Miracle-dz/UniFAR-FTUs huggingface dataset:
Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation

Identifying unexpected objects on roads in semantic segmentation (e.g., identifying dogs on roads) is crucial in safetycritical applications. Existing approaches use images of unexpected objects from external datasets or require additional training (e.g., retraining segmentation networks or training an extra network), which necessitate a non-trivial amount of labor intensity or lengthy inference time. One possible alternative is to use prediction scores of a pretrained network such as the max logits (i.e., maximum values among classes before the final softmax layer) for detecting such objects. However, the distribution of max logits of each predicted class is significantly different from each other, which degrades the performance of identifying unexpected objects in urban-scene segmentation. To address this issue, we propose a simple yet effective approach that standardizes the max logits in order to align the different distributions and reflect the relative meanings of max logits within each predicted class. Moreover, we consider the local regions from two different perspectives based on the intuition that neighboring pixels share similar semantic information. In contrast to previous approaches, our method does not utilize any external datasets or require additional training, which makes our method widely applicable to existing pretrained segmentation models. Such a straightforward approach achieves a new state-of-the-art performance on the publicly available Fishyscapes Lost & Found leader-board with a large margin. Our code is publicly available at this link1.
On the Certified Robustness for Ensemble Models and Beyond

show deep neural networks (DNN) are vulnerable to adversarial which aim to mislead DNNs by adding perturbations with small magnitude. To defend against such attacks, both empirical and theoretical defense approaches have been extensively studied for a single ML model . In this work, we aim to analyze and provide the certiﬁed robustness for ensemble ML models , together with the sufﬁcient and necessary conditions of robustness for different ensemble protocols. Although ensemble models are shown more robust than a single model empirically; surprisingly, we ﬁnd that in terms of the certiﬁed robustness the standard ensemble models only achieve marginal improvement compared to a single model. Thus, to explore the conditions that guarantee to provide certiﬁably robust ensemble ML models, we ﬁrst prove that diversiﬁed gradient and large conﬁdence margin are sufﬁcient and necessary conditions for certiﬁably robust ensemble models under the model-smoothness assumption. We then provide the bounded model-smoothness analysis based on the proposed Ensemble-before-Smoothing strategy. We also prove that an ensemble model can always achieve higher certiﬁed robustness than a single base model under mild conditions. Inspired by the theoretical ﬁndings, we propose the lightweight Diversity Regularized Training (DRT) to train certiﬁably robust ensemble ML models. Extensive experiments show that our DRT enhanced ensembles can consistently achieve higher certiﬁed robustness than existing single and ensemble ML models, demonstrating the state-of-the-art certiﬁed L 2 -robustness on MNIST, CIFAR-10, base justiﬁcation of the regularization-based training approach DRT. Extensive experiments showed that DRT-enhanced ensembles achieve the highest certiﬁed robustness compared with existing baselines.
Statistical evaluation of fitted models applied to $\bf p_t$ spectrum data from 5 TeV and 13 TeV $\bf p$-$\bf p$ collisions at the large hadron collider

In a recent analysis of high-statistics pt spectra from 5 and 13 TeV p-p collisions a two-component (soft+hard) model (TCM) of hadron production near midrapidity, based on fixed model functions independent of collision multiplicity nch or event selection criteria, was employed as a reference to determine data biases induced by the selection criteria. As in previous studies the fixed TCM accurately isolated jet-related and nonjet contributions to spectra. It was observed that two selection criteria (V0M and SPD), based on different pseudorapidity η acceptances, bias the spectrum hard component (jet fragment distribution) in different but complementary ways whereas the soft component is not significantly biased. In the present study TCM model functions are adjusted (i.e. fitted) to accommodate data. The effect of selection bias is then represented by smooth evolution of model parameters with nch. To evaluate the quality of model fits, Z-scores (data-model differences divided by statistical uncertainties) are evaluated and compared with data/model ratios that are often used for such comparisons. Based on Z-scores the variable TCM is compared with two other frequently-invoked models: the Tsallis model and the blast-wave model as they have been recently applied to the same spectrum data. The results are relevant to recent claims that collectivity (various flow manifestations) as a possible manifestation of QGP formation is observed in small systems. Data systematics suggest that these p-p pt spectra are consistent with conventional QCD.
Stability of backward stochastic differential equations: the general Lipschitz case

In this paper, we obtain stability results for backward stochastic differential equations with jumps (BSDEs) in a very general framework. More specifically, we consider a convergent sequence of standard data, each associated to their own filtration, and we prove that the associated sequence of (unique) solutions is also convergent. The current result extends earlier contributions in the literature of stability of BSDEs and unifies several frameworks for numerical approximations of BSDEs and their implementations.
Modeling Bilingual Conversational Characteristics for Neural Chat Translation

Neural chat translation aims to translate bilingual conversational text, which has a broad application in international exchanges and cooperation. Despite the impressive performance of sentence-level and context-aware Neural Machine Translation (NMT), there still remain challenges to translate bilingual conversational text due to its inherent characteristics such as role preference, dialogue coherence, and translation consistency. In this paper, we aim to promote the translation quality of conversational text by modeling the above properties. Specifically, we design three latent variational modules to learn the distributions of bilingual conversational characteristics. Through sampling from these learned distributions, the latent variables, tailored for role preference, dialogue coherence, and translation consistency, are incorporated into the NMT model for better translation. We evaluate our approach on the benchmark dataset BConTrasT (English<->German) and a self-collected bilingual dialogue corpus, named BMELD (English<->Chinese). Extensive experiments show that our approach notably boosts the performance over strong baselines by a large margin and significantly surpasses some state-of-the-art context-aware NMT models in terms of BLEU and TER. Additionally, we make the BMELD dataset publicly available for the research community.
Broad-band X-ray observations of the 2018 outburst of the changing-look active galactic nucleus NGC 1566

We study the nature of the changing-look Active Galactic Nucleus NGC 1566 during its June 2018 outburst. During the outburst, the X-ray intensity of the source rises up to ∼ 25 − 30 times compared to its quiescent state intensity. We perform timing and spectral analysis of the source during pre-outburst, outburst and post-outburst epochs using semi-simultaneous observations with the XMM-Newton, Nuclear Spectroscopic Telescope Array (NuSTAR) and Neil Gehrels Swift Observatories. We calculate variance, normalized variance, and fractional rms amplitude in different energy bands to study the variability. The broad-band 0.5–70 keV spectra are fitted with phenomenological models, as well as physical models. A strong soft X-ray excess is detected in the spectra during the outburst. The soft excess emission is found to be complex and could originate in the warm Comptonizing region in the inner accretion disc. We find that the increase in the accretion rate is responsible for the sudden rise in luminosity. This is supported by the ‘q’-shape of the hardness-intensity diagram that is generally found in outbursting black hole X-ray binaries. From our analysis, we find that NGC 1566 most likely harbours a low-spinning black hole with the spin parameter 0 ∼ 0.2. We also discuss a scenario where the central core of NGC 1566 could be a merging supermassive black hole.
Posttreatment confounding in causal mediation studies: A cutting‐edge problem and a novel solution via sensitivity analysis

In causal mediation studies that decompose an average treatment effect into indirect and direct effects, examples of posttreatment confounding are abundant. In the presence of treatment‐by‐mediator interactions, past research has generally considered it infeasible to adjust for a posttreatment confounder of the mediator–outcome relationship due to incomplete information: for any given individual, a posttreatment confounder is observed under the actual treatment condition while missing under the counterfactual treatment condition. This paper proposes a new sensitivity analysis strategy for handling posttreatment confounding and incorporates it into weighting‐based causal mediation analysis. The key is to obtain the conditional distribution of the posttreatment confounder under the counterfactual treatment as a function of not only pretreatment covariates but also its counterpart under the actual treatment. The sensitivity analysis then generates a bound for the natural indirect effect and that for the natural direct effect over a plausible range of the conditional correlation between the posttreatment confounder under the actual and that under the counterfactual conditions. Implemented through either imputation or integration, the strategy is suitable for binary as well as continuous measures of posttreatment confounders. Simulation results demonstrate major strengths and potential limitations of this new solution. A reanalysis of the National Evaluation of Welfare‐to‐Work Strategies (NEWWS) Riverside data reveals that the initial analytic results are sensitive to omitted posttreatment confounding.
Communication Efficiency in Federated Learning: Achievements and Challenges

Federated Learning (FL) is known to perform Machine Learning tasks in a distributed manner. Over the years, this has become an emerging technology especially with various data protection and privacy policies being imposed FL allows performing machine learning tasks whilst adhering to these challenges. As with the emerging of any new technology, there are going to be challenges and benefits. A challenge that exists in FL is the communication costs, as FL takes place in a distributed environment where devices connected over the network have to constantly share their updates this can create a communication bottleneck. In this paper, we present a survey of the research that is performed to overcome the communication constraints in an FL setting.
Bayesian Precision Factor Analysis for High-dimensional Sparse Gaussian Graphical Models

Gaussian graphical models are popular tools for studying the dependence relationships between diﬀerent random variables. We propose a novel approach to Gaussian graphical models that relies on decomposing the precision matrix encoding the conditional independence relationships into a low rank and a diagonal component. Such decompositions are already popular for modeling large covariance matrices as they admit a latent factor based representation that allows easy inference but are yet to garner widespread use in precision matrix models due to their computational intractability. We show that a simple latent variable representation for such decomposition in fact exists for precision matrices as well. The latent variable construction provides fundamentally novel insights into Gaussian graphical models. It is also immediately useful in Bayesian settings in achieving eﬃcient posterior inference via a straightforward Gibbs sampler that scales very well to high-dimensional problems far beyond the limits of the current state-of-the-art. The ability to eﬃciently explore the full posterior space allows the model uncertainty to be easily assessed and the underlying graph to be determined via a novel posterior false discovery rate control procedure. The decomposition also crucially allows us to adapt sparsity inducing priors to shrink insigniﬁcant oﬀ-diagonal entries toward zero, making the approach adaptable to high-dimensional small-sample-size sparse settings. We evaluate the method’s empirical performance through synthetic experiments and illustrate its practical utility in data sets from two diﬀerent application domains.
Pruning Ternary Quantization

We propose pruning ternary quantization (PTQ), a simple, yet effective, symmetric ternary quantization method. The method signiﬁcantly compresses neural network weights to a sparse ternary of {− 1 , 0 , 1 } and thus reduces computational, storage, and memory footprints. We show that PTQ can convert regular weights to ternary orthonormal bases by simply using pruning and L2 projection. In addition, we introduce a reﬁned straight-through estimator to ﬁnalize and stabilize the quantized weights. Our method can provide at most 48 × compression ratio on the ResNet-18 structure, with an acceptable accuracy of 65.36%, outperforming leading methods. Furthermore, PTQ can compress a ResNet-18 model from 46 MB to 1.4 MB ( ∼ 33 × ) and a ResNet-50 model from 99 MB to 3.3 MB ( ∼ 30 × ), while the top-1 accuracy on ImageNet drops slightly from 69.7% to 67.03% and from 76.15% to 74.47%, respectively. Our method uniﬁes pruning and quantization and thus provides a range of size-accuracy trade-off. [Our code and trained models are uploaded to the reviewing sys-tem].
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with state-of-the-art machine learning (ML) techniques. This article provides a structured overview of such techniques. Application-centric objective areas for which these approaches have been applied are summarized, and then classes of methodologies used to construct physics-guided ML models and hybrid physics-ML frameworks are described. We then provide a taxonomy of these existing techniques, which uncovers knowledge gaps and potential crossovers of methods between disciplines that can serve as ideas for future research.
Pulsed characteristic-function measurement of a thermalizing harmonic oscillator

We present a method for the direct measurement of the Wigner characteristic function of a thermalizing harmonic oscillator that is completely inaccessible for control or measurement. The strategy employs a recently proposed probe-measurement-based scheme [Phys. Rev. Lett. 122, 110406 (2019)] which relies on the pulsed control of a two-level probe. We generalize this scheme to the case of a nonunitary time evolution of the target harmonic oscillator, describing its thermalization through contact to a finite-temperature environment, given in the form of a Lindblad master equation. This generalization is achieved using a superoperator formalism and yields analytical expressions for the direct measurement of the characteristic function, accounting for the decoherence during the measurement process.
Selling Impressions : Effi ciency vs . Competition ∗

In digital advertising, a publisher selling impressions faces a trade-off in deciding how precisely to match advertisers with viewers. A more precise match generates effi ciency gains that the publisher can hope to exploit. A coarser match will generate a thicker market and thus more competition. The publisher can control the precision of the match by controlling the amount of information that advertisers have about viewers. We characterize the optimal tradeoffwhen impressions are sold by auction. The publisher pools premium matches for advertisers (when there will be less competition on average) but gives advertisers full information about lower quality matches. Jel Classification: D44, D47, D83, D84.
Modern language: Interaction in conversational NS-NNS video SCMC eTandem exchanges

This study investigates the interaction of native speaker–non-native speaker (NS-NNS) dyads engaged in conversational interaction as part of a video-based synchronous computer mediated communication (VidSCMC) eTandem language program. Previous work has indicated certain advantages of NNS-NS conversational interaction for language learning (e.g., Nakahama et al., 2001); however, this potential has not been tested empirically for interaction in voice-based synchronous computer mediated communication (SVCMC) platforms, despite their growing popularity among language learners (Yanguas & Bergin, 2018). Participants were 18 university students (9 L1 English-L2 Spanish; 9 L1 Spanish-L2 English) taking part in a VidSCMC eTandem language partnership between two universities, in the United States and Mexico. Building on previous interactionist work on synchronous computer mediated communication (SCMC), language related episodes (LREs) were analyzed for quantity, trigger type, initiator, reactive or preemptive status, instances of negotiation, and the provision of negative feedback. Results show a remarkably even distribution of LRE types and linguistic foci, with NSs and NNSs taking equal responsibility in promoting globally-and locally-focused target language (TL) comprehension and output through their engagement in LREs. These patterns are reflective of the simultaneously shared status of learner and expert by the members of NS-NNS eTandem exchange dyads. Results are discussed in light of the changing relationship between language and language learners brought on by modern technologies.
Measuring the Academic Impact of Course Sequencing using Student Grade Data

Undergraduate college students have substantial flexibility in choosing the order in which they take courses, since most courses either have no prerequisites or only a single prerequisite. However, the specific order that courses are taken can have an impact on student performance. This paper describes a general methodology for assessing the impact of course sequencing on student performance, as measured by course grades, and applies this methodology to eight years of undergraduate academic data from Fordham University. The results demonstrate that certain course orderings are associated with improved student grade performance. This study introduces a methodology, new metrics, and a publicly available data-processing tool that can be applied to any student course-grade data set to measure course sequencing effects. The results can be used to inform student decisions, modify course recommendations, and even modify course prerequisites.
Monologues, dialogues, and common priors

The main purpose of this paper is to provide a simple criterion enabling to conclude that two agents do not share a common prior. The criterion is simple, as it does not require information about the agents' knowledge and beliefs, but rather only the record of a dialogue between the agents. In each stage of the dialogue, the agents tell each other the probability they ascribe to a fixed event and update their beliefs about the event. To characterize dialogues consistent with a common prior, we first study monologues, which are sequences of probabilities assigned by a single agent to a given event in an exogenous learning process. A dialogue is consistent with a common prior if and only if each selection sequence from the two monologues comprising the dialogue is itself a monologue.
An Analysis of the MTHFR Gene and Clinical Phenotypes in Familial Non-Syndromic Cleft Palate

Non-syndromic Cleft Lip and/or Palate (NSCL/P) constitutes the most common form of Orofacial Cleft (OFC). The methylene tetra hydrofolate reductase (MTHFR) encoding gene has been reported as playing a vital role in the pathogenesis of NSCL/P. Allele-specific Oligonucleotide (ASO) PCR analysis of A1298C and C677T of MTHFR gene polymorphism was performed using DNA from both the mother and father, in addition to circulating fetal cell-free DNA followed by electrophoresis. Homozygote mutation of A1298C was identified in the circulating fetal cell-free DNA, while heterozygote mutation was found to be present in both parents. Surprisingly, the affected infant possessed normal allele of C677T, despite both parents being heterozygotemutated. Following birth, the infant presented cleft palate defect, even though both parents were phonetically normal. The mutation of both C677T and 1298C genes can cause fusion failure during oral and maxillofacial development. The latter may occur without following typical Mendelian hereditary patterns, despite the fact that NSCL/P is inherited in numerous cases. The irregular hereditary pattern is probably due to an interaction between genetic susceptibility and environmental stimulation. Clinical Article (J Int Dent Med Res 2020; 13(3): 1160-1164)
Gastrointestinal Tolerance and Protein Absorption Markers with a New Peptide Enteral Formula Compared to a Standard Intact Protein Enteral Formula in Critically Ill Patients

The aim of this exploratory study was to investigate gastrointestinal tolerance and protein absorption markers with a new enteral peptide formula (PF) compared to an isocaloric enteral intact protein standard formula (SF) containing the same amount of protein in ICU patients. Patients admitted to a cardio-thoracic intensive care unit expected to receive tube feeding for ≥5 days were randomized to receive either PF (1.5 kcal/mL) or SF in a double-blind manner for ≤14 days. Twenty-six patients were randomized (13 SF and 13 PF) and 23 (12 SF and 11 PF) completed at least 5 days of product administration. There were no statistically significant differences between the feeds during the first 5 days of intervention for diarrhea (SF:3 (23%); PF:5 (39%), p = 0.388), vomiting (SF:1 (8%); PF:2 (15%), p = 0.549), constipation (SF:7 (54%), PF:3 (23%), p = 0.115), and high gastric residual volume (>500 mL: SF:1 (8%); PF: 2 (15%), p = 0.535). There were no differences in plasma amino acids or urinary markers of protein absorption and metabolism. In conclusion, no major differences were found in tolerability and protein absorption markers between the standard intact protein formula and the peptide formula.