Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing possible effects of circumgalactic media on the metal content of galaxies through the mass–metallicity relationship

The circumgalactic medium (CGM) connects the gas between the interstellar medium (ISM) and the intergalactic medium, which plays an important role in galaxy evolution. We use the stellar mass-metallicity relationship to investigate whether sharing the CGM will affect the distribution of metals in galaxy pairs. The optical emission lines from the Sloan Digital Sky Survey Data Release (SDSS DR7) are used to measure the gas-phase metallicity. We find that there is no significant difference in the distribution of the metallicity difference between two members in star forming-star forming pairs (∆log(O/H)diff), metallicity offset from the best-fitted stellar mass-metallicity relationship of galaxies in pairs (∆log(O/H)MS), as compared to “fake” pairs. By looking at ∆log(O/H)diff and ∆log(O/H)MS as a function of the star formation rate (SFR), specific star formation rate (sSFR), and stellar mass ratio, no difference is seen between galaxies in pairs and control galaxies. From our results, the share of the CGM may not play an important role in shaping the evolution of metal contents of galaxies.
Engineering the collapse of lifetime distribution of nitrogen-vacancy centers in nanodiamonds

We demonstrate experimentally that the distribution of the decay rates of nitrogen-vacancy centers in diamond becomes narrower by over five times for nanodiamonds embedded in thin chalcogenide films. The nitrogen-vacancy (NV) center is a point defect in diamond formed by a lattice vacancy paired with a nitrogen atom, which replaces the carbon atom adjacent to the vacancy. 1 NV centers are fluorescent in the red part of the optical spectrum and, individually, can act as well- localized sources of single photons with high photostability even at room temperature. 2-5 Two charge states are known for NV centers, namely NV 0 and NV - . 1 They exhibit zero phonon lines (ZPLs) centered at 575 nm and 637 nm, respectively, with characteristic broad phonon sidebands at room temperature. 1 There are also two distinct lifetimes for NV 0 and NV - centres. 6 Typically only emission from the NV 0 state is observed in cathodoluminescence experiments at room temperature. 7-16 The intensity and spectrum of NV emission are highly sensitive to external perturbations, such as temperature, strain, electric and magnetic fields. 1, 9 Moreover, subwavelength confinement of optical fields in metallic nanostructures can strongly modify the emission decay rates of the NV centres when coupled to surface plasmon resonance. 17-20 Although naturally present in large single-crystal diamonds, NV centers have been routinely produced in nanocrystals (nanodiamonds), thermally) between amorphous and crystalline phases, 39-41 our approach offers a straightforward way of implementing active control over the emission statistics in nanodiamonds.
Prospective Artificial Intelligence Approaches for Active Cyber Defence

Cybercriminals are rapidly developing new malicious tools that leverage artificial intelligence (AI) to enable new classes of adaptive and stealthy attacks. New defensive methods need to be developed to counter these threats. Some cybersecurity professionals are speculating AI will enable corresponding new classes of active cyber defence measures -- is this realistic, or currently mostly hype? The Alan Turing Institute, with expert guidance from the UK National Cyber Security Centre and Defence Science Technology Laboratory, published a research roadmap for AI for ACD last year. This position paper updates the roadmap for two of the most promising AI approaches -- reinforcement learning and causal inference - and describes why they could help tip the balance back towards defenders.
Multiscale deep context modeling for lossless point cloud geometry compression

We propose a practical deep generative approach for lossless point cloud geometry compression, called MSVoxelDNN, and show that it significantly reduces the rate compared to the MPEG G-PCC codec. Our previous work based on autoregressive models (VoxelDNN [1]) has a fast training phase, however, inference is slow as the occupancy probabilities are predicted sequentially, voxel by voxel. In this work, we employ a multiscale architecture which models voxel occupancy in coarse-to-fine order. At each scale, MSVoxelDNN divides voxels into eight conditionally independent groups, thus requiring a single network evaluation per group instead of one per voxel. We evaluate the performance of MSVoxelDNN on a set of point clouds from Microsoft Voxelized Upper Bodies (MVUB) and MPEG, showing that the current method speeds up encoding/decoding times significantly compared to the previous VoxelDNN, while having average rate saving over G-PCC of 17.5%. The implementation is available at https://github.com/Weafre/MSVoxelDNN.
Automated problem setting selection in multi-target prediction with AutoMTP

Algorithm Selection (AS) is concerned with the selection of the best-suited algorithm out of a set of candidates for a given problem. The area of AS has received a lot of attention from machine learning researchers and practitioners, as positive results along this line of research can make expertise in ML more readily accessible to experts in other domains as well as to the general public. Another quickly expanding area is that of Multi-Target Prediction (MTP). The ability to simultaneously predict multiple target variables of diverse types makes MTP of interest for a plethora of applications. MTP embraces several subﬁelds of machine learning, such as multi-label classiﬁcation, multi-target regression, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. This work combines the two above-mentioned areas by proposing AutoMTP, an automated framework that performs algorithm selection for MTP. AutoMTP is realized by adopting a rule-based system for the algorithm selection step and a ﬂexible neural network architecture that can be used for the several subﬁelds of MTP.
GPS-denied Navigation: Attitude, Position, Linear Velocity, and Gravity Estimation with Nonlinear Stochastic Observer

Successful navigation of a rigid-body traveling with six degrees of freedom (6 DoF) requires accurate estimation of attitude, position, and linear velocity. The true navigation dynamics are highly nonlinear and are modeled on the matrix Lie group of SE2(3). This paper presents novel geometric nonlinear continuous stochastic navigation observers on SE2(3) capturing the true nonlinearity of the problem. The proposed observers combines IMU and landmark measurements. It efficiently handles the IMU measurement noise. The proposed observers are guaranteed to be almost semi-globally uniformly ultimately bounded in the mean square. Quaternion representation is provided. A real-world quadrotor measurement dataset is used to validate the effectiveness of the proposed observers in its discrete form.
Bitcoin Address Clustering Method Based on Multiple Heuristic Conditions

We analyzed the associations between Bitcoin transactions and addresses to cluster address and further find groups of addresses controlled by the same entity. It revealed the vulnerabilities of Bitcoin anonymity mechanism, which could be used by the law enforcement agencies to track and crack down illegal transactions. However, single heuristic method and incomplete heuristic conditions were difficult to cluster a large number of addresses comprehensively and accurately. Therefore, this paper reviewed a variety of heuristics, and used multiple heuristics comprehensively to cluster addresses to improve the degree of address aggregation and address recall rate, which laid a foundation for further inferring of entity identity.
Protocluster detection in simulations of HSC–SSP and the 10-yr LSST forecast, using PCcones

The progenitors of present-day galaxy clusters give important clues about the evolution of the large scale structure, cosmic mass assembly, and galaxy evolution. Simulations are a major tool for these studies since they are used to interpret observations. In this work, we introduce a set of “protocluster-lightcones”, dubbed PCcones. They are mock galaxy catalogs generated from the Millennium Simulation with the L-GALAXIES semi-analytic model. These lightcones were constructed by placing a desired structure at the redshift of interest in the centre of the cone. This approach allows to adopt a set of observational constraints, such as magnitude limits and uncertainties in magnitudes and photometric redshifts (photo-zs), to produce realistic simulations of photometric surveys. We show that photo-zs obtained with PCcones are more accurate than those obtained directly with the Millennium Simulation, mostly due to the difference in how apparent magnitudes are computed. We apply PCcones in the determination of the expected accuracy of protocluster detection using photo-zs in the z = 1 − 3 range in the wide-layer of HSC-SSP and the 10-year LSST forecast. With our technique, we expect to recover only ∼ 38% and ∼ 43% of all massive galaxy cluster progenitors with more than 70% of purity for HSC-SSP and LSST, respectively. Indeed, the combination of observational constraints and photo-z uncertainties affects the detection of structures critically for both emulations, indicating the need of spectroscopic redshifts to improve detection. We also compare our mocks of the Deep CFHTLS at z < 1.5 with observed cluster catalogs, as an extra validation of the lightcones and methods.
A Dynamical Measurement of the Disk Mass in Elias 2–27

Recent multi-wavelength Atacama Large Millimeter/submillimeter Array (ALMA) observations of the protoplanetary disk orbiting around Elias 2–27 revealed a two-armed spiral structure. The observed morphology, together with the young age of the star and the disk-to-star mass ratio estimated from dust-continuum emission, make this system a perfect laboratory to investigate the role of self-gravity in the early phases of star formation. This is particularly interesting if we consider that gravitational instabilities could be a fundamental first step for the formation of planetesimals and planets. In this Letter, we model the rotation curve obtained by CO data of Elias 2–27 with a theoretical rotation curve, including both the disk self-gravity and the star contribution to the gravitational potential. We compare this model with a purely Keplerian one and with a simple power-law function. We find that (especially for the 13CO isotopologue) the rotation curve is better described by considering not only the star, but also the disk self-gravity. We are thus able to obtain for the first time a dynamical estimate of the disk mass of 0.08 ± 0.04 M ⊙ and the star mass of 0.46 ± 0.03 M ⊙ (in the more general case), the latter being comparable with previous estimates. From these values, we derive that the disk is 17% of the star mass, meaning that it could be prone to gravitational instabilities. This result would strongly support the hypothesis that the two spiral arms are generated by gravitational instabilities.
Towards Guaranteed Safety Assurance of Automated Driving Systems With Scenario Sampling: An Invariant Set Perspective

How many scenarios are sufficient to validate the safe Operational Design Domain (ODD) of an Automated Driving System (ADS) equipped vehicle? Is a more significant number of sampled scenarios guaranteeing a more accurate safety assessment of the ADS? Despite the various empirical success of ADS safety evaluation with scenario sampling in practice, some of the fundamental properties are largely unknown. This paper seeks to remedy this gap by formulating and tackling the scenario sampling safety assurance problem from a set invariance perspective. First, a novel conceptual equivalence is drawn between the scenario sampling safety assurance problem and the data-driven robustly controlled forward invariant set validation and quantification problem. This paper then provides a series of complete solutions with finite-sampling analyses for the safety validation problem that authenticates a given ODD. On the other hand, the quantification problem escalates the validation challenge and starts looking for a safe sub-domain of a particular property. This inspires various algorithms that are provably probabilistic incomplete, probabilistic complete but sub-optimal, and asymptotically optimal. Finally, the proposed asymptotically optimal scenario sampling safety quantification algorithm is also empirically demonstrated through simulation experiments.
Lackadaisical quantum walks on 2D grids with multiple marked vertices

Lackadaisical quantum walk (LQW) is a quantum analog of a classical lazy walk, where each vertex has a self-loop of weight l. For a regular N×N 2D grid LQW can find a single marked vertex with O(1) probability in O(NlogN) steps using l = d/N, where d is the degree of the vertices of the grid [11]. For multiple marked vertices, however, l = d/N is not optimal as the success probability decreases with the increase of the number of marked vertices [12]. In this paper, we numerically study search by LQW for different types of 2D grids—triangular, rectangular and honeycomb—with multiple marked vertices. We show that in all cases the weight l = m ⋅ d/N, where m is the number of marked vertices, still leads to O(1) success probability.
Seed Word Selection for Weakly-Supervised Text Classification with Unsupervised Error Estimation

Weakly-supervised text classification aims to induce text classifiers from only a few user-provided seed words. The vast majority of previous work assumes high-quality seed words are given. However, the expert-annotated seed words are sometimes non-trivial to come up with. Furthermore, in the weakly-supervised learning setting, we do not have any labeled document to measure the seed words’ efficacy, making the seed word selection process “a walk in the dark”. In this work, we remove the need for expert-curated seed words by first mining (noisy) candidate seed words associated with the category names. We then train interim models with individual candidate seed words. Lastly, we estimate the interim models’ error rate in an unsupervised manner. The seed words that yield the lowest estimated error rates are added to the final seed word set. A comprehensive evaluation of six binary classification tasks on four popular datasets demonstrates that the proposed method outperforms a baseline using only category name seed words and obtained comparable performance as a counterpart using expert-annotated seed words.
Inferring Drop-in Binary Parsers from Program Executions

We present BIEBER (Byte-IdEntical Binary parsER), the first system to model and regenerate a full working parser from instrumented program executions. To achieve this, BIEBER exploits the regularity (e.g., header fields and array-like data structures) that is commonly found in file formats. Key generalization steps derive strided loops that parse input file data and rewrite concrete loop bounds with expressions over input file header bytes. These steps enable BIEBER to generalize parses of specific input files to obtain parsers that operate over input files of arbitrary size. BIEBER also incrementally and efficiently infers a decision tree that reads file header bytes to route input files of different types to inferred parsers of the appropriate type. The inferred parsers and decision tree are expressed in an IR; separate backends (C and Perl in our prototype) can translate the IR into the same language as the original program (for a safer drop-in replacement), or automatically port to a different language. An empirical evaluation shows that BIEBER can successfully regenerate parsers for six file formats (waveform audio [1654 files], MT76x0 .BIN firmware containers [5 files], OS/2 1.x bitmap images [9 files], Windows 3.x bitmaps [9971 files], Windows 95/NT4 bitmaps [133 files], and Windows 98/2000 bitmaps [859 files]), correctly parsing 100% (>= 99.98% when using standard held-out cross-validation) of the corresponding corpora. The regenerated parsers contain automatically inserted safety checks that eliminate common classes of errors such as memory errors. We find that BIEBER can help reverse-engineer file formats, because it automatically identifies predicates for the decision tree that relate to key semantics of the file format. We also discuss how BIEBER helped us detect and fix two new bugs in stb_image as well as independently rediscover and fix a known bug.
Particle swarm optimization in constrained maximum likelihood estimation a case study

The aim of paper is to apply two types of particle swarm optimization, global best andlocal best PSO to a constrained maximum likelihood estimation problem in pseudotime anal-ysis, a sub-field in bioinformatics. The results have shown that particle swarm optimizationis extremely useful and efficient when the optimization problem is non-differentiable and non-convex so that analytical solution can not be derived and gradient-based methods can not beapplied.
Differentiable Model Compression via Pseudo Quantization Noise

We propose DiffQ a differentiable method for model compression for quantizing model parameters without gradient approximations (e.g., Straight Through Estimator). We suggest adding independent pseudo quantization noise to model parameters during training to approximate the effect of a quantization operator. DiffQ is differentiable both with respect to the unquantized weights and the number of bits used. Given a single hyper-parameter balancing between the quantized model size and accuracy, DiffQ optimizes the number of bits used per individual weight or groups of weights, in end-to-end training. We experimentally verify that our method is competitive with STE based quantization techniques on several benchmarks and architectures for image classification, language modeling, and audio source separation. For instance, on the ImageNet dataset, DiffQ compresses a 12 layers transformer-based model by more than a factor of 8, (lower than 4 bits precision per weight on average), with a loss of 0.3% in model accuracy. Code is available at github.com/facebookresearch/diffq.
Robust parameter design for Wiener-based binaural noise reduction methods in hearing aids

This work presents a method for designing the weighting parameter required by Wiener-based binaural noise reduction methods. This parameter establishes the desired tradeoff between noise reduction and binaural cue preservation in hearing aid applications. The proposed strategy was specially derived for the preservation of interaural level difference, interaural time difference and interaural coherence binaural cues. It is defined as a function of the average input noise power at the microphones, providing robustness against the influence of joint changes in noise and speech power (Lombard effect), as well as to signal to noise ratio (SNR) variations. A theoretical framework, based on the mathematical definition of the homogeneity degree, is presented and applied to a generic augmented Wiener-based cost function. The theoretical insights obtained are supported by computational simulations and psychoacoustic experiments using the multichannel Wiener filter with interaural transfer function preservation technique (MWF-ITF), as a case study. Statistical analysis indicates that the proposed dynamic structure for the weighting parameter and the design method of its fixed part provide significant robustness against changes in the original binaural cues of both speech and residual noise, at the cost of a small decrease in the noise reduction performance, as compared to the use of a purely fixed weighting parameter.
Singular Perturbation-based Reinforcement Learning of Two-Point Boundary Optimal Control Systems

We solve the problem of two-point boundary optimal control of linear time-varying systems with unknown model dynamics using reinforcement learning. Leveraging singular perturbation theory techniques, we transform the time-varying optimal control problem into two time-invariant subproblems. This allows the utilization of an off-policy iteration method to learn the controller gains. We show that the performance of the learning-based controller approximates that of the model-based optimal controller and the approximation accuracy improves as the control problem’s time horizon increases. We also provide a simulation example to verify the results.
Fine-grained anomaly detection via multi-task self-supervision

Detecting anomalies using deep learning has become a major challenge over the last years, and is becoming increasingly promising in several fields. The introduction of self-supervised learning has greatly helped many methods including anomaly detection where simple geometric transformation recognition tasks are used. However these methods do not perform well on fine-grained problems since they lack finer features. By combining both high-scale shape features and low-scale fine features in a multi-task framework, our method greatly improves fine-grained anomaly detection. It outperforms state-of-the-art with up to 31% relative error reduction measured with AUROC on various anomaly detection problems including one-vs-all, out-of-distribution detection and face presentation attack detection.
How Heavy Is It? Humanoid Robot Estimating Physical Properties of Unknown Objects Without Force/Torque Sensors

—Many robots utilize commercial force/torque sensors to identify physical properties of a priori unknown objects. However, such sensors can be difﬁcult to apply to smaller-sized robots due to their weight, size, and high-cost. In this paper, we propose a framework for smaller-sized humanoid robots to estimate the inertial properties of unknown objects without using force/torque sensors. In our framework, a neural network is designed and trained to predict joint torque outputs. The neural network’s inputs are robot’s joint angle, steady-state joint error, and motor current. All of these can be easily obtained from many existing smaller-sized robots. As the joint rotation direction is taken into account, the neural network can be trained with a smaller sample size, but still maintains accurate torque estimation capability. Eventually, the inertial properties of the objects are identiﬁed using a nonlinear optimization method. Our proposed framework has been demonstrated on a NAO humanoid robot.
Using Chandra Localizations and Gaia Distances and Proper Motions to Classify Hard X-Ray Sources Discovered by INTEGRAL

Here, we report on X-ray observations of ten 17–60 keV sources discovered by the International Gamma-Ray Astrophysics Laboratory satellite. The primary new information is sub-arcsecond positions obtained by the Chandra X-ray Observatory. In six cases (IGR J17040-4305, IGR J18017-3542, IGR J18112-2641, IGR J18434-0508, IGR J19504+3318, and IGR J20084+3221), a unique Chandra counterpart is identified with a high degree of certainty, and for five of these sources (all but J19504), Gaia distances or proper motions indicate that they are Galactic sources. For four of these, the most likely classifications are that the sources are magnetic cataclysmic variables (CVs). J20084 could be either a magnetic CV or a high-mass X-ray binary. We classify the sixth source (J19504) as a likely active galactic nucleus (AGN). In addition, we find likely Chandra counterparts to IGR J18010-3045 and IGR J19577+3339, and the latter is a bright radio source and probable AGN. The other two sources, IGR J12529-6351 and IGR J18013-3222, do not have likely Chandra counterparts, indicating that they are transient, highly variable, or highly absorbed.