Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality Assurance in MLOps Setting: An Industrial Perspective

Today, machine learning (ML) is widely used in industry to provide the core functionality of production systems. However, it is practically always used in production systems as part of a larger end-to-end software system that is made up of several other components in addition to the ML model. Due to production demand and time constraints, automated software engineering practices are highly applicable. The increased use of automated ML software engineering practices in industries such as manufacturing and utilities requires an automated Quality Assurance (QA) approach as an integral part of ML software. Here, QA helps reduce risk by offering an objective perspective on the software task. Although conventional software engineering has automated tools for QA data analysis for data-driven ML, the use of QA practices for ML in operation (MLOps) is lacking. This paper examines the QA challenges that arise in industrial MLOps and conceptualizes modular strategies to deal with data integrity and Data Quality (DQ). The paper is accompanied by real industrial use-cases from industrial partners. The paper also presents several challenges that may serve as a basis for future studies.
Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition

Value Decomposition (VD) aims to deduce the contributions of agents for decentralized policies in the presence of only global rewards, and has recently emerged as a powerful credit assignment paradigm for tackling cooperative Multi-Agent Reinforcement Learning (MARL) problems. One of the main challenges in VD is to promote diverse behaviors among agents, while existing methods directly encourage the diversity of learned agent networks with various strategies. However, we argue that these dedicated designs for agent networks are still limited by the indistinguishable VD network, leading to homogeneous agent behaviors and thus downgrading the cooperation capability. In this paper, we propose a novel Contrastive Identity-Aware learning (CIA) method, explicitly boosting the credit-level distinguishability of the VD network to break the bottleneck of multi-agent diversity. Specifically, our approach leverages contrastive learning to maximize the mutual information between the temporal credits and identity representations of different agents, encouraging the full expressiveness of credit assignment and further the emergence of individualities. The algorithm implementation of the proposed CIA module is simple yet effective that can be readily incorporated into various VD architectures. Experiments on the SMAC benchmarks and across different VD backbones demonstrate that the proposed method yields results superior to the state-of-the-art counterparts. Our code is available at https://github.com/liushunyu/CIA.
Round table on Standard Model Anomalies

This contribution to “The XVth Quark confinement and the Hadron spectrum conference" covers a description, both theoretical and experimental, of the present status of a set of very different anomalies. The discussion ranges from the long standing b → sℓℓ anomalies, (g − 2) and the new MW anomaly.
Wide-band Timing of the Parkes Pulsar Timing Array UWL Data

In 2018 an ultra–wide-bandwidth low-frequency (UWL) receiver was installed on the 64 m Parkes Radio Telescope, enabling observations with an instantaneous frequency coverage from 704 to 4032 MHz. Here we present the analysis of a 3 yr data set of 35 ms pulsars observed with the UWL by the Parkes Pulsar Timing Array, using wide-band timing methods. The two key differences compared to typical narrowband methods are (1) generation of two-dimensional templates accounting for pulse shape evolution with frequency and (2) simultaneous measurements of the pulse time of arrival (TOA) and dispersion measure (DM). This is the first time that wide-band timing has been applied to a uniform data set collected with a single large fractional bandwidth receiver, for which such techniques were originally developed. As a result of our study, we present a set of profile evolution models and new timing solutions, including initial noise analysis. Precision of our TOA and DM measurements is in the range of 0.005–2.08 μs and (0.043–14.24) × 10−4 cm−3 pc, respectively, with 94% of the pulsars achieving a median TOA uncertainty of less than 1 μs.
IMPLEMENTING FACIAL LANDMARK TRACKING FOR AMERICAN SIGN LANGUAGE RECOGNITION USING RANDOM FOREST CLASSIFICATION TREE MODEL AND PRINCIPAL COMPONENT ANALYSIS

The deaf and hard of hearing community relies on American Sign Language (ASL) as their primary mode of communication, but communication with others who do not know ASL can be difﬁcult, especially during emergencies where no interpreter is available. As an effort to alleviate this problem, research in computer vision based real time ASL interpreting models is ongoing. However, most of these models are hand shape (gesture) based and lack the integration of facial cues, which are crucial in ASL to convey tone and distinguish similar looking signs. Thus, the integration of facial cues in computer vision based ASL interpreting models has the potential to improve performance and reliability. In this paper, we introduce a new facial expression based classiﬁcation model that can be used to improve ASL interpreting models. This model utilizes the relative angles of facial landmarks with principal component analysis and a Random Forest Clas-siﬁcation tree model to classify frames taken from videos of ASL users signing a complete sentence. The model classiﬁes the frames as statements or assertions. The model was able to achieve an accuracy of 82%.
Sensor sensitivity based on synthetic magnetism exceptional point

An efficient mass sensor based on exceptional points (EPs), engineered under synthetic magnetism requirement, is proposed. The benchmark system consists of an electromechanical (optomechanical) system where an electric (optical) field is driving two mechanical resonators which are mechanically coupled through a phase-dependent phonon-hopping. This phase induces series of EPs once it matches the condition of $\frac{\pi}{2}(2n +1)$. For any perturbation of the system, the phase-matched condition is no longer satisfied and this lifts the EP-degeneracies leading to a frequency splitting that scales as the square root of the perturbation strength, resulting in a giant sensitivity-factor enhancement. Owing to the set of EPs, our proposal allows multiple sensing scheme and performs better than its anti-PT-symmetric sensor counterpart. This work sheds light on new platforms that can be used for mass sensing purposes, opening up new opportunities in nanoparticles or pollutants detection, and to water treatment.
A Generic Approach for Statistical Stability in Model Distillation

Model distillation has been a popular method for producing interpretable machine learning. It uses an interpretable “student” model to mimic the predictions made by the black box “teacher” model. However, when the student model is sensitive to the variability of the data sets used for training, the corresponded interpretation is not reliable. Existing strategies stabilize model distillation by checking whether a large enough corpus of pseudo-data is generated to reliably reproduce student models, but methods to do so have so far been developed for a speciﬁc student model. In this paper, we develop a generic approach for stable model distillation based on central limit theorem for the average loss. We start with a collection of candidate student models and search for candidates that reasonably agree with the teacher. Then we construct a multiple testing framework to select a corpus size such that the consistent student model would be selected under diﬀerent pseudo sample. We demonstrate the application of our proposed approach on three commonly used intelligible models: decision trees, falling rule lists and symbolic regression. Finally, we conduct simulation experiments on Mammographic Mass and Breast Cancer datasets and illustrate the testing procedure throughout a theoretical analysis with Markov process.
Monitoring and Improving Personalized Sleep Quality from Long-Term Lifelogs

Sleep plays a vital role in our physical, cognitive, and psychological well-being. Despite its importance, long-term monitoring of personalized sleep quality (SQ) in real-world contexts is still challenging. Many sleep researches are still developing clinically and far from accessible to the general public. Fortunately, wearables and IoT devices provide the potential to explore the sleep insights from multimodal data, and have been used in some SQ researches. However, most of these studies analyze the sleep related data and present the results in a delayed manner (i.e., today’s SQ obtained from last night’s data), it is sill difficult for individuals to know how their sleep will be before they go to bed and how they can proactively improve it. To this end, this paper proposes a computational framework to monitor the individual SQ based on both the objective and subjective data from multiple sources, and moves a step further towards providing the personalized feedback to improve the SQ in a data-driven manner. The feedback is implemented by referring the insights from the PMData dataset based on the discovered patterns between life events and different levels of SQ. The deep learning based personal SQ model (PerSQ), using the long-term heterogeneous data and considering the carry-over effect, achieves higher prediction performance compared with baseline models. A case study also shows reasonable results for an individual to monitor and improve the SQ in the future.
Existence and non-existence of the non-central Wishart distributions.

The problem considered in this paper is to find when the non-central Wishart distribution, defined on the cone Pd of semi positive definite matrices of order d and with a real valued shape parameter, exists. We reduce this problem to the problem of existence of the measures m(n,k,d) defined on Pd and with Laplace transform (dets) n/2 exp tr(s 1 w) where n is an integer and where w = diag(0,.,0,1,.,1) has order d and rank k. We compute m(d 1,d,d) and we show that neither m(d 2,d,d) nor m(d 2,d 1,d) exist. This proves a conjecture of E. Mayerhofer.
Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling

Existing research generally treats Chinese character as a minimum unit for representation. However, such Chinese character representation will suffer two bottlenecks: 1) Learning bottleneck, the learning cannot benefit from its rich internal features (e.g., radicals and strokes); and 2) Parameter bottleneck, each individual character has to be represented by a unique vector. In this paper, we introduce a novel representation method for Chinese characters to break the bottlenecks, namely StrokeNet, which represents a Chinese character by a Latinized stroke sequence (e.g., “凹 (concave)” to “ajaie” and “凸 (convex)” to “aeaqe”). Specifically, StrokeNet maps each stroke to a specific Latin character, thus allowing similar Chinese characters to have similar Latin representations. With the introduction of StrokeNet to neural machine translation (NMT), many powerful but not applicable techniques to non-Latin languages (e.g., shared subword vocabulary learning and ciphertext-based data augmentation) can now be perfectly implemented. Experiments on the widely-used NIST Chinese-English, WMT17 Chinese-English and IWSLT17 Japanese-English NMT tasks show that StrokeNet can provide a significant performance boost over the strong baselines with fewer model parameters, achieving 26.5 BLEU on the WMT17 Chinese-English task which is better than any previously reported results without using monolingual data. Code and scripts are freely available at https://github.com/zjwang21/StrokeNet.
One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learning

Reliance on vast annotations to achieve leading performance severely restricts the practicality of large-scale point cloud semantic segmentation. For the purpose of reducing data annotation costs, effective labeling schemes are developed and contribute to attaining competitive results under weak supervision strategy. Revisiting current weak label forms, we introduce One Class One Click (OCOC), a low cost yet informative quasi scene-level label, which encapsulates point-level and scene-level annotations. An active weakly supervised framework is proposed to leverage scarce labels by involving weak supervision from global and local perspectives. Contextual constraints are imposed by an auxiliary scene classification task, respectively based on global feature embedding and point-wise prediction aggregation, which restricts the model prediction merely to OCOC labels. Furthermore, we design a context-aware pseudo labeling strategy, which effectively supplement point-level supervisory signals. Finally, an active learning scheme with a uncertainty measure - temporal output discrepancy is integrated to examine informative samples and provides guidance on sub-clouds query, which is conducive to quickly attaining desirable OCOC annotations and reduces the labeling cost to an extremely low extent. Extensive experimental analysis using three LiDAR benchmarks collected from airborne, mobile and ground platforms demonstrates that our proposed method achieves very promising results though subject to scarce labels. It considerably outperforms genuine scene-level weakly supervised methods by up to 25\% in terms of average F1 score and achieves competitive results against full supervision schemes. On terrestrial LiDAR dataset - Semantics3D, using approximately 2\textpertenthousand{} of labels, our method achieves an average F1 score of 85.2\%, which increases by 11.58\% compared to the baseline model.
Comparison of the Core-collapse Evolution of Two Nearly Equal-mass Progenitors

We compare the core-collapse evolution of a pair of 15.8 M ☉ stars with significantly different internal structures, a consequence of the bimodal variability exhibited by massive stars during their late evolutionary stages. The 15.78 and 15.79 M ☉ progenitors have core masses (masses interior to an entropy of 4 k B baryon−1) of 1.47 and 1.78 M ☉ and compactness parameters ξ 1.75 of 0.302 and 0.604, respectively. The core-collapse simulations are carried out in 2D to nearly 3 s postbounce and show substantial differences in the times of shock revival and explosion energies. The 15.78 M ☉ model begins exploding promptly at 120 ms postbounce when a strong density decrement at the Si–Si/O shell interface, not present in the 15.79 M ☉ progenitor, encounters the stalled shock. The 15.79 M ☉ model takes 100 ms longer to explode but ultimately produces a more powerful explosion. Both the larger mass accretion rate and the more massive core of the 15.79 M ☉ model during the first 0.8 s postbounce time result in larger ν e / ν¯e luminosities and RMS energies along with a flatter and higher-density heating region. The more-energetic explosion of the 15.79 M ☉ model resulted in the ejection of twice as much 56Ni. Most of the ejecta in both models are moderately proton rich, though counterintuitively the highest electron fraction (Y e = 0.61) ejecta in either model are in the less-energetic 15.78 M ☉ model, while the lowest electron fraction (Y e = 0.45) ejecta in either model are in the 15.79 M ☉ model.
Data-Driven Feature Tracking for Event Cameras

Because of their high temporal resolution, increased resilience to motion blur, and very sparse output, event cameras have been shown to be ideal for low-latency and low-bandwidth feature tracking, even in challenging scenarios. Existing feature tracking methods for event cameras are either handcrafted or derived from first principles but require extensive parameter tuning, are sensitive to noise, and do not generalize to different scenarios due to unmodeled effects. To tackle these deficiencies, we introduce the first data-driven feature tracker for event cameras, which leverages low-latency events to track features detected in a grayscale frame. We achieve robust performance via a novel frame attention module, which shares information across feature tracks. By directly transferring zero-shot from synthetic to real data, our data-driven tracker outperforms existing approaches in relative feature age by up to 120 % while also achieving the lowest latency. This performance gap is further increased to 130 % by adapting our tracker to real data with a novel self-supervision strategy. Multimedia Material A video is available at https://youtu.be/dtkXvNXcWRY and code at https://github.com/uzh-rpg/deep_ev_tracker
Absolute X-Ray Measurement of the Atomic Scattering Factor of Chromium

Absolute measurements were made to determine the atomic scattering factor of chromium by x-ray diffraction. This study was made at room temperature, using an imperfect single crystal in transmission and applying a measured secondary-extinction correction to the integrated intensities obtained with monochromatic $\mathrm{Mo}K\ensuremath{\alpha}$ radiation. The measurements were placed on an absolute scale using a value of the Debye temperature of 525\ifmmode^\circ\else\textdegree\fi{}K and a measured mass-absorption coefficient of 30.15 ${\mathrm{cm}}^{2}$/g. As in previous measurements on nickel, the apparent scattering factors of the (110) and (200) reflections were found to depend linearly on the reciprocal of the reflection half-width, thus indicating the presence of primary extinction. The true atomic scattering factors for the (110) and (200) reflections, determined from an extrapolation to zero reciprocal half-width, were found to be considerably lower than the theoretical values obtained from free-atom Hartree-Fock calculations. From the paired reflections (330)-(411) and (600)-(442) the ${t}_{2g}$ population of $3d$ electrons was found to be about 72%, thus indicating a marked departure from spherical symmetry.
Universal Variable-to-Fixed Length Lossy Compression at Finite Blocklengths

We consider universal variable-to-fixed length compression of memoryless sources with a fidelity criterion. We design a dictionary codebook over the reproduction alphabet which is used to parse the source stream. Once a source subsequence is within a specified distortion of a dictionary codeword, the index of the codeword is emitted as the reproduced string. Our proposed dictionary consists of coverings of type classes in the boundary of transition from low to high empirical lossy rate. We derive the asymptotics of the \epsilon-coding rate (up to the third-order term) of our coding scheme for large enough dictionaries.
Topological rigidity and Gromov simplicial volume

A natural problem in the theory of 3-manifolds is the question of whether two 3-manifolds are homeomorphic or not. The aim of this paper is to study this problem for the class of closed Haken manifolds using degree one maps. To this purpose we introduce an invariant �. N/ D.Vol . N /; kN k/, where kN k denotes the Gromov simplicial volume of N and Vol.N / is a 2-dimensional simplicial volume which measures the volume of the base 2-orbifolds of the Seifert pieces of N. After studying the behavior of �. N/ under the action of non-zero degree maps, we prove that if M and N are closed Haken manifolds such that kM kDj deg.f /jkN k and Vol.M / D Vol . N / then any non-zero degree map f W M ! N is homotopic to a covering map. As a corollary we prove that if M and N are closed Haken manifolds such that �. N/ is sufficiently close to �. M/ then any degree one map f W M ! N is homotopic to a homeomorphism.
Promises and Pitfalls of Threshold-based Auto-labeling

Creating large-scale high-quality labeled datasets is a major bottleneck in supervised machine learning workflows. Threshold-based auto-labeling (TBAL), where validation data obtained from humans is used to find a confidence threshold above which the data is machine-labeled, reduces reliance on manual annotation. TBAL is emerging as a widely-used solution in practice. Given the long shelf-life and diverse usage of the resulting datasets, understanding when the data obtained by such auto-labeling systems can be relied on is crucial. This is the first work to analyze TBAL systems and derive sample complexity bounds on the amount of human-labeled validation data required for guaranteeing the quality of machine-labeled data. Our results provide two crucial insights. First, reasonable chunks of unlabeled data can be automatically and accurately labeled by seemingly bad models. Second, a hidden downside of TBAL systems is potentially prohibitive validation data usage. Together, these insights describe the promise and pitfalls of using such systems. We validate our theoretical guarantees with extensive experiments on synthetic and real datasets.
Projection-free Adaptive Regret with Membership Oracles

In the framework of online convex optimization, most iterative algorithms require the computation of projections onto convex sets, which can be computationally expensive. To tackle this problem HK12 proposed the study of projection-free methods that replace projections with less expensive computations. The most common approach is based on the Frank-Wolfe method, that uses linear optimization computation in lieu of projections. Recent work by GK22 gave sublinear adaptive regret guarantees with projection free algorithms based on the Frank Wolfe approach. In this work we give projection-free algorithms that are based on a different technique, inspired by Mhammedi22, that replaces projections by set-membership computations. We propose a simple lazy gradient-based algorithm with a Minkowski regularization that attains near-optimal adaptive regret bounds. For general convex loss functions we improve previous adaptive regret bounds from $O(T^{3/4})$ to $O(\sqrt{T})$, and further to tight interval dependent bound $\tilde{O}(\sqrt{I})$ where $I$ denotes the interval length. For strongly convex functions we obtain the first poly-logarithmic adaptive regret bounds using a projection-free algorithm.
Embedding Compression for Text Classification Using Dictionary Screening

In this paper, we propose a dictionary screening method for embedding compression in text classification tasks. The key purpose of this method is to evaluate the importance of each keyword in the dictionary. To this end, we first train a pre-specified recurrent neural network-based model using a full dictionary. This leads to a benchmark model, which we then use to obtain the predicted class probabilities for each sample in a dataset. Next, to evaluate the impact of each keyword in affecting the predicted class probabilities, we develop a novel method for assessing the importance of each keyword in a dictionary. Consequently, each keyword can be screened, and only the most important keywords are reserved. With these screened keywords, a new dictionary with a considerably reduced size can be constructed. Accordingly, the original text sequence can be substantially compressed. The proposed method leads to significant reductions in terms of parameters, average text sequence, and dictionary size. Meanwhile, the prediction power remains very competitive compared to the benchmark model. Extensive numerical studies are presented to demonstrate the empirical performance of the proposed method.