Miracle-dz/UniFAR-FTUs huggingface dataset:
Longitudinal dynamics and particle production in relativistic nuclear collisions

This work presents a three-dimensional dynamical initialization model for relativistic heavy-ion collisions, implementing local energy-momentum conservation and baryon charge fluctuations at string junctions. Constraining parameters using experimental data from p+p collisions at various collision energies, the model provides a very good description of the charged hadron and net proton rapidity distributions in Au+Au collisions from 7.7 to 200 GeV and Pb+Pb collisions at 8.77 and 17.3 GeV. We demonstrate the importance of fluctuations of baryon densities to string junctions for describing net-proton distributions at collision energies of 62.4 and 200 GeV. Including this improved baryon stopping description along with the requirement of strangeness neutrality also yields a good description of identified particle yields as functions of the collision energy above 7.7 GeV. We further study asymmetric p+Al and (p, d, He)+Au collisions at the top RHIC energy and p+Pb, Xe+Xe, and Pb+Pb collisions at LHC energies. We identify the produced particle rapidity distributions in asymmetric collision systems as particularly useful for constraining models of the early-time longitudinal dynamics.
Correlated quantization for distributed mean estimation and optimization

We study the problem of distributed mean estimation and optimization under communication constraints. We propose a correlated quantization protocol whose error guarantee depends on the deviation of data points instead of their absolute range. The design doesn’t need any prior knowl-edge on the concentration property of the dataset, which is required to get such dependence in previous works. We show that applying the proposed protocol as a sub-routine in distributed optimization algorithms leads to better convergence rates. We also prove the optimality of our protocol under mild assumptions. Experimental results show that our proposed algorithm outperforms existing mean estimation protocols on a diverse set of tasks.
Art-Attack: Black-Box Adversarial Attack via Evolutionary Art

Deep neural networks (DNNs) have achieved state-of-the-art performance in many tasks but have shown extreme vulnerabilities to attacks generated by adversarial examples. Many works go with a white-box attack that assumes total access to the targeted model including its architecture and gradients. A more realistic assumption is the black-box scenario where an attacker only has access to the targeted model by querying some input and observing its predicted class probabilities. Different from most prevalent black-box attacks that make use of substitute models or gradient estimation, this paper proposes a gradient-free attack by using a concept of evolutionary art to generate adversarial examples that iteratively evolves a set of overlapping transparent shapes. To evaluate the effectiveness of our proposed method, we attack three state-of-the-art image classification models trained on the CIFAR-10 dataset in a targeted manner. We conduct a parameter study outlining the impact the number and type of shapes have on the proposed attack's performance. In comparison to state-of-the-art black-box attacks, our attack is more effective at generating adversarial examples and achieves a higher attack success rate on all three baseline models.
Assessing and mitigating systematic errors in forest attribute maps utilizing harvester and airborne laser scanning data

Cut-to-length harvesters collect useful information for modeling relationships between forest attributes and airborne laser scanning (ALS) data. However, harvesters operate in mature forests, which may introduce selection biases that can result in systematic errors in harvester data-based forest attribute maps. We fitted regression models (harvester models) for volume (V), height (HL), stem frequency (N), above-ground biomass, basal area, and quadratic mean diameter (QMD) using harvester and ALS data. Performances of the harvester models were evaluated using national forest inventory plots in an 8.7 Mha study area. We estimated biases of large-area synthetic estimators and compared efficiencies of model-assisted (MA) estimators with field data-based direct estimators. The harvester models performed better in productive than unproductive forests, but systematic errors occurred in both. The use of MA estimators resulted in efficiency gains that were largest for HL (relative efficiency, RE=6.0) and smallest for QMD (RE=1.5). The bias of the synthetic estimator was largest for N (39%) and smallest for V (1%). The latter was due to an overestimation of deciduous and an underestimation of spruce forests that by chance balanced. We conclude that a probability sample of reference observations may be required to ensure the unbiasedness of estimators utilizing harvester data.
Formation Control of Nonlinear Multi-Agent Systems Using Three-Layer Neural Networks

This paper considers a leader-following formation control problem for heterogeneous, second-order, uncertain, input-affine, nonlinear multi-agent systems modeled by a directed graph. A tunable, three-layer neural network (NN) is proposed with an input layer, two hidden layers, and an output layer to approximate an unknown nonlinearity. Unlike commonly used trial and error efforts to select the number of neurons in a conventional NN, in this case an \textit{a priori} knowledge allows one to set up the number of neurons in each layer. The NN weights tuning laws are derived using the Lyapunov theory. The leader-following and formation control problems are addressed by a robust integral of the sign of the error (RISE) feedback and a NN-based control. The RISE feedback term compensates for unknown leader dynamics and the unknown, bounded disturbance in the agent error dynamics. The NN-based term compensates for the unknown nonlinearity in the dynamics of multi-agent systems, and semi-global asymptotic tracking results are rigorously proven using the Lyapunov stability theory. The results of the paper are compared with two previous results to evaluate the efficiency and performance of the proposed method.
Upper limit on the axion-photon coupling from magnetic white dwarf polarization

Polarization measurements of thermal radiation from magnetic white dwarf (MWD) stars have been proposed as a probe of axion-photon mixing. The radiation leaving the surface of the MWD is unpolarized, but if low-mass axions exist then photons polarized parallel to the direction of the MWD ’ s magnetic field may convert into axions, which induces a linear polarization dependent on the strength of the axion-photon coupling g a γγ . We model this process by using the formalism of axion-photon mixing in the presence of strong-field vacuum birefringence to show that of all stellar types MWDs are the most promising targets for axion-induced polarization searches. We then consider linear polarization data from multiple MWDs, including SDSS J135141 and Grw þ 70 ° 8247 , to show that after rigorously accounting for astrophysical uncertainties the axion-photon coupling is constrained to j g a γγ j ≲ 5 . 4 × 10 − 12 GeV − 1 at 95% confidence for axion masses m a ≲ 3 × 10 − 7 eV. This upper limit puts in tension the previously-suggested explanation of the anomalous transparency of the Universe to TeV gamma-rays in terms of axions. We identify MWD targets for which future data and modeling efforts could further improve the sensitivity to axions. statistical uncertainty is treated as a hyperparameter that is determined by maximum likelihood estimation. The red band illustrates the allowed axion contribution at 1 σ confidence. At the best-fit point the astrophysical normalization is zero. Still, we illustrate the astrophysical linear polarization model, with an arbitrary normalization.
Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference

We introduce a neuro-symbolic natural logic framework based on reinforcement learning with introspective revision. The model samples and rewards specific reasoning paths through policy gradient, in which the introspective revision algorithm modifies intermediate symbolic reasoning steps to discover reward-earning operations as well as leverages external knowledge to alleviate spurious reasoning and training inefficiency. The framework is supported by properly designed local relation models to avoid input entangling, which helps ensure the interpretability of the proof paths. The proposed model has built-in interpretability and shows superior capability in monotonicity inference, systematic generalization, and interpretability, compared with previous models on the existing datasets.
Temporal Difference Learning for Model Predictive Control

Data-driven model predictive control has two key advantages over model-free methods: a potential for improved sample efficiency through model learning, and better performance as computational budget for planning increases. However, it is both costly to plan over long horizons and challenging to obtain an accurate model of the environment. In this work, we combine the strengths of model-free and model-based methods. We use a learned task-oriented latent dynamics model for local trajectory optimization over a short horizon, and use a learned terminal value function to estimate long-term return, both of which are learned jointly by temporal difference learning. Our method, TD-MPC, achieves superior sample efficiency and asymptotic performance over prior work on both state and image-based continuous control tasks from DMControl and Meta-World. Code and video results are available at https://nicklashansen.github. io/td-mpc.
Multi-sublinear operators and their commutators on product generalized mixed Morrey spaces

In this paper, we study the boundedness for a large class of multisublinear operators Tm generated by multilinear Calderón-Zygmund operators and their commutators T b m,i (i = 1, · · · ,m) on the product generalized mixed Morrey spaces M φ1 ~ q1 (R) × · · · × M φm ~ qm (R). We find the sufficient conditions on (φ1, · · · , φm, φ) which ensure the boundedness of the operator Tm from M φ1 ~ q1 (R)× · · ·×M φm ~ qm (R) to M φ ~q (R). Moreover, the sufficient conditions for the boundeness of T b m,i from M φ1 ~ q1 (R) × · · · × M φm ~ qm (R) to M φ ~q (R) are also studied. As applications, we obtain the boundedness for the multi-sublinear maximal operator, the multilinear Calderón-Zygmund operator and their commutators on product generalzied mixed Morrey spaces. Mathematics Subject Classification (2010). Primary 42B20; Secondary 42B25, 42B35.
Bayesian Multi-Arm De-Intensification Designs

In recent years new cancer treatments improved survival in multiple histologies. Some of these therapeutics, and in particular treatment combinations, are often associated with severe treatment-related adverse events (AEs). It is therefore important to identify alternative de-intensified therapies, for example dose-reduced therapies, with reduced AEs and similar efficacy. We introduce a sequential design for multiarm de-intensification studies. The design evaluates multiple de-intensified therapies at different dose levels, one at the time, based on modeling of toxicity and efficacy endpoints. We study the utility of the design in oropharynx cancer de-intensification studies. We use a Bayesian nonparametric model for efficacy and toxicity outcomes to define decision rules at interim and final analysis. Interim decisions include early termination of the study due to inferior survival of experimental arms compared to the standard of care (SOC), and transitions from one de-intensified treatment arm to another with a further reduced dose when there is sufficient evidence of non-inferior survival. We evaluate the operating characteristics of the design using simulations and ∗steffen.ventz. 81@gmail.com 1 ar X iv :2 20 3. 04 82 9v 1 [ st at . A P] 9 M ar 2 02 2 data from recent de-intensification studies in human papillomavirus (HPV)-associated oropharynx cancer.
Update Compression for Deep Neural Networks on the Edge

An increasing number of artificial intelligence (AI) applications involve the execution of deep neural networks (DNNs) on edge devices. Many practical reasons motivate the need to update the DNN model on the edge device post-deployment, such as refining the model, concept drift, or outright change in the learning task. In this paper, we consider the scenario where retraining can be done on the server side based on a copy of the DNN model, with only the necessary data transmitted to the edge to update the deployed model. However, due to bandwidth constraints, we want to minimise the transmission required to achieve the update. We develop a simple approach based on matrix factorisation to compress the model update—this differs from compressing the model itself. The key idea is to preserve existing knowledge in the current model and optimise only small additional parameters for the update which can be used to reconstitute the model on the edge. We compared our method to similar techniques used in federated learning; our method usually requires less than half of the update size of existing methods to achieve the same accuracy.
Spectral stability of small-amplitude dispersive shocks in quantum hydrodynamics with viscosity

<p style='text-indent:20px;'>A compressible viscous-dispersive Euler system in one space dimension in the context of quantum hydrodynamics is considered. The dispersive term is due to quantum effects described through the Bohm potential and the viscosity term is of linear type. It is shown that small-amplitude viscous-dispersive shock profiles for the system under consideration are spectrally stable, proving in this fashion a previous numerical observation by Lattanzio <i>et al.</i> [<xref ref-type="bibr" rid="b28">28</xref>,<xref ref-type="bibr" rid="b29">29</xref>]. The proof is based on spectral energy estimates which profit from the monotonicty of the profiles in the small-amplitude regime. </p>
Efficient Image Representation Learning with Federated Sampled Softmax

Learning image representations on decentralized data can bring many benefits in cases where data cannot be aggregated across data silos. Softmax cross entropy loss is highly effective and commonly used for learning image representations. Using a large number of classes has proven to be particularly beneficial for the descriptive power of such representations in centralized learning. However, doing so on decentralized data with Federated Learning is not straightforward as the demand on FL clients' computation and communication increases proportionally to the number of classes. In this work we introduce federated sampled softmax (FedSS), a resource-efficient approach for learning image representation with Federated Learning. Specifically, the FL clients sample a set of classes and optimize only the corresponding model parameters with respect to a sampled softmax objective that approximates the global full softmax objective. We examine the loss formulation and empirically show that our method significantly reduces the number of parameters transferred to and optimized by the client devices, while performing on par with the standard full softmax method. This work creates a possibility for efficiently learning image representations on decentralized data with a large number of classes under the federated setting.
Data-Efficient Structured Pruning via Submodular Optimization

Structured pruning is an effective approach for compressing large pre-trained neural networks without significantly affecting their performance. However, most current structured pruning methods do not provide any performance guarantees, and often require fine-tuning, which makes them inapplicable in the limited-data regime. We propose a principled data-efficient structured pruning method based on submodular optimization. In particular, for a given layer, we select neurons/channels to prune and corresponding new weights for the next layer, that minimize the change in the next layer's input induced by pruning. We show that this selection problem is a weakly submodular maximization problem, thus it can be provably approximated using an efficient greedy algorithm. Our method is guaranteed to have an exponentially decreasing error between the original model and the pruned model outputs w.r. t the pruned size, under reasonable assumptions. It is also one of the few methods in the literature that uses only a limited-number of training data and no labels. Our experimental results demonstrate that our method outperforms state-of-the-art methods in the limited-data regime.
DUAL: Textless Spoken Question Answering with Speech Discrete Unit Adaptive Learning

Spoken Question Answering (SQA) has 001 gained research attention and made remark-002 able progress in recent years. However, ex-003 isting SQA methods rely on Automatic Speech 004 Recognition (ASR) transcriptions, which is 005 time and cost-prohibitive to collect. This work 006 proposes an ASR transcription-free SQA frame-007 work named Discrete Unit Adaptive Learning 008 (DUAL), which leverages unlabeled data for 009 pre-training and is fine-tuned by the SQA down-010 stream task. DAUL can directly predict the 011 time interval of the spoken answer from the 012 spoken document. We also release a new SQA 013 benchmark corpus Natural Multi-speaker Spo-014 ken Question Answering (NMSQA) for testing 015 SQA in realistic scenarios. The experimen-016 tal results show that DUAL performs competi-017 tively with the cascade approach (ASR + text 018 QA), and DUAL is robust to real-world speech. 019 We will open-source our code and model to 020 inspire more SQA innovations from the com-021 munity. 022
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms

In this paper, we consider nonconvex minimax optimization, which is gaining prominence in many modern machine learning applications such as GANs. Large-scale edge-based collection of training data in these applications calls for communication-efficient distributed optimization algorithms, such as those used in federated learning, to process the data. In this paper, we analyze Local stochastic gradient descent ascent (SGDA), the local-update version of the SGDA algorithm. SGDA is the core algorithm used in minimax optimization, but it is not well-understood in a distributed setting. We prove that Local SGDA has \textit{order-optimal} sample complexity for several classes of nonconvex-concave and nonconvex-nonconcave minimax problems, and also enjoys \textit{linear speedup} with respect to the number of clients. We provide a novel and tighter analysis, which improves the convergence and communication guarantees in the existing literature. For nonconvex-PL and nonconvex-one-point-concave functions, we improve the existing complexity results for centralized minimax problems. Furthermore, we propose a momentum-based local-update algorithm, which has the same convergence guarantees, but outperforms Local SGDA as demonstrated in our experiments.
Learning from Few Examples: A Summary of Approaches to Few-Shot Learning

Few-Shot Learning refers to the problem of learning the underlying pattern in the data just from a few training samples. Requiring a large number of data samples, many deep learning solutions suffer from data hunger and extensively high computation time and resources. Furthermore, data is often not available due to not only the nature of the problem or privacy concerns but also the cost of data preparation. Data collection, preprocessing, and labeling are strenuous human tasks. Therefore, few-shot learning that could drastically reduce the turnaround time of building machine learning applications emerges as a low-cost solution. This survey paper comprises a representative list of recently proposed few-shot learning algorithms. Given the learning dynamics and characteristics, the approaches to few-shot learning problems are discussed in the perspectives of meta-learning, transfer learning, and hybrid approaches (i.e., different variations of the few-shot learning problem).
Learning Progressive Distributed Compression Strategies From Local Channel State Information

This paper proposes a deep learning framework to design distributed compression strategies in which distributed agents need to compress high-dimensional observations of a source, then send the compressed bits via bandwidth limited links to a fusion center for source reconstruction. Further, we require the compression strategy to be progressive so that it can adapt to the varying link bandwidths between the agents and the fusion center. Moreover, to ensure scalability, we investigate strategies that depend only on the local channel state information (CSI) at each agent. Toward this end, we use a data-driven approach in which the progressive linear combination and uniform quantization strategy at each agent are trained as a function of its local CSI. To deal with the challenges of modeling the quantization operations (which always produce zero gradients in the training of neural networks), we propose a novel approach of exploiting the statistics of the batch training data to set the dynamic ranges of the uniform quantizers. Numerically, we show that the proposed distributed estimation strategy designed with only local CSI can significantly reduce the signaling overhead and can achieve a lower mean-squared error distortion for source reconstruction than state-of-the-art designs that require global CSI at comparable overall communication cost.
Direct laser cooling of calcium monohydride molecules

We demonstrate optical cycling and laser cooling of a cryogenic buffer-gas beam of calcium monohydride (CaH) molecules. We measure vibrational branching ratios for laser cooling transitions for both excited electronic states A and B. Furthermore, we measure that repeated photon scattering via the A ← X transition is achievable at a rate of ∼1.6×106 photons s−1 and demonstrate interaction-time limited scattering of ∼200 photons by repumping the largest vibrational decay channel. We also demonstrate a sub-Doppler cooling technique, namely the magnetically assisted Sisyphus effect, and use it to cool the transverse temperature of a molecular beam of CaH. Using a standing wave of light, we lower the transverse temperature from 12.2(1.2) mK to 5.7(1.1) mK. We compare these results to a model that uses optical Bloch equations and Monte Carlo simulations of the molecular beam trajectories. This work establishes a clear pathway for creating a magneto-optical trap (MOT) of CaH molecules. Such a MOT could serve as a starting point for production of ultracold hydrogen gas via dissociation of a trapped CaH cloud.
An accurate IoT Intrusion Detection Framework using Apache Spark

The internet has caused tremendous changes since its appearance in the 1980s, and now, the Internet of Things (IoT) seems to be doing the same. The potential of IoT has made it the center of attention for many people, but, where some see an opportunity to contribute, others may see IoT networks as a target to be exploited. The high number of IoT devices makes them the perfect setup for staging denial-of-service attacks (DoS) that can have devastating consequences. This renders the need for cybersecurity measures such as intrusion detection systems (IDSs) evident. The aim of this paper is to build an IDS using the big data platform, Apache Spark. Apache Spark was used along with its ML library (MLlib) and the BoT-IoT dataset. The IDS was then tested and evaluated based on F-Measure (f1), as was the standard when evaluating imbalanced data. Two rounds of tests were performed, a partial dataset for minimizing bias, and the full BoT-IoT dataset for exploring big data and ML capabilities in a security setting. For the partial dataset, the Random Forest algorithm had the highest performance for binary classification at an average f1 measure of 99.7%, as well as 99.6% for main category classification, and an 88.5% f1 measure for sub category classification. As for the complete dataset, the Decision Tree algorithm scored the highest f1 measures for all conducted tests; 97.9% for binary classification, 79% for main category classification, and 77% for sub category classification.