Miracle-dz/UniFAR-FTUs huggingface dataset:
A Learned Cache Eviction Framework with Minimal Overhead

Recent work shows the effectiveness of Machine Learning (ML) to reduce cache miss ratios by making better eviction decisions than heuristics. However, state-of-the-art ML caches require many predictions to make an eviction decision, making them impractical for high-throughput caching systems. This paper introduces Machine learning At the Tail (MAT), a framework to build efficient ML-based caching systems by integrating an ML module with a traditional cache system based on a heuristic algorithm. MAT treats the heuristic algorithm as a filter to receive high-quality samples to train an ML model and likely candidate objects for evictions. We evaluate MAT on 8 production workloads, spanning storage, in-memory caching, and CDNs. The simulation experiments show MAT reduces the number of costly ML predictions-per-eviction from 63 to 2, while achieving comparable miss ratios to the state-of-the-art ML cache system. We compare a MAT prototype system with an LRU-based caching system in the same setting and show that they achieve similar request rates.
Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systems

The dazzling promises of AI systems to augment humans in various tasks hinge on whether humans can appropriately rely on them. Recent research has shown that appropriate reliance is the key to achieving complementary team performance in AI-assisted decision making. This paper addresses an under-explored problem of whether the Dunning-Kruger Effect (DKE) among people can hinder their appropriate reliance on AI systems. DKE is a metacognitive bias due to which less-competent individuals overestimate their own skill and performance. Through an empirical study (N = 249), we explored the impact of DKE on human reliance on an AI system, and whether such effects can be mitigated using a tutorial intervention that reveals the fallibility of AI advice, and exploiting logic units-based explanations to improve user understanding of AI advice. We found that participants who overestimate their performance tend to exhibit under-reliance on AI systems, which hinders optimal team performance. Logic units-based explanations did not help users in either improving the calibration of their competence or facilitating appropriate reliance. While the tutorial intervention was highly effective in helping users calibrate their self-assessment and facilitating appropriate reliance among participants with overestimated self-assessment, we found that it can potentially hurt the appropriate reliance of participants with underestimated self-assessment. Our work has broad implications on the design of methods to tackle user cognitive biases while facilitating appropriate reliance on AI systems. Our findings advance the current understanding of the role of self-assessment in shaping trust and reliance in human-AI decision making. This lays out promising future directions for relevant HCI research in this community.
DAG Learning on the Permutahedron

We propose a continuous optimization framework for discovering a latent directed acyclic graph (DAG) from observational data. Our approach optimizes over the polytope of permutation vectors, the so-called Permutahedron, to learn a topological ordering. Edges can be optimized jointly, or learned conditional on the ordering via a non-differentiable subroutine. Compared to existing continuous optimization approaches our formulation has a number of advantages including: 1. validity: optimizes over exact DAGs as opposed to other relaxations optimizing approximate DAGs; 2. modularity: accommodates any edge-optimization procedure, edge structural parameterization, and optimization loss; 3. end-to-end: either alternately iterates between node-ordering and edge-optimization, or optimizes them jointly. We demonstrate, on real-world data problems in protein-signaling and transcriptional network discovery, that our approach lies on the Pareto frontier of two key metrics, the SID and SHD.
Gravity can lead to multiple peaks in the early stages of coffee ring formation

We consider the role of gravity in solute transport when a thin droplet evaporates. Under the physically-relevant assumptions that the contact line is pinned and the solutal P\'{e}clet number, $\mbox{Pe}$ is large, we identify two fundamental regimes that depend on the size of the Bond number, $\mbox{Bo}$. When $\mbox{Bo} = O(1)$, the asymptotic structure of solute transport follows directly from the surface tension-dominated regime, whereby advection drives solute towards the contact line, only to be countered by local diffusive effects, leading to the formation of the famous ``coffee ring". For larger Bond numbers, we identify the distinguished limit in which $\mbox{Bo}^{-1/2}\mbox{Pe}^{2/3} = O(1)$, where the diffusive boundary layer is comparable to the surface tension boundary layer. In each regime, we perform a systematic asymptotic analysis of the solute transport and compare our predictions to numerical simulations of the full model. Our analysis identifies the effect of gravity on the nascent coffee ring, providing quantitative predictions of the size, location and shape of the solute mass profile. Furthermore, we reveal that, for certain values of $\mbox{Bo}$, $\mbox{Pe}$ and the evaporation time, a secondary peak may exist inside the classical coffee ring. We find that the onset of this secondary peak is linked to the change in behaviour of the critical point in the droplet centre. Both the onset and the peak characteristics are shown to be independent of $\mbox{Pe}$, but solutal diffusion may act to remove the secondary peak when the classical coffee ring becomes so large as to subsume it.
Optical Flow Estimation in 360$^\circ$ Videos: Dataset, Model and Application

Optical flow estimation has been a long-lasting and fundamental problem in the computer vision community. However, despite the advances of optical flow estimation in perspective videos, the 360$^\circ$ videos counterpart remains in its infancy, primarily due to the shortage of benchmark datasets and the failure to accommodate the omnidirectional nature of 360$^\circ$ videos. We propose the first perceptually realistic 360$^\circ$ filed-of-view video benchmark dataset, namely FLOW360, with 40 different videos and 4,000 video frames. We then conduct comprehensive characteristic analysis and extensive comparisons with existing datasets, manifesting FLOW360's perceptual realism, uniqueness, and diversity. Moreover, we present a novel Siamese representation Learning framework for Omnidirectional Flow (SLOF) estimation, which is trained in a contrastive manner via a hybrid loss that combines siamese contrastive and optical flow losses. By training the model on random rotations of the input omnidirectional frames, our proposed contrastive scheme accommodates the omnidirectional nature of optical flow estimation in 360$^\circ$ videos, resulting in significantly reduced prediction errors. The learning scheme is further proven to be efficient by expanding our siamese learning scheme and omnidirectional optical flow estimation to the egocentric activity recognition task, where the classification accuracy is boosted up to $\sim$26%. To summarize, we study the optical flow estimation in 360$^\circ$ videos problem from perspectives of the benchmark dataset, learning model, and also practical application. The FLOW360 dataset and code are available at https://siamlof.github. io.
The Cartesian Closed Bicategory of Thin Spans of Groupoids

Recently, there has been growing interest in bicategorical models of programming languages, which are "proof-relevant" in the sense that they keep distinct account of execution traces leading to the same observable outcomes, while assigning a formal meaning to reduction paths as isomorphisms.In this paper we introduce a new model, a bicategory called thin spans of groupoids. Conceptually it is close to Fiore et al.’s generalized species of structures and to Melliès’ homotopy template games, but fundamentally differs as to how replication of resources and the resulting symmetries are treated. Where those models are saturated – the interpretation is inflated by the fact that semantic individuals may carry arbitrary symmetries – our model is thin, drawing inspiration from thin concurrent games: the interpretation of terms carries no symmetries, but semantic individuals satisfy a subtle invariant defined via biorthogonality, which guarantees their invariance under symmetry.We first build the bicategory Thin of thin spans of groupoids. Its objects are certain groupoids with additional structure, its morphisms are spans composed via plain pullback with identities the identity spans, and its 2-cells are span morphisms making the induced triangles commute only up to natural isomorphism. We then equip Thin with a pseudocomonad ! , and finally show that the Kleisli bicategory Thin! is cartesian closed.
Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers

Since the recent advent of regulations for data protection (e.g., the General Data Protection Regulation), there has been increasing demand in deleting information learned from sensitive data in pre-trained models without retraining from scratch. The inherent vulnerability of neural networks towards adversarial attacks and unfairness also calls for a robust method to remove or correct information in an instance-wise fashion, while retaining the predictive performance across remaining data. To this end, we consider instance-wise unlearning, of which the goal is to delete information on a set of instances from a pre-trained model, by either misclassifying each instance away from its original prediction or relabeling the instance to a different label. We also propose two methods that reduce forgetting on the remaining data: 1) utilizing adversarial examples to overcome forgetting at the representation-level and 2) leveraging weight importance metrics to pinpoint network parameters guilty of propagating unwanted information. Both methods only require the pre-trained model and data instances to forget, allowing painless application to real-life settings where the entire training set is unavailable. Through extensive experimentation on various image classification benchmarks, we show that our approach effectively preserves knowledge of remaining data while unlearning given instances in both single-task and continual unlearning scenarios.
Rational curves in a quadric threefold via an $\text{SL}(2,\mathbb{C})$-representation

In this paper, we regard the smooth quadric threefold $Q_{3}$ as Lagrangian Grassmannian and search for fixed rational curves of low degree in $Q_{3}$ with respect to a torus action, which is the maximal subgroup of the special linear group $\text{SL}(2,\mathbb{C})$. Most of them are confirmations of very well-known facts. If the degree of a rational curve is $3$, it is confirmed using the Lagrangian's geometric properties that the moduli space of twisted cubic curves in $Q_3$ has a specific projective bundle structure. From this, we can immediately obtain the cohomology ring of the moduli space.
Synthetic A/B Testing using Synthetic Interventions

Suppose there are N units and D interventions. We aim to learn the average potential outcome associated with every unit-intervention pair, i.e., N × D causal parameters. While running N × D experiments is conceivable, it can be expensive or infeasible. This work introduces an experiment design, synthetic A/B testing, and the synthetic interventions (SI) estimator to recover all N ×D causal parameters while observing each unit under at most two interventions, independent of D. Under a novel tensor factor model for potential outcomes across units, measurements, and interventions, we establish the identification of each parameter. Further, we show the SI estimator is finite-sample consistent and asymptotically normal. Collectively, these also lead to novel results for panel data settings, particularly for synthetic controls. We empirically validate our experiment design using real e-commerce data from a large-scale A/B test.
SparseVLR: A Framework for Veriﬁed Locally Robust Sparse Neural Networks Search

Developing robust sparse models ﬁt for safety-critical and resource-constrained systems such as drones, autonomous robots, etc., has been an issue of longstanding interest. The inability of adversarial training mechanisms to provide a formal robustness guarantee kindles the requirement for veriﬁed local robustness mechanisms. This work aims to compute sparse veriﬁed locally robust networks which exhibit (benign) accuracy and veriﬁed local robustness comparable to their dense counterparts. Towards this objective, we examine several model sparsiﬁcation approaches and present ‘SparseVLR’– a framework to search veriﬁed locally robust sparse networks. We empirically investigated SparseVLR’s efﬁcacy and generalizability by evaluating various benchmark and application-speciﬁc datasets across several models. Above all, we provide an in-depth study and reasoning to unveil the causes for the ascen-dancy of SparseVLR.
The Down Under Doppler and Electricity Experiment (DUNDEE): Overview and Preliminary Results

Abstract DUNDEE (Down Under Doppler and Electricity Experiment) is described. DUNDEE was carded out in the vicinity of Darwin, Northern Territory, Australia, during the wet seasons of November 1988 through February 1989, and November 1989 through February 1990. The general goal of DUNDEE was to investigate the dynamical and electrical properties of tropical mesoscale convective systems and isolated deep convective storms. Darwin, situated at the southern tip of the “maritime continent,” experiences both monsoon and “break” period conditions during the wet season. We discuss the observational network deployed for DUNDEE and present preliminary scientific results. One particularly interesting observation is a large contrast in the frequency of total lightning between break period convection (high lightning rates) and convection in the monsoon trough (low lightning rates). A relationship between CAPE (convective available potentional energy) and total flash rate is presented and discussed to explain this obs.
KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP

This paper focuses on the data augmentation for low-resource NLP tasks where the training set is limited. The existing solutions either leverage task-independent heuristic rules (e.g., Synonym Replacement) or fine-tune general-purpose pre-trained language models (e.g., GPT2) using the limited training instances to produce new synthetic data. Consequently, they have trivial task-specific knowledge and are limited to yielding low-quality synthetic data. To combat this issue, we propose Knowledge Mixture Data Augmentation Model (KnowDA) which is an Seq2Seq language model pre-trained on a mixture of diverse NLP tasks under a novel framework of Knowledge Mixture Training (KoMT). The goal of KoMT is to condense diverse NLP task-specific knowledge into the single KnowDA model (i.e., all-in-one) such that KnowDA could utilize these knowledge to quickly grasp the inherent synthesis law of the target task through limited training instances. Specifically, KoMT reformulates input examples from various heterogeneous NLP tasks into a unified text-to-text format, and employs denoising training objectives in different granularity to learn to reconstruct partial or complete samples. To the best of our knowledge, we are the first attempt to apply 100+ NLP multi-task training for data augmentation. Extensive experiments show that i) the synthetic data produced by KnowDA successfully improves performance of the strong pre-trained language models (i.e., Bert, ALBert and Deberta) by a large margin on the low-resource NLP benchmark FewGLUE, CoNLL'03 and WikiAnn; ii) KnowDA successfully transfers the task knowledge to NLP tasks whose types are seen and unseen in KoMT.
Acceleration of osteogenesis by using barium titanate piezoelectric ceramic as an implant material

As bone has piezoelectric properties, it is expected that activity of bone cells and bone formation can be accelerated by applying piezoelectric ceramics to implants. Since lead ions, included in ordinary piezoelectric ceramics, are harmful, a barium titanate (BTO) ceramic, which is a lead-free piezoelectric ceramic, was used in this study. The purpose of this study was to investigate piezoelectric effects of surface charge of BTO on cell differentiation under dynamic loading in vitro. Rat bone marrow cells seeded on surfaces of BTO ceramics were cultured in culture medium supplemented with dexamethasone, β-glycerophosphate and ascorbic acid while a dynamic load was applied to the BTO ceramics. After 10 days of cultivation, the cell layer and synthesized matrix on the BTO surfaces were scraped off, and then DNA content, alkaline phosphtase (ALP) activity and calcium content were measured, to evaluate osteogenic differentiation. ALP activity on the charged BTO surface was slightly higher than that on the non-charged BTO surface. The amount of calcium on the charged BTO surface was also higher than that on the non-charged BTO surface. These results showed that the electric charged BTO surface accelerated osteogenesis.
The balance between critical thinking and paradigm thinking in the Arctic: Scientific cooperation across theoretical divides

Abstract Kuhnian’ paradigms are a commonly used method of explaining the structure of knowledge production within the social sciences; however, in some ways, they are also in opposition with Popperian’ critical thinking. The opposing approaches surmount to a comparative analytic method – Kuhn advocates undertaking science that is incommensurable, discipline-specific and ideologically and metaphysically fixed in nature; whilst Popper advocates science that is pluralistic, rebellious, interdisciplinary, and ideologically and metaphysically adaptable. This article utilises a systematic literature review of key peer-reviewed articles, book chapters and online articles from respected sources relating to Arctic scientific cooperation during and since the Cold War in order to provide a qualitative data source for comparative theoretical analysis. This article analyses key trends in Arctic environmental decision-making since the Cold War utilising a comparative critical constructivist framework based on epistemological challenges visible in the “Science Wars” between Thomas Kuhn and Karl Popper. By applying two of the foundations of social science (critical thinking and paradigms) to Arctic International Relations and Geopolitics, this article assesses the state of Arctic science cooperation and; the potential for Arctic science cooperation to solve wicked environmental problems. The article concludes that there are power relationships within the epistemological background to environmental decision-making which impacts science cooperation in the Arctic and; current trends in Arctic decision-making further propels the Arctic along a trajectory of environmental degradation.
Overview of Doppler Radar Observations of Precipitating Cloud Systems in Sumatera Island During the First CPEA Campaign

During the latter half of the first CPEA campaign (CPEA-I), X-band Doppler radar (XDR) observation was carried out from 10 April to 9 May 2004 in west Sumatera. In this paper, characteristics of precipitating systems and their relation to the Madden-Julian Oscillation (MJO) are investigated based on the analysis of the XDR data. Significant diurnal variations of precipitation were observed both in the convectively inactive and active phases of MJO, in which the area of intense echoes with reflectivity greater than 40 dBZ attained a maximum around 16 LST, whereas the area of weaker echoes attained a maximum in the evening. However, while the area of weaker echoes showed significant drop in the evening (21 LST) in the inactive phase, such a drop occurred in the early morning in the active phase. During the convectively inactive phase from 10 to 22 April, the precipitation was caused by systems that formed within the observation area. Shallow convective cells appeared in the mountain range around midday, which subsequently developed into deep convective systems. These convective systems then migrated southwestward in many cases, but in some cases they were observed to split and then migrated both southwestward and northeastward. During the convectively active phase from 23 April to 6 May, much of the precipitation occurred associated with the development of pre-existing cloud systems within super cloud clusters (SCCs). While the precipitation area with weak to moderate reflectivity became considerably larger than that in the convectively inactive phase, the area and the top heights of intense echoes were generally suppressed. Environmental factors responsible for these modulations in precipitation are also discussed. The structure and evolution of precipitation systems were further investigated for some selected cases. The precipitation system observed on 17 April showed typical evolution during the convectively inactive phase, whereas the precipitation system on 11 April was observed to split into two systems that migrated southwestward and northeastward. The northeastward migrations of the precipitation systems occurred as a result of successive generation of new convective elements on the northeastern side of old ones. The precipitation systems observed on 23-24 April and 5-6 May occurred during passages of SCCs, and moved eastward associated with intrusions of low-level westerlies. These systems were composed of convective regions around the leading portions of the low-level westerlies, and stratiform regions behind. Analyses of reflectivity and Doppler velocity fields for the latter case suggested that the elevated orography in the west Sumatera temporarily blocked the eastward propagation of the system and the intrusion of the low-level westerly.
COVID-19 information exposure and vaccine hesitancy: The influence of trust in government and vaccine confidence

ABSTRACT As the novel coronavirus (COVID-19) continues to spread, vaccine hesitancy increasingly threats public health worldwide. Health information from traditional, online and social media may influence vaccine hesitancy. The purpose of this study was to explore the influence of exposure to COVID-19 information from various media on vaccine hesitancy, as well as the mediating roles of public trust in government and vaccine confidence. With a sample of 438 online participants (mean age = 30.69 years) responding to an anonymous questionnaire, the study tested a mediation model using bias-corrected bootstrap. The results indicated that exposure to COVID-19 information from online news media and traditional media can reduce vaccine hesitancy indirectly. Whereas a positive and indirect relationship between COVID-19 information exposure on social media and vaccine hesitancy was revealed. Trust in government and vaccine confidence were found to be salient mediators between exposure to COVID-19 information from various media and vaccine hesitancy. Findings from this study offer implications for strategies to address vaccine hesitancy.