Miracle-dz/UniFAR-FTUs huggingface dataset:
Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning

Learning to reach goal states and learning diverse skills through mutual information (MI) maximization have been proposed as principled frameworks for self-supervised reinforcement learning, allowing agents to acquire broadly applicable multitask policies with minimal reward engineering. Starting from a simple observation that the standard goal-conditioned RL (GCRL) is encapsulated by the optimization objective of variational empowerment, we discuss how GCRL and MI-based RL can be generalized into a single family of methods, which we name variational GCRL (VGCRL), interpreting variational MI maximization, or variational empowerment, as representation learning methods that acquire functionally-aware state representations for goal reaching. This novel perspective allows us to: (1) derive simple but unexplored variants of GCRL to study how adding small representation capacity can already expand its capabilities; (2) investigate how discriminator function capacity and smoothness determine the quality of discovered skills, or latent goals, through modifying latent dimensionality and applying spectral normalization; (3) adapt techniques such as hindsight experience replay (HER) from GCRL to MI-based RL; and lastly, (4) propose a novel evaluation metric, named latent goal reaching (LGR), for comparing empowerment algorithms with different choices of latent dimensionality and discriminator parameterization. Through principled mathematical derivations and careful experimental studies, our work lays a novel foundation from which to evaluate, analyze, and develop representation learning techniques in goal-based RL.
Relaxation Dynamics in the Energy Landscape of Glass-Forming Liquids

We numerically study the zero-temperature relaxation dynamics of several glass-forming models to their inherent structures, following quenches from equilibrium configurations sampled across a wide range of initial temperatures. In a mean-field Mari-Kurchan model, we find that relaxation changes from a power-law to an exponential decay below a well-defined temperature, consistent with recent findings in mean-field $p$-spin models. By contrast, for finite-dimensional systems, the relaxation is always algebraic, with a non-trivial universal exponent at high temperatures crossing over to a harmonic value at low temperatures. We demonstrate that this apparent evolution is controlled by a temperature-dependent population of localised glassy excitations. Our work unifies several recent lines of studies aiming at a detailed characterisation of the complex potential energy landscape of glass-formers, and challenges both mean-field and real space descriptions of glasses.
Rich dynamics caused by known biological brain network features resulting in stateful networks

The mammalian brain could contain dense and sparse network connectivity structures, including both excitatory and inhibitory neurons, but is without any clearly defined output layer. The neurons have time constants, which mean that the integrated network structure has state memory. The network structure contains complex mutual interactions between the neurons under different conditions, which depend on the internal state of the network. The internal state can be defined as the distribution of activity across all individual neurons across the network. Therefore, the state of a neuron/network becomes a defining factor for how information is represented within the network. Towards this study, we constructed a fully connected (with dense/sparse coding strategies) recurrent network comprising of both excitatory and inhibitory neurons, driven by pseudo-random inputs of varying frequencies. In this study we assessed the impact of varying specific intrinsic parameters of the neurons that enriched network state dynamics, such as initial neuron activity, amount of inhibition in combination with thresholded neurons and conduction delays. The impact was assessed by quantifying the changes in mutual interactions between the neurons within the network for each given input. We found such effects were more profound in sparsely connected networks than in densely connected networks. However, also densely connected networks could make use of such dynamic changes in the mutual interactions between neurons, as a given input could induce multiple different network states.
SIMLR: Machine Learning inside the SIR model for COVID-19 Forecasting

Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the epidemiological SIR model. For each region, SIMLR tracks the changes in the policies implemented at the government level, which it uses to estimate the time-varying parameters of an SIR model for forecasting the number of new infections one to four weeks in advance. It also forecasts the probability of changes in those government policies at each of these future times, which is essential for the longer-range forecasts. We applied SIMLR to data from in Canada and the United States, and show that its mean average percentage error is as good as state-of-the-art forecasting models, with the added advantage of being an interpretable model. We expect that this approach will be useful not only for forecasting COVID-19 infections, but also in predicting the evolution of other infectious diseases.
On using distributed representations of source code for the detection of C security vulnerabilities

This paper presents an evaluation of the code representation model Code2vec when trained on the task of detecting security vulnerabilities in C source code. We leverage the open-source library astminer to extract path-contexts from the abstract syntax trees of a corpus of labeled C functions. Code2vec is trained on the resulting path-contexts with the task of classifying a function as vulnerable or non-vulnerable. Using the CodeXGLUE benchmark, we show that the accuracy of Code2vec for this task is comparable to simple transformer-based methods such as pre-trained RoBERTa, and outperforms more naive NLP-based methods. We achieved an accuracy of 61.43% while maintaining low computational requirements relative to larger models.
Semi-supervised Conditional Density Estimation for Imputation and Classiﬁcation of Incomplete Instances

—Incomplete instances with various missing attributes in many real-world scenes have brought challenges to the classiﬁ-cation task. There are some missing values imputation methods to ﬁll the missing values with substitute values before classiﬁcation. However, the separation between imputation and classiﬁcation may lead to inferior performance since label information are ignored during imputation. Moreover, these imputation meth-ods tend to initialize these missing values with strong prior assumptions, while the unreliability of such initialization is rarely considered. To tackle these problems, a novel semi-supervised conditional normalizing ﬂow (SSCFlow) is proposed in this paper. SSCFlow explicitly utilizes the observed labels to facilitate the imputation and classiﬁcation simultaneously by employing a semi-supervised algorithm to estimate the conditional probability density of missing values. Moreover, SSCFlow takes the initialized missing values as corrupted initial imputation and iteratively reconstructs their latent representations with an overcomplete denoising autoencoder to approximate the true conditional probability density of missing values. Experiments have been conducted with real-world datasets to demonstrate the robustness and efﬁciency of the proposed algorithm.
Preparation of Many-body Ground States by Time Evolution with Variational Microscopic Magnetic Fields and Incomplete Interactions

State preparation is of fundamental importance in quantum physics, which can be realized by constructing the quantum circuit as a unitary that transforms the initial state to the target, or implementing a quantum control protocol to evolve to the target state with a designed Hamiltonian. In this work, we study the latter on quantum many-body systems by the time evolution with fixed couplings and variational magnetic fields. In specific, we consider to prepare the ground states of the Hamiltonians containing certain interactions that are missing in the Hamiltonians for the time evolution. An optimization method is proposed to optimize the magnetic fields by "fine-graining" the discretization of time, in order to gain high precision and stability. The back propagation technique is utilized to obtain the gradients of the fields against the logarithmic fidelity. Our method is tested on preparing the ground state of Heisenberg chain with the time evolution by the XY and Ising interactions, and its performance surpasses two baseline methods that use local and global optimization strategies, respectively. Our work can be applied and generalized to other quantum models such as those defined on higher dimensional lattices. It enlightens to reduce the complexity of the required interactions for implementing quantum control or other tasks in quantum information and computation by means of optimizing the magnetic fields.
Low-efficiency long gamma-ray bursts: A case study with AT2020blt

The Zwicky Transient Facility recently announced the detection of an optical transient AT2020blt at redshift z = 2 . 9, consistent with the afterglow of an on-axis gamma-ray burst. However, no prompt emission was observed. We analyse AT2020blt with detailed models, showing the data are best explained as the afterglow of an on-axis long gamma-ray burst, ruling out other hypotheses such as a cocoon and a low-Lorentz factor jet. We search Fermi data for prompt emission, setting deeper upper limits on the prompt emission than in the original detection paper. Together with KONUS- Wind observations, we show that the gamma-ray eﬃciency of AT2020blt is (cid:46) 0 . 3 − 4 . 5%. We speculate that AT2020blt and AT2021any belong to the low-eﬃciency tail of long gamma-ray burst distributions that are beginning to be readily observed due to the capabilities of new observatories like the Zwicky Transient Facility.
What and How long: Prediction of Mobile App Engagement

User engagement is crucial to the long-term success of a mobile app. Several metrics, such as dwell time, have been used for measuring user engagement. However, how to effectively predict user engagement in the context of mobile apps is still an open research question. For example, do the mobile usage contexts (e.g., time of day) in which users access mobile apps impact their dwell time? Answers to such questions could help mobile operating system and publishers to optimize advertising and service placement. In this article, we first conduct an empirical study for assessing how user characteristics, temporal features, and the short/long-term contexts contribute to gains in predicting users’ app dwell time on the population level. The comprehensive analysis is conducted on large app usage logs collected through a mobile advertising company. The dataset covers more than 12K anonymous users and 1.3 million log events. Based on the analysis, we further investigate a novel mobile app engagement prediction problem—can we predict simultaneously what app the user will use next and how long he/she will stay on that app? We propose several strategies for this joint prediction problem and demonstrate that our model can improve the performance significantly when compared with the state-of-the-art baselines. Our work can help mobile system developers in designing a better and more engagement-aware mobile app user experience.
Personalizing Pre-trained Models

Self-supervised or weakly supervised models trained on large-scale datasets have shown sample-efficient transfer to diverse datasets in few-shot settings. We consider how upstream pretrained models can be leveraged for downstream few-shot, multilabel, and continual learning tasks. Our model CLIPPER (CLIP PERsonalized) uses image representations from CLIP, a large-scale image representation learning model trained using weak natural language supervision. We developed a technique, called Multi-label Weight Imprinting (MWI), for multi-label, continual, and few-shot learning, and CLIPPER uses MWI with image representations from CLIP. We evaluated CLIPPER on 10 single-label and 5 multi-label datasets. Our model shows robust and competitive performance, and we set new benchmarks for few-shot, multi-label, and continual learning. Our lightweight technique is also compute-efficient and enables privacy-preserving applications as the data is not sent to the upstream model for fine-tuning.
Improving the Transferability of Adversarial Examples with New Iteration Framework and Input Dropout

Deep neural networks(DNNs) is vulnerable to be attacked by adversarial examples. Black-box attack is the most threatening attack. At present, black-box attack methods mainly adopt gradient-based iterative attack methods, which usually limit the relationship between the iteration step size, the number of iterations, and the maximum perturbation. In this paper, we propose a new gradient iteration framework, which redefines the relationship between the above three. Under this framework, we easily improve the attack success rate of DI-TI-MIM. In addition, we propose a gradient iterative attack method based on input dropout, which can be well combined with our framework. We further propose a multi dropout rate version of this method. Experimental results show that our best method can achieve attack success rate of 96.2\% for defense model on average, which is higher than the state-of-the-art gradient-based attacks.
Greenberger–Horne–Zeilinger states: Their identifications and robust violations

The [Formula: see text]-qubit Greenberger–Horne–Zeilinger (GHZ) states are the maximally entangled states of [Formula: see text] qubits, which have had many important applications in quantum information processing, such as quantum key distribution and quantum secret sharing. Thus how to distinguish the GHZ states from other quantum states becomes a significant problem. In this work, by presenting a family of the generalized Clauser–Horne–Shimony–Holt (CHSH) inequality, we show that the [Formula: see text]-qubit GHZ states can be indeed identified by the maximal violations of the generalized CHSH inequality under some specific measurement settings. The generalized CHSH inequality is simple and contains only four correlation functions for any [Formula: see text]-qubit system, thus has the merit of facilitating experimental verification. Furthermore, we present a quantum phenomenon of robust violations of the generalized CHSH inequality in which the maximal violation of Bell’s inequality can be robust under some specific noises adding to the [Formula: see text]-qubit GHZ states.
Fast Improvement of TEM Images with Low-Dose Electrons by Deep Learning

Abstract Low electron dose observation is indispensable for observing various samples using a transmission electron microscope; consequently, image processing has been used to improve transmission electron microscopy (TEM) images. To apply such image processing to in situ observations, we here apply a convolutional neural network to TEM imaging. Using a dataset that includes short-exposure images and long-exposure images, we develop a pipeline for processed short-exposure images, based on end-to-end training. The quality of images acquired with a total dose of approximately $5$ $e^{-}$ per pixel becomes comparable to that of images acquired with a total dose of approximately $1{,}000$ $e^{-}$ per pixel. Because the conversion time is approximately 8 ms, in situ observation at 125 fps is possible. This imaging technique enables in situ observation of electron-beam-sensitive specimens.
Understanding the Formation of Indomethacin–Saccharin Cocrystals by Anti-Solvent Crystallization

Pharmaceutical cocrystals are a novel drug form with the potential to enhance pharmaceutical properties, including the solubility and dissolution behavior for BCS class II drug substances such as indomethacin (IMC). Recently, we reported that pure indomethacin–saccharin (IMC–SAC) cocrystals were prepared via anti-solvent crystallization. In this study, we investigated the solubility behavior of IMC–SAC cocrystals and individual components in methanol–water cosolvent. Also, the phase solubility diagram (PSD) of the cocrystal was determined to increase our understanding of cocrystallization. The criterion for pure IMC–SAC cocrystal formation was proposed and verified through supporting experiments performed with different concentrations. We also found that Scocrystal and Scocrystal/Sdrug are critical factors for the design of the cocrystallization process via anti-solvent addition. Real-time monitoring of the cocrystallization process was performed using an in-line near-infrared (NIR) system. Principal comp.
Improved Gate Reliability of p-GaN Gate HEMTs by Gate Doping Engineering

We present a novel p-GaN gate HEMT structure with reduced hole concentration near the Schottky interface by doping engineering in MOCVD, which aims at lowering the electric field across the gate. By employing an additional unintentionally doped GaN layer, the gate leakage current is suppressed and the gate breakdown voltage is boosted from 10.6 to 14.6 V with negligible influence on the threshold voltage and on-resistance. Time-dependent gate breakdown measurements reveal that the maximum gate drive voltage increases from 6.2 to 10.6 V for a 10-year lifetime with a 1% gate failure rate. This method effectively expands the operating voltage margin of the p-GaN gate HEMTs without any other additional process steps.
Weakly Supervised Learning Creates a Fusion of Modeling Cultures

Abstract:The past two decades have witnessed the great success of the algorithmic modeling framework advocated by Breiman et al. (2001). Nevertheless, the excellent prediction performance of these black-box models rely heavily on the availability of strong supervision, i.e. a large set of accurate and exact ground-truth labels. In practice, strong supervision can be unavailable or expensive, which calls for modeling techniques under weak supervision. In this comment, we summarize the key concepts in weakly supervised learning and discuss some recent developments in the field. Using algorithmic modeling alone under a weak supervision might lead to unstable and misleading results. A promising direction would be integrating the data modeling culture into such a framework.
Dynamic Correlation Estimators for Bivariate Brownian and Geometric Brownian Motions

Estimating dynamic correlation between a pair of time series is of importance in many applications. We present new estimators for the dynamic correlation between a pair of correlated Brownian motions and separately for dynamic correlation between a pair of correlated Geometric Brownian motions. We show that, as the sample size increases, all estimators presented in this paper converge in probability to the underlying true dynamic correlation.
PII: S0925-4773(01)00489-0

Two to three megabase deletions on chromosome 22q11 are the cytogenetic findings most commonly associated with cardiac and craniofacial defects in humans. The constellation of clinical findings associated with these deletions is termed the 22q11 deletion syndrome. We had earlier described a patient with the 22q11 deletion phenotype who was hemizygous for an atypical 20 kb microdeletion in this region. The deletion included coding regions of two genes organized head-to-head, UFD1L and CDC45L, along with an 884 bp CpG-rich intervening region. Based on this genomic organization, we hypothesized that both genes may be co-expressed and co-regulated by sequences within this region. We demonstrate that expression of both genes is enhanced in a similar pattern in precursors of structures affected by the deletion. The intergenic region is sufficient to direct transcription most strongly in the developing pharyngeal arches and limb buds of transgenic mice and can also direct bi-directional transcriptional activation in a neural crest-derived cell line. Deletion analyses revealed that a 404 bp fragment closest to UFD1L is necessary and sufficient to direct this bi-directional transcriptional activity. These results reveal the presence of a conserved regulatory region in the 22q11 deletion locus that can direct simultaneous transcription of genes involved in ubiquitin mediated protein processing (UFD1L) and cell cycle control (CDC45L). q 2001 Elsevier Science Ireland Ltd. All rights reserved.
PII: S0925-4773(99)00103-3

Mammalian NEFA and nucleobindin are calcium-binding proteins containing a signal peptide, two EF-hand motifs, acidic and basic regions and a leucine-zipper motif. Although they have been discussed to be involved in autoimmunity, apoptosis and calcium homeostasis in the Golgi apparatus and bone matrix, their exact role remains unknown. Here we report the cloning of their Drosophila homolog, nucb1, as well as the analysis of its expression pattern during embryogenesis and the subcellular localization of the NUCB1 protein. The nucb1 mRNA and the NUCB1 protein were found to be expressed maternally and zygotically, and they accumulate ubiquitously at low levels during all embryonic stages due to a maternal component. From stage 11 onward, high levels of zygotic expression can be detected speci®cally in the salivary glands and their placodes. In contrast to the known mammalian family members, the NUCB1 protein localizes in a subpattern of cytoplasmic substructures, probably the Golgi apparatus. q 1999 Elsevier Science Ireland Ltd. All rights reserved.
PII: S0925-4773(02)00012-6

Mab21 homolog in Caenorhabditis elegans is expressed in the sensory ray cells and determines the ray identity (Dev. Dyn. 221 (2001) 422). Similarly, vertebrate homologs of this gene have been shown to express in the craniofacial tissues, limb bud and neural tissues. We report here the cloning and expression analysis of two zebrafish mab21 genes. Both genes have a conserved open reading frame of 1080 nucleotides. Phylogenetic analysis suggested that the two family subgroups existed prior to the divergence of vertebrates. Their expression profiles revealed that mab21l1 was turned on in embryos as early as 8 h post-fertilization (hpf) while mab21l2 was first detected at around 11 hpf. In situ hybridization results showed that expression of these mab21 genes marked the early differentiating olfactory bulbs, eye primordia, midbrain and subsequently the branchial pouches and neural tube. q 2002 Elsevier Science Ireland Ltd. All rights reserved.
PII: S0925-4773(01)00609-8

The embryonic tracheal system of Drosophila provides an important model for understanding the process of epithelial branching morphogenesis. Here we report the sequence and expression analysis of a novel tracheal gene, named windpipe (wdp). wdp is identical to the predicted gene CG3413 and encodes a transmembrane, leucine-rich repeat family member. wdp transcripts appear abruptly at stage 15 and are restricted to primary tracheal branches that give rise to secondary branches. q 2002 Elsevier Science Ireland Ltd. All rights reserved.