Miracle-dz/UniFAR-FTUs huggingface dataset:
Perseus: A Simple High-Order Regularization Method for Variational Inequalities

This paper settles an open and challenging question pertaining to the design of simple high-order regularization methods for solving smooth and monotone variational inequalities (VIs). A VI involves ﬁnding x ⋆ ∈ X such that h F ( x ) , x − x ⋆ i ≥ 0 for all x ∈ X and we consider the setting where F : R d 7→ R d is smooth with up to ( p − 1) th -order derivatives. For the case of p = 2, Nesterov [2006] extended the cubic regularized Newton’s method to VIs with a global rate of O ( ǫ − 1 ). Monteiro and Svaiter [2012] proposed another second-order method which achieved an improved rate of O ( ǫ − 2 / 3 log(1 /ǫ )), but this method required a nontrivial binary search procedure as an inner loop. High-order methods based on similar binary search procedures have been further developed and shown to achieve a rate of O ( ǫ − 2 / ( p +1) log(1 /ǫ )) [Bullins and Lai, 2020, Lin and Jordan, 2021b, Jiang and Mokhtari, 2022]. However, such search procedure can be computationally prohibitive in practice [Nesterov, 2018] and the problem of ﬁnding a simple high-order regularization methods remains as an open and challenging question in optimization theory. We propose a p th -order method which does not require any binary search scheme and is guaranteed to converge to a weak solution with a global rate of O ( ǫ − 2 / ( p +1) ). A version with restarting attains a global linear and local superlinear convergence rate for smooth and strongly monotone VIs. Further, our method achieves a global rate of O ( ǫ − 2 /p ) for solving smooth and non-monotone VIs satisfying the Minty condition; moreover, the restarted version again attains a global linear and local superlinear convergence rate if the strong Minty condition holds.
Contextualizing Learning: Becoming A Tourism and Hospitality Professional

Over three years, a final semester, undergraduate project-based tourism and hospitality course was successively refined using action research processes. The course required students to integrate and put into practice learnings from all courses. Each offering of the course required students to develop, implement and evaluate a tourism and hospitality project specifically related to their interests. The majority of students developed projects in conjunction with industry partners, a few with hypothetical industry partners, and several with themselves as entrepreneurs. Initially, role-play was variously used to engage students in experiential profession-based learning. By the third year of offering, role-play was replaced by simulation. Students became a community of practice, wherein each week’s face-to-face contact involved a focused round-table departmental meeting. During meetings, there was a strong emphasis on intrapersonal, interpersonal and oral communication skill development. Project assessment involved 5-minute pitches, a written proposal, ethics “test”, an oral presentation and a written report. Students were required to self-evaluate all assessment tasks. In addition, students anonymously voted and provided written feedback on the best project pitch, as well as written feedback on all oral presentations. Student involvement in self and peer assessment aided personal reflection and constructive feedback with respect to professional practice.
Cosmology-informed neural networks to solve the background dynamics of the Universe

The field of machine learning has drawn increasing interest from various other fields due to the success of its methods at solving a plethora of different problems. An application of these has been to train artificial neural networks to solve differential equations without the need of a numerical solver. This particular application offers an alternative to conventional numerical methods, with advantages such as lower memory required to store solutions, parallelization, and, in some cases, a lower overall computational cost than its numerical counterparts. In this work, we train artificial neural networks to represent a bundle of solutions of the differential equations that govern the background dynamics of the Universe for four different models. The models we have chosen are $\Lambda \mathrm{CDM}$, the Chevallier-Polarski-Linder parametric dark energy model, a quintessence model with an exponential potential, and the Hu-Sawicki $f(R)$ model. We use the solutions that the networks provide to perform statistical analyses to estimate the values of each model's parameters with observational data; namely, estimates of the Hubble parameter from cosmic chronometers, type Ia supernovae data from the Pantheon compilation, and measurements from baryon acoustic oscillations. The results we obtain for all models match similar estimations done in the literature using numerical solvers. In addition, we estimate the error of the solutions that the trained networks provide by comparing them with the analytical solution when there is one, or to a high-precision numerical solution when there is not. Through these estimations we find that the error of the solutions is at most $\sim1\%$ in the region of the parameter space that concerns the $95\%$ confidence regions that we find using the data, for all models and all statistical analyses performed in this work.
Explainable multi-class anomaly detection on functional data

In this paper we describe an approach for anomaly detection and its explainability in multivariate functional data. The anomaly detection procedure consists of transforming the series into a vector of features and using an Isolation forest algorithm. The explainable procedure is based on the computation of the SHAP coefficients and on the use of a supervised decision tree. We apply it on simulated data to measure the performance of our method and on real data coming from industry.
Leveraging Application Data Constraints to Optimize Database-Backed Web Applications

Exploiting the relationships among data is a classical query optimization technique. As persistent data is increasingly being created and maintained programmatically, prior work that infers data relationships from data statistics misses an important opportunity. We present Coco, the first tool that identifies data relationships by analyzing database-backed applications. Once identified, Coco leverages the constraints to optimize the application's physical design and query execution. Instead of developing a fixed set of predefined rewriting rules, Coco employs an enumerate-test-verify technique to automatically exploit the discovered data constraints to improve query execution. Each resulting rewrite is provably equivalent to the original query. Using 14 real-world web applications, our experiments show that Coco can discover numerous data constraints from code analysis and improve real-world application performance significantly.
Ontology Reuse: the Real Test of Ontological Design

Reusing ontologies in practice is still very challenging, especially when multiple ontologies are (jointly) involved. Moreover, despite recent advances, the realization of systematic ontology quality assurance remains a difficult problem. In this work, the quality of thirty biomedical ontologies, and the Computer Science Ontology are investigated, from the perspective of a practical use case. Special scrutiny is given to cross-ontology references, which are vital for combining ontologies. Diverse methods to detect potential issues are proposed, including natural language processing and network analysis. Moreover, several suggestions for improving ontologies and their quality assurance processes are presented. It is argued that while the advancing automatic tools for ontology quality assurance are crucial for ontology improvement, they will not solve the problem entirely. It is ontology reuse that is the ultimate method for continuously verifying and improving ontology quality, as well as for guiding its future development. Specifically, multiple issues can be found and fixed primarily through practical and diverse ontology reuse scenarios.
A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior

Learning personalized cancer treatment with machine learning holds great promise to improve cancer patients’ chance of survival. Despite recent advances in machine learning and precision oncology, this approach remains challenging as collecting data in preclini-cal/clinical studies for modeling multiple treatment eﬃcacies is often an expensive, time-consuming process. Moreover, the randomization in treatment allocation proves to be suboptimal since some participants/samples are not receiving the most appropriate treatments during the trial. To address this challenge, we formulate drug screening study as a ‘contextual bandit’ problem, in which an algorithm selects anticancer therapeutics based on contextual information about cancer cell lines while adapting its treatment strategy to maximize treatment response in an “online” fashion. We propose using a novel deep Bayesian bandits framework that uses functional prior to approximate posterior for drug response prediction based on multi-modal information consisting of genomic features and drug structure. We empirically evaluate our method on three large-scale in vitro pharmacogenomic datasets and show that our approach outperforms several benchmarks in identifying optimal treatment for a given cell line.
Rigorous scaling laws for internally heated convection at infinite Prandtl number

We prove rigorous scaling laws for measures of the vertical heat transport enhancement in two models of convection driven by uniform internal heating at infinite Prandtl number. In the first model, a layer of incompressible fluid is bounded by horizontal plates held at the same constant temperature and convection reduces the fraction of the total dimensionless heat input per unit volume and time escaping the layer through the bottom boundary. We prove that this fraction decreases no faster than O( R−2), where R is a “flux” Rayleigh number quantifying the strength of the internal heating relative to diffusion. The second model, instead, has a perfectly insulating bottom boundary, so all heat must escape through the top one. In this case, we prove that the Nusselt number, defined as the ratio of the total-to-conductive vertical heat flux, grows no faster than O( R4). These power-law bounds improve on exponential results available for fluids with finite Prandtl number. The proof combines the background method with a minimum principle for the fluid’s temperature and with Hardy–Rellich inequalities to exploit the link between the vertical velocity and temperature available at infinite Prandtl number.
Semiclassical roots of universality in many-body quantum chaos

Quantum chaos of many-body (MB) systems has been swiftly developing into a vibrant research area at the interface between various disciplines, ranging from statistical physics to condensed matter to quantum information and to cosmology. In quantum systems with a classical limit, advanced semiclassical methods provide the crucial link between classically chaotic dynamics and corresponding universal features at the quantum level. Recently, single-particle (SP) techniques dealing with ergodic wave interference in the usual semiclassical limit ℏ→0 have begun to be transformed into the field theoretical domain of N-particle systems in the analogous semiclassical limit ℏeff=1/N→0 , thereby accounting for genuine MB quantum interference. This semiclassical MB theory provides a unified framework for understanding random-matrix correlations of both SP and MB quantum chaotic systems. Certain braided bundles of classical orbits, and of mean field modes, govern interference, respectively, and provide the key to the foundation of universality. Case studies presented include an MB version of Gutzwiller’s trace formula for the spectral density and out-of-time-order correlators along with brief remarks on where further progress may be forthcoming.
Fusion: Efficient and Secure Inference Resilient to Malicious Server and Curious Clients

—In secure machine learning inference, most current schemes assume that the server is semi-honest and honestly follows the protocol but attempts to infer additional information. However, in real-world scenarios, the server may behave maliciously, e.g., using low-quality model parameters as inputs or deviating from the protocol. Although a few studies investigate the security against the malicious server, they do not consider the veriﬁcation of model accuracy meanwhile preserving the privacy of both server’s model and the client’s inputs. Furthermore, an honest-but-curious client may perform model extraction attacks to steal the server’s model. To address these issues, we propose Fusion , an efﬁcient and privacy-preserving inference scheme that is secure against the malicious server, and a curious client who may perform model extraction attacks. Without leveraging expensive cryptographic techniques, Fusion can be used as a general compiler for converting any semi-honest inference scheme into a maliciously secure one. The experimental results indicate that Fusion is 48.06 × faster and uses 30.90 × less communication than the existing maliciously secure inference protocol which does not achieve the veriﬁcation of the model accuracy. In addition, to show the scalability, we conduct ImageNet-scale inference on the practical ResNet50 model and it costs 8.678 minutes and 10.117 GiB of communication in a WAN setting, which is 1.18 × faster and has 2.64 × less communication than those of semi-honest C RYP TF LOW 2 (ACM CCS 2020) which is efﬁcient and one of the most popular secure inference over ImageNet-scale DNNs.
Overview of solution methods for elliptic partial differential equations on cartesian and hierarchical grids

Elliptic partial diﬀerential equations (PDEs) arise in many areas of computational sciences such as computational ﬂuid dynamics, biophysics, engineering, geophysics and more. They are diﬃcult to solve due to their global nature and sometimes ill-conditioned operators. We review common discretization methods for elliptic PDEs such as the ﬁnite diﬀerence, ﬁnite volume, ﬁnite element, and spectral methods and the linear systems they form. We also provide an overview of classic to modern solution methods for the linear systems formed by these discretization methods. These methods include splitting and Krylov methods, direct methods, and hierarchical methods. Finally, we show applications that would beneﬁt from fast and eﬃcient solvers for elliptic PDEs, including projection methods for the incompressible Navier-Stokes equations and the shallow water wave equations with dispersive corrections.
Integration of NOMA With Reflecting Intelligent Surfaces: A Multi-Cell Optimization With SIC Decoding Errors

Reflecting intelligent surfaces (RIS) has gained significant attention due to its high energy and spectral efficiency in next-generation wireless networks. By using low-cost passive reflecting elements, RIS can smartly reconfigure the signal propagation to extend the wireless communication coverage. On the other hand, non-orthogonal multiple access (NOMA) has been proven as a key air interface technique for supporting massive connections over limited resources. Utilizing the superposition coding and successive interference cancellation (SIC) techniques, NOMA can multiplex multiple users over the same spectrum and time resources by allocating different power levels. This paper proposes a new optimization scheme in a multi-cell RIS-NOMA network to enhance the spectral efficiency under SIC decoding errors. In particular, the power budget of the base station and the transmit power of NOMA users while the passive beamforming of RIS is simultaneously optimized in each cell. Due to objective function and quality of service constraints, the joint problem is formulated as non-convex, which is very complex and challenging to obtain the optimal global solution. To reduce the complexity and make the problem tractable, we first decouple the original problem into two sub-problems for power allocation and passive beamforming. Then, the efficient solution of each sub-problem is obtained in two-steps. In the first-step of For power allocation sub-problem, we transform it to a convex problem by the inner approximation method and then solve it through a standard convex optimization solver in the second-step. Accordingly, in the first-step of passive beamforming, it is transformed into a standard semi-definite programming problem by successive convex approximation and different of convex programming methods. Then, penalty based method is used to achieve a Rank-1 solution for passive beamforming in second-step. Numerical results demonstrate the benefits of the proposed optimization scheme in the multi-cell RIS-NOMA network.
Long Context Question Answering via Supervised Contrastive Learning

Long-context question answering (QA) tasks require reasoning over a long document or multiple documents. Addressing these tasks often benefits from identifying a set of evidence spans (e.g., sentences), which provide supporting evidence for answering the question.In this work, we propose a novel method for equipping long-context QA models with an additional sequence-level objective for better identification of the supporting evidence.We achieve this via an additional contrastive supervision signal in finetuning, where the model is encouraged to explicitly discriminate supporting evidence sentences from negative ones by maximizing question-evidence similarity. The proposed additional loss exhibits consistent improvements on three different strong long-context transformer models, across two challenging question answering benchmarks – HotpotQA and QAsper.
SOPHIE: viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework

Genomic epidemiology is now widely used for viral outbreak investigations. Still, this methodology faces many challenges. First, few methods account for intra-host viral diversity. Second, maximum parsimony principle continues to be employed, even though maximum likelihood or Bayesian models are usually more consistent. Third, many methods utilize case-specific data, such as sampling times or infection exposure intervals. This impedes study of persistent infections in vulnerable groups, where such information has a limited use. Finally, most methods implicitly assume that transmission events are independent, while common source outbreaks violate this assumption. We propose a maximum likelihood framework SOPHIE (SOcial and PHilogenetic Investigation of Epidemics) based on integration of phylogenetic and random graph models. It infers transmission networks from viral phylogenies and expected properties of inter-host social networks modelled as random graphs with given expected degree distributions. SOPHIE is scalable, accounts for intra-host diversity and accurately infers transmissions without case-specific epidemiological data. SOPHIE code is freely available at https://github.com/compbel/SOPHIE/
Hierarchical cue control of drug seeking in the face of cost

Addiction is characterized by intermittent drug seeking despite rising costs. This behavior is heavily influenced by environmental stimuli that signal drug availability and reinforce seeking. We aimed to establish the relationship between three key aspects of human drug use in rats: the intermittent, binge nature of drug intake, the motivational conflict of drug seeking in the face of escalating negative costs, and the ability of different drug cues to interact to modulate relapse. Rats were trained to self administer cocaine on an intermittent access schedule, where brief drug availability states were signaled by a shift in the ambient lighting of the environment and cocaine delivery was signaled by a separate proximal cue. Rats then went through a conflict procedure, where foot shock intensity associated with cocaine seeking was escalated until intake was suppressed. We completed relapse tests where the drug delivery cue was non contingently presented alone, or in the context of dynamic drug availability state transitions. Intermittent access spurred psychomotor sensitization and binge-like cocaine intake. The intensity of binge-like drug taking during training was predictive of later drug seeking despite escalating costs. In relapse tests, the ability of a proximal drug cue to trigger relapse was gated by the presence of a global cue signaling drug-availability state transitions. Our results suggest that the pattern of drug intake plays a role in many features of addiction, including modifying an individual’s willingness to endure high costs associated with drug seeking. Further, our results indicate that drug-related sensory information can be hierarchically organized to exert a dynamic modulating influence on drug-seeking motivation.
Single-cell phenotypic characteristics of phenotypic resistance under recurring antibiotic exposure in Escherichia coli

Despite increasing interest, non-heritable, phenotypic drug resistance, such as tolerance and persistence towards antibiotics, remains less characterized compared to genetic resistance. Non-heritable drug resistance challenges antibiotic treatment and have implications towards heritable resistance evolution. Phenotypically resistant cells have commonly been characterized as growth arrested cells prior and during antibiotic application that quickly resume growth post-application. Here, we add novel combinations of characteristics of phenotypic resistant E. coli single cells—that are of particular interest towards genetically fixed resistance—, and contrast those to susceptible cells of the isoclonal initial population by exposure to different levels of recurrent antibiotic. We find that phenotypic resistant cells reduced their growth rate by about 50% compared to growth rates prior to antibiotic exposure, but cells do not go into near growth arrest. The growth reduction is induced by antibiotic exposure and not caused by a stochastic switch or predetermined state as frequently described. Cells exhibiting constant intermediate growth survived best under antibiotic exposure and, contrary to expectations, selection did not primarily act on fast growing cells. Our findings support diverse modes of phenotypic resistance, and we revealed resistant cell characteristics that supports acclaims of an underappreciated role of phenotypic resistant cells towards resistance evolution.
Cross-link assisted spatial proteomics to map sub-organelle proteomes and membrane protein topology

The specific functions of cellular organelles and sub-compartments depend on their protein content, which can be characterized by spatial proteomics approaches. However, many spatial proteomics methods are limited in their ability to resolve organellar sub-compartments, profile multiple sub-compartments in parallel, and/or characterize membrane-associated proteomes. Here, we develop a cross-link assisted spatial proteomics (CLASP) strategy that addresses these shortcomings. Using human mitochondria as a model system, we show that CLASP can elucidate spatial proteomes of all mitochondrial sub-compartments and provide topological insight into the mitochondrial membrane proteome in a single experiment. Biochemical and imaging-based follow-up studies demonstrate that CLASP allows discovering mitochondria-associated proteins and revising previous protein sub-compartment localization and membrane topology data. This study extends the scope of cross-linking mass spectrometry beyond protein structure and interaction analysis towards spatial proteomics, establishes a method for concomitant profiling of sub-organelle and membrane proteomes, and provides a resource for mitochondrial spatial biology.