Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceived Hearing Loss and Availability of Audiologists in Appalachia

ABSTRACT:Introduction: There is a high demand for audiologists throughout the United States. Previous research has supported an additional demand for these providers within Appalachia.Purpose: The purpose of the study was to determine if Appalachia has a disproportionally high demand for audiologists compared to the rest of the United States.Methods: A cross-sectional retrospective study was performed with population data from the Appalachian Regional Commission, the American Academy of Audiology, and the United States Census Bureau. County-level population-weighted averages of individuals with perceived hearing loss and number of audiologists per capita were compared between Appalachian and non-Appalachian counties.Results: A mean weighted 5.76 % of individuals reported hearing loss within Appalachia, which was 1.1% higher than the rest of the United States. The 1.14 audiologists per 100,000 individuals in Appalachian counties was not significantly lower than the 1.32 audiologists per 100,000 individuals found in non-Appalachian counties. Audiologists per capita decreased with increases in Beale code and percent reporting hearing loss.Conclusion: The high number of individuals reporting hearing loss supports an increased demand for audiologists in rural Appalachia. More research is needed to determine how to meet this demand or improve the efficacy of the limited number of providers.
SECOND ORDER REFLECTED BACKWARD STOCHASTIC DIFFERENTIAL EQUATIONS 1

In this article, we build upon the work of Soner, Touzi and Zhang [ Probab. Theory Related Fields 153 (2012) 149–190] to deﬁne a notion of a second order backward stochastic differential equation reﬂected on a lower càdlàg obstacle. We prove existence and uniqueness of the solution under a Lipschitz-type assumption on the generator, and we investigate some links between our reﬂected 2BSDEs and nonclassical optimal stopping problems. Finally, we show that reﬂected 2BSDEs provide a super-hedging price for American options in a market with volatility uncertainty.
Migratory Dendritic Cells of the CD8 Mouse Lymphoid-Tissue Resident and Cutting Edge: Expression of XCR1 Defines

Subsets of dendritic cells (DCs) have been described according to their functions and anatomical locations. Conventional DC subsets are deﬁned by reciprocal expression of CD11b and CD8 a in lymphoid tissues (LT), and of CD11b and CD103 in non-LT (NLT). Spleen CD8 a + and dermal CD103 + DCs share a high efﬁciency for Ag cross-presentation and a developmen-tal dependency on speciﬁc transcription factors. How-ever, it is not known whether all NLT-derived CD103 + DCs and LT-resident CD8 a + DCs are similar despite their different anatomical locations. XCR1 was previously described as exclusively expressed on mouse spleen CD8 a + DCs and human blood BDCA3 + DCs. In this article, we showed that LT-resident CD8 a + DCs and NLT-derived CD103 + DCs speciﬁcally express XCR1 and are characterized by a unique transcriptional ﬁngerprint, irrespective of their tissue of origin. Therefore, CD8 a + DCs and CD103 + DCs belong to a common DC subset which is unequivocally identiﬁed by XCR1 expression throughout the body. The of
Comparing Conventional Pitch Detection Algorithms with a Neural Network Approach

Despite much research, traditional methods to pitch prediction are still not perfect. With the emergence of neural networks (NNs), researchers hope to create a NN-based pitch predictor that outperforms traditional methods. Three pitch detection algorithms (PDAs), pYIN, YAAPT, and CREPE are compared in this paper. pYIN and YAAPT are conventional approaches considering time domain and frequency domain processing. CREPE utilizes a data-trained deep convolutional neural network to estimate pitch. It involves 6 densely connected convolutional hidden layers and determines pitch probabilities for a given input signal. The performance of CREPE representing neural network pitch predictors is compared to more classical approaches represented by pYIN and YAAPT. The figure of merit (FOM) will include the amount of unvoiced-to-voiced errors, voiced-to-voiced errors, gross pitch errors, and fine pitch errors.
Supervised Deep Hashing for High-dimensional and Heterogeneous Case-based Reasoning

Case-based Reasoning (CBR) on high-dimensional and heterogeneous data is a trending yet challenging and computationally expensive task in the real world. A promising approach is to obtain low-dimensional hash codes representing cases and perform a similarity retrieval of cases in a Hamming space. However, previous methods based on data-independent hashing rely on random projections or manual construction, inapplicable to address specific data issues (e.g., high-dimensionality and heterogeneity) due to their insensitivity to data characteristics. To address these issues, this work introduces a novel deep hashing network to learn similarity-preserving compact hash codes for efficient case retrieval and proposes a deep-hashing-enabled CBR model HeCBR. Specifically, we introduce position embedding to represent heterogeneous features and utilize a multilinear interaction layer to obtain case embeddings, which effectively filtrates zero-valued features to tackle high-dimensionality and sparsity and captures inter-feature couplings. Then, we feed the case embeddings into fully-connected layers, and subsequently a hash layer generates hash codes with a quantization regularizer to control the quantization loss during relaxation. To cater for incremental learning of CBR, we further propose an adaptive learning strategy to update the hash function. Extensive experiments on public datasets show HeCBR greatly reduces storage and significantly accelerates the case retrieval. HeCBR achieves desirable performance compared with the state-of-the-art CBR methods and performs significantly better than hashing-based CBR methods in classification. typical CBR models to investigate the effectiveness of our proposed HeCBR. All experimental results demonstrate that our HeCBR significantly outperforms the hashing-enabled methods in terms of classification tasks and achieves desirable performance compared with typical CBR methods. Theoretical and empirical analysis show that HeCBR can greatly reduce storage cost and significantly improve efficiency in relation to typical CBR models.
Left Heavy Tails and the Effectiveness of the Policy and Value Networks in DNN-based best-first search for Sokoban Planning

Despite the success of practical solvers in various NP-complete domains such as SAT and CSP as well as using deep reinforcement learning to tackle two-player games such as Go, certain classes of PSPACE-hard planning problems have remained out of reach. Even carefully designed domain-specialized solvers can fail quickly due to the exponential search space on hard instances. Recent works that combine traditional search methods, such as best-first search and Monte Carlo tree search, with Deep Neural Networks' (DNN) heuristics have shown promising progress and can solve a significant number of hard planning instances beyond specialized solvers. To better understand why these approaches work, we studied the interplay of the policy and value networks of DNN-based best-first search on Sokoban and show the surprising effectiveness of the policy network, further enhanced by the value network, as a guiding heuristic for the search. To further understand the phenomena, we studied the cost distribution of the search algorithms and found that Sokoban instances can have heavy-tailed runtime distributions, with tails both on the left and right-hand sides. In particular, for the first time, we show the existence of \textit{left heavy tails} and propose an abstract tree model that can empirically explain the appearance of these tails. The experiments show the critical role of the policy network as a powerful heuristic guiding the search, which can lead to left heavy tails with polynomial scaling by avoiding exploring exponentially sized subtrees. Our results also demonstrate the importance of random restarts, as are widely used in traditional combinatorial solvers, for DNN-based search methods to avoid left and right heavy tails.
Generalized Permutants and Graph GENEOs

This paper is part of a line of research devoted to developing a compositional and geometric theory of Group Equivariant Non-Expansive Operators (GENEOs) for Geometric Deep Learning. It has two objectives. The first objective is to generalize the notions of permutants and permutant measures, originally defined for the identity of a single “perception pair”, to a map between two such pairs. The second and main objective is to extend the application domain of the whole theory, which arose in the set-theoretical and topological environments, to graphs. This is performed using classical methods of mathematical definitions and arguments. The theoretical outcome is that, both in the case of vertex-weighted and edge-weighted graphs, a coherent theory is developed. Several simple examples show what may be hoped from GENEOs and permutants in graph theory and how they can be built. Rather than being a competitor to other methods in Geometric Deep Learning, this theory is proposed as an approach that can be integrated with such methods.
Generative Adversarial Networks for Pseudo-Radio-Signal Synthesis

—For many wireless communication applications, traf- ﬁc pattern modeling of radio signals combined with channel effects is much needed. While analytical models are used to capture these phenomena, real world non-linear effects (e.g. device responses, interferences, distortions, noise) and especially the combination of such effects can be difﬁcult to capture by these models. This is simply due to their complexity and degrees of freedom which can be hard to explicitize in compact expressions. In this paper, we propose a more model-free approach to jointly approximate an end-to-end black-boxed wireless communication scenario using software-deﬁned radio platforms and optimize for an efﬁcient synthesis of subsequently similar “pseudo-radio-signals”. More precisely, we implement a generative adversarial network based solution that automatically learns radio properties from recorded prototypes in speciﬁc scenarios. This allows for a high degree of expressive freedom. Numerical results show that the prototypes’ trafﬁc patterns jointly with channel effects are learned without the introduction of assumptions about the scenario or the simpliﬁcation to a parametric model.
Challenges of mapping Vulnerabilities and Exposures to Open-Source Packages

—Much of the current software depends on open-source components, which in turn have complex dependencies on other open-source libraries. Vulnerabilities in open source therefore have potentially huge impacts. The goal of this work is to get a quantitative overview of the frequency and evolution of existing vulnerabilities in popular software repositories and package managers. To this end, we provide an up-to-date overview of the open source landscape and its most popular package managers. We discuss approaches to map entries of the Common Vulnerabilities and Exposures (CVE) list to open-source libraries. Based on this mapping approaches, we show the frequency and distribution of CVE entries with respect to popular programming languages.
mt5se: An Open Source Framework for Building Autonomous Trading Robots

Autonomous trading robots have been studied in artificial intelligence area for quite some time. Many AI techniques have been tested for building autonomous agents able to trade financial assets. These initiatives include traditional neural networks, fuzzy logic, reinforcement learning but also more recent approaches like deep neural networks and deep reinforcement learning. Many developers claim to be successful in creating robots with great performance when simulating execution with historical price series, so called backtesting. However, when these robots are used in real markets frequently they present poor performance in terms of risks and return. In this paper, we propose an open source framework (mt5se) that helps the development, backtesting, live testing and real operation of autonomous traders. We built and tested several traders using mt5se. The results indicate that it may help the development of better traders. Furthermore, we discuss the simple architecture that is used in many studies and propose an alternative multiagent architecture. Such architecture separates two main concerns for portfolio manager (PM) : price prediction and capital allocation. More than achieve a high accuracy, a PM should increase profits when it is right and reduce loss when it is wrong. Furthermore, price prediction is highly dependent of asset's nature and history, while capital allocation is dependent only on analyst's prediction performance and assets' correlation. Finally, we discuss some promising technologies in the area.
Using cognitive psychology to understand GPT-3

Significance Language models are trained to predict the next word for a given text. Recently, it has been shown that scaling up these models causes them to not only generate language but also to solve challenging reasoning problems. The present article lets a large language model (GPT-3) do experiments from the cognitive psychology literature. We find that GPT-3 can solve many of these tasks reasonably well, despite being only taught to predict future word occurrences on a vast amount of text from the Internet and books. We additionally utilize analysis tools from the cognitive psychology literature to demystify how GPT-3 solves different tasks and use the thereby acquired insights to make recommendations for how to improve future model iterations.
Target alignment in truncated kernel ridge regression

Kernel ridge regression (KRR) has recently attracted renewed interest due to its potential for explaining the transient effects, such as double descent, that emerge during neural network training. In this work, we study how the alignment between the target function and the kernel affects the performance of the KRR. We focus on the truncated KRR (TKRR) which utilizes an additional parameter that controls the spectral truncation of the kernel matrix. We show that for polynomial alignment, there is an \emph{over-aligned} regime, in which TKRR can achieve a faster rate than what is achievable by full KRR. The rate of TKRR can improve all the way to the parametric rate, while that of full KRR is capped at a sub-optimal value. This shows that target alignemnt can be better leveraged by utilizing spectral truncation in kernel methods. We also consider the bandlimited alignment setting and show that the regularization surface of TKRR can exhibit transient effects including multiple descent and non-monotonic behavior. Our results show that there is a strong and quantifable relation between the shape of the \emph{alignment spectrum} and the generalization performance of kernel methods, both in terms of rates and in finite samples.
Resilient Synchrophasor Networks for the Real-Time Monitoring, Protection and Control of Power Grids: from Theory to Validation

Operational practices of both power transmission and distribution grids are impacted by the increasing connection of renewable energy resources, electric vehicles and energy storage systems. A timely and accurate knowledge of the system state, coupled with a level of automation able to protect or control the system, enables a better operation of the electrical grids. In this respect, synchrophasor networks, might provide system operators with wide-area, synchronized, low latency, high refresh-rate measurements from phasor measurement units (PMUs). This thesis focuses on the design and validation of resilient synchrophasor networks in order to address some of the challenges that are preventing the adoption in real-fields of what proposed by the literature. First, the architectural layers of synchrophasor networks are described with particular focus on the time dissemination techniques suitable for phasor measurement units. Then, we propose a new data-pushing logic able to minimize the latency introduced at the concentration point without increasing the data incompleteness. The data-pushing logic is validated in three real synchrophasor networks adopting different telecom infrastructures (i.e., 4G-LTE, optical fiber links and twisted pairs). In the context of reliable operation of synchrophasor networks, we propose an algorithm able to deal with intentional or unintentional bad data measurements. We also impersonate an attacker and we show that it is possible to forge delay attacks undetectable by state-of-the-art bad-data detection algorithms that can lead to physical grid damage. We use the proposed bad data algorithm as an effective way of neutralizing these attacks. The IEEE C37.118 Class-P Std compliant synchrophasor extraction algorithm adopted in the real-fields is also implemented and validated in a GPS-synchronized real-time simulator. The simulated PMUs are used to validate two applications for the real-time protection and control of electrical networks by means of synchrophasor technology. The first one is a protection scheme that relies on PMU-based real-time state estimation processes to detect a fault and identify the faulted line. Then, we validate in a real-time hardware-in-the-loop (HIL) setup, the Grid Explicit Congestion Notification (GECN) voltage control mechanism for distribution networks, already presented in the literature. We show that, by leveraging on the accurate knowledge of the system state, GECN is able to control the state of the system in time-scales of seconds, in order to maintain its voltage level within predefined limits.
Forgetting Data from Pre-trained GANs

Large pre-trained generative models are known to occasionally provide samples that may be undesirable for various reasons. The standard way to mitigate this is to re-train the models differently. In this work, we take a different, more compute-friendly approach and investigate how to post-edit a model after training so that it forgets certain kinds of samples. We provide three different algorithms for GANs that differ on how the samples to be forgotten are described. Extensive evaluations on real-world image datasets show that our algorithms are capable of forgetting data while retaining high generation quality at a fraction of the cost of full re-training
Continuous Switch Model and Heuristics for Mixed-Integer Problems in Power Systems

Many power systems operation and planning computations (e.g., transmission and generation switching and placement) solve a mixed-integer nonlinear problem (MINLP) with binary variables representing the decision to connect devices to the grid. Binary variables with nonlinear AC network constraints make this problem NP-hard. For large real-world networks, obtaining an AC feasible optimum solution for these problems is computationally challenging and often unattainable with state-of-the-art tools today. In this work, we map the MINLP decision problem into a set of equivalent circuits by representing binary variables with a circuit-based continuous switch model. We characterize the continuous switch model by a controlled nonlinear impedance that more closely mimics the physical behavior of a real-world switch. This mapping effectively transforms the MINLP problem into an NLP problem. We mathematically show that this transformation is a tight relaxation of the MINLP problem. For fast and robust convergence, we develop physics-driven homotopy and Newton-Raphson damping methods. To validate this approach, we empirically show robust convergences for large, realistic systems ($>$ 70,000 buses) in a practical wall-clock time to an AC-feasible optimum. We compare our results and show improvement over industry-standard tools and other binary relaxation methods.
Low Star Formation Activity and Low Gas Content of Quiescent Galaxies at z = 3.5–4.0 Constrained with ALMA

The discovery in deep near-infrared surveys of a population of massive quiescent galaxies at z > 3 has given rise to the question of how they came to be quenched so early in the history of the universe. Measuring their molecular gas properties can distinguish between physical processes where they stop forming stars due to a lack of fuel versus those where the star formation efficiency is reduced and the gas is retained. We conducted Atacama Large Millimeter/submillimeter Array observations of four quiescent galaxies at z = 3.5–4.0 found by the Fourstar Galaxy Evolution Survey and a serendipitous optically dark galaxy at z = 3.71. We aim to investigate the presence of dust-obscured star formation and their gas content by observing the dust continuum emission at Band 7 and the atomic carbon [C i](3 P 1–3 P 0) line at 492.16 GHz. Among the four quiescent galaxies, only one source is detected in the dust continuum at λ obs = 870 μm. The submillimeter observations confirm their passive nature, and all of them are located more than four times below the main sequence of star-forming galaxies at z = 3.7. None of the targets are detected in [C i], constraining their gas-mass fractions to be <20%. These gas-mass fractions are more than 3 times lower than the scaling relation for star-forming galaxies at z = 3.7. These results support scenarios where massive galaxies at z = 3.5–4.0 quench by consuming/expelling all the gas rather than by reducing the efficiency of the conversion of their gas into stars.
A Mildly Relativistic Outflow Launched Two Years after Disruption in Tidal Disruption Event AT2018hyz

We present late-time radio/millimeter (as well as optical/UV and X-ray) detections of tidal disruption event (TDE) AT2018hyz, spanning 970–1300 d after optical discovery. In conjunction with earlier deeper limits, including those at ≈700 days, our observations reveal rapidly rising emission at 0.8–240 GHz, steeper than F ν ∝ t 5 relative to the time of optical discovery. Such a steep rise cannot be explained in any reasonable scenario of an outflow launched at the time of disruption (e.g., off-axis jet, sudden increase in the ambient density), and instead points to a delayed launch. Our multifrequency data allow us to directly determine the radius and energy of the radio-emitting outflow, and we find from our modeling that the outflow was launched ≈750 days after optical discovery. The outflow velocity is mildly relativistic, with β ≈ 0.25 and ≈0.6 for a spherical geometry and a 10° jet geometry, respectively, and the minimum kinetic energy is E K ≈ 5.8 × 1049 and ≈6.3 × 1049 erg, respectively. This is the first definitive evidence for the production of a delayed mildly relativistic outflow in a TDE; a comparison to the recently published radio light curve of ASASSN-15oi suggests that the final rebrightening observed in that event (at a single frequency and time) may be due to a similar outflow with a comparable velocity and energy. Finally, we note that the energy and velocity of the delayed outflow in AT2018hyz are intermediate between those of past nonrelativistic TDEs (e.g., ASASSN-14li, AT2019dsg) and the relativistic TDE Sw J1644+57. We suggest that such delayed outflows may be common in TDEs.
Lower Bounds on the Error Probability for Invariant Causal Prediction

It is common practice to collect observations of feature and response pairs from different environments. A natural question is how to identify features that have consistent prediction power across environments. The invariant causal prediction framework proposes to approach this problem through invariance, assuming a linear model that is invariant under different environments. In this work, we make an attempt to shed light on this framework by connecting it to the Gaussian multiple access channel problem. Specifically, we incorporate optimal code constructions and decoding methods to provide lower bounds on the error probability. We illustrate our findings by various simulation settings.