Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes

The alternating current (AC) chance-constrained optimal power flow (CC-OPF) problem addresses the economic efficiency of electricity generation and delivery under generation uncertainty. The latter is intrinsic to modern power grids because of the high amount of renewables. Despite its academic success, the AC CC-OPF problem is highly nonlinear and computationally demanding, which limits its practical impact. For improving the AC-OPF problem complexity/accuracy trade-off, the paper proposes a fast data-driven setup that uses the sparse and hybrid Gaussian processes (GP) framework to model the power flow equations with input uncertainty. We advocate the efficiency of the proposed approach by a numerical study over multiple IEEE test cases showing up to two times faster and more accurate solutions compared to the state-of-the-art methods.
Fuse: In-Situ Sensemaking Support in the Browser

People spend a significant amount of time trying to make sense of the internet, collecting content from a variety of sources and organizing it to make decisions and achieve their goals. While humans are able to fluidly iterate on collecting and organizing information in their minds, existing tools and approaches introduce significant friction into the process. We introduce Fuse, a browser extension that externalizes users’ working memory by combining low-cost collection with lightweight organization of content in a compact card-based sidebar that is always available. Fuse helps users simultaneously extract key web content and structure it in a lightweight and visual way. We discuss how these affordances help users externalize more of their mental model into the system (e.g., saving, annotating, and structuring items) and support fast reviewing and resumption of task contexts. Our 22-month public deployment and follow-up interviews provide longitudinal insights into the structuring behaviors of real-world users conducting information foraging tasks.
The Emergence of Fads in a Changing World

We study how fads emerge from social learning in a changing environment. We consider a sequential learning model in which rational agents arrive in order, each acting only once, and the underlying unknown state is constantly evolving. Each agent receives a private signal, observes all past actions of others, and chooses an action to match the current state. Since the state changes over time, cascades cannot last forever, and actions ﬂuctuate too. We show that in the long run, actions change more often than the state. This describes many real-life faddish behaviors in which people often change their actions more frequently than what is necessary.
ELMformer: Efficient Raw Image Restoration with a Locally Multiplicative Transformer

In order to get raw images of high quality for downstream Image Signal Process (ISP), in this paper we present an Efficient Locally Multiplicative Transformer called ELMformer for raw image restoration. ELMformer contains two core designs especially for raw images whose primitive attribute is single-channel. The first design is a Bi-directional Fusion Projection (BFP) module, where we consider both the color characteristics of raw images and spatial structure of single-channel. The second one is that we propose a Locally Multiplicative Self-Attention (L-MSA) scheme to effectively deliver information from the local space to relevant parts. ELMformer can efficiently reduce the computational consumption and perform well on raw image restoration tasks. Enhanced by these two core designs, ELMformer achieves the highest performance and keeps the lowest FLOPs on raw denoising and raw deblurring benchmarks compared with state-of-the-arts. Extensive experiments demonstrate the superiority and generalization ability of ELMformer. On SIDD benchmark, our method has even better denoising performance than ISP-based methods which need huge amount of additional sRGB training images.
LawBreaker: An Approach for Specifying Traffic Laws and Fuzzing Autonomous Vehicles

Autonomous driving systems (ADSs) must be tested thoroughly before they can be deployed in autonomous vehicles. High-fidelity simulators allow them to be tested against diverse scenarios, including those that are difficult to recreate in real-world testing grounds. While previous approaches have shown that test cases can be generated automatically, they tend to focus on weak oracles (e.g. reaching the destination without collisions) without assessing whether the journey itself was undertaken safely and satisfied the law. In this work, we propose LawBreaker, an automated framework for testing ADSs against real-world traffic laws, which is designed to be compatible with different scenario description languages. LawBreaker provides a rich driver-oriented specification language for describing traffic laws, and a fuzzing engine that searches for different ways of violating them by maximising specification coverage. To evaluate our approach, we implemented it for Apollo+LGSVL and specified the traffic laws of China. LawBreaker was able to find 14 violations of these laws, including 173 test cases that caused accidents.
Gyrokinetic simulations of turbulence and zonal flows driven by steep profile gradients using a delta-f approach with an evolving background Maxwellian

Long global gyrokinetic turbulence simulations are particularly challenging in situations where the system deviates strongly from its initial state and when fluctuation levels are high, for example, in strong gradient regions. For particle-in-cell simulations, statistical sampling noise accumulation from large marker weights due to large deviations from the control variate of a delta-f scheme makes such simulations often impractical. An adaptive control variate in the form of a flux-surface-averaged Maxwellian with a time-dependent temperature profile is introduced in an attempt to alleviate the former problem. Under simplified collisionless physics, this adaptive delta-f scheme is shown to reduce noise accumulation in the zonal flows and the simulated heat flux in a quasi-steady turbulent state. The method also avoids the collapse of the signal-to-noise ratio, which occurs in the standard non-adaptive scheme, and, therefore, allows one to reach numerically converged results even with lower marker numbers.
On the Lorentz symmetry in conformally reduced quantum gravity

The functional renormalization group treatment of the conform reduced Einstein–Hilbert gravity is extended by following the evolution of the time and space derivatives separately, in order to consider the Lorentz symmetry during the evolution. We found the Reuter fixed point in the ultraviolet region. It is shown that starting from the Gaussian fixed point the Lorentz symmetry breaks down in the vicinity of the Reuter fixed point. Similarly, in the broken phase it also breaks down in the infrared region close to a critical singularity scale. By calculating the anomalous dimension form the kinetic term of the action, we found a new relevant coupling belonging to the curvature.
Structural versus pipeline composition of higher-order functions (experience report)

In teaching students to program with compositions of higher-order functions, we have encountered a sharp distinction in the difficulty of problems as perceived by students. This distinction especially matters as growing numbers of programmers learn about functional programming for data processing. We have made initial progress on identifying this distinction, which appears counter-intuitive to some. We describe the phenomenon, provide some preliminary evidence of the difference in difficulty, and suggest consequences for functional programming pedagogy.
Enhancing UK Computing Education Research Infrastructure with Doctoral Consortia

The research infrastructure of a mature discipline includes many components, one of which is the steady production of PhD graduates. In a young discipline such as computing education, there are relatively few supervisors and students, supervisory norms have not matured and networking between students and with other researchers is challenging. In such a context, the development of a doctoral consortium (DC) is particularly important. This editorial captures the need to provide a regular DC for the UK computing education research community, and directions for development.
Learning models of shape from 3D range data

Constructing shape models of complex articulated and deformable objects is a fundamental capability that enables a variety of applications in computer graphics, biomechanics, arts and entertainment. Current approaches require a significant amount of manual intervention in the model construction process. In this thesis, we present algorithms for learning models of shape that reduce the need for human input. First, we describe an unsupervised algorithm for registering 3D surface scans of an object undergoing significant deformations. Our algorithm does not use markers, nor does it assume prior knowledge about object shape, the dynamics of its deformation, or scan alignment. It is based on a probabilistic model, which minimizes deformation and attempts to preserve geodesic distances and local mesh geometry. Second, we describe an algorithm whose input is a set of meshes corresponding to different configurations of an articulated object. The algorithm automatically recovers a decomposition of the object into approximately rigid parts, the location of the parts in the different object instances, and the articulated object skeleton linking the parts. We also address the problem of learning the space of human body deformations from 3D scans. Unlike existing example-based approaches, our model spans variation in both subject shape and pose. We learn a model of surface deformation as a function of the joint angles of the articulated human skeleton. We also learn a separate model of the variation between different body shapes. We show how to combine these two models to produce realistic deformation for different people in different poses. Finally, we show how our framework can be used for shape completion – generating a complete surface mesh given a limited set of markers specifying the target shape. We use this capability to complete partial mesh geometry and to animate marker motion capture sequences.
Entanglement detection with near-zero cost

Recent research on parallel functional programming has culminated in a provably efficient (in work and space) parallel memory manager, which has been incorporated into the MPL (MaPLe) compiler for Parallel ML and shown to deliver practical efficiency and scalability. The memory manager exploits a property of parallel programs called disentanglement, which restricts computations from accessing concurrently allocated objects. Disentanglement is closely related to race-freedom, but subtly differs from it. Unlike race-freedom, however, no known techniques exists for ensuring disentanglement, leaving the task entirely to the programmer. This is a challenging task, because it requires reasoning about low-level memory operations (e.g., allocations and accesses), which is especially difficult in functional languages. In this paper, we present techniques for detecting entanglement dynamically, while the program is running. We first present a dynamic semantics for a functional language with references that checks for entanglement by consulting parallel and sequential dependency relations in the program. Notably, the semantics requires checks for mutable objects only. We prove the soundness of the dynamic semantics and present several techniques for realizing it efficiently, in particular by pruning away a large number of entanglement checks. We also provide bounds on the work and space of our techniques. We show that the entanglement detection techniques are practical by implementing them in the MPL compiler for Parallel ML. Considering a variety of benchmarks, we present an evaluation and measure time and space overheads of less than 5% on average with up to 72 cores. These results show that entanglement detection has negligible cost and can therefore remain deployed with little or no impact on efficiency, scalability, and space.
Speeding Up Automated Assessment of Programming Exercises

Introductory programming courses around the world use automatic assessment. Automatic assessment for programming code is typically performed via unit tests which require computation time to execute, at times in significant amounts, leading to computation costs and delay in feedback to students. We present a step-based approach for speeding up automated assessment to address the issue, consisting of (1) a cache of past programming exercise submissions and their associated test results to avoid retesting equivalent new submissions; (2) static analysis to detect e.g. infinite loops (heuristically) ; (3) a machine learning model to evaluate programs without running them ; and (4) a traditional set of unit tests. When a student submits code for an exercise, the code is evaluated sequentially through each step, providing feedback to the student at the earliest possible time, reducing the need to run tests. We evaluate the impact of the proposed approach using data collected from an introductory programming course and demonstrate a considerable reduction in the number of exercise submissions that require running the tests (up to 80% of exercises). Using the approach leads to faster feedback in a more sustainable way, and also provides opportunities for precise non-exercise specific feedback in steps (2) and (3).
Using Machine Learning Algorithms for Analysing the Factors That Affect Pupil Engagement and Learning Outcomes in CSE

Student engagement in computing science education (CSE) is crucial for student learning. However, little is known about the effects of all four student engagement dimensions on pupils’ learning outcomes in CSE. Moreover, little is known about measuring behavioural engagement (BE), cognitive engagement (CE), emotional engagement (EE), and social engagement (SE) and how to identify student engagement levels in CS classes in high schools. The study investigates the effects of BE, CE, EE, and SE on pupils’ learning outcomes in high schools’ CS classes and uses machine learning approaches to better understand and optimise the learning process and environments in which it occurs.
Reference counting with frame limited reuse

The recently introduced _Perceus_ algorithm can automatically insert reference count instructions such that the resulting (cycle-free) program is _garbage free_: objects are freed at the very moment they can no longer be referenced. An important extension is reuse analysis. This optimization pairs objects of known size with fresh allocations of the same size and tries to reuse the object in-place at runtime if it happens to be unique. Unfortunately, current implementations of reuse analysis are fragile with respect to small program transformations, or can cause an arbitrary increase in the peak heap usage. We present a novel _drop-guided_ reuse algorithm that is simpler and more robust than previous approaches. Moreover, we generalize the linear resource calculus to precisely characterize garbage-free and frame-limited evaluations. On each function call, a frame-limited evaluation may hold on to memory longer if the size is bounded by a constant factor. Using this framework we show that our drop-guided reuse _is_ frame-limited and find that an implementation of our new reuse approach in Koka can provide significant speedups.
A Comprehensive Artificial Intelligence Based User Intention Assessment Model from Online Reviews and Social Media

ABSTRACT Predictive analytics is being increasingly used to predict various aspects of applications and users. It offers vast opportunities in the growth of the modern era’s business transformation by enabling automated decision-making processes. Being able to determine the intention of users in an automated way is one of the important factors in enabling automated decision-making for applications and businesses using such applications. In this paper, we utilize and build upon the existing works, and propose a comprehensive intention assessment model that detects different possible intents of users by analyzing their text-based reviews on online forums, retail market websites, or on social media. If the information about a product or service experience is present somewhere in a review or post, our technique can accurately segregate different possible purchase intention labels (i.e., positive, negative, and unknown). Our proposed comprehensive model for intention assessment includes extensive data pre-processing, extended feature selection model, utilization of artificial intelligence (machine learning and deep learning) techniques, and customized cost and loss functions. We built a comprehensive testbed and carried out evaluations and comparisons. Our solution demonstrates high accuracy, precision, and F1 score. The proposed solution helps in mining and gaining deeper insights into behavior of consumers and market tendencies and can help in making informed decisions.
Deep Learning Approach for Aspect-Based Sentiment Classification: A Comparative Review

ABSTRACT The emergence of various e-commerce sites has led to an increase in review sites for various services and products. People nowadays easily get information about products and services that will be used through reviews. Here sentiment analysis plays an important role in classifying the polarity of product reviews. However, with a large number of reviews, a sentiment analysis that only gives overall polarity is not sufficient. This will make it difficult to find the reviews of certain aspects (features) of the product. Aspect-based sentiment analysis as fine-grained sentiment analysis is able to provide specific polarity for each aspect contained in a sentence. Various kinds of development methods have been carried out to provide accurate results in aspect-based sentiment analysis. This paper will discuss the various deep learning methods that have been carried out and provide the possibility of research that can be carried out from Aspect-Based Sentiment Analysis.
Physical and feasible: Climate change adaptation in Longyearbyen, Svalbard

Abstract Longyearbyen, Svalbard, has become showcase of Arctic climate change. However, we know little about how these changes are dealt with locally. This article aims to fill this gap by examining climate change impacts and adaptation in a non-Indigenous “community of experts” and sets out to 1) describe observed changes and perceived societal impacts of climate change and 2) discuss adaptation measures and related understandings of adaptation. The research consists of ethnographic fieldwork and interviews with planners, engineers, architects, scientists, construction workers and local politicians. The research finds that climate change impacts the built environment in Longyearbyen, and that there is vast awareness of and concern related to these impacts. There is a substantial knowledge base for adaptation, and a special trust in scientific knowledge, skills and experts. The interview partners consider adaptation as necessary and feasible. Adaptation is understood and implemented as technical responses to physical problems, rooted in a modernist understanding of the environment as separated from humans, who can control it through technical means. This suggests a narrow understanding of adaptation that might fail to address more socially transformative processes.
Derived equivalence for stratified Mukai flop on G(2,4)

Conjecture 1.1. Let (X,B) and (Y, C) be pairs of quasi-projective varieties with Q-divisors such that there exist quasi-finite and surjective morphisms π : U → X and σ : V → Y from smooth varieties, which may be reducible, such that π(KX+B) = KU and σ (KY +C) = KV . Let X → X and Y → Y be natural morphisms from the associated Deligne-Mumford stacks. Assume that there are proper birational morphisms μ : W → X and ν : W → Y from a third variety W such that μ(KX +B) ≤ ν (KY +C). Then there exists a fully faithful exact functor D(Coh(X ))→ D(Coh(Y)).
A neurocognitive model for predicting the fate of individual memories

One goal of cognitive science is to build theories of mental function that predict individual behavior. In this project we focus on predicting, for individual participants, which specific items in a list will be remembered at some point in the future. If you want to know if an individual will remember something, one commonsense approach is to give them a quiz or test such that a correct answer likely indicates later memory for an item. In this project we attempt to predict later memory without explicit assessments by jointly modeling both neural and behavioral data in a computational cognitive model which captures the dynamics of memory acquisition and decay. In this paper, we lay out a novel hierarchical Bayesian approach for combining neural and behavioral data and present results showing how fMRI signals recorded during the study phase of a memory task can improve our ability to predict (in held-out data) which items will be remembered or forgotten 72 hours later.
Integrating dependent evidence: naïve reasoning in the face of complexity

When reasoning about evidence under conditions of uncertainty, one important consideration for accurate updating is the presence (and influence) of dependencies. For instance, if considering whether a patient has a disease, the value of two doctors’ diagnoses indicating the presence of the disease may carry more value if such diagnoses were conducted independently, rather than if, all else being equal, one doctor has seen the other’s diagnosis before making their own. In the present paper, we demonstrate that lay reasoners prefer to avoid dependencies when considering evidential support. However, we additionally illustrate two cases in which dependencies may carry evidential advantage: namely, when information is partial or contradictory. Lay reasoners erroneously remain averse to dependencies even in such cases, reflecting the difficulties inherent to considerations of dependence.
SHEAR AND BULK VISCOSITIES OF A HADRON GAS WITHIN RELAXATION TIME APPROXIMATION AND ITS TEST

We concentrate on the calculation of the shear and bulk viscosities of a hadron gas. They deﬁne its dissipative dynamics and inﬂuence its experimentally measurable elliptic ﬂow. Due to the diﬃculty of this calculation, the relaxation time approximation (RTA) was used in previous works. As those results have approached the realistic ones, there is a need to ﬁnd out how accurate RTA is. For this sake, we calculate the viscosities in RTA, by using the cross sections extracted from the ultrarelativistic quantum molecular dynamics (UrQMD) model and compare them with the same ones calculated without RTA. This allows us to ﬁnd the estimates of errors due to the application of RTA in the calculations of viscosities, which are valid also for other similar models. For instance, in the temperature region 100 MeV (cid:2) T (cid:2) 160 MeV at zero chemical potentials, the shear viscosity becomes smaller up to 1.57 times or up to 1.45 times if the averaged relaxation time is used. This has important consequences for the interpretation of the previously made calculations of viscosities and some other related calculations. Within RTA, we also ﬁnd estimation of the enhancement of the bulk viscosity of a hadron gas because of the nonconservation of particle numbers.