Miracle-dz/UniFAR-FTUs huggingface dataset:
Fully Neural Network based Model for General Temporal Point Processes

A temporal point process is a mathematical model for a time series of discrete events, which covers various applications. Recently, recurrent neural network (RNN) based models have been developed for point processes and have been found effective. RNN based models usually assume a specific functional form for the time course of the intensity function of a point process (e.g., exponentially decreasing or increasing with the time since the most recent event). However, such an assumption can restrict the expressive power of the model. We herein propose a novel RNN based model in which the time course of the intensity function is represented in a general manner. In our approach, we first model the integral of the intensity function using a feedforward neural network and then obtain the intensity function as its derivative. This approach enables us to both obtain a flexible model of the intensity function and exactly evaluate the log-likelihood function, which contains the integral of the intensity function, without any numerical approximations. Our model achieves competitive or superior performances compared to the previous state-of-the-art methods for both synthetic and real datasets.
Decentralized Learning of Generative Adversarial Networks from Multi-Client Non-iid Data

This work addresses a new problem of learning generative adversarial networks (GANs) from multiple data collections that are each i) owned separately and privately by different clients and ii) drawn from a non-identical distribution that comprises different classes. Given such multi-client and non-iid data as input, we aim to achieve a distribution involving all the classes input data can belong to, while keeping the data decentralized and private in each client storage. Our key contribution to this end is a new decentralized approach for learning GANs from non-iid data called Forgiver-First Update (F2U), which a) asks clients to train an individual discriminator with their own data and b) updates a generator to fool the most `forgiving' discriminators who deem generated samples as the most real. Our theoretical analysis proves that this updating strategy indeed allows the decentralized GAN to learn a generator's distribution with all the input classes as its global optimum based on f-divergence minimization. Moreover, we propose a relaxed version of F2U called Forgiver-First Aggregation (F2A), which adaptively aggregates the discriminators while emphasizing forgiving ones to perform well in practice. Our empirical evaluations with image generation tasks demonstrated the effectiveness of our approach over state-of-the-art decentralized learning methods.
Multiple Exclusion Statistics: the $k$-mers problem.

A new distribution for systems of particles obeying statistical exclusion of correlated states is presented following the Haldane's state counting. It relies upon a conjecture to deal with the multiple exclusion that takes place when the states available to single particles are spatially correlated and it can be simultaneously excluded by more than one particle. The Haldane's statistics [F. D. M. Haldane, Phys. Rev. Lett. 67, 937 (1991)] and Wu's distribution [Y. -S. Wu, Phys. Rev. Lett. 52, 2103 (1984)] are recovered in the limit of non-correlated states (constant statistical exclusion) of the multiple exclusion statistics. In addition, the exclusion spectrum function $G(n)$ is introduced to account for the dependence of the statistical exclusion on the occupation-number $n$. Results of thermodynamics and state occupation are shown for ideal lattice gases of linear particles of size $k$ ($k$-mers) where multiple exclusion occurs. Remarkable agreement is found with simulations from $k=2$ to $10$ where multiple exclusion dominates as $k$ increases.
Accelerating DNN Training in Wireless Federated Edge Learning Systems

Training task in classical machine learning models, such as deep neural networks, is generally implemented at a remote cloud center for centralized learning, which is typically time-consuming and resource-hungry. It also incurs serious privacy issue and long communication latency since a large amount of data are transmitted to the centralized node. To overcome these shortcomings, we consider a newly-emerged framework, namely federated edge learning, to aggregate local learning updates at the network edge in lieu of users’ raw data. Aiming at accelerating the training process, we first define a novel performance evaluation criterion, called learning efficiency. We then formulate a training acceleration optimization problem in the CPU scenario, where each user device is equipped with CPU. The closed-form expressions for joint batchsize selection and communication resource allocation are developed and some insightful results are highlighted. Further, we extend our learning framework to the GPU scenario. The optimal solution in this scenario is manifested to have the similar structure as that of the CPU scenario, recommending that our proposed algorithm is applicable in more general systems. Finally, extensive experiments validate the theoretical analysis and demonstrate that the proposed algorithm can reduce the training time and improve the learning accuracy simultaneously.
SN REFSDAL: PHOTOMETRY AND TIME DELAY MEASUREMENTS OF THE FIRST EINSTEIN CROSS SUPERNOVA

We present the first year of Hubble Space Telescope imaging of the unique supernova (SN) “Refsdal,” a gravitationally lensed SN at z = 1.488 ± 0.001 with multiple images behind the galaxy cluster MACS J1149.6+2223. The first four observed images of SN Refsdal (images S1–S4) exhibited a slow rise (over ∼150 days) to reach a broad peak brightness around 2015 April 20. Using a set of light curve templates constructed from SN 1987A-like peculiar Type II SNe, we measure time delays for the four images relative to S1 of 4 ± 4 (for S2), 2 ± 5 (S3), and 24 ± 7 days (S4). The measured magnification ratios relative to S1 are 1.15 ± 0.05 (S2), 1.01 ± 0.04 (S3), and 0.34 ± 0.02 (S4). None of the template light curves fully captures the photometric behavior of SN Refsdal, so we also derive complementary measurements for these parameters using polynomials to represent the intrinsic light curve shape. These more flexible fits deliver fully consistent time delays of 7 ± 2 (S2), 0.6 ± 3 (S3), and 27 ± 8 days (S4). The lensing magnification ratios are similarly consistent, measured as 1.17 ± 0.02 (S2), 1.00 ± 0.01 (S3), and 0.38 ± 0.02 (S4). We compare these measurements against published predictions from lens models, and find that the majority of model predictions are in very good agreement with our measurements. Finally, we discuss avenues for future improvement of time delay measurements—both for SN Refsdal and for other strongly lensed SNe yet to come.
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement

We address the problem of unsupervised disentanglement of discrete and continuous explanatory factors of data. We first show a simple procedure for minimizing the total correlation of the continuous latent variables without having to use a discriminator network or perform importance sampling, via cascading the information flow in the $\beta$-vae framework. Furthermore, we propose a method which avoids offloading the entire burden of jointly modeling the continuous and discrete factors to the variational encoder by employing a separate discrete inference procedure. This leads to an interesting alternating minimization problem which switches between finding the most likely discrete configuration given the continuous factors and updating the variational encoder based on the computed discrete factors. Experiments show that the proposed method clearly disentangles discrete factors and significantly outperforms current disentanglement methods based on the disentanglement score and inference network classification score. The source code is available at this https URL.
Abigaille II: toward the development of a spider-inspired climbing robot

SUMMARY This paper presents a novel robotic platform, Abigaille II, designed to climb vertical surfaces using dry adhesion. Abigaille II is a lightweight hexapod prototype actuated by 18 miniaturized motors. The robot's feet consist of adhesive patches, which have microhairs with mushroom-shaped caps fixed on the top of millimeter-scale flexible posts. A pentapedal gait is used to climb flat vertical surfaces as this gait maximizes the number of legs in contact to the surface. Abigaille can however also walk by using other gaits, including the tripod gait.
Sparse Minimax Optimality of Bayes Predictive Density Estimates from Clustered Discrete Priors

We consider the problem of predictive density estimation under Kullback-Leibler loss in a high-dimensional Gaussian model with exact sparsity constraints on the location parameters. We study the first order asymptotic minimax risk of Bayes predictive density estimates based on product discrete priors where the proportion of non-zero coordinates converges to zero as dimension increases. Discrete priors that are product of clustered univariate priors provide a tractable configuration for diversification of the future risk and are used for constructing efficient predictive density estimates. We establish that the Bayes predictive density estimate from an appropriately designed clustered discrete prior is asymptotically minimax optimal. The marginals of our proposed prior have infinite clusters of identical sizes. The within cluster support points are equi-probable and the clusters are periodically spaced with geometrically decaying probabilities as they move away from the origin. The cluster periodicity depends on the decay rate of the cluster probabilities. Under different sparsity regimes, through numerical experiments, we compare the maximal risk of the Bayes predictive density estimates from the clustered prior with varied competing estimators including those based on geometrically decaying non-clustered priors of Johnstone (1994) and Mukherjee & Johnstone (2017) and obtain encouraging results.
Low density lipoprotein receptor‐related protein gene polymorphisms and risk for late‐onset Alzheimer's disease in a Japanese population

Low density lipoprotein (LDL) receptor‐related protein (LRP) gene polymorphisms located in the 5′ region and in exon 3, and the apolipoprotein E (APOE) genotype were determined in 100 Japanese patients affected by late‐onset Alzheimer's disease (AD). We matched 246 controls for age and found no association between the polymorphism located in the 5′ region of the LRP gene. The distribution of LRP exon 3 genotypes and alleles did not differ between AD and the control groups. However, the frequency of T allele in the Alzheimer's group having APOE‐ɛ4 was lower than that in the control group having APOE‐ɛ4, but it was only marginally significant (p=0.022). Age of onset was significantly younger in the patients with CC genotype than those carrying the T allele (p=0.03), and this trend was more evident among non‐APOE‐ɛ4 carriers (p=0.008). These results support the possibility that ApoE and LRP may contribute to the development of AD.
Introduction to the Issue on Data Science: Machine Learning for Audio Signal Processing

AUDIO signal processing is currently undergoing a paradigm change, where data-driven machine learning is replacing hand-crafted feature design. This has led some to ask whether audio signal processing is still useful in the “era of machine learning. ” This special issue aims to promote progress, systematization, understanding, and convergence of applying machine learning in the area of audio signal processing. Specifically, this issue is focused on work that demonstrates novel applications of machine learning techniques in the area of sound, speech and music signal processing, as well as methodological considerations of merging machine learning with audio signal processing. This special issue covers various topics, from a review of deep learning in the audio domain (Purwins et al.) to speech recognition (Bavu et al.), from voice activity detection (Ariav and Cohen) to musical brain state decoding (Ntalampiras and Potamitis), from music information retrieval (Kim et al.) to bioacoustic classification for species monitoring (Thakur and Rajan), from polyphonic acoustic event detection (Vesperini et al.) to heart sound segmentation (Oliveira et al.) and from speech enhancement (Wood and Rouat) to source separation (Le Roux et al.).
Mutual-interference-aware available spectrum resource distribution in TV white space

To utilize TV white space (TVWS), mutual interference should be restrained to guarantee both TV and secondary systems normal operation. Introducing guard bands is a feasible solution and it naturally constrains the available resources in TVWS. In this paper, we investigate the available spectrum resource distribution under aggregate interference constraints within the TV coverage. A statistical approach is brought up to analyze the mutual interference and based on that, available resource determination is formulated as an optimization problem. In terms of its non-polynomial time complexity, we put forward the near-optimal spectrum resource retreat algorithm (SRRA) to solve this problem. Simulation results verify the accuracy of SRRA and prove that our proposed statistical model matches the actual scenario closely. And we find that the spectrum resource distribution consists of a series of resource contours (RCs) within coverage of TV transmitter and available resources can reach the upper bound at certain RC, which can guide secondary systems for better usage of TVWS.
Moving Forward With Molecular Communication: From Theory to Human Health Applications

The birth of wireless communication systems nearly 1 century ago has transformed and redefined the way humans communicate and interact. This transformation, which has opened boundaries between societies and eliminated cultural barriers, has evolved from the original paradigm of wireless electromagnetic communication systems. This paradigm has experienced numerous evolutionary developments that have not only brought along seamless connectivity for human interactions but also communication between machines and devices.
Molecular Basis for Interaction between Icap1α PTB Domain and β1 Integrin*

Icap1α is a 200-amino acid protein that binds to the COOH-terminal 13 amino acids (786AVTTVVNPKYEGK798) of the integrin β1 subunit. Alanine scanning mutagenesis of this region revealed that Val787, Val790, and792NPKY795 are critical for Icap1α binding. The NPXY motif is a known binding substrate for phosphotyrosine binding (PTB) domain proteins. The sequences of Icap1α, residues 58–200, and the β1 integrin, residues 786–797, were aligned to the available PTB-peptide structures to generate a high quality structural model. Site-directed mutagenesis showed that Leu135, Ile138, and Ile139 of Icap1α, residues predicted by the model to be in close proximity to 792NPKY795, and Leu82 and Tyr144, residues expected to form a hydrophobic pocket near Val787, are required for the Icap1α-β1 integrin interaction. These findings indicate that Icap1α is a PTB domain protein, which recognizes the NPXY motif of β1 integrin. Furthermore, our date suggest that an interaction between Val787 and the hydrophobic pocket created by Leu82 and Tyr144of Icap1α forms the basis for the specificity of Icap1α for the β1 integrin subunit.
Storm-Water Bioretention for Runoff Quality and Quantity Mitigation

In response to water quality and quantity issues within the Stroubles Creek watershed in Blacksburg, Virginia, a retrofit bioretention cell (BRC) was installed to collect and treat runoff from an existing parking lot. The BRC was completed in July 2007, and 28 precipitation events were monitored between October 2007 and June 2008. For each storm, inflow and outflow flow-weighted composite samples were collected and analyzed for suspended sediment, total nitrogen, and total phosphorus. The inflow and outflow concentrations and loads, as well as total inflow and outflow volumes and peak flow rates, were analyzed to evaluate BRC efficiency. Overall, the BRC successfully reduced flow volumes and peak flow rates leaving the parking lot by 97 and 99%, respectively. Cumulative mass reductions for sediment, total nitrogen, and total phosphorus all exceeded 99% by mass. The findings of this study have significant implications for areas with karst geology: (1) current design recommendations of lining the bottom of .
Principles of Transactional Memory

Transactional memory (TM) is an appealing paradigm for concurrent programming on shared memory architectures. With a TM, threads of an application communicate, and synchronize their actions, via in-memory transactions. Each transaction can perform any number of operations on shared data, and then either commit or abort. When the transaction commits, the effects of all its operations become immediately visible to other transactions; when it aborts, however, those effects are entirely discarded. Transactions are atomic: programmers get the illusion that every transaction executes all its operations instantaneously, at some single and unique point in time. Yet, a TM runs transactions concurrently to leverage the parallelism offered by modern processors. The aim of this book is to provide theoretical foundations for transactional memory. This includes defining a model of a TM, as well as answering precisely when a TM implementation is correct, what kind of properties it can ensure, what are the power and limitations of a TM, and what inherent trade-offs are involved in designing a TM algorithm. While the focus of this book is on the fundamental principles, its goal is to capture the common intuition behind the semantics of TMs and the properties of existing TM implementations.
Neuronal Replacement as a Tool for Basal Ganglia Circuitry Repair: 40 Years in Perspective

The ability of new neurons to promote repair of brain circuitry depends on their capacity to re-establish afferent and efferent connections with the host. In this review article, we give an overview of past and current efforts to restore damaged connectivity in the adult mammalian brain using implants of fetal neuroblasts or stem cell-derived neuronal precursors, with a focus on strategies aimed to repair damaged basal ganglia circuitry induced by lesions that mimic the pathology seen in humans affected by Parkinson’s or Huntington’s disease. Early work performed in rodents showed that neuroblasts obtained from striatal primordia or fetal ventral mesencephalon can become anatomically and functionally integrated into lesioned striatal and nigral circuitry, establish afferent and efferent connections with the lesioned host, and reverse the lesion-induced behavioral impairments. Recent progress in the generation of striatal and nigral progenitors from pluripotent stem cells have provided compelling evidence that they can survive and mature in the lesioned brain and re-establish afferent and efferent axonal connectivity with a remarkable degree of specificity. The studies of cell-based circuitry repair are now entering a new phase. The introduction of genetic and virus-based techniques for brain connectomics has opened entirely new possibilities for studies of graft-host integration and connectivity, and the access to more refined experimental techniques, such as chemo- and optogenetics, has provided new powerful tools to study the capacity of grafted neurons to impact the function of the host brain. Progress in this field will help to guide the efforts to develop therapeutic strategies for cell-based repair in Huntington’s and Parkinson’s disease and other neurodegenerative conditions involving damage to basal ganglia circuitry.
EFFECTS OF BETA MERCAPTOETHANOL ON THE PROLIFERATION AND DIFFERENTIATION OF HUMAN OSTEOPROGENITOR CELLS

Antioxidants are known to influence metabolism and promote cell survival in a number of cell culture systems. However, their effects on the modulation of bone cell differentiationin vitroare not clearly defined. In the present studies we have investigated the effects of β‐mercaptoethanol (βME) and ascorbate alone and in combination on human osteoprogenitors derived from bone marrow fibroblasts. In primary marrow cultures, βME stimulated colony formation (2‐fold), alkaline phosphatase activity (3.5‐fold) and, increased DNA synthesis (8‐fold) after 21 days. Cell proliferation was increased significantly by βME during the first 4 days of a 10‐day culture period, indicating stimulation of marrow osteoprogenitor proliferation. Ascorbate did not significantly augment the effects of βME in primary cultures or long‐term cultures of passaged bone marrow fibroblasts. These findings indicate a potential beneficial role for βME addition for the optimal maintenance of colony formation, cell proliferation and differentiation of marrow osteoprogenitor cells in primary human bone marrow fibroblast cultures.
Toward a Mechanistic Understanding of Marine Invertebrate Behavior at Elevated CO2

Elevated carbon dioxide (CO2) levels can alter ecologically important behaviors in a range of marine invertebrate taxa; however, a clear mechanistic understanding of these behavioral changes is lacking. The majority of mechanistic research on the behavioral effects of elevated CO2 has been done in fish, focusing on disrupted functioning of the GABAA receptor (a ligand-gated ion channel, LGIC). Yet, elevated CO2 could induce behavioral alterations through a range of mechanisms that disturb different components of the neurobiological pathway that produces behavior, including disrupted sensation, altered behavioral choices and disturbed LGIC-mediated neurotransmission. Here, we review the potential mechanisms by which elevated CO2 may affect marine invertebrate behaviors. Marine invertebrate acid–base physiology and pharmacology is discussed in relation to altered GABAA receptor functioning. Alternative mechanisms for behavioral change at elevated CO2 are considered and important topics for future research have been identified. A mechanistic understanding will be important to determine why there is variability in elevated CO2-induced behavioral alterations across marine invertebrate taxa, why some, but not other, behaviors are affected within a species and to identify which marine invertebrates will be most vulnerable to rising CO2 levels.