of our solution is a \emph{topology-aware} optimization strategy, which leverages the unique network characteristics of decentralized systems to effectively reduce the noise scale and improve the model usability. Besides, we design a novel learning protocol for both synchronous and asynchronous decentralized systems by restricting the sensitivity of the SGD algorithm and maximizing the noise reduction. We formally analyze and prove the DP requirement of our proposed algorithms. Experimental evaluations demonstrate that our algorithm achieves a better trade-off between usability and privacy than prior works.
Extracting N-ary Cross-sentence Relations using Constrained Subsequence Kernel

Most of the past work in relation extraction deals with relations occurring within a sentence and having only two entity arguments. We propose a new formulation of the relation extraction task where the relations are more general than intra-sentence relations in the sense that they may span multiple sentences and may have more than two arguments. Moreover, the relations are more specific than corpus-level relations in the sense that their scope is limited only within a document and not valid globally throughout the corpus. We propose a novel sequence representation to characterize instances of such relations. We then explore various classifiers whose features are derived from this sequence representation. For SVM classifier, we design a Constrained Subsequence Kernel which is a variant of Generalized Subsequence Kernel. We evaluate our approach on three datasets across two domains: biomedical and general domain.
Combining Model-Based and Model-Free Methods for Nonlinear Control: A Provably Convergent Policy Gradient Approach

Model-free learning-based control methods have seen great success recently. However, such methods typically suffer from poor sample complexity and limited convergence guarantees. This is in sharp contrast to classical model-based control, which has a rich theory but typically requires strong modeling assumptions. In this paper, we combine the two approaches to achieve the best of both worlds. We consider a dynamical system with both linear and non-linear components and develop a novel approach to use the linear model to define a warm start for a model-free, policy gradient method. We show this hybrid approach outperforms the model-based controller while avoiding the convergence issues associated with model-free approaches via both numerical experiments and theoretical analyses, in which we derive sufficient conditions on the non-linear component such that our approach is guaranteed to converge to the (nearly) global optimal controller.