Miracle-dz/UniFAR-FTUs huggingface dataset:
Caramel: Accelerating Decentralized Distributed Deep Learning with Computation Scheduling

The method of choice for parameter aggregation in Deep Neural Network (DNN) training, a network-intensive task, is shifting from the Parameter Server model to decentralized aggregation schemes (AllReduce) inspired by theoretical guarantees of better performance. However, current implementations of AllReduce overlook the interdependence of communication and computation, resulting in significant performance degradation. In this paper, we develop Caramel, a system that accelerates decentralized distributed deep learning through model-aware computation scheduling and communication optimizations for AllReduce. Caramel achieves this goal through (a) computation DAG scheduling that expands the feasible window of transfer for each parameter (transfer boundaries), and (b) network optimizations for smoothening of the load including adaptive batching and pipelining of parameter transfers. Caramel maintains the correctness of the dataflow model, is hardware-independent, and does not require any user-level or framework-level changes. We implement Caramel over TensorFlow and show that the iteration time of DNN training can be improved by up to 3.62x in a cloud environment.
Measurements of Absolute Branching Fractions of Fourteen Exclusive Hadronic $D$ Decays to $\eta$

Using $2.93\,\rm fb^{-1}$ of $e^+e^-$ collision data taken at a center-of-mass energy of 3.773\,GeV with the BESIII detector, we report the first measurements of the absolute branching fractions of fourteen hadronic $D^{0(+)}$ decays to exclusive final states with an $\eta$, e.g., $D^0\to K^-\pi^+\eta$, $K^0_S\pi^0\eta$, $K^+K^-\eta$, $K^0_SK^0_S\eta$, $K^-\pi^+\pi^0\eta$, $K^0_S\pi^+\pi^-\eta$, $K^0_S\pi^0\pi^0\eta$, and $\pi^+\pi^-\pi^0\eta$; $D^+\to K^0_S\pi^+\eta$, $K^0_SK^+\eta$, $K^-\pi^+\pi^+\eta$, $K^0_S\pi^+\pi^0\eta$, $\pi^+\pi^+\pi^-\eta$, and $\pi^+\pi^0\pi^0\eta$. Among these decays, the $D^0\to K^-\pi^+\eta$ and $D^+\to K^0_S\pi^+\eta$ decays have the largest branching fractions, which are $\mathcal{B} (D^0\to K^-\pi^+\eta )=(1.853\pm0.025_{\rm stat}\pm0.031_{\rm syst})\%$ and $\mathcal{B}(D^+\to K^0_S\pi^+\eta)=(1.309\pm0.037_{\rm stat}\pm0.031_{\rm syst})\%$, respectively. We also determine the $CP$ asymmetries for the six decays with highest event yields. No evidence of $CP$ violation is found.
Time-fractional Birth and Death Processes

In this article, we provide different representations for a time-fractional birth and death process $N_{\alpha}(t)$, whose transition probabilities are governed by a time-fractional system of differential equations. More specifically, we present two equivalent characterizations for its trajectories: the first one as a time-changed classic birth and death process, whereas the second one is a Markov renewal process. Also, we provide results for the asymptotic behavior of the process conditioned not to be killed. The most important is that the concept of quasi-limiting distribution and quasi-stationary distribution do not coincide, which is a consequence of the long-memory nature of the process. As an application example, we revisit the linear case to show the consequences of our main theorems.
Universal approach for unsupervised classification of univariate data

Unsupervised machine learning, and in particular data clustering, is a powerful approach for the analysis of datasets and identification of characteristic features occurring throughout a dataset. It is gaining popularity across scientific disciplines and is particularly useful for applications without a priori knowledge of the data structure. Here, we introduce a universal approach for unsupervised data classification relevant for any dataset consisting of a series of univariate measurements. It is therefore ideally suited for a wide range of measurement types. Here, we apply it to the field of nanoelectronics and spectroscopy to identify meaningful structures in data sets, providing physically relevant information about the system under study. An important step in our approach is the guidelines for the estimation of the optimum number of clusters. In addition, we have performed an extensive benchmark of ML approaches found in literature for the classification of molecular break junction traces. We find that several feature space construction methods we have introduced and clustering algorithms yield accuracies up to 20% higher than methods reported so far, increasing the Fowlkes-Mallows index from 0.77 up to 0.91
Hierarchical multidimensional scaling for the comparison of musical performance styles

Quantification of stylistic differences between musical artists is of academic interest to the music community, and is also useful for other applications such as music information retrieval and recommendation systems. Information about stylistic differences can be obtained by comparing the performances of different artists across common musical pieces. In this article, we develop a statistical methodology for identifying and quantifying systematic stylistic differences among artists that are consistent across audio recordings of a common set of pieces, in terms of several musical features. Our focus is on a comparison of ten different orchestras, based on data from audio recordings of the nine Beethoven symphonies. As generative or fully parametric models of raw audio data can be highly complex, and more complex than necessary for our goal of identifying differences between orchestras, we propose to reduce the data from a set of audio recordings down to pairwise distances between orchestras, based on different musical characteristics of the recordings, such as tempo, dynamics, and timbre. For each of these characteristics, we obtain multiple pairwise distance matrices, one for each movement of each symphony. We develop a hierarchical multidimensional scaling (HMDS) model to identify and quantify systematic differences between orchestras in terms of these three musical characteristics, and interpret the results in the context of known qualitative information about the orchestras. This methodology is able to recover several expected systematic similarities between orchestras, as well as to identify some more novel results. For example, we find that modern recordings exhibit a high degree of similarity to each other, as compared to older recordings.
Normalizing Compositional Structures Across Graphbanks

The emergence of a variety of graph-based meaning representations (MRs) has sparked an important conversation about how to adequately represent semantic structure. MRs exhibit structural differences that reflect different theoretical and design considerations, presenting challenges to uniform linguistic analysis and cross-framework semantic parsing. Here, we ask the question of which design differences between MRs are meaningful and semantically-rooted, and which are superficial. We present a methodology for normalizing discrepancies between MRs at the compositional level (Lindemann et al., 2019), finding that we can normalize the majority of divergent phenomena using linguistically-grounded rules. Our work significantly increases the match in compositional structure between MRs and improves multi-task learning (MTL) in a low-resource setting, serving as a proof of concept for future broad-scale cross-MR normalization.
Trajectory deflection of spinning magnetic microparticles: The Magnus effect at the microscale

The deflection due to the Magnus force of magnetic particles with a diameter of 80 micrometer dropping through fluids and rotating in a magnetic field was measured. With Reynolds number for this experiment around 1, we found trajectory deflections of the order of 1 degree, in agreement within measurement error with theory. This method holds promise for the sorting and analysis of the distribution in magnetic moment and particle diameter of suspensions of microparticles, such as applied in catalysis, or objects loaded with magnetic particles.
Airborne laser scanning for vegetation structure quantification in a south east Australian scrubby forest-woodland

Airborne laser scanning (ALS) has the potential to capture a range vegetation structural metrics, but most studies have focussed on conifer or mixed conifer-deciduous cool-temperate or boreal forests. This study focuses on a warm-temperate eucalypt forest, where two epochs of ALS data, captured approximately 2 years apart, were compared with plot and transect field data collected after the second ALS epoch. Linear regression was used to compare metrics from field and ALS data, and Student's t-tests were used to compare metrics from the two ALS epochs. Statistically significant relationships were found for tree height (R2 = 0.915; SE = 2.08 m; P < 0.01) and canopy cover (R2 = 0.508; SE = 16.4%; P < 0.01). Foliage projective cover was also significantly correlated (R2 = 0.916; SE = 4.5%; P < 0.01) at a 10-m stratification, but not at the typically computed 2-m stratification, because of the presence of a tall scrubby understorey. Statistically significant values were also obtained from ALS data captured 2 years earlier, although correlation was not as strong, most likely because of the greater interval between fieldwork and ALS capture. Importantly, significant agreement was found for all metrics when the two ALS epochs were compared, suggesting that the metrics are robust.
Efficient binary-level coverage analysis

Code coverage analysis plays an important role in the software testing process. More recently, the remarkable effectiveness of coverage feedback has triggered a broad interest in feedback-guided fuzzing. In this work, we introduce bcov, a tool for binary-level coverage analysis. Our tool statically instruments x86-64 binaries in the ELF format without compiler support. We implement several techniques to improve efficiency and scale to large real-world software. First, we bring Agrawal’s probe pruning technique to binary-level instrumentation and effectively leverage its superblocks to reduce overhead. Second, we introduce sliced microexecution, a robust technique for jump table analysis which improves CFG precision and enables us to instrument jump table entries. Additionally, smaller instructions in x86-64 pose a challenge for inserting detours. To address this challenge, we aggressively exploit padding bytes and systematically host detours in neighboring basic blocks. We evaluate bcov on a corpus of 95 binaries compiled from eight popular and well-tested packages like FFmpeg and LLVM. Two instrumentation policies, with different edge-level precision, are used to patch all functions in this corpus - over 1.6 million functions. Our precise policy has average performance and memory overheads of 14% and 22% respectively. Instrumented binaries do not introduce any test regressions. The reported coverage is highly accurate with an average F-score of 99.86%. Finally, our jump table analysis is comparable to that of IDA Pro on gcc binaries and outperforms it on clang binaries.
UDapter: Language Adaptation for Truly Universal Dependency Parsing

Recent advances in the field of multilingual dependency parsing have brought the idea of a truly universal parser closer to reality. However, cross-language interference and restrained model capacity remain a major obstacle to this pursuit. To address these issues, we propose a novel multilingual task adaptation approach based on recent work in parameter-efficient transfer learning, which allows for an easy but effective integration of existing linguistic typology features into the parsing network. The resulting parser, UDapter, consistently outperforms strong monolingual and multilingual baselines on both high-resource and low-resource (zero-shot) languages, setting a new state of the art in multilingual UD parsing. Our in-depth analyses show that soft parameter sharing via typological features is key to this success.
Dense Steiner problems: Approximation algorithms and inapproximability

The Steiner Tree problem is a classical problem in combinatorial optimization: the goal is to connect a set $T$ of terminals in a graph $G$ by a tree of minimum size. Karpinski and Zelikovsky (1996) studied the $\delta$-dense version of {\sc Steiner Tree}, where each terminal has at least $\delta |V(G)\setminus T|$ neighbours outside $T$, for a fixed $\delta > 0$. They gave a PTAS for this problem. We study a generalization of pairwise $\delta$-dense {\sc Steiner Forest}, which asks for a minimum-size forest in $G$ in which the nodes in each terminal set $T_1,\dots,T_k$ are connected, and every terminal in $T_i$ has at least $\delta |T_j|$ neighbours in $T_j$, and at least $\delta|S|$ nodes in $S = V(G)\setminus (T_1\cup\dots\cup T_k)$, for each $i, j$ in $\{1,\dots, k\}$ with $i\neq j$. Our first result is a polynomial-time approximation scheme for all $\delta > 1/2$. Then, we show a $(\frac{13}{12}+\varepsilon)$-approximation algorithm for $\delta = 1/2$ and any $\varepsilon > 0$. We also consider the $\delta$-dense Group Steiner Tree problem as defined by Hauptmann and show that the problem is $\mathsf{APX}$-hard.
On the complexity of Winner Verification and Candidate Winner for Multiwinner Voting Rules

The Chamberlin-Courant and Monroe rules are fundamental and well-studied rules in the literature of multi-winner elections. The problem of determining if there exists a committee of size k that has a Chamberlin-Courant (respectively, Monroe) dissatisfaction score of at most r is known to be NP-complete. We consider the following natural problems in this setting: a) given a committee S of size k as input, is it an optimal k-sized committee? , and b) given a candidate c and a committee size k, does there exist an optimal k-sized committee that contains c? In this work, we resolve the complexity of both problems for the Chamberlin-Courant and Monroe voting rules in the settings of rankings as well as approval ballots. We show that verifying if a given committee is optimal is coNP-complete whilst the latter problem is complete for Theta_2^P. Our contribution fills an essential gap in the literature for these important multi-winner rules.
L'hostilité aux OGM survit-elle à des produits attractifs ?

Nous testons ici les dispositions a payer des consommateurs pour des aliments transgeniques de seconde generation, c'est-a-dire ayant des caracteristiques innovantes attractives pour le consommateur par rapport au produit conventionnel. Nous testons egalement les dispositions a payer de tels produits lorsqu'ils sont obtenus avec des technologies alternatives aux biotechnologies transgeniques. Les resultats indiquent que la disposition a payer positive pour une caracteristique desirable obtenue grâce a un organisme genetiquement modifie compense la perte de disposition a payer qu'entraine, pour le consommateur, l'usage d'une telle technologie. Pour un meme produit ameliore pour le consommateur, le recours a la technologie transgenique conduit a une disposition a payer toujours inferieure a celles proposee pour les autres technologies. L'ecart est neanmoins d'une ampleur reduite, meme si les OGM continuent a subir le cout d'une image negative.
UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues

Building an end-to-end conversational agent for multi-domain task-oriented dialogues has been an open challenge for two main reasons. First, tracking dialogue states of multiple domains is non-trivial as the dialogue agent must obtain complete states from all relevant domains, some of which might have shared slots among domains as well as unique slots specifically for one domain only. Second, the dialogue agent must also process various types of information across domains, including dialogue context, dialogue states, and database, to generate natural responses to users. Unlike the existing approaches that are often designed to train each module separately, we propose "UniConv" -- a novel unified neural architecture for end-to-end conversational systems in multi-domain task-oriented dialogues, which is designed to jointly train (i) a Bi-level State Tracker which tracks dialogue states by learning signals at both slot and domain level independently, and (ii) a Joint Dialogue Act and Response Generator which incorporates information from various input components and models dialogue acts and target responses simultaneously. We conduct comprehensive experiments in dialogue state tracking, context-to-text, and end-to-end settings on the MultiWOZ2.1 benchmark, achieving superior performance over competitive baselines.
Large deviations and rate of escape for hyperbolic random walks

Let $\Gamma$ be a countable group acting on a geodesic hyperbolic metric space $X$ and $\mu$ a probability measure on $\Gamma$ which generates a non elementary semi-group. Under the necessary assumption that $\mu$ has a finite exponential moment, we establish large deviations results for the distance of a random walk with driving measure $\mu$.
Assessing Car Damage using Mask R-CNN

Picture based vehicle protection handling is a significant region with enormous degree for mechanization. In this paper we consider the issue of vehicle harm characterization, where a portion of the classifications can be fine-granular. We investigate profound learning based procedures for this reason. At first, we attempt legitimately preparing a CNN. In any case, because of little arrangement of marked information, it doesn't function admirably. At that point, we investigate the impact of space explicit pre-preparing followed by tweaking. At last, we explore different avenues regarding move learning and outfit learning. Trial results show that move learning works superior to space explicit tweaking. We accomplish precision of 89.5% with blend of move and gathering learning.
HST Survey of the Orion Nebula Cluster in the H2O 1.4 μm Absorption Band. I. A Census of Substellar and Planetary-mass Objects

In order to obtain a complete census of the stellar and substellar population, down to a few in the ∼1 Myr old Orion Nebula Cluster, we used the infrared channel of the Wide Field Camera 3 of the Hubble Space Telescope with the F139M and F130N filters. These bandpasses correspond to the 1.4 μm H2O absorption feature and an adjacent line-free continuum region. Out of 4504 detected sources, 3352 (about 75%) appear fainter than m130 = 14 (Vega mag) in the F130N filter, a brightness corresponding to the hydrogen-burning limit mass ( ) at ∼1 Myr. Of these, however, only 742 sources have a negative F130M–F139N color index, indicative of the presence of H2O vapor in absorption, and can therefore be classified as bona fide M and L dwarfs, with effective temperatures T ≲ 2850 K at an assumed 1 Myr cluster age. On our color–magnitude diagram (CMD), this population of sources with H2O absorption appears clearly distinct from the larger background population of highly reddened stars and galaxies with positive F130M–F139N color index and can be traced down to the sensitivity limit of our survey, m130 ≃ 21.5, corresponding to a 1 Myr old ≃3 planetary-mass object under about 2 mag of visual extinction. Theoretical models of the BT-Settl family predicting substellar isochrones of 1, 2, and 3 Myr down to ∼1 fail to reproduce the observed H2O color index at M ≲ 20 . We perform a Bayesian analysis to determine extinction, mass, and effective temperature of each substellar member of our sample, together with its membership probability.
Selective machine learning of doubly robust functionals.

While model selection is a well-studied topic in parametric and nonparametric regression or density estimation, model selection of possibly high-dimensional nuisance parameters in semiparametric problems is far less developed. In this paper, we propose a selective machine learning framework for making inferences about a finite-dimensional functional defined on a semiparametric model, when the latter admits a doubly robust estimating function. We introduce two model selection criteria for bias reduction of functional of interest, each based on a novel definition of pseudo-risk for the functional that embodies this double robustness property and thus may be used to select the candidate model that is nearest to fulfilling this property even when all models are wrong. We establish an oracle property for a multi-fold cross-validation version of the new model selection criteria which states that our empirical criteria perform nearly as well as an oracle with a priori knowledge of the pseudo-risk for each candidate model. We also describe a smooth approximation to the selection criteria which allows for valid post-selection inference. Finally, we apply the approach to model selection of a semiparametric estimator of average treatment effect given an ensemble of candidate machine learners to account for confounding in an observational study.
Contrastive Multi-document Question Generation

Multi-document question generation focuses on generating a question that covers the common aspect of multiple documents. Such a model is useful in generating clarifying options. However, a naive model trained only using the targeted (‘positive’) document set may generate too generic questions that cover a larger scope than delineated by the document set. To address this challenge, we introduce the contrastive learning strategy where given ‘positive’ and ‘negative’ sets of documents, we generate a question that is closely related to the ‘positive’ set but is far away from the ‘negative’ set. This setting allows generated questions to be more specific and related to the target document set. To generate such specific questions, we propose Multi-Source Coordinated Question Generator (MSCQG), a novel framework that includes a supervised learning (SL) stage and a reinforcement learning (RL) stage. In the SL stage, a single-document question generator is trained. In the RL stage, a coordinator model is trained to find optimal attention weights to align multiple single-document generators, by optimizing a reward designed to promote specificity of generated questions. We also develop an effective auxiliary objective, named Set-induced Contrastive Regularization (SCR) that improves the coordinator’s contrastive learning during the RL stage. We show that our model significantly outperforms several strong baselines, as measured by automatic metrics and human evaluation. The source repository is publicly available at ‘www.github. com/woonsangcho/contrast_qgen’.
Maternal Heart Rate and Heart Rate Variability During Pregnancy and Exercise Training

Exercise in non‐pregnant women increases parasympathetic control resulting in lower resting heart rates (HR) and increased heart rate variability (HRV). This is important given the relationships between pregnancy complications (hypertension, pre‐eclampsia and gestational diabetes) and altered autonomic control. This study tested the hypothesis that maternal exercise throughout gestation alters maternal cardiac autonomic control as indicated by changes in HR and HRV. We used magnetocardiography to record maternal HR between 28 and 32 wks GA from 20 women who exercised regularly (>30 min aerobic activity, 3 times per week) and 19 women who did not exercise during pregnancy. R‐R intervals were used to determine HR and HRV. HR (82.9 + 9.1 vs 90.4 + 9.9 bpm; F=6.2(1,37);p<0.05) was lower, pNN50 was greater (16.3 + 19.4 vs. 6.1 + 6.0; F= 4.7(1,37);p<0.05), and there was a trend of higher SDNN (p=0.05) indicating increased parasympathetic control in the exercise relative to the non‐exercise group. LF/HF, an index of sympathovagal balance, was lower (0.55 + 0.36 vs. 0.82 + 0.53; F=4.3(1,37), p<0.05) in the exercise group. Indices of parasympathetic control are altered in women who exercise regularly throughout their pregnancy. Therefore, exercise can be used as an intervention to decrease the risk of pregnancy complications associated with increased sympathetic input.