Miracle-dz/UniFAR-FTUs huggingface dataset:
More Dominantly Truthful Multi-Task Peer Prediction with a Finite Number of Tasks

In the setting where we ask participants multiple similar possibly subjective multi-choice questions (e.g. Do you like Bulbasaur? Y/N; do you like Squirtle? Y/N), peer prediction aims to design mechanisms that encourage honest feedback without verification. A series of works have successfully designed multi-task peer prediction mechanisms where reporting truthfully is better than any other strategy (dominantly truthful), while they require an infinite number of tasks. A recent work proposes the first multi-task peer prediction mechanism, Determinant Mutual Information (DMI)-Mechanism, where not only is dominantly truthful but also works for a finite number of tasks (practical). However, the existence of other practical dominantly-truthful multi-task peer prediction mechanisms remains to be an open question. This work answers the above question by providing 1. a new family of information-monotone information measures: volume mutual information (VMI), where DMI is a special case; 2. a new family of practical dominantly-truthful multi-task peer prediction mechanisms, VMI-Mechanisms. To illustrate the importance of VMI-Mechanisms, we also provide a tractable effort incentive optimization goal. We show that DMI-Mechanism may not be not optimal but we can construct a sequence of VMI-Mechanisms that are approximately optimal. The main technical highlight in this paper is a novel geometric information measure, Volume Mutual Information, that is based on a simple idea: we can measure an object A's information amount by the number of objects that is less informative than A. Different densities over the object lead to different information measures. This also gives Determinant Mutual Information a simple geometric interpretation.
Production of Very Light Elements and Strontium in the Early Ejecta of Neutron Star Mergers

We study the production of very light elements (Z < 20) in the dynamical and spiral-wave wind ejecta of binary neutron star mergers by combining detailed nucleosynthesis calculations with the outcome of numerical relativity merger simulations. All our models are targeted to GW170817 and include neutrino radiation. We explore different finite-temperature, composition-dependent nuclear equations of state, and binary mass ratios, and find that hydrogen and helium are the most abundant light elements. For both elements, the decay of free neutrons is the driving nuclear reaction. In particular, ∼0.5–2 × 10−6 M ⊙ of hydrogen are produced in the fast expanding tail of the dynamical ejecta, while ∼1.5–11 × 10−6 M ⊙ of helium are synthesized in the bulk of the dynamical ejecta, usually in association with heavy r-process elements. By computing synthetic spectra, we find that the possibility of detecting hydrogen and helium features in kilonova spectra is very unlikely for fiducial masses and luminosities, even when including nonlocal thermodynamic equilibrium effects. The latter could be crucial to observe helium lines a few days after merger for faint kilonovae or for luminous kilonovae ejecting large masses of helium. Finally, we compute the amount of strontium synthesized in the dynamical and spiral-wave wind ejecta, and find that it is consistent with (or even larger than, in the case of a long-lived remnant) the one required to explain early spectral features in the kilonova of GW170817.
Not All Fabrics Are Created Equal: Exploring eFPGA Parameters for IP Redaction

Semiconductor design houses rely on third-party foundries to manufacture their integrated circuits (ICs). While this trend allows them to tackle fabrication costs, it introduces security concerns as external (and potentially malicious) parties can access critical parts of the designs and steal or modify the intellectual property (IP). Embedded field-programmable gate array (eFPGA) redaction is a promising technique to protect critical IPs of an ASIC by redacting (i.e., removing) critical parts and mapping them onto a custom reconfigurable fabric. Only trusted parties will receive the correct bitstream to restore the redacted functionality. While previous studies imply that using an eFPGA is a sufficient condition to provide security against IP threats like reverse-engineering, whether this truly holds for all eFPGA architectures is unclear, thus motivating the study in this article. We examine the security of eFPGA fabrics generated by varying different FPGA design parameters. We characterize the power, performance, and area (PPA) characteristics and evaluate each fabric’s resistance to Boolean satisfiability (SAT)-based bitstream recovery. Our results encourage designers to work with custom eFPGA fabrics rather than off-the-shelf commercial FPGAs and reveals that only considering a redaction fabric’s bitstream size is inadequate for gauging security.
Information-Theoretic Bayes Risk Lower Bounds for Realizable Models

We derive information-theoretic lower bounds on the Bayes risk and generalization error of realizable machine learning models. In particular, we employ an analysis in which the rate-distortion function of the model parameters bounds the required mutual information between the training samples and the model parameters in order to learn a model up to a Bayes risk constraint. For realizable models, we show that both the rate distortion function and mutual information admit expressions that are convenient for analysis. For models that are (roughly) lower Lipschitz in their parameters, we bound the rate distortion function from below, whereas for VC classes, the mutual information is bounded above by $d_\mathrm{vc}\log(n)$. When these conditions match, the Bayes risk with respect to the zero-one loss scales no faster than $\Omega(d_\mathrm{vc}/n)$, which matches known outer bounds and minimax lower bounds up to logarithmic factors. We also consider the impact of label noise, providing lower bounds when training and/or test samples are corrupted.
Significant optoelectrical entanglement and mechanical squeezing in a multimodulated optoelectromechanical system

We propose eﬀective generation of entangled and squeezed states in an optoelectromechanical system comprising of a macroscopic LC electrical circuit and an optomechanical system. We obtain enhanced entanglement between optical and LC circuit modes that are coupled via a common mechanical mode in the microwave regime. We achieve this enhancement by a stepwise application of modulation in the laser drive, the voltage drive and the spring constant of the moveable end-mirror. The maximum amount of entanglement is observed to be primarily dependent on the voltage modulation and changes slightly with the parameters of the spring constant. Alongside the generated entanglement, we also study the variation of the maximum degree of squeezing in the mechanical mode for diﬀerent parameter regimes.
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines

Input pipelines, which ingest and transform input data, are an essential part of training Machine Learning (ML) models. However, it is challenging to implement efficient input pipelines, as it requires reasoning about parallelism, asynchrony, and variability in fine-grained profiling information. Our analysis of over two million ML jobs in Google datacenters reveals that a significant fraction of model training jobs could benefit from faster input data pipelines. At the same time, our analysis indicates that most jobs do not saturate host hardware, pointing in the direction of software-based bottlenecks. Motivated by these findings, we propose Plumber, a tool for finding bottlenecks in ML input pipelines. Plumber uses an extensible and interpretable operational analysis analytical model to automatically tune parallelism, prefetching, and caching under host resource constraints. Across five representative ML pipelines, Plumber obtains speedups of up to 47x for misconfigured pipelines. By automating caching, Plumber obtains end-to-end speedups of over 50% compared to state-of-the-art tuners.
A Private and Computationally-Efficient Estimator for Unbounded Gaussians

We give the first polynomial-time, polynomial-sample, differentially private estimator for the mean and covariance of an arbitrary Gaussian distribution $\mathcal{N}(\mu,\Sigma)$ in $\mathbb{R}^d$. All previous estimators are either nonconstructive, with unbounded running time, or require the user to specify a priori bounds on the parameters $\mu$ and $\Sigma$. The primary new technical tool in our algorithm is a new differentially private preconditioner that takes samples from an arbitrary Gaussian $\mathcal{N}(0,\Sigma)$ and returns a matrix $A$ such that $A \Sigma A^T$ has constant condition number.
Biaxial Strain Modulated Valence Band Engineering in III-V Digital Alloys

Some III-V digital alloy avalanche photodiodes exhibit low excess noise. These alloys have low hole ionization coefficients due to presence of small 'minigaps', enhanced effective mass and large separation between light-hole and split-off bands in the valence band. In this letter, an explanation for the formation of the minigaps using a tight binding picture is provided. Furthermore, we demonstrate that decreasing substrate lattice constant can increase the minigap size and mass in the transport direction. This leads to reduced quantum tunneling and phonon scattering of the holes. Finally, we illustrate the band structure modification with substrate lattice constant for other III-V digital alloys.
Quantum Field Theories with Tensor Renormalization Group

We report recent progress on the application of the tensor renormalization group (TRG) to quantum ﬁeld theories pursued by the Tsukuba group. We explain how to treat the scalar, fermion, and gauge theories with the TRG method presenting the results for the phase transitions in the (3+1)-dimensional ((3+1) 𝑑 ) complex 𝜙 4 theory at ﬁnite density, (1+1) 𝑑 pure U(1) lattice gauge theory with a 𝜃 term, (3+1) 𝑑 Nambu–Jona-Lasinio model at ﬁnite density and (1+1) 𝑑 and (2+1) 𝑑 Hubbard models at an arbitrary chemical potential. It is demonstrated that the TRG method is free from the sign problem in practical calculations and applicable to the four-dimensional models.
LiMuSE: Lightweight Multi-Modal Speaker Extraction

Multi-modal cues, including spatial information, facial expression and voiceprint, are introduced to the speech separation and speaker extraction tasks to serve as complementary information to achieve better performance. However, the introduction of these cues brings about an increasing number of parameters and model complexity, which makes it harder to deploy these models on resource-constrained devices. In this paper, we alleviate the aforementioned problem by proposing a Lightweight Multi-modal framework for Speaker Extraction (LiMuSE). We propose to use GC-equipped TCN, which incorporates Group Communication (GC) and Temporal Convolutional Network (TCN) in the Context Codec module, the audio block and the fusion block. The experiments on the MC_GRID dataset demonstrate that LiMuSE achieves on par or better performance with a much smaller number of parameters and less model complexity. We further investigate the impacts of the quantization of LiMuSE. Our code and dataset are provided11https://github.com/aispeech-lab/LiMuSE.
Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination

Automated image processing algorithms can improve the quality, efficiency, and consistency of classifying the morphology of heterogeneous carbonate rock and can deal with a massive amount of data and images seamlessly. Geoscientists and petroleum engineers face difficulties in setting the direction of the optimum method for determining petrophysical properties from core plug images of optical thin-sections, Micro-Computed Tomography (μCT), or Magnetic Resonance Imaging (MRI). Most of the successful work is from the homogeneous and clastic rocks focusing on 2D images with less focus on 3D and requiring numerical simulation. Currently, image analysis methods converge to three approaches: image processing, artificial intelligence, and combined image processing with artificial intelligence. In this work, we propose two methods to determine the porosity from 3D μCT and MRI images: an image processing method with Image Resolution Optimized Gaussian Algorithm (IROGA); advanced image recognition method enabled by Machine Learning Difference of Gaussian Random Forest (MLDGRF). Meanwhile, we have built reference 3D micro models and collected images for calibration of the IROGA and MLDGRF methods. To evaluate the predictive capability of these calibrated approaches, we ran them on 3D μCT and MRI images of natural heterogeneous carbonate rock. We also measured the porosity and lithology of the carbonate rock using three and two industry-standard ways, respectively, as reference values. Notably, IROGA and MLDGRF have produced porosity results with an accuracy of 96.2% and 97.1% on the training set and 91.7% and 94.4% on blind test validation, respectively, in comparison with the three experimental measurements. We measured limestone and pyrite reference values using two methods, X-ray powder diffraction, and grain density measurements. MLDGRF has produced lithology (limestone and pyrite) volume fractions with an accuracy of 97.7% in comparison to reference measurements.
Soliton boson stars, Q-balls and the causal Buchdahl bound

Self-gravitating non-topological solitons whose potential admits multiple vacua are promising candidates for exotic compact objects. Such objects can arise in several extensions of the Standard Model and could be produced in the early Universe. In this work, we focus on objects made from complex scalars (gravitating Q-balls/soliton boson stars), deriving analytic solutions in spherical symmetry and comparing them with fully numerical ones. In the high-compactness limit we find that these objects present an effectively linear equation of state, thus saturating the Buchdahl limit with the causality constraint. Far from that limit, these objects behave either as flat space-time Q-balls or (in the low-compactness limit) as mini boson stars stabilized by quantum pressure. We establish the robustness of this picture by analyzing a variety of potentials (including cosine, quartic and sextic ones).
Defect Detection on Semiconductor Wafers by Distribution Analysis

A method for object classification that is based on distribution analysis is proposed. In addition, a method for finding relevant features and the unification of this algorithm with another classification algorithm is proposed. The presented classification algorithm has been applied successfully to real-world measurement data from wafer fabrication of close to hundred thousand chips of several product types. The presented algorithm prefers finding the best rater in a low-dimensional search space over finding a good rater in a high-dimensional search space. Our approach is interesting in that it is fast (quasi-linear) and reached good to excellent prediction or detection quality for real-world wafer data.
Natural Adversarial Objects

Although state-of-the-art object detection methods have shown compelling performance, models often are not robust to adversarial attacks and out-of-distribution data. We introduce a new dataset, Natural Adversarial Objects (NAO), to evaluate the robustness of object detection models. NAO contains 7,934 images and 9,943 objects that are unmodified and representative of real-world scenarios, but cause state-of-the-art detection models to misclassify with high confidence. The mean average precision (mAP) of EfficientDet-D7 drops 74.5% when evaluated on NAO compared to the standard MSCOCO validation set. Moreover, by comparing a variety of object detection architectures, we find that better performance on MSCOCO validation set does not necessarily translate to better performance on NAO, suggesting that robustness cannot be simply achieved by training a more accurate model. We further investigate why examples in NAO are difficult to detect and classify. Experiments of shuffling image patches reveal that models are overly sensitive to local texture. Additionally, using integrated gradients and background replacement, we find that the detection model is reliant on pixel information within the bounding box, and insensitive to the background context when predicting class labels. NAO can be downloaded at https://drive.google. com/drive/folders/15P8sOWoJku6SSEiHLEts86ORfytGezi8.
Optimal Antenna Configurations for Digital Oilfield Implementations

Antennas play a very pivotal role in the development and advancement of digital oil fields. They provide the last mile communication link to the field locations and thus must be properly sized to ensure the link availability and reliability. There are different types of antennas currently being deployed with each having different impacts on the communication link performance. This paper discusses typical antenna configurations for implementing last mile communications to the field in digital oilfield applications. It analyzes the characteristic of each configuration, the impact these different configurations have on data security, deployment speed and communication range and presents an optimal configuration that improves data security, deployment speed and communication range.
On Jacobian group and complexity of the Y-graph

In the present paper we suggest a simple approach for counting Jacobian group of the Y -graph Y (n; k, l,m). In the case Y (n; 1, 1, 1) the structure of the Jacobian group will be find explicitly. Also, we obtain a closed formula for the number of spanning trees of Y -graph in terms of Chebyshev polynomials and give its asymtotics.
A Study of Magnetized White Dwarf + Helium Star Binary Evolution to Type Ia Supernovae

The white dwarf (WD) + helium (He) star binary channel plays an important role in the single degenerate scenario for the progenitors of type Ia supernovae (SNe Ia). Previous studies on the WD + main sequence star evolution have shown that the magnetic fields of WDs may significantly influence their accretion and nuclear burning processes. In this work we focus on the evolution of magnetized WD + He star binaries with detailed stellar evolution and binary population synthesis (BPS) calculations. In the case of magnetized WDs, the magnetic fields may disrupt the inner regions of the accretion disk, funnel the accretion flow onto the polar caps and even confine helium burning within the caps. We find that, for WDs with sufficiently strong magnetic fields, the parameter space of the potential SN Ia progenitor systems shrinks toward shorter orbital periods and lower donor masses compared with that in the non-magnetized WD case. The reason is that the magnetic confinement usually works with relatively high mass transfer rates, which can trigger strong wind mass loss from the WD, thus limiting the He-rich mass accumulation efficiency. The surviving companion stars are likely of low-mass at the moment of the SN explosions, which can be regarded as a possible explanation for the non-detection of surviving companions after the SNe or inside the SN remnants. However, the corresponding birthrate of Galactic SNe Ia in our high-magnetic models is estimated to be ∼(0.08–0.13) × 10−3 yr−1 ( ∼0.17–0.28 × 10−3 yr−1 for the non-magnetic models), significantly lower than the observed Galactic SN Ia birthrate.
Ordered $k$-Median with Outliers and Fault-Tolerance

In this paper, we study two natural generalizations of the ordered k-median problem, named robust ordered k-median and fault-tolerant ordered k-median. In ordered k-median, given a finite metric space (X, d), we seek to open a set of k facilities S ⊆ X which induces a service cost vector ~c = {d(j, S) : j ∈ X}, and minimize the ordered objective w~c. Here d(j, S) = mini∈S d(j, i) is the minimum distance between j and open facilities in S, w ∈ R is a predefined non-increasing non-negative vector, and ~c is the nonincreasingly sorted version of ~c. The current best result for ordered k-median is a (5 + ǫ)-approximation [Chakrabarty and Swamy, STOC 2019]. We first consider robust ordered k-median, also known as ordered k-median with outliers, where the input consists of an ordered k-median instance and an additional parameter m ∈ Z+. The goal is to open k facilities S, select m clients T ⊆ X and assign the nearest open facility to each client j ∈ T . In this case, the service cost vector is ~c = {d(j, S) : j ∈ T}, the vector w is in R, and the objective is w~c. We introduce a novel yet simple objective function that enables linear analysis of the non-linear ordered objective, apply an iterative rounding framework [Krishnaswamy et al., STOC 2018] and obtain a constant-factor approximate solution. We devise the first constant-factor approximations for ordered matroid median and ordered knapsack median using the same method. We also consider the fault-tolerant ordered k-median problem, where besides the same input as standard ordered k-median, we are also given additional client requirements {rj ∈ Z+ : j ∈ X} and need to assign rj distinct open facilities to each client j ∈ X. The service cost of j is the sum of distances to its assigned facilities, and the objective is still w~c. We obtain a constant-factor approximation algorithm using a novel LP relaxation with constraints created via a new sparsification technique.
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation

Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios. One representative of such challenging scenarios is to deploy a translation system for a conference with a specific topic, e.g., global warming or coronavirus, where there are usually extremely less resources due to the limited schedule. To motivate wider investigation in such a scenario, we present a real-world fine-grained domain adaptation task in machine translation (FGraDA). The FGraDA dataset consists of Chinese-English translation task for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone. Each sub-domain is equipped with a development set and test set for evaluation purposes. To be closer to reality, FGraDA does not employ any in-domain bilingual training data but provides bilingual dictionaries and wiki knowledge base, which can be easier obtained within a short time. We benchmark the fine-grained domain adaptation task and present in-depth analyses showing that there are still challenging problems to further improve the performance with heterogeneous resources.
Neural Network-based Face Pose Tracking for Interactive Face Recognition System

Security system has long been a very important aspect in almost every field. Technology advancement has made biometric systems applicable to provide higher level of security. Face recognition is one of the popular biometric modalities. An interactive face recognition system has been developed to increase the yield of the face recognition yet still uses a simple recognition algorithm. The system provides responses to the user in order to obtain a frontal face image. A neural-based face pose estimation is proposed to generate the proper response based on the captured face image. The overall accuracy of the pose recognition is 95.6%.