Miracle-dz/UniFAR-FTUs huggingface dataset:
Structured Prediction with Partial Labelling through the Infimum Loss

Annotating datasets is one of the main costs in nowadays supervised learning. The goal of weak supervision is to enable models to learn using only forms of labelling which are cheaper to collect, as partial labelling. This is a type of incomplete annotation where, for each datapoint, supervision is cast as a set of labels containing the real one. The problem of supervised learning with partial labelling has been studied for specific instances such as classification, multi-label, ranking or segmentation, but a general framework is still missing. This paper provides a unified framework based on structured prediction and on the concept of infimum loss to deal with partial labelling over a wide family of learning problems and loss functions. The framework leads naturally to explicit algorithms that can be easily implemented and for which proved statistical consistency and learning rates. Experiments confirm the superiority of the proposed approach over commonly used baselines.
RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework

Real-time traffic accident forecasting is increasingly important for public safety and urban management (e.g., real-time safe route planning and emergency response deployment). Previous works on accident forecasting are often performed on hour levels, utilizing existed neural networks with static region-wise correlations taken into account. However, it is still challenging when the granularity of forecasting step improves as the highly dynamic nature of road network and inherent rareness of accident records in one training sample, which leads to biased results and zero-inflated issue. In this work, we propose a novel framework RiskOracle, to improve the prediction granularity to minute levels. Specifically, we first transform the zero-risk values in labels to fit the training network. Then, we propose the Differential Time-varying Graph neural network (DTGN) to capture the immediate changes of traffic status and dynamic inter-subregion correlations. Furthermore, we adopt multi-task and region selection schemes to highlight citywide most-likely accident subregions, bridging the gap between biased risk values and sporadic accident distribution. Extensive experiments on two real-world datasets demonstrate the effectiveness and scalability of our RiskOracle framework.
Quantifying polaronic effects on the scattering and mobility of charge carriers in lead halide perovskites

The formation of polarons due to the interaction between charge carriers and the crystal lattice has been proposed to have wide-ranging effects on charge carrier dynamics in lead--halide perovskites (LHPs). The hypothesis underlying many of those proposals is that charge carriers are "protected" from scattering by their incorporation into polarons. We test that hypothesis by deriving expressions for the rates of scattering of polarons by polar-optical and acoustic phonons, and ionised impurities, which we compute for electrons in the LHPs MAPbI$_{3}$ , MAPbBr$_{3}$ and CsPbI$_{3}$. We then use the ensemble Monte Carlo method to compute electron-polaron distribution functions which satisfy a Boltzmann equation incorporating the same three scattering mechanisms. By carrying out analogous calculations for band electrons and comparing their results to those for polarons, we conclude that polaron formation impacts charge-carrier scattering rates and mobilities to a limited degree in LHPs, contrary to claims in the recent literature.
ETH-Tight Algorithms for Long Path and Cycle on Unit Disk Graphs

We present an algorithm for the extensively studied Long Path and Long Cycle problems on unit disk graphs that runs in time $2^{O(\sqrt{k})}(n+m)$. Under the Exponential Time Hypothesis, Long Path and Long Cycle on unit disk graphs cannot be solved in time $2^{o(\sqrt{k})}(n+m)^{O(1)}$ [de Berg et al., STOC 2018], hence our algorithm is optimal. Besides the $2^{O(\sqrt{k})}(n+m)^{O(1)}$-time algorithm for the (arguably) much simpler Vertex Cover problem by de Berg et al. [STOC 2018] (which easily follows from the existence of a $2k$-vertex kernel for the problem), this is the only known ETH-optimal fixed-parameter tractable algorithm on UDGs. Previously, Long Path and Long Cycle on unit disk graphs were only known to be solvable in time $2^{O(\sqrt{k}\log k)}(n+m)$. This algorithm involved the introduction of a new type of a tree decomposition, entailing the design of a very tedious dynamic programming procedure. Our algorithm is substantially simpler: we completely avoid the use of this new type of tree decomposition. Instead, we use a marking procedure to reduce the problem to (a weighted version of) itself on a standard tree decomposition of width $O(\sqrt{k})$.
Authentication, Access Control, Privacy, Threats and Trust Management Towards Securing Fog Computing Environments: A Review

Fog computing is an emerging computing paradigm that has come into consideration for the deployment of IoT applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern in the Fog computing environment. Furthermore, mitigating and preventing those security measures is a research issue. Therefore, to provide the necessary security for Fog devices, we need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works needs to be identified and need to be aggregate all issues in Fog security. It needs to be noted that computation devices consist of many ordinary users, and are not managed by any central entity or managing body. Therefore, trust and privacy is also a key challenge to gain market adoption for Fog. To provide the required trust and privacy, we need to also focus on authentication, threats and access control mechanisms as well as techniques in Fog computing. In this paper, we perform a survey and propose a taxonomy, which presents an overview of existing security concerns in the context of the Fog computing paradigm. We discuss the Blockchain-based solutions towards a secure Fog computing environment and presented various research challenges and directions for future research.
A critical analysis of models and experimental evidence of negative capacitance stabilization in a ferroelectric by capacitance matching to an adjacent dielectric layer

We present a thorough analysis of the foundations of models of stabilization of negative capacitance (NC) in a ferroelectric (FE) layer by capacitance matching to a dielectric layer, which claim that the FE is stabilized in a low polarization state without FE polarization switching (non-switching), showing that the concept is fundamentally flawed and unphysical. We also analyze experimental evidence concluding that there is no data supporting the need to invoke such stabilization; rather, conventional models of ferroelectric polarization switching suffice to account for the effects observed. We analyze experimental evidence that at least in some of the model systems for which this effect has been claimed, categorically rule out stabilized non-switching NC. Microscopic measurements recently published as supporting non-switching stabilized NC actually rule them out, since the ferroelectric in a stack sandwiched between two dielectric layers was found to be in a mixed domain state (high polarizations within each domain) rather than in the low polarization state predicted by non-switching stabilized NC models. Nonetheless, since stabilized NC (corresponding to a minimum in free energy) is not physically impossible, it would be useful to move the research efforts to investigating scenarios and systems in which this effect is possible and expected and assess whether they are useful and practical for low power electronics.
Learning Cross-domain Generalizable Features by Representation Disentanglement

Deep learning models exhibit limited generalizability across different domains. Specifically, transferring knowledge from available entangled domain features(source/target domain) and categorical features to new unseen categorical features in a target domain is an interesting and difficult problem that is rarely discussed in the current literature. This problem is essential for many real-world applications such as improving diagnostic classification or prediction in medical imaging. To address this problem, we propose Mutual-Information-based Disentangled Neural Networks (MIDNet) to extract generalizable features that enable transferring knowledge to unseen categorical features in target domains. The proposed MIDNet is developed as a semi-supervised learning paradigm to alleviate the dependency on labeled data. This is important for practical applications where data annotation requires rare expertise as well as intense time and labor. We demonstrate our method on handwritten digits datasets and a fetal ultrasound dataset for image classification tasks. Experiments show that our method outperforms the state-of-the-art and achieve expected performance with sparsely labeled data.
CheXclusion: Fairness gaps in deep chest X-ray classifiers

Machine learning systems have received much attention recently for their ability to achieve expert-level performance on clinical tasks, particularly in medical imaging. Here, we examine the extent to which state-of-the-art deep learning classifiers trained to yield diagnostic labels from X-ray images are biased with respect to protected attributes. We train convolution neural networks to predict 14 diagnostic labels in 3 prominent public chest X-ray datasets: MIMIC-CXR, Chest-Xray8, CheXpert, as well as a multi-site aggregation of all those datasets. We evaluate the TPR disparity - the difference in true positive rates (TPR) - among different protected attributes such as patient sex, age, race, and insurance type as a proxy for socioeconomic status. We demonstrate that TPR disparities exist in the state-of-the-art classifiers in all datasets, for all clinical tasks, and all subgroups. A multi-source dataset corresponds to the smallest disparities, suggesting one way to reduce bias. We find that TPR disparities are not significantly correlated with a subgroup's proportional disease burden. As clinical models move from papers to products, we encourage clinical decision makers to carefully audit for algorithmic disparities prior to deployment. Our supplementary materials can be found at, http://www.marzyehghassemi. com/chexclusion-supp-3/.
Detection and Mitigation of Bias in Ted Talk Ratings

Unbiased data collection is essential to guaranteeing fairness in artificial intelligence models. Implicit bias, a form of behavioral conditioning that leads us to attribute predetermined characteristics to members of certain groups and informs the data collection process. This paper quantifies implicit bias in viewer ratings of TEDTalks, a diverse social platform assessing social and professional performance, in order to present the correlations of different kinds of bias across sensitive attributes. Although the viewer ratings of these videos should purely reflect the speaker's competence and skill, our analysis of the ratings demonstrates the presence of overwhelming and predominant implicit bias with respect to race and gender. In our paper, we present strategies to detect and mitigate bias that are critical to removing unfairness in AI.
On the formation and recurrent shedding of ligaments in droplet aerobreakup

Abstract The breakup of water droplets when exposed to high-speed gas flows is investigated using both high-magnification shadowgraphy experiments as well as fully three-dimensional numerical simulations, which account for viscous as well as capillary effects. After thorough validation of the simulations with respect to the experiments, we elucidate the ligament formation process and the effect of surface tension. By Fourier decomposition of the flow field, we observe the development of specific azimuthal modes, which destabilize the liquid sheet surrounding the droplet. Eventually, the liquid sheet is ruptured, which leads to the formation of ligaments. We further observe the ligament formation and shedding to be a recurrent process. While the first ligament shedding weakly depends on the Weber number, subsequent shedding processes seem to be driven primarily by inertia and the vortex shedding in the wake of the deformed droplet.
Spectral Signatures of Quasar Ages at z ≈ 3

Insight into quasar ages may be obtained from the proximity effect, but so far only in a limited number of bright quasars. Based on ∼2600 Sloan Digital Sky Survey quasar spectra at 2.5 ≤ z ≤ 3.5, a search for spectral voids between Lyα forest lines finds proximity zones over a wide range of radial distances. The majority of zone sizes are less than 5 Mpc, with their numbers decreasing exponentially toward larger distances. After a normalization by luminosities, the zone sizes are distributed with an e-folding scale of 0.64 as compared with the anticipated values. A group of quasars are selected for their large proximity zones of ≳10 Mpc. Their composite spectrum displays strong narrow cores and large equivalent widths in Lyα and other major UV emission lines. If the proximity zones along lines of sight are indicative of quasar ages, these features may be the signatures of old quasars. Another group of quasars are selected as they show no proximity zone and exhibit intrinsic absorption lines at zab > zem. They are likely young quasars and exhibit weaker narrow emission-line components. The significant difference of spectral features between the two groups may reflect an evolution pattern over quasars’ lifetimes.
Novelty-Prepared Few-Shot Classification

Few-shot classification algorithms can alleviate the data scarceness issue, which is vital in many real-world problems, by adopting models pre-trained from abundant data in other domains. However, the pre-training process was commonly unaware of the future adaptation to other concept classes. We disclose that a classically fully trained feature extractor can leave little embedding space for unseen classes, which keeps the model from well-fitting the new classes. In this work, we propose to use a novelty-prepared loss function, called self-compacting softmax loss (SSL), for few-shot classification. The SSL can prevent the full occupancy of the embedding space. Thus the model is more prepared to learn new classes. In experiments on CUB-200-2011 and mini-ImageNet datasets, we show that SSL leads to significant improvement of the state-of-the-art performance. This work may shed some light on considering the model capacity for few-shot classification tasks.
Deep Learning for Content-Based Personalized Viewport Prediction of 360-Degree VR Videos

In this letter, the problem of head movement prediction for virtual reality videos is studied. In the considered model, a deep learning network is introduced to leverage position data as well as video frame content to predict future head movement. For optimizing data input into this neural network, data sample rate, reduced data, and long-period prediction length are also explored for this model. Simulation results show that the proposed approach yields 16.1% improvement in terms of prediction accuracy compared to a baseline approach that relies only on the position data.
Retrofitting Fine Grain Isolation in the Firefox Renderer (Extended Version)

Firefox and other major browsers rely on dozens of third-party libraries to render audio, video, images, and other content. These libraries are a frequent source of vulnerabilities. To mitigate this threat, we are migrating Firefox to an architecture that isolates these libraries in lightweight sandboxes, dramatically reducing the impact of a compromise. Retrofitting isolation can be labor-intensive, very prone to security bugs, and requires critical attention to performance. To help, we developed RLBox, a framework that minimizes the burden of converting Firefox to securely and efficiently use untrusted code. To enable this, RLBox employs static information flow enforcement, and lightweight dynamic checks, expressed directly in the C++ type system. RLBox supports efficient sandboxing through either software-based-fault isolation or multi-core process isolation. Performance overheads are modest and transient, and have only minor impact on page latency. We demonstrate this by sandboxing performance-sensitive image decoding libraries ( libjpeg and libpng ), video decoding libraries ( libtheora and libvpx ), the libvorbis audio decoding library, and the zlib decompression library. RLBox, using a WebAssembly sandbox, has been integrated into production Firefox to sandbox the libGraphite font shaping library.
Learning Generalizable Features Across Domains by Disentangling Representations

Deep learning models exhibit limited generalizability across different domains. Specifically, transferring knowledge from available entangled domain features (source/target domain) and categorical features to new unseen categorical features in a target domain is an interesting and difficult problem that is rarely discussed in the current literature. This problem is essential for many realworld applications such as improving diagnostic classification or prediction in medical imaging. To address this problem, we propose Mutual-Information-based Disentangled Neural Networks (MIDNet) to extract generalizable features that enable transferring knowledge to unseen categorical features in target domains. The proposed MIDNet is developed as a semi-supervised learning paradigm to alleviate the dependency on labeled data. This is important for practical applications where data annotation requires rare expertise as well as intense time and labor. We demonstrate our method on handwritten digits datasets and a fetal ultrasound dataset for image classification tasks. Experiments show that our method outperforms the state-of-the-art and achieve expected performance with sparsely labeled data.
Harmonics Based Representation in Clarinet Tone Quality Evaluation

Music tone quality evaluation is generally performed by experts. It could be subjective and short of consistency and fairness as well as time-consuming. In this paper we present a new method for identifying the clarinet reed quality by evaluating tone quality based on the harmonic structure and energy distribution. We first decouple the quality of reed and clarinet pipe based on the acoustic harmonics, and discover that the reed quality is strongly relevant to the even parts of the harmonics. Then we construct a features set consisting of the even harmonic envelope and the energy distribution of harmonics in spectrum. The annotated clarinet audio data are recorded from 3 levels of performers and the tone quality is classified by machine learning. The results show that our new method for identifying low and medium high tones significantly outperforms previous methods.
Bridging the Gap Between Theory and Practice on Insertion-Intensive Database

With the prevalence of online platforms, today, data is being generated and accessed by users at a very high rate. Besides, applications such as stock trading or high frequency trading require guaranteed low delays for performing an operation on a database. It is consequential to design databases that guarantee data insertion and query at a consistently high rate without introducing any long delay during insertion. In this paper, we propose Nested B-trees (NB-trees), an index that can achieve a consistently high insertion rate on large volumes of data, while providing asymptotically optimal query performance that is very efficient in practice. Nested B-trees support insertions at rates higher than LSM-trees, the state-of-the-art index for insertion-intensive workloads, while avoiding their long insertion delays and improving on their query performance. They approach the query performance of B-trees when complemented with Bloom filters. In our experiments, NB-trees had worst-case delays up to 1000 smaller than LevelDB, RocksDB and bLSM, commonly used LSM-tree data-stores, could perform queries more than 4 times faster than LevelDB and 1.5 times faster than bLSM and RocksDB, while also outperforming them in terms of average insertion rate.
Effects of dense quark matter on gluon propagators in lattice 
QC2D

The transverse and longitudinal gluon propagators in the Landau gauge are studied in the two-color lattice QCD at nonzero quark chemical potential $\mu_q$. Parameterization of the momentum dependence of the propagators is provided for all values of chemical potential under study. We find that the longitudinal propagator is infrared suppressed at nonzero $\mu_q$ with suppression increasing with increasing $\mu_q$. The transverse propagator dependence on $\mu_q$ was found to be opposite: it is enhanced at large $\mu_q$. It is found, respectively, that the electric screening mass is increasing while the magnetic screening mass is decreasing with increasing $\mu_q$. Nice agreement between the electric screening mass computed from the longitudinal propagator and the Debye mass computed earlier from the singlet static quark-antiquark potential was found. We discuss how the dependence of the propagators on the chemical potential correlates with the respective dependence of the string tension. Additionally, we consider the difference between two propagators as a function of the momentum and make interesting observations.
Learning Depth via Interaction

Motivated by the astonishing capabilities of natural intelligent agents and inspired by theories from psychology, this paper explores the idea that perception gets coupled to 3D properties of the world via interaction with the environment. Existing works for depth estimation require either massive amounts of annotated training data or some form of hard-coded geometrical constraint. This paper explores a new approach to learning depth perception requiring neither of those. Specifically, we train a specialized global-local network architecture with what would be available to a robot interacting with the environment: from extremely sparse depth measurements down to even a single pixel per image. From a pair of consecutive images, our proposed network outputs a latent representation of the observer's motion between the images and a dense depth map. Experiments on several datasets show that, when ground truth is available even for just one of the image pixels, the proposed network can learn monocular dense depth estimation up to 22.5% more accurately than state-of-the-art approaches. We believe that this work, despite its scientific interest, lays the foundations to learn depth from extremely sparse supervision, which can be valuable to all robotic systems acting under severe bandwidth or sensing constraints.
Undergraduate Researcher Personas

Personalized undergraduate research programs can help increase undergraduate students' participation in research. Personas of undergraduate researchers are a powerful means of encapsulating the richness of students' various goals, motivations, and experiences in research. Student personas can support the design of research programs with student-centered approaches that better engage undergraduate students across multiple disciplines. We used interview data and drew on Self-Determination Theory to identify students' various goals and motivations in order to build three undergraduate researcher personas in physics.