Miracle-dz/UniFAR-FTUs huggingface dataset:
GEV Beamforming Supported by DOA-Based Masks Generated on Pairs of Microphones

Distant speech processing is a challenging task, especially when dealing with the cocktail party effect. Sound source separation is thus often required as a preprocessing step prior to speech recognition to improve the signal to distortion ratio (SDR). Recently, a combination of beamforming and speech separation networks have been proposed to improve the target source quality in the direction of arrival of interest. However, with this type of approach, the neural network needs to be trained in advance for a specific microphone array geometry, which limits versatility when adding/removing microphones, or changing the shape of the array. The solution presented in this paper is to train a neural network on pairs of microphones with different spacing and acoustic environmental conditions, and then use this network to estimate a time-frequency mask from all the pairs of microphones forming the array with an arbitrary shape. Using this mask, the target and noise covariance matrices can be estimated, and then used to perform generalized eigenvalue (GEV) beamforming. Results show that the proposed approach improves the SDR from 4.78 dB to 7.69 dB on average, for various microphone array geometries that correspond to commercially available hardware.
An Empirical Investigation of Cash Conversion Cycle of Manufacturing Firms and its Association with Firm Size and Profitability

The purpose of this empirical study is to investigate Cash Conversion Cycle of thirty manufacturing firms listed in Dhaka Stock Exchanges under six different categories, which are, Food and allied, Pharmaceuticals and chemical, Cement, Textile, Engineering and Miscellaneous. This paper sets industry average Cash Conversion Cycle for these six industries and examines the relationship of Cash Conversion Cycle with firm size and profitability. This study did not find statistically significant differences among the Cash Conversion Cycle of varying manufacturing industries. The result of this study indicates a statistically significant negative relationship between the Cash Conversion Cycle and profitability, especially in terms of Return on Equity. The result also shows that the Cash Conversion Cycle of manufacturing firm also has significant negative relationship with firm size, when measured in terms of net sales. The present study contributes to the literature on working capital management written in the context of Bangladesh.
Adversarial Attacks for Embodied Agents

Adversarial attacks are valuable for providing insights into the blind-spots of deep learning models and help improve their robustness. Existing work on adversarial attacks have mainly focused on static scenes; however, it remains unclear whether such attacks are effective against embodied agents, which could navigate and interact with a dynamic environment. In this work, we take the first step to study adversarial attacks for embodied agents. In particular, we generate spatiotemporal perturbations to form 3D adversarial examples, which exploit the interaction history in both the temporal and spatial dimensions. Regarding the temporal dimension, since agents make predictions based on historical observations, we develop a trajectory attention module to explore scene view contributions, which further help localize 3D objects appeared with the highest stimuli. By conciliating with clues from the temporal dimension, along the spatial dimension, we adversarially perturb the physical properties (e.g., texture and 3D shape) of the contextual objects that appeared in the most important scene views. Extensive experiments on the EQA-v1 dataset for several embodied tasks in both the white-box and black-box settings have been conducted, which demonstrate that our perturbations have strong attack and generalization abilities.
Temporal and spatial variation in soil resources in a deciduous woodland

1 Plants can respond to nutrient‐rich patches by proliferation of roots or by increased rates of ion uptake; such patches are therefore considered to be an important source of nutrients for plant growth. However, little is known about the spatial and temporal heterogeneity of nutrient concentration at scales that are applicable to the development of root systems of herbaceous perennial plants.
Resolution Limits for the Noisy Non-Adaptive 20 Questions Problem

We establish fundamental limits on estimation accuracy for the noisy 20 questions problem with measurement-dependent noise and introduce optimal non-adaptive procedures that achieve these limits. The minimal achievable resolution is defined as the absolute difference between the estimated and the true locations of a target over a unit cube, given a finite number of queries constrained by the excess-resolution probability. Inspired by the relationship between the 20 questions problem and the channel coding problem, we derive non-asymptotic bounds on the minimal achievable resolution to estimate the target location. Furthermore, applying the Berry–Esseen theorem to our non-asymptotic bounds, we obtain a second-order asymptotic approximation to the achievable resolution of optimal non-adaptive query procedures with a finite number of queries subject to the excess-resolution probability constraint. We specialize our second-order results to measurement-dependent versions of several channel models including the binary symmetric, the binary erasure and the binary Z- channels. As a complement, we establish a second-order asymptotic achievability bound for adaptive querying and use this to bound the benefit of adaptive querying.
Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An In Silico Validation

People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although the artificial pancreas and continuous glucose monitoring have been proven to be effective in achieving closed-loop control, significant challenges still remain due to the high complexity of glucose dynamics and limitations in the technology. In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In particular, the delivery strategies are developed by double Q-learning with dilated recurrent neural networks. For designing and testing purposes, the FDA-accepted UVA/Padova Type 1 simulator was employed. First, we performed long-term generalized training to obtain a population model. Then, this model was personalized with a small data-set of subject-specific data. In silico results show that the single and dual-hormone delivery strategies achieve good glucose control when compared to a standard basal-bolus therapy with low-glucose insulin suspension. Specifically, in the adult cohort (n = 10), percentage time in target range 70, 180 mg/dL improved from 77.6% to 80.9% with single-hormone control, and to 85.6% with dual-hormone control. In the adolescent cohort (n = 10), percentage time in target range improved from 55.5% to $\text{65.9}\%$ with single-hormone control, and to 78.8% with dual-hormone control. In all scenarios, a significant decrease in hypoglycemia was observed. These results show that the use of deep reinforcement learning is a viable approach for closed-loop glucose control in T1D.
Human-Perception-Oriented Pseudo Analog Video Transmissions With Deep Learning

Recently, pseudo analog transmission has gained increasing attentions due to its ability to alleviate the cliff effect in video multicast scenarios. The existing pseudo analog systems are optimized under the minimum mean squared error criterion. However, their power allocation strategies do not take the perceptual video quality into consideration. In this article, we propose a human-perception-based pseudo analog video transmission system named ROIC-Cast, which aims to intelligently enhance the transmission quality of the region-of-interest (ROI) parts. Firstly, the classic deep learning based saliency detection algorithm is adopted to decompose the continuous video sequences into ROI and non-ROI blocks. Secondly, an effective compression method is used to reduce the data amount of side information generated by the ROI extraction module. Then, the power allocation scheme is formulated as a convex problem, and the optimal transmission power for both ROI and non-ROI blocks is derived in a closed form. Finally, the simulations are conducted to validate the proposed system by comparing with a few of existing systems, e.g., KMV-Cast, SoftCast, and DAC-RAN. The proposed ROIC-Cast can achieve over 4.1 dB peak signal-to-noise ratio gains of ROI compared with other systems, given the channel signal-to-noise ratio as −5 dB, 0 dB, 5 dB, and 10 dB, respectively. This significant performance improvement is due to the automatic ROI extraction, high-efficiency data compression as well as adaptive power allocation.
Learning and Inference in Imaginary Noise Models

Inspired by recent developments in learning smoothed densities with empirical Bayes, we study variational autoencoders with a decoder that is tailored for the random variable $Y=X+N(0,\sigma^2 I_d)$. A notion of smoothed variational inference emerges where the smoothing is implicitly enforced by the noise model of the decoder; "implicit", since during training the encoder only sees clean samples. This is the concept of imaginary noise model, where the noise model dictates the functional form of the variational lower bound $\mathcal{L}(\sigma)$, but the noisy data are never seen during learning. The model is named $\sigma$-VAE. We prove that all $\sigma$-VAEs are equivalent to each other via a simple $\beta$-VAE expansion: $\mathcal{L}(\sigma_2) \equiv \mathcal{L}(\sigma_1,\beta)$, where $\beta=\sigma_2^2/\sigma_1^2$. We prove a similar result for the Laplace distribution in exponential families. Empirically, we report an intriguing power law $\mathcal{D}_{\rm KL} \sim \sigma^{-\nu}$ for the learned models and we study the inference in the $\sigma$-VAE for unseen noisy data. The experiments were performed on MNIST, where we show that quite remarkably the model can make reasonable inferences on extremely noisy samples even though it has not seen any during training. The vanilla VAE completely breaks down in this regime. We finish with a hypothesis (the XYZ hypothesis) on the findings here.
Computing Lyapunov functions using deep neural networks

We propose a deep neural network architecture and a training algorithm for computing approximate Lyapunov functions of systems of nonlinear ordinary differential equations. Under the assumption that the system admits a compositional Lyapunov function, we prove that the number of neurons needed for an approximation of a Lyapunov function with fixed accuracy grows only polynomially in the state dimension, i.e., the proposed approach is able to overcome the curse of dimensionality. We show that nonlinear systems satisfying a small-gain condition admit compositional Lyapunov functions. Numerical examples in up to ten space dimensions illustrate the performance of the training scheme.
On the theoretical properties of the exchange algorithm

The exchange algorithm is one of the most popular extensions of the Metropolis--Hastings algorithm to sample from doubly-intractable distributions. However, the theoretical exploration of the exchange algorithm is very limited. For example, natural questions like `Does exchange algorithm converge at a geometric rate? ' or `Does the exchange algorithm admit a Central Limit Theorem? ' have not been answered yet. In this paper, we study the theoretical properties of the exchange algorithm, in terms of asymptotic variance and convergence speed. We compare the exchange algorithm with the original Metropolis--Hastings algorithm and provide both necessary and sufficient conditions for the geometric ergodicity of the exchange algorithm. Moreover, we prove that our results can be applied to various practical applications such as location models, Gaussian models, Poisson models, and a large class of exponential families, which includes most of the practical applications of the exchange algorithm. A central limit theorem for the exchange algorithm is also established. Our results justify the theoretical usefulness of the exchange algorithm.
Observations on the tolerance of young dogs (puppies) to infection with Trypanosoma congolense

Studies were undertaken to assess the susceptibility of young local dogs to infection with Trypanosoma congolensei. Six puppies (7 weeks old) were used for the study. Although the puppies became parasitaemic 6 to 7 days post infection, they were tolerant to infection as the parasitaemia remained low through out the first seven weeks of the eight week observation period. The packed cell volume (PCV) also only dropped slightly during the last four weeks attaining the value of 25.6 + 3.8 (p>0.05) by the eighth week while the mean body weight continued to increase. Similarly, the mean daily body temperature did not differ significantly from those of un-infected control. The significance of trypanotolerance in Nigerian local dogs is discussed. Key Words: local puppies, low parasitaemia, packed cell volume, Trypanosoma congolense , trypanotolerance, Nigerian Afr. J. Clin. Exper. Microbiol. Vol.6(1) 2005: 28-33
Invasive Silene latifolia May Benefit from a Native Pollinating Seed Predator, Hadena ectypa, in North America

The acquisition of new mutualists and escape from enemies are often essential for the establishment of invasive species. With its introduction to North America, Silene latifolia successfully escaped a number of generalist and specialist enemies, including the seed predator/specialist pollinator Hadena bicruris, but information regarding the acquisition of new mutualists in a community context has not been examined. We used field observations of mixed species arrays and laboratory feeding trials and compared floral scent and plant/pollinator morphological match to explore the interaction in North America of the invasive S. latifolia with the native pollinating seed predator, Hadena ectypa, in order to understand mechanisms of enemy release and mutualist facilitation underlying the successful invasion of S. latifolia. In mixed arrays, H. ectypa visited S. latifolia at a low frequency similar to the combined visitation of other non-Hadena nocturnal moth species. Differences in the floral scent profiles of S. latifolia and Silene stellata, a native coflowering congener and natural host of H. ectypa, combined with the lack of morphological match between H. ectypa and S. latifolia, likely contribute to these results. In the field study, only one H. ectypa egg was oviposited on S. latifolia, and this did not result in a successful fruit attack. Larvae feeding trials in the lab showed no initial feeding preference for pistils of either Silene species. Therefore, our study suggests that S. latifolia has escaped the cost of seed predation typically associated with visitation and oviposition by Hadena pollinators, a potential natural enemy, while taking advantage of pollination services provided by both H. ectypa and other native North American nocturnal moth pollinators.
Effective Learning of a GMRF Mixture Model

Learning a Gaussian Mixture Model (GMM) is hard when the number of parameters is too large given the amount of available data. As a remedy, we propose restricting the GMM to a Gaussian Markov Random Field Mixture Model (GMRF-MM), as well as a new method for estimating the latter’s sparse precision (i.e., inverse covariance) matrices. When the sparsity pattern of each matrix is known, we propose an efficient optimization method for the Maximum Likelihood Estimate (MLE) of that matrix. When it is unknown, we utilize the popular Graphical Least Absolute Shrinkage and Selection Operator (GLASSO) to estimate that pattern. However, we show that even for a single Gaussian, when GLASSO is tuned to successfully estimate the sparsity pattern, it does so at the price of a substantial bias of the values of the nonzero entries of the matrix, and we show that this problem only worsens in a mixture setting. To overcome this, we discard the nonzero values estimated by GLASSO, keep only its pattern estimate and use it within the proposed MLE method. This yields an effective two-step procedure that removes the bias. We show that our “debiasing” approach outperforms GLASSO in both the single-GMRF and the GMRF-MM cases. We also show that when learning priors for image patches, our method outperforms GLASSO even if we merely use an educated guess about the sparsity pattern, and that our GMRF-MM outperforms the baseline GMM on real and synthetic high-dimensional datasets.
Standard Numbering Scheme for Class B β-Lactamases

Unlike for classes A and B, a standardized amino acid numbering scheme has not been proposed for the class C (AmpC) β-lactamases, which complicates communication in the field. Here, we propose a scheme developed through a collaborative approach that considers both sequence and structure, preserves traditional numbering of catalytically important residues (Ser64, Lys67, Tyr150, and Lys315), is adaptable to new variants or enzymes yet to be discovered and includes a variation for genetic and epidemiological applications.
Pathology and Genetics of Tumours of the Nervous System

Pathology and Genetics of Tumours of the Nervous System. Paul Kleihues, Webster K. Cavenee, eds. IARC, Lyon, France. 2000, 314 pp. The new edition of this publication opens with the recently revised WHO Classification of Tumors of the Nervous System (Working Group, 1999) and then systematically presents each of these tumors and familial tumor syndromes in a …
Notes on Hamiltonian threshold and chain graphs

We revisit results obtained in [F. Harary, U. Peled, Hamiltonian threshold graphs, Discrete Appl. Math., 16 (1987), 11-15], where several necessary and necessary and sufficient conditions for a connected threshold graph to be Hamiltonian were obtained. We present these results in new forms, now stated in terms of structural parameters that uniquely define the threshold graph and we extend them to chain graphs. We also identify the chain graph with minimum number of Hamilton cycles within the class of Hamiltonian chain graphs of a given order.
Synthesizing Unrestricted False Positive Adversarial Objects Using Generative Models

Adversarial examples are data points misclassified by neural networks. Originally, adversarial examples were limited to adding small perturbations to a given image. Recent work introduced the generalized concept of unrestricted adversarial examples, without limits on the added perturbations. In this paper, we introduce a new category of attacks that create unrestricted adversarial examples for object detection. Our key idea is to generate adversarial objects that are unrelated to the classes identified by the target object detector. Different from previous attacks, we use off-the-shelf Generative Adversarial Networks (GAN), without requiring any further training or modification. Our method consists of searching over the latent normal space of the GAN for adversarial objects that are wrongly identified by the target object detector. We evaluate this method on the commonly used Faster R-CNN ResNet-101, Inception v2 and SSD Mobilenet v1 object detectors using logo generative iWGAN-LC and SNGAN trained on CIFAR-10. The empirical results show that the generated adversarial objects are indistinguishable from non-adversarial objects generated by the GANs, transferable between the object detectors and robust in the physical world. This is the first work to study unrestricted false positive adversarial examples for object detection.
Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning

Exploration of the high-dimensional state action space is one of the biggest challenges in Reinforcement Learning (RL), especially in multi-agent domain. We present a novel technique called Experience Augmentation, which enables a time-efficient and boosted learning based on a fast, fair and thorough exploration to the environment. It can be combined with arbitrary off-policy MARL algorithms and is applicable to either homogeneous or heterogeneous environments. We demonstrate our approach by combining it with MADDPG and verifing the performance in two homogeneous and one heterogeneous environments. In the best performing scenario, the MADDPG with experience augmentation reaches to the convergence reward of vanilla MADDPG with 1/4 realistic time, and its convergence beats the original model by a significant margin. Our ablation studies show that experience augmentation is a crucial ingredient which accelerates the training process and boosts the convergence.
Addiction by Any Other Name is Still Addiction: Embracing Molecular Neurogenetic/Epigenetic Basis of Reward Deficiency

The Human Genome Project and the database it created established a plausible observatory, so to speak, for scientists to identify the etiology of genetic variants and their expression. It is well-known that “Single Nucleotide Polymorphisms” (SNPs) which involve the cumulative presence of nucleic acids in sufficient volume and proximity along the DNA strands to create novel
Differential effects of inotropes and inodilators on renal function in acute cardiac care

Abstract Pathological interplay between the heart and kidneys is widely encountered in heart failure (HF) and is linked to worse prognosis and quality of life. Inotropes, along with diuretics and vasodilators, are a core medical response to HF but decompensated patients who need inotropic support often present with an acute worsening of renal function. The impact of inotropes on renal function is thus potentially an important influence on the choice of therapy. There is currently relatively little objective data available to guide the selection of inotrope therapy but recent direct observations on the effects of levosimendan and milrinone on glomerular filtration favour levosimendan. Other lines of evidence indicate that in acute decompensated HF levosimendan has an immediate renoprotective effect by increasing renal blood flow through preferential vasodilation of the renal afferent arterioles and increases in glomerular filtration rate: potential for renal medullary ischaemia is avoided by an offsetting increase in renal oxygen delivery. These indications of a putative reno-protective action of levosimendan support the view that this calcium-sensitizing inodilator may be preferable to dobutamine or other adrenergic inotropes in some settings by virtue of its renal effects. Additional large studies will be required, however, to clarify the renal effects of levosimendan in this and other relevant clinical situations, such as cardiac surgery.
Risk of Ill-Informed Decision-Making When Choosing Your Favorite Fish

ABSTRACT Risk to children of women who choose a favorite fish without regard to its methylmercury or omega-3 content was estimated under three consumption scenarios: (1) current fish consumption rate by U.S. women if limited to orange roughy (Hoplostethus atlanticus), (2) 12 oz of roughy per week, and (3) roughy consumption to meet docosahexaenoic acid (DHA) requirements. Risks were similarly assessed if king mackerel (Scomberomorus cavalla) were eaten. Based on mercury concentrations in fillets purchased from 2004–2007 (0.73 ± 0.29 mg Hg/kg; n = 45), women would have an 85% probability of exceeding USEPA's reference dose (RfD) if they ate only roughy. Based on literature-derived concentrations, they would have a 91% probability of exceeding the RfD if they ate only mackerel. Increasing consumption of either fish to 12 oz per week would increase their probability of exceeding the RfD to 100%. Attempting to meet DHA requirements through eating these fish also results in a 100% probability of exceeding the RfD; however, owing to its very low DHA content, roughy consumption would result in exceedance by 100-fold. These results highlight recommendations of others that benefits and risks of fish consumption should be presented together to enable consumers to make informed decisions.