Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints

Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex. Despite this, most commonly used methods in practice focus on identifying a Maximum Tolerated Dose (MTD) by learning only from toxicity events. We present a novel adaptive clinical trial methodology, called Safe Efficacy Exploration Dose Allocation (SEEDA), that aims at maximizing the cumulative efficacies while satisfying the toxicity safety constraint with high probability. We evaluate performance objectives that have operational meanings in practical clinical trials, including cumulative efficacy, recommendation/allocation success probabilities, toxicity violation probability, and sample efficiency. An extended SEEDA-Plateau algorithm that is tailored for the increase-then-plateau efficacy behavior of molecularly targeted agents (MTA) is also presented. Through numerical experiments using both synthetic and real-world datasets, we show that SEEDA outperforms state-of-the-art clinical trial designs by finding the optimal dose with higher success rate and fewer patients.
Contextual policy transfer in reinforcement learning domains via deep mixtures-of-experts

In reinforcement learning, agents that consider the context, or current state, when selecting source policies for transfer have been shown to outperform context-free approaches. However, none of the existing approaches transfer knowledge contextually from model-based learners to a model-free learner. This could be useful, for instance, when source policies are intentionally learned on diverse simulations with plentiful data but transferred to a real-world setting with limited data. In this paper, we assume knowledge of estimated source task dynamics and policies, and common sub-goals but different dynamics. We introduce a novel deep mixture-of-experts formulation for learning state-dependent beliefs over source task dynamics that match the target dynamics using state trajectories collected from the target task. The mixture model is easy to interpret, demonstrates robustness to estimation errors in dynamics, and is compatible with most learning algorithms. We then show how this model can be incorporated into standard policy reuse frameworks, and demonstrate its effectiveness on benchmarks from OpenAI-Gym.
Driven-state relaxation of a coupled qubit-defect system in spin-locking measurements

It is widely known that spin-locking noise-spectroscopy is a powerful technique for the characterization of low-frequency noise mechanisms in superconducting qubits. Here we show that the relaxation rate of the driven spin-locking state of a qubit can be significantly affected by the presence of an off-resonant high-frequency two-level-system defect. Thus, both low- and high-frequency defects should be taken into account in the interpretation of spin-locking measurements and other types of driven-state noise-spectroscopy.
Methodology for Realizing VMM with Binary RRAM Arrays: Experimental Demonstration of Binarized-ADALINE using OxRAM Crossbar

In this paper, we present an efficient hardware mapping methodology for realizing vector matrix multiplication (VMM) on resistive memory (RRAM) arrays. Using the proposed VMM computation technique, we experimentally demonstrate a binarized-ADALINE (Adaptive Linear) classifier on an OxRAM crossbar. An 8×8 OxRAM crossbar with Ni/3-nm HfO2/7 nm Al-doped-TiO2/TiN device stack is used. Weight training for the binarized-ADALINE classifier is performed ex-situ on UCI cancer dataset. Post weight generation the OxRAM array is carefully programmed to binary weight-states using the proposed weight mapping technique on a custom-built testbench. Our VMM powered binarized-ADALINE network achieves a classification accuracy of 78% in simulation and 67% in experiments. Experimental accuracy was found to drop mainly due to crossbar inherent sneak-path issues and RRAM device programming variability.
Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network

In this paper, we explore the slot tagging with only a few labeled support sentences (a.k.a. few-shot). Few-shot slot tagging faces a unique challenge compared to the other fewshot classification problems as it calls for modeling the dependencies between labels. But it is hard to apply previously learned label dependencies to an unseen domain, due to the discrepancy of label sets. To tackle this, we introduce a collapsed dependency transfer mechanism into the conditional random field (CRF) to transfer abstract label dependency patterns as transition scores. In the few-shot setting, the emission score of CRF can be calculated as a word’s similarity to the representation of each label. To calculate such similarity, we propose a Label-enhanced Task-Adaptive Projection Network (L-TapNet) based on the state-of-the-art few-shot classification model – TapNet, by leveraging label name semantics in representing labels. Experimental results show that our model significantly outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting.
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization

In many real-world scenarios, decision makers seek to efficiently optimize multiple competing objectives in a sample-efficient fashion. Multi-objective Bayesian optimization (BO) is a common approach, but many existing acquisition functions do not have known analytic gradients and suffer from high computational overhead. We leverage recent advances in programming models and hardware acceleration for multi-objective BO using Expected Hypervolume Improvement (EHVI)---an algorithm notorious for its high computational complexity. We derive a novel formulation of $q$-Expected Hypervolume Improvement ($q$EHVI), an acquisition function that extends EHVI to the parallel, constrained evaluation setting. $q$EHVI is an exact computation of the joint EHVI of $q$ new candidate points (up to Monte-Carlo (MC) integration error). Whereas previous EHVI formulations rely on gradient-free acquisition optimization or approximated gradients, we compute exact gradients of the MC estimator via auto-differentiation, thereby enabling efficient and effective optimization using first-order and quasi-second-order methods. Lastly, our empirical evaluation demonstrates that $q$EHVI is computationally tractable in many practical scenarios and outperforms state-of-the-art multi-objective BO algorithms at a fraction of their wall time.
Cell type nomenclature for the mammalian brain: Development and application of a systematic, extensible convention

The recent advent of single cell RNA-sequencing and other high-throughput technologies has led to an explosion of cell type definitions across multiple organ systems. Consortia like the BRAIN Initiative Cell Census Network (BICCN) and the Human Cell Atlas (HCA) have begun to standardize and centralize the intake of data and associated metadata from these projects; however, the naming and organization of cell types has largely been left to individual investigators, resulting in widely varying nomenclature and limited alignment between taxonomies derived from overlapping datasets. To facilitate cross-dataset alignment and comparison, we created a working nomenclature convention for matching and tracking cell types across studies. The convention is qualitatively similar to how gene transcripts are tracked across different versions of GENCODE genome builds, allowing comparison of matched types with a common reference or any other taxonomy. It augments but does not change existing cell type names provided in original publications and can be directly applied to data from new or published studies. We applied this nomenclature to a series of published cell type data from the Allen Institute, highlighting its extensibility to classifications defined using multiple quantifiable modalities. Finally, we propose conventions for assigning accurate yet flexible cell types names in the mammalian cortex as a step towards a much larger community-wide effort to organize multi-source, data-driven information related to cell types. The convention presented here can be easily applied to cell type taxonomies from any organ system or organism.
Agent Programming for Industrial Applications: Some Advantages and Drawbacks

Autonomous agents are seen as a prominent technology to be applied in industrial scenarios. Classical automation solutions are struggling with challenges related to high dynamism, prompt actuation, heterogeneous entities, including humans, and decentralised decision-making. Besides promoting concepts, languages, and tools to face such challenges, agents must also provide high reliability. To assess how appropriate and mature are agents for industrial applications, we have investigated its application in two scenarios of the gas and oil industry. This paper presents the development of systems and the initial results highlighting the advantages and drawbacks of the agents approach when compared with the existing automation solutions.
Automated Design Space Exploration for Optimized Deployment of DNN on Arm Cortex-A CPUs

The spread of deep learning on embedded devices has prompted the development of numerous methods to optimize the deployment of deep neural networks (DNNs). Works have mainly focused on: 1) efficient DNN architectures; 2) network optimization techniques, such as pruning and quantization; 3) optimized algorithms to speed up the execution of the most computational intensive layers; and 4) dedicated hardware to accelerate the data flow and computation. However, there is a lack of research on cross-level optimization as the space of approaches becomes too large to test and obtain a globally optimized solution. Thus, leading to suboptimal deployment in terms of latency, accuracy, and memory. In this work, we first detail and analyze the methods to improve the deployment of DNNs across the different levels of software optimization. Building on this knowledge, we present an automated exploration framework to ease the deployment of DNNs. The framework relies on a reinforcement learning search that, combined with a deep learning inference framework, automatically explores the design space and learns an optimized solution that speeds up the performance and reduces the memory on embedded CPU platforms. Thus, we present a set of results for state-of-the-art DNNs on a range of Arm Cortex-A CPU platforms achieving up to $4\times $ improvement in performance and over $2\times $ reduction in memory with negligible loss in accuracy with respect to the BLAS floating-point implementation.
Stochastic Geometry-Based Modeling and Analysis of Beam Management in 5G

Beam management is central in the operation of dense 5G cellular networks. Focusing the energy radiated to mobile terminals (MTs) by increasing the number of beams per cell increases signal power and decreases interference, and has hence the potential to bring major improvements on area spectral efficiency (ASE). This benefit, however, comes with unavoidable overheads that increase with the number of beams and the MT speed. This paper proposes a first system-level stochastic geometry model encompassing major aspects of the beam management problem: frequencies, antennas, and propagation; physical layer, wireless links, and coding; network geometry, interference, and resource sharing; sensing, signaling, and mobility management. This model leads to a simple analytical expression for the effective ASE that the typical user gets in this context. This in turn allows one to find, for a wide variety of 5G network scenarios including millimeter wave (mmWave) and sub-6 GHz, the number of beams per cell that offers the best global trade-off between these benefits and costs. We finally provide numerical results that discuss the effects of different systemic trade-offs and performances of mmWave and sub-6 GHz 5G deployments.
Gradient effects on false vacuum decay in gauge theory

We study false vacuum decay for a gauged complex scalar field in a polynomial potential with nearly degenerate minima. Radiative corrections to the profile of the nucleated bubble as well as the full decay rate are computed in the planar thin-wall approximation using the effective action. This allows to account for the inhomogeneity of the bounce background and the radiative corrections in a self-consistent manner. In contrast to scalar or fermion loops, for gauge fields one must deal with a coupled system that mixes the Goldstone boson and the gauge fields, which considerably complicates the numerical calculation of Green's functions. In addition to the renormalization of couplings, we employ a covariant gradient expansion in order to systematically construct the counterterm for the wave-function renormalization. The result for the full decay rate however does not rely on such an expansion and accounts for all gradient corrections at the chosen truncation of the loop expansion. The ensuing gradient effects are shown to be of the same order of magnitude as non-derivative one-loop corrections.
Compartmental organization of Golgi-specific protein modification and vacuolar protein sorting events defined in a yeast sec18 (NSF) mutant

The sec18 and sec23 secretory mutants of Saccharomyces cerevisiae have previously been shown to exhibit temperature-conditional defects in protein transport from the ER to the Golgi complex (Novick, P., S. Ferro, and R. Schekman, 1981. Cell. 25:461-469). We have found that the Sec18 and Sec23 protein functions are rapidly inactivated upon shifting mutant cells to the nonpermissive temperature (less than 1 min). This has permitted an analysis of the potential role these SEC gene products play in transport events distal to the ER. The sec-dependent transport of alpha-factor (alpha f) and carboxypeptidase Y (CPY) biosynthetic intermediates present throughout the secretory pathway was monitored in temperature shift experiments. We found that Sec18p/NSF function was required sequentially for protein transport from the ER to the Golgi complex, through multiple Golgi compartments and from the Golgi complex to the cell surface. In contrast, Sec23p function was required in the Golgi complex, but only for transport of alpha f out of an early compartment. Together, these studies define at least three functionally distinct Golgi compartments in yeast. From cis to trans these compartments contain: (a) An alpha 1----6 mannosyltransferase; (b) an alpha 1----3 mannosyltransferase; and (c) the Kex2 endopeptidase. Surprisingly, we also found that a pool of Golgi-modified CPY (p2 CPY) located in a compartment distal to the alpha 1----3 mannosyltransferase does not require Sec18p function for final delivery to the vacuole. This compartment appears to be equivalent to the Kex2 compartment as we show that a novel vacuolar CPY-alpha f-invertase fusion protein undergoes efficient Kex2-dependent cleavage resulting in the secretion of invertase. We propose that this Kex2 compartment is the site in which vacuolar proteins are sorted from proteins destined to be secreted.
Time-Resolved FMRI Shared Response Model Using Gaussian Process Factor Analysis

Multi-subject fMRI studies are challenging due to the high variability of both brain anatomy and functional brain topographies across participants. An effective way of aggregating multi-subject fMRI data is to extract a shared representation that filters out unwanted variability among subjects. Some recent work has implemented probabilistic models to extract a shared representation in task fMRI. In the present work, we improve upon these models by incorporating temporal information in the common latent structures. We introduce a new model, Shared Gaussian Process Factor Analysis (S-GPFA), that discovers shared latent trajectories and subject-specific functional topographies, while modeling temporal correlation in fMRI data. We demonstrate the efficacy of our model using the time-segment matching experiment on the publicly available Raider dataset. We further test the utility of our model by analyzing its learned model parameters in the large multi-site SPINS dataset, on a social cognition task from participants with and without schizophrenia.
Zooplankton—Life in a Nutritionally Dilute Environment

The classic concept of “filtering rate” often used to describe feeding by zooplankton may be outmoded. The Ivlev (1945) type of fitted curve may describe more precisely the relation between a zooplanktonic organism and its food supply. Once food is captured it is assimilated with a high degree of efficiency. There does not appear to be any relationship between the quantity of food eaten and the digestive efficiency. Examination is made of the relationship between respiratory rate and size in zooplankton. The exponential constant varies considerably from one organism to another, but may not be affected by physical parameters such as temperature. A multiple regression equation relating respiration to weight and temperature simultaneously gives a moderately good estimate of respiration for a variety of marine copepods. The relationship between uptake of oxygen and excretion of nitrogen (O:N ratio) is a useful indicator of metabolic substrate used in respiration and is related to the chemical composition of the animals. The “specific dynamic effect” (SDA) of food should be considered in studies of zooplanktonic metabolism. The literature on growth efficiency for zooplankton is reviewed and the applicability of recent growth models for fish (Paloheimo and Dickie, 1965, 1966) to zooplanktonic problems is considered.
Regret Balancing for Bandit and RL Model Selection

We consider model selection in stochastic bandit and reinforcement learning problems. Given a set of base learning algorithms, an effective model selection strategy adapts to the best learning algorithm in an online fashion. We show that by estimating the regret of each algorithm and playing the algorithms such that all empirical regrets are ensured to be of the same order, the overall regret balancing strategy achieves a regret that is close to the regret of the optimal base algorithm. Our strategy requires an upper bound on the optimal base regret as input, and the performance of the strategy depends on the tightness of the upper bound. We show that having this prior knowledge is necessary in order to achieve a near-optimal regret. Further, we show that any near-optimal model selection strategy implicitly performs a form of regret balancing.
Response of Ragweed Parthenium (Parthenium hysterophorus) to Saflufenacil and Glyphosate

Abstract Field and greenhouse studies were conducted in Belle Glade, FL, in 2010 and 2011 to evaluate saflufenacil and glyphosate efficacy on POST burndown of ragweed parthenium. Log-logistic models were used to determine the herbicide dose required to produce 90% control (ED90). The ED90 for rosette ragweed parthenium control in the greenhouse was saflufenacil at 8.0 g ai ha−1 at 14 d after treatment. The rate required to cause 90% growth reduction of rosette ragweed parthenium at 14 d after treatment was 8.9 g ha−1 of saflufenacil. The probability of rosette ragweed parthenium survival decreased with increasing rates of saflufenacil. The ED90 value for bolted ragweed parthenium control in the field was 5.7 g ha−1 of saflufenacil at 21 d after treatment. Ragweed parthenium had no response to glyphosate either in the field or in the greenhouse studies. This demonstrates that saflufenacil can be used as a POST burndown of ragweed parthenium populations that have no response to glyphosate. Nomenclature: Glyphosate; saflufenacil; ragweed parthenium, Parthenium hysterophorus L.
The cloudbow of planet Earth observed in polarisation

Context. Scattering processes in the atmospheres of planets cause characteristic features that can be particularly well observed in polarisation. For planet Earth, both molecular scattering (Rayleigh) and scattering by small particles (Mie) imprint specific signatures in its phase curve. Polarised phase curves allow us to infer physical and chemical properties of the atmosphere like the composition of the gaseous and liquid components, droplet sizes, and refraction indices. Aims. An unequivocal prediction of a liquid-water-loaded atmosphere is the existence of a rainbow feature at a scattering angle of around 138–144°. Earthshine allows us to observe the primary rainbow in linear polarisation. Methods. We observed polarisation spectra of Earthshine using FORS2 at the Very Large Telescope for phase angles from 33° to 65° (Sun–Earth–Moon angle). The spectra were used to derive the degree of polarisation in the B, V, R, and I passbands and the phase curve from 33° to 136°. The new observations extend to the smallest phases that can be observed from the ground. Results. The degree of polarisation of planet Earth is increasing for decreasing phase angles downwards of 45° in the B, V, R, and I passbands. From comparison of the phase curve observed with models of an Earth-type atmosphere we are able to determine the refractive index of water and to constrain the mean water droplet sizes to 6−7μm. Furthermore, we can retrieve the mean cloud fraction of liquid water clouds to 0.3, and the mean optical depth of the water clouds to values between 10 and 20. Conclusions. Our observations allow us to discern two fundamentally different scattering mechanisms of the atmosphere of planet Earth: molecular and particle scattering. The physical and chemical properties can be retrieved with high fidelity through suitable inversion of the phase curve. Observations of polarimetric phase curves of planets beyond the Solar System shall be extremely valuable for a thorough characterisation of their atmospheres.
Can Synthetic Data Improve Object Detection Results for Remote Sensing Images?

Deep learning approaches require enough training samples to perform well, but it is a challenge to collect enough real training data and label them manually. In this letter, we propose the use of realistic synthetic data with a wide distribution to improve the performance of remote sensing image aircraft detection. Specifically, to increase the variability of synthetic data, we randomly set the parameters during rendering, such as the size of the instance and the class of background images. In order to make the synthetic images more realistic, we then refine the synthetic images at the pixel level using CycleGAN with real unlabeled images. We also fine-tune the model with a small amount of real data, to obtain a higher accuracy. Experiments on NWPU VHR-10, UCAS-AOD and DIOR datasets demonstrate that the proposed method can be applied for augmenting insufficient real data.
InFocus: A Spatial Coding Technique to Mitigate Misfocus in Near-Field LoS Beamforming

Phased arrays, commonly used in IEEE 802.11ad and 5G radios, are capable of focusing radio frequency signals in a specific direction or a spatial region. Beamforming achieves such directional or spatial concentration of signals and enables phased array-based radios to achieve high data rates. Designing beams for millimeter wave and terahertz communication using massive phased arrays, however, is challenging due to hardware constraints and the wide bandwidth in these systems. For example, beams which are optimal at the center frequency may perform poor in wideband communication systems where the radio frequencies differ substantially from the center frequency. The poor performance in such systems is due to differences in the optimal beamformers corresponding to distinct radio frequencies within the wide bandwidth. Such a mismatch leads to a misfocus effect in near-field systems and the beam squint effect in far-field systems. In this paper, we investigate the misfocus effect and propose InFocus, a low complexity technique to construct beams that are well suited for massive wideband phased arrays. The beams are constructed using a carefully designed frequency modulated waveform in the spatial dimension. InFocus mitigates beam misfocus and beam squint when applied to near-field and far-field systems.