cssi/SciSciGPT-SciSciNet huggingface dataset:
5-fluorocytosine resistance is associated with hypermutation and alterations in capsule biosynthesis in Cryptococcus

Patients infected with the fungal pathogen Cryptococcus are most effectively treated with a combination of 5-fluorocytosine (5FC) and amphotericin B. 5FC acts as a prodrug, which is converted into toxic 5-fluorouracil (5FU) upon uptake into fungal cells. However, the pathogen frequently develops resistance through unclear mechanisms. Here we show that resistance to 5FC in Cryptococcus deuterogattii is acquired more frequently in isolates with defects in DNA mismatch repair that confer an elevated mutation rate. We use whole genome sequencing of 16 independent isolates to identify mutations associated with 5FC resistance in vitro. We find mutations in known resistance genes (FUR1 and FCY2) and in a gene UXS1, previously shown to encode an enzyme that converts UDP-glucuronic acid to UDP-xylose for capsule biosynthesis, but not known to play a role in 5FC metabolism. Mutations in UXS1 lead to accumulation of UDP-glucuronic acid and alterations in nucleotide metabolism, which appear to suppress toxicity of both 5FC and its toxic derivative 5FU.
Cryptanalysis of the Lifted Unbalanced Oil Vinegar Signature Scheme

In 2017, Ward Beullens et al. submitted Lifted Unbalanced Oil and Vinegar (LUOV) [4], a signature scheme based on the famous multivariate public key cryptosystem (MPKC) called Unbalanced Oil and Vinegar (UOV), to NIST for the competition for post-quantum public key scheme standardization. The defining feature of LUOV is that, though the public key \(\mathscr {P}\) works in the extension field of degree r of \(\mathbb {F}_2\), the coefficients of \(\mathscr {P}\) come from \(\mathbb {F}_2\). This is done to significantly reduce the size of \(\mathscr {P}\). The LUOV scheme is now in the second round of the NIST PQC standardization process.
Cooperation and stability for complex systems in resource-limited environments

Resource-limited complex systems are ubiquitous in the natural world, as is the potential for instability in such systems. Classic models of interacting species have provided a basis for our understanding of stability in these systems, and suggest that stable coexistence requires weak, rare, and asymmetric interactions. But missing from these models is an explicit understanding of how resource exchange and resource limitation can drive or prevent instability. Here, we show that systems based on general rules for the consumption and exchange of resources are guaranteed to be stable when exchange of resources is reciprocated by each pair of partners. These cooperative, mutualistic interactions can be arbitrarily strong and yet not disrupt stability. More general modes of exchange will lead to instability when supply rates are low, but when resource supply from outside the system is sufficiently high, arbitrary exchange is consistent with a stable equilibrium.
Effects of Longitudinal Vortices on Turbulent Junction Flow Heat Transfer

Turbulent junction flow is a vortex system upstream of a blockage embedded in a turbulent boundary layer, which leads to augmented heat transfer near the blockage. Vortex legs wrap around the block...
Stabilizer-free weak Galerkin methods for monotone quasilinear elliptic PDEs

Abstract In this paper, we study the stabilizer-free weak Galerkin methods on polytopal meshes for a class of second order elliptic boundary value problems of divergence form and with gradient nonlinearity in the principal coefficient. With certain assumptions on the nonlinear coefficient, we show that the discrete problem has a unique solution. This is achieved by showing that the associated operator satisfies certain continuity and monotonicity properties. With the help of these properties, we derive optimal error estimates in the energy norm. We present several numerical examples to verify the error estimates.
Relative Navigation Approach for Vision-Based Aerial GPS-Denied Navigation

GPS-denied aerial flight is a challenging research problem and requires knowledge of complex elements from several distinct disciplines. Additionally, aerial vehicles can present challenging constraints such as stringent payload limits and fast vehicle dynamics. In this paper we propose a new architecture to simplify some of the challenges that constrain GPS-denied aerial flight. At the core, the approach combines visual graph-SLAM with a multiplicative extended Kalman filter. More importantly, for the front end we depart from the common practice of estimating global states and instead keep the position and yaw states of the MEKF relative to the current node in the map. This relative navigation approach provides simple application of sensor measurement updates, intuitive definition of map edges and covariances, and the flexibility of using a globally consistent map when desired. We verify the approach with hardware flight-test results.
Effect of interictal epileptiform discharges on EEG-based functional connectivity networks.

Abstract Objective Functional connectivity networks (FCNs) based on interictal electroencephalography (EEG) can identify pathological brain networks associated with epilepsy. FCNs are altered by interictal epileptiform discharges (IEDs), but it is unknown whether this is due to the morphology of the IED or the underlying pathological activity. Therefore, we characterized the impact of IEDs on the FCN through simulations and EEG analysis. Methods We introduced simulated IEDs to sleep EEG recordings of eight healthy controls and analyzed the effect of IED amplitude and rate on the FCN. We then generated FCNs based on epochs with and without IEDs and compared them to the analogous FCNs from eight subjects with infantile spasms (IS), based on 1340 visually marked IEDs. Differences in network structure and strength were assessed. Results IEDs in IS subjects caused increased connectivity strength but no change in network structure. In controls, simulated IEDs with physiological amplitudes and rates did not alter network strength or structure. Conclusions Increases in connectivity strength in IS subjects are not artifacts caused by the interictal spike waveform and may be related to the underlying pathophysiology of IS. Significance Dynamic changes in EEG-based FCNs during IEDs may be valuable for identification of pathological networks associated with epilepsy.
{TinySDR}: {Low-Power} {SDR} Platform for {Over-the-Air} Programmable {IoT} Testbeds

Wireless protocol design for IoT networks is an active area of research which has seen significant interest and developments in recent years. The research community is however handicapped by the lack of a flexible, easily deployable platform for prototyping IoT endpoints that would allow for ground up protocol development and investigation of how such protocols perform at scale. We introduce tinySDR, the first software-defined radio platform tailored to the needs of power-constrained IoT endpoints. TinySDR provides a standalone, fully programmable low power software-defined radio solution that can be duty cycled for battery operation like a real IoT endpoint, and more importantly, can be programmed over the air to allow for large scale deployment. We present extensive evaluation of our platform showing it consumes as little as 30 uW of power in sleep mode, which is 10,000x lower than existing SDR platforms. We present two case studies by implementing LoRa and BLE beacons on the platform and achieve sensitivities of -126 dBm and -94 dBm respectively while consuming 11% and 3% of the FPGA resources. Finally, using tinySDR, we explore the research question of whether an IoT device can demodulate concurrent LoRa transmissions in real-time, within its power and computing constraints.
Risk and security tradeoffs in graphical coordination games

A system relying on the collective behavior of decision-makers can be vulnerable to a variety of adversarial attacks. How well can a system operator protect performance in the face of these risks? We frame this question in the context of graphical coordination games, where the agents in a network choose among two conventions and derive benefits from coordinating neighbors, and system performance is measured in terms of the agents’ welfare. In this paper, we assess an operator’s ability to mitigate two types of adversarial attacks-1) broad attacks, where the adversary attaches a fake neighbor to each agent in the network and 2) focused attacks, where the adversary can force a selected subset of the agents to commit to a prescribed convention. As a mitigation strategy, the system operator can implement a class of distributed algorithms that govern the agents’ decision-making process. We evaluate the extent to which the system is vulnerable to both types of attack, as well as characterize the operator’s fundamental trade-off between security against worst-case broad attacks and vulnerability from focused attacks. Our work highlights the design challenges a system operator faces in maintaining resilience of networked distributed systems.
n-m-Variant Systems: Adversarial-Resistant Software Rejuvenation for Cloud-Based Web Applications

Web servers are a popular target for adversaries as they are publicly accessible and often vulnerable to compromise. Compromises can go unnoticed for months, if not years, and recovery often involves a complete system rebuild. In this paper, we propose n-m-Variant Systems, an adversarial-resistant software rejuvenation framework for cloud-based web applications. We improve the state-of-the-art by introducing a variable m that provides a knob for administrators to tune an environment to balance resource usage, performance overhead, and security guarantees. Using m, security guarantees can be tuned for seconds, minutes, days, or complete resistance. We design and implement an n-m-Variant System prototype to protect a Mediawiki PHP application serving dynamic content from an external SQL persistent storage. Our performance evaluation shows a throughput reduction of 65% for 108 seconds of resistance and 83% for 12 days of resistance to sophisticated adversaries, given appropriate resource allocation. Furthermore, we use theoretical analysis and simulation to characterize the impact of system parameters on resilience to adversaries. Through these efforts, our work demonstrates how properties of cloud-based servers can enhance the integrity of Web servers.
Growth-Regulated Hsp70 Phosphorylation Regulates Stress Responses and Prion Maintenance.

Maintenance of protein homeostasis in eukaryotes under normal growth and stress conditions requires the functions of Hsp70 chaperones and associated cochaperones. Here, we investigate an evolutionarily conserved serine phosphorylation that occurs at the site of communication between the nucleotide-binding and substrate-binding domains of Hsp70. Ser151 phosphorylation in yeast Hsp70 (Ssa1) is promoted by cyclin-dependent kinase (Cdk1) during normal growth. Phosphomimetic substitutions at this site (S151D) dramatically downregulate heat shock responses, a result conserved with HSC70 S153 in human cells. Phosphomimetic forms of Ssa1 also fail to relocalize in response to starvation conditions, do not associate in vivo with Hsp40 cochaperones Ydj1 and Sis1, and do not catalyze refolding of denatured proteins in vitro in cooperation with Ydj1 and Hsp104. Despite these negative effects on HSC70/HSP70 function, the S151D phosphomimetic allele promotes survival of heavy metal exposure and suppresses the Sup35-dependent [PSI+ ] prion phenotype, consistent with proposed roles for Ssa1 and Hsp104 in generating self-nucleating seeds of misfolded proteins. Taken together, these results suggest that Cdk1 can downregulate Hsp70 function through phosphorylation of this site, with potential costs to overall chaperone efficiency but also advantages with respect to reduction of metal-induced and prion-dependent protein aggregate production.
Treatment induced neuropathy of diabetes.

Abstract Background Treatment induced neuropathy of diabetes (TIND) is an iatrogenic painful sensory and autonomic neuropathy. Although the prevalence is not known, it is seen in up to 10% of tertiary cases referred for evaluation of diabetic neuropathy. Evidence TIND is associated with a decrease in the glycosylated hemoglobin A1C in individuals with longstanding hyperglycemia. TIND is more common in individuals with type 1 diabetes, but can occur in anyone with diabetes through the use of insulin, oral hypoglycemic medications or diet control. There is an acute or subacute onset of neuropathy that is linked to the change in glucose control. Although the primary clinical manifestation is neuropathic pain there is a concurrent development of autonomic dysfunction, retinopathy and nephropathy. Conclusion TIND is more common than previously suspected. The number of cases reported over the past 10 years is much greater than historical literature predicted. Increased attention to target glucose control as a physician metric could suggest a possible explanation for the increased in TIND cases reported in recent years. At present, supportive care is the only recommended treatment. Future research is necessary to define the underlying mechanism, prevent development and to guide treatment recommendations.
Provable Low Rank Phase Retrieval

We study the Low Rank Phase Retrieval (LRPR) problem defined as follows: recover an $n \times q$ matrix ${ \boldsymbol {X}^{*}}$ of rank $r$ from a different and independent set of $m$ phaseless (magnitude-only) linear projections of each of its columns. To be precise, we need to recover ${ \boldsymbol {X}^{*}}$ from $\boldsymbol {y}_{k}:= | \boldsymbol {A}_{k}{}' \boldsymbol {x}^{*}_{k}|, k=1,2, {\dots }, q$ when the measurement matrices $\boldsymbol {A}_{k}$ are mutually independent. Here $\boldsymbol {y}_{k}$ is an $m$ length vector, $\boldsymbol {A}_{k}$ is an $n \times m$ matrix, and $'$ denotes matrix transpose. The question is when can we solve LRPR with $m \ll n$ ? A reliable solution can enable fast and low-cost phaseless dynamic imaging, e.g., Fourier ptychographic imaging of live biological specimens. In this work, we develop the first provably correct approach for solving this LRPR problem. Our proposed algorithm, Alternating Minimization for Low-Rank Phase Retrieval (AltMinLowRaP), is an AltMin based solution and hence is also provably fast (converges geometrically). Our guarantee shows that AltMinLowRaP solves LRPR to $\epsilon $ accuracy, with high probability, as long as $m q \ge C n r^{4} \log (1/\epsilon)$ , the matrices $\boldsymbol {A}_{k}$ contain i.i.d. standard Gaussian entries, and the right singular vectors of ${ \boldsymbol {X}^{*}}$ satisfy the incoherence assumption from matrix completion literature. Here $C$ is a numerical constant that only depends on the condition number of ${ \boldsymbol {X}^{*}}$ and on its incoherence parameter. Its time complexity is only $C mq nr \log ^{2}(1/\epsilon)$ . Since even the linear (with phase) version of the above problem is not fully solved, the above result is also the first complete solution and guarantee for the linear case. Finally, we also develop a simple extension of our results for the dynamic LRPR setting.
An exclusive fine-needle biopsy approach to sampling solid lesions under EUS guidance: a prospective cohort study

Background Endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) is increasingly utilized to enhance the cytological yield of sampling solid lesions, but its superiority over existing fine-needle aspiration (FNA) platforms has not been clearly demonstrated. The aim of our study was to compare the diagnostic accuracy and procedural outcomes of FNB using a new Franseen-tip needle to that of a traditional FNA in sampling solid lesions under EUS guidance. Methods Consecutive patients with solid lesions referred for EUS-FNB sampling were included. Procedure-related outcomes were collected prospectively including patient demographics, number of passes performed, diagnostic sample adequacy, adverse events, and recovery time. The Acquire needle was used to sample all lesions in the study group. Consecutive EUS-FNA procedures performed to sample solid lesions using the Expect needle were utilized as controls. Results There were 180 patients undergoing EUS-FNB compared to 183 patients undergoing EUS-FNA procedures for solid-lesion sampling. The procedure time was significantly shorter in patients who underwent FNB compared to FNA (mean: 37.4 vs 44.9 minutes, P < 0.001). Significantly fewer passes were performed in the FNB cohort compared to the FNA group (mean: 2.9 vs 3.8, P < 0.001). The cytologic diagnostic yield was significantly higher in the FNB group compared to the FNA group (98.3% vs 90.2%, P = 0.003). No significant difference in the incidence of adverse events was observed between the FNB and FNA groups (1.1% vs 0.5%, P = 0.564). Conclusions An FNB-exclusive approach to sampling solid lesions under EUS guidance is safe and feasible, and may result in fewer overall passes, shorter procedure time, and improved diagnostic adequacy. FNB may replace FNA as the primary sampling modality of choice in all solid lesions.
Astrophysical consequences of a violation of the strong equivalence principle

A previous analysis of the consequences of a violation of the strong equivalence principle (SEP) for massive particles has now been extended to the case of photons and leads to the prediction that the photon number is conserved. Comparison of the predictions of our theory with observations, especially those on big-bang nucleosynthesis, shows that a violation of the SEP is not ruled out by the data available, and that further direct measurements are required.
Mitochondrial Encephalomyopathy, Lactic Acidosis, and Stroke (MELAS)

Mitochondrial encephalomyopathy, lactic acidosis, and stroke (MELAS) is a mitochondrial encephalomyopathy, caused by an inherited mutation in a mitochondrial gene. The classic MELAS syndrome consists of mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes. Variability in the clinical and genetic features of this syndrome presents many difficulties for clinicians. Treatment primarily is based on theory and anecdotes, although guidance also is available from a limited number of controlled clinical studies.
Recovering the QNEC from the ANEC

We study the relative entropy in QFT comparing the vacuum state to a special family of purifications determined by an input state and constructed using relative modular flow. We use this to prove a conjecture by Wall that relates the shape derivative of relative entropy to a variational expression over the averaged null energy (ANE) of possible purifications. This variational expression can be used to easily prove the quantum null energy condition (QNEC). We formulate Wall’s conjecture as a theorem pertaining to operator algebras satisfying the properties of a half-sided modular inclusion, with the additional assumption that the input state has finite averaged null energy. We also give a new derivation of the strong superadditivity property of relative entropy in this context. We speculate about possible connections to the recent methods used to strengthen monotonicity of relative entropy with recovery maps.
On Convex Hulls of Epigraphs of QCQPs

Quadratically constrained quadratic programs (QCQPs) are a fundamental class of optimization problems well-known to be NP-hard in general. In this paper we study sufficient conditions for a convex hull result that immediately implies that the standard semidefinite program (SDP) relaxation of a QCQP is tight. We begin by outlining a general framework for proving such sufficient conditions. Then using this framework, we show that the convex hull result holds whenever the quadratic eigenvalue multiplicity, a parameter capturing the amount of symmetry present in a given problem, is large enough. Our results also imply new sufficient conditions for the tightness (as well as convex hull exactness) of a second order cone program relaxation of simultaneously diagonalizable QCQPs.
Sliding Basis Optimization for Heterogeneous Material Design

Abstract We present the sliding basis computational framework to automatically synthesize heterogeneous (graded or discrete) material fields for parts designed using constrained optimization. Our framework uses the fact that any spatially varying material field over a given domain may be parameterized as a weighted sum of the Laplacian eigenfunctions. We bound the parameterization of all material fields using a small set of weights to truncate the Laplacian eigenfunction expansion, which enables efficient design space exploration with the weights as a small set of design variables. We further improve computational efficiency by using the property that the Laplacian eigenfunctions form a spectrum and may be ordered from lower to higher frequencies. Starting the optimization with a small set of weighted lower frequency basis functions we iteratively include higher frequency bases by sliding a window over the space of ordered basis functions as the optimization progresses. This approach allows greater localized control of the material distribution as the sliding window moves through higher frequencies. The approach also reduces the number of optimization variables per iteration, thus the design optimization process speeds up independent of the domain resolution without sacrificing analysis quality. While our method is useful for problems where analytical gradients are available, it is most beneficial when the gradients may not be computed easily (i.e., optimization problems coupled with external black-box analysis) thereby enabling optimization of otherwise intractable design problems. The sliding basis framework is independent of any particular physics analysis, objective and constraints, providing a versatile and powerful design optimization tool for various applications. We demonstrate our approach on graded solid rocket fuel design and multi-material topology optimization applications and evaluate its performance.
A review of multi-objective deep learning speech denoising methods

Abstract This paper presents a review of multi-objective deep learning methods that have been introduced in the literature for speech denoising. After stating an overview of conventional, single objective deep learning, and hybrid or combined conventional and deep learning methods, a review of the mathematical framework of the multi-objective deep learning methods for speech denoising is provided. A representative method from each speech denoising category, whose codes are publicly available, is selected and a comparison is carried out by considering the same public domain dataset and four widely used objective metrics. The comparison results indicate the effectiveness of the multi-objective method compared with the other methods, in particular when the signal-to-noise ratio is low. Possible future improvements that can be achieved are also mentioned.
Your Noise, My Signal: Exploiting Switching Noise for Stealthy Data Exfiltration from Desktop Computers

Attacks based on power analysis have been long existing and studied, with some recent works focused on data exfiltration from victim systems without using conventional communications (e.g., WiFi). Nonetheless, prior works typically rely on intrusive direct power measurement, either by implanting meters in the power outlet or tapping into the power cable, thus jeopardizing the stealthiness of attacks. In this paper, we propose NoDE (Noise for Data Exfiltration), a new system for stealthy data exfiltration from enterprise desktop computers. Specifically, NoDE achieves data exfiltration over a building's power network by exploiting high-frequency voltage ripples (i.e., switching noises) generated by power factor correction circuits built into today's computers. Located at a distance and even from a different room, the receiver can non-intrusively measure the voltage of a power outlet to capture the high-frequency switching noises for online information decoding without supervised training/learning. To evaluate NoDE, we run experiments on seven different computers from top vendors and using top-brand power supply units. Our results show that for a single transmitter, NoDE achieves a rate of up to 28.48 bits/second with a distance of 90 feet (27.4 meters) without the line of sight, demonstrating a practically stealthy threat. Based on the orthogonality of switching noise frequencies of different computers, we also demonstrate simultaneous data exfiltration from four computers using only one receiver. Finally, we present a few possible defenses, such as installing noise filters, and discuss their limitations.
Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling.

To simultaneously address the rising need of expressing uncertainties in deep learning models along with producing model outputs which are consistent with the known scientific knowledge, we propose a novel physics-guided architecture (PGA) of neural networks in the context of lake temperature modeling where the physical constraints are hard coded in the neural network architecture. This allows us to integrate such models with state of the art uncertainty estimation approaches such as Monte Carlo (MC) Dropout without sacrificing the physical consistency of our results. We demonstrate the effectiveness of our approach in ensuring better generalizability as well as physical consistency in MC estimates over data collected from Lake Mendota in Wisconsin and Falling Creek Reservoir in Virginia, even with limited training data. We further show that our MC estimates correctly match the distribution of ground-truth observations, thus making the PGA paradigm amenable to physically grounded uncertainty quantification.
19-Month-Old Male Presenting with Near Strangulation by Human Hair

Introduction: Hair tourniquet syndrome is a well-documented phenomenon in which hair can become tightly wrapped around an appendage resulting in ischemic injury and necrosis. When a hair tourniquet involves the neck as the appendage, fatality can result due to asphyxia, specifically strangulation. Strangulation can be fatal, and death can result within minutes. In the pediatric population, suffocation is a concern when assessing the sleep of newborns and infants. There are inherent dangers of co-sleeping that are routinely discussed during pediatric office visits, particularly about newborns. Parents are alerted to the …
Multi-color Forcing in Graphs

Let $$G=(V,E)$$ be a finite connected graph along with a coloring of the vertices of G using the colors in a given set X. In this paper, we introduce multi-color forcing, a generalization of zero-forcing on graphs, and give conditions in which the multi-color forcing process terminates regardless of the number of colors used. We give an upper bound on the number of steps required to terminate a forcing procedure in terms of the number of vertices in the graph on which the procedure is being applied. We then focus on multi-color forcing with three colors and analyze the end states of certain families of graphs, including complete graphs, complete bipartite graphs, and paths, based on various initial colorings. We end with a few directions for future research.
Diffuser-based computational imaging funduscope

Poor access to eye care is a major global challenge that could be ameliorated by low-cost, portable, and easy-to-use diagnostic technologies. Diffuser-based imaging has the potential to enable inexpensive, compact optical systems that can reconstruct a focused image of an object over a range of defocus errors. Here, we present a diffuser-based computational funduscope that reconstructs important clinical features of a model eye. Compared to existing diffuser-imager architectures, our system features an infinite-conjugate design by relaying the ocular lens onto the diffuser. This offers shift-invariance across a wide field-of-view (FOV) and an invariant magnification across an extended depth range. Experimentally, we demonstrate fundus image reconstruction over a 33° FOV and robustness to ±4D refractive error using a constant point-spread-function. Combined with diffuser-based wavefront sensing, this technology could enable combined ocular aberrometry and funduscopic screening through a single diffuser sensor.
DIY-NAMIC Behavior: A High-Throughput Method to Measure Complex Phenotypes in the Homecage.

Complex behavioral assessment is necessary to comprehensively assess in vivo manipulations in rodent models for neuropsychiatric disorders. Operant behavioral paradigms provide rich datasets and allow for the careful analysis of behavioral phenotypes. However, one major limitation in these studies is the expense and work-load that are required using traditional methods. The equipment for commercial operant boxes can be prohibitively expensive, and the daily experimenter effort and mouse costs required for these studies is extensive. Rodents are generally trained on task-specific paradigms for months, tested every day for 5–7 d/week. Additionally, appetitive paradigms usually require food restriction and are also commonly run in the non-active light phase of the rodent circadian rhythm. These limitations make operant behavioral testing especially difficult during adolescence, a time period of interest with regards to the development of adult-like phenotypes and a high-risk period for the development of neuropsychiatric disorders, including those which involve impulsive behavior. In order to address these issues, we developed an automated, inexpensive, open-source method which allows the implementation of most standard operant paradigms in the homecage of rodents in shorter time frames without food restriction, and with much less experimenter effort. All construction and code for the do-it-yourself Nautiyal Automated Modular Instrumental Conditioning (DIY-NAMIC) system are open source. We demonstrate their utility here by measuring impulsive behavior in a pharmacology experiment, as well as in adolescent mice.
Strategic flexibility analysis of HRD research and practice post COVID-19 pandemic

While governments, intergovernmental organizations, non-profits, corporations are all aware that disruptions through pandemics and other natural bio-disasters like the COVID-19 pandemic can happen,...
Energy Constraints Improve Liquid State Machine Performance

A model of metabolic energy constraints is applied to a liquid state machine in order to analyze its effects on network performance. It was found that, in certain combinations of energy constraints, a significant increase in testing accuracy emerged; an improvement of 4.25% was observed on a seizure detection task using a digital liquid state machine while reducing overall reservoir spiking activity by 6.9%. The accuracy improvements appear to be linked to the energy constraints’ impact on the reservoir’s dynamics, as measured through metrics such as the Lyapunov exponent and the separation of the reservoir.
Evaluation of Amorphous Oxide Coatings for High-Voltage Li-Ion Battery Applications Using a First-Principles Framework

Cathode surface coatings are widely used industrially as a means to suppress degradation and improve electrochemical performance of lithium-ion batteries. However, developing an optimal coating is challenging, as different coating materials may enhance one aspect of performance while hindering another. To elucidate the fundamental thermodynamic and transport properties of amorphous cathode coating materials, here, we present a framework for calculating and analyzing the Li+ and O2- transport and the stability against delithiation in such materials. Our framework includes systematic workflows of ab-initio molecular dynamics calculations to obtain amorphous structures and diffusion trajectories coupled with an analysis of critical changes of the active-ion local environment during diffusion. Based on these data, we provide an estimate of room-temperature diffusivities, including statistical error bars, and the evaluation of the coating suitability in terms of its ability to facilitate Li+ transport while blocking O2- transport. Finally, we add the thermodynamic stability analysis of the coating chemistry within the operating voltage of common Li-ion cathodes. We apply this framework to two commonly used amorphous coating materials, Al2O3 and ZnO. We find that (1) in general, a higher Li+ content increases both Li+ and O2- diffusivities in both Al2O3 and ZnO. Also, Li+ and O2- diffuse much faster in ZnO than in Al2O3. (2) However, neither Al2O3 nor ZnO is expected to retain a significant concentration of Li+ at high charge. (3) ZnO performs much more poorly in terms of O2- blocking, and hence, Al2O3 is preferred for high-voltage cathode applications. These results will help to quantitatively evaluate amorphous materials, such as metal oxides and fluorides, for different performance metrics and facilitate the development of optimal cathode coatings.
Atypical response inhibition and error processing in 22q11.2 Deletion Syndrome and schizophrenia: Towards neuromarkers of disease progression and risk

Abstract 22q11.2 deletion syndrome (also known as DiGeorge syndrome or velo-cardio-facial syndrome) is characterized by increased vulnerability to neuropsychiatric symptoms, with approximately 30% of individuals with the deletion going on to develop schizophrenia. Clinically, deficits in executive function have been noted in this population, but the underlying neural processes are not well understood. Using a Go/No-Go response inhibition task in conjunction with high-density electrophysiological recordings (EEG), we sought to investigate the behavioral and neural dynamics of inhibition of a prepotent response (a critical component of executive function) in individuals with 22q11.2DS with and without psychotic symptoms, when compared to individuals with idiopathic schizophrenia and age-matched neurotypical controls. Twenty-eight participants diagnosed with 22q11.2DS (14–35 years old; 14 with at least one psychotic symptom), 15 individuals diagnosed with schizophrenia (18–63 years old) and two neurotypical control groups (one age-matched to the 22q11.2DS sample, the other age-matched to the schizophrenia sample) participated in this study. Analyses focused on the N2 and P3 no-go responses and error-related negativity (Ne) and positivity (Pe). Atypical inhibitory processing was shown behaviorally and by significantly reduced P3, Ne, and Pe responses in 22q11.2DS and schizophrenia. Interestingly, whereas P3 was only reduced in the presence of psychotic symptoms, Ne and Pe were equally reduced in schizophrenia and 22q11.2DS, regardless of the presence of symptoms. We argue that while P3 may be a marker of disease severity, Ne and Pe might be candidate markers of risk.
Byzantine-Resilient Distributed Optimization of Multi-Dimensional Functions

The problem of distributed optimization requires a group of agents to reach agreement on a parameter that minimizes the average of their local cost functions using information received from their neighbors. While there are a variety of distributed optimization algorithms that can solve this problem, they are typically vulnerable to malicious (or "Byzantine") agents that do not follow the algorithm. Recent attempts to address this issue focus on single dimensional functions, or provide analysis under certain assumptions on the statistical properties of the functions at the agents. In this paper, we propose a resilient distributed optimization algorithm for multidimensional convex functions. Our scheme involves two filtering steps at each iteration of the algorithm: (1) distance-based and (2) component-wise removal of extreme states. We show that this algorithm can mitigate the impact of up to F Byzantine agents in the neighborhood of each regular node, without knowing the identities of the Byzantine agents in advance. In particular, we show that if the network topology satisfies certain conditions, all of the regular states are guaranteed to asymptotically converge to a bounded region that contains the global minimizer.
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration

We undertake a precise study of the asymptotic and non-asymptotic properties of stochastic approximation procedures with Polyak-Ruppert averaging for solving a linear system $\bar{A} \theta = \bar{b}$. When the matrix $\bar{A}$ is Hurwitz, we prove a central limit theorem (CLT) for the averaged iterates with fixed step size and number of iterations going to infinity. The CLT characterizes the exact asymptotic covariance matrix, which is the sum of the classical Polyak-Ruppert covariance and a correction term that scales with the step size. Under assumptions on the tail of the noise distribution, we prove a non-asymptotic concentration inequality whose main term matches the covariance in CLT in any direction, up to universal constants. When the matrix $\bar{A}$ is not Hurwitz but only has non-negative real parts in its eigenvalues, we prove that the averaged LSA procedure actually achieves an $O(1/T)$ rate in mean-squared error. Our results provide a more refined understanding of linear stochastic approximation in both the asymptotic and non-asymptotic settings. We also show various applications of the main results, including the study of momentum-based stochastic gradient methods as well as temporal difference algorithms in reinforcement learning.
On the Load Distribution of Vehicular Users Modeled by a Poisson Line Cox Process

In this letter, we characterize the load on the cellular macro base stations (MBSs) due to vehicular users modeled by a Poisson line Cox process (PLCP). Modeling the locations of MBSs by a homogeneous 2D Poisson point process (PPP), we first characterize the total chord length distribution of the lines of the Poisson line process (PLP) intersecting the typical Poisson Voronoi (PV) cell. Using this result, we derive the exact probability mass function (PMF) of the load on the typical MBS. Considering the computational complexity of this expression, we propose an easy-to-use approximation for the PMF that is also remarkably accurate. Building on this result, we also compute the PMF of the load on the tagged MBS that serves the typical vehicular user. This result enables the characterization of the rate coverage of the typical receiver in the network, which is also included as a useful case study.
A MODEL FOR OPTIMAL HUMAN NAVIGATION WITH STOCHASTIC EFFECTS

We present a method for optimal path planning of human walking paths in mountainous terrain using a control theoretic formulation and a Hamilton--Jacobi--Bellman equation. Previous models for human...
RNA–protein interaction mapping via MS2- or Cas13-based APEX targeting

RNA–protein interactions underlie a wide range of cellular processes. Improved methods are needed to systematically map RNA–protein interactions in living cells in an unbiased manner. We used two approaches to target the engineered peroxidase APEX2 to specific cellular RNAs for RNA-centered proximity biotinylation of protein interaction partners. Both an MS2-MCP system and an engineered CRISPR-Cas13 system were used to deliver APEX2 to the human telomerase RNA hTR with high specificity. One-minute proximity biotinylation captured candidate binding partners for hTR, including more than a dozen proteins not previously linked to hTR. We validated the interaction between hTR and the N6-methyladenosine (m6A) demethylase ALKBH5 and showed that ALKBH5 is able to erase the m6A modification on endogenous hTR. ALKBH5 also modulates telomerase complex assembly and activity. MS2- and Cas13-targeted APEX2 may facilitate the discovery of novel RNA–protein interactions in living cells.
Missing Value Imputation for Mixed Data via Gaussian Copula

Missing data imputation forms the first critical step of many data analysis pipelines. The challenge is greatest for mixed data sets, including real, Boolean, and ordinal data, where standard techniques for imputation fail basic sanity checks: for example, the imputed values may not follow the same distributions as the data. This paper proposes a new semiparametric algorithm to impute missing values, with no tuning parameters. The algorithm models mixed data as a Gaussian copula. This model can fit arbitrary marginals for continuous variables and can handle ordinal variables with many levels, including Boolean variables as a special case. We develop an efficient approximate EM algorithm to estimate copula parameters from incomplete mixed data. The resulting model reveals the statistical associations among variables. Experimental results on several synthetic and real datasets show the superiority of our proposed algorithm to state-of-the-art imputation algorithms for mixed data.
Y-Net for Chest X-Ray Preprocessing: Simultaneous Classification of Geometry and Segmentation of Annotations

Over the last decade, convolutional neural networks (CNNs) have emerged as the leading algorithms in image classification and segmentation. Recent publication of large medical imaging databases have accelerated their use in the biomedical arena. While training data for photograph classification benefits from aggressive geometric augmentation, medical diagnosis – especially in chest radiographs – depends more strongly on feature location. Diagnosis classification results may be artificially enhanced by reliance on radiographic annotations. This work introduces a general pre-processing step for chest x-ray input into machine learning algorithms. A modified Y-Net architecture based on the VGG11 encoder is used to simultaneously learn geometric orientation (similarity transform parameters) of the chest and segmentation of radiographic annotations. Chest x-rays were obtained from published databases. The algorithm was trained with 1000 manually labeled images with augmentation. Results were evaluated by expert clinicians, with acceptable geometry in 95.8% and annotation mask in 96.2% (n = 500), compared to 27.0% and 34.9% respectively in control images (n = 241). We hypothesize that this pre-processing step will improve robustness in future diagnostic algorithms.Clinical relevance— This work demonstrates a universal pre-processing step for chest radiographs – both normalizing geometry and masking radiographic annotations – for use prior to further analysis.
Single-seed cascades on clustered networks

We consider a dynamic network cascade process developed by Duncan Watts applied to a class of random networks, developed independently by Newman and Miller, which allows a specified amount of clustering (short loops). We adapt existing methods for locally tree-like networks to formulate an appropriate two-type branching process to describe the spread of a cascade started with a single active node and obtain a fixed-point equation to implicitly express the extinction probability of such a cascade. In so doing, we also recover a formula that has appeared in various forms in works by Hackett et al. and Miller which provides a threshold condition for certain extinction of the cascade. We find that clustering impedes cascade propagation for networks of low average degree by reducing the connectivity of the network, but for networks with high average degree, the presence of small cycles makes cascades more likely.
A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining

With the abundance of automatic meeting transcripts, meeting summarization is of great interest to both participants and other parties. Traditional methods of summarizing meetings depend on complex multi-step pipelines that make joint optimization intractable. Meanwhile, there are a handful of deep neural models for text summarization and dialogue systems. However, the semantic structure and styles of meeting transcripts are quite different from articles and conversations. In this paper, we propose a novel abstractive summary network that adapts to the meeting scenario. We design a hierarchical structure to accommodate long meeting transcripts and a role vector to depict the difference among speakers. Furthermore, due to the inadequacy of meeting summary data, we pretrain the model on large-scale news summary data. Empirical results show that our model outperforms previous approaches in both automatic metrics and human evaluation. For example, on ICSI dataset, the ROUGE-1 score increases from 34.66% to 46.28%.
Interfacial phenomena, evaporation and stress in a constrained capillary system

The heat transfer and interfacial characteristics of a small constrained system under stress in the form of an evaporating extended meniscus at the exit of a slot feeder were evaluated. The stress level in the completely wetting fluid-solid system was increased by decreasing the overall size of the film and thereby increasing the relative importance of interfacial phenomena. Recent data concerning the effect of interfacial phenomena on transport processes in a constrained capillary system will be presented.The liquid thickness profile, which was representative of the pressure field in the extended meniscus was measured using ellipsometry and microcomputer enhanced video microscopy. The analysis demonstrates that the capillary and disjoining pressures are coupled and they lead to fluid flow and a reduction in vapor pressure. The interfacial forces have a large effect on the heat transfer characteristics. A model equation based on the augmented Young-Laplace equation was numerically solved and compared with the film thickness data to determine the Hamaker constant, in situ. The effects of system resistance on the mass and the heat flux distribution were also investigated.
Regulation of SETD2 stability is important for the fidelity of H3K36me3 deposition

The histone H3K36me3 mark regulates transcription elongation, pre-mRNA splicing, DNA methylation, and DNA damage repair. However, knowledge of the regulation of the enzyme SETD2, which deposits this functionally important mark, is very limited. Here, we show that the poorly characterized N-terminal region of SETD2 plays a determining role in regulating the stability of SETD2. This stretch of 1–1403 amino acids contributes to the robust degradation of SETD2 by the proteasome. Besides, the SETD2 protein is aggregate prone and forms insoluble bodies in nuclei especially upon proteasome inhibition. Removal of the N-terminal segment results in the stabilization of SETD2 and leads to a marked increase in global H3K36me3 which, uncharacteristically, happens in a Pol II-independent manner. The functionally uncharacterized N-terminal segment of SETD2 regulates its half-life to maintain the requisite cellular amount of the protein. The absence of SETD2 proteolysis results in a Pol II-independent H3K36me3 deposition and protein aggregation.
Accelerating the distributed Kaczmarz algorithm by strong over-relaxation

Abstract The distributed Kaczmarz algorithm is an adaptation of the standard Kaczmarz algorithm to the situation in which data is distributed throughout a network represented by a tree. We isolate substructures of the network and study convergence of the distributed Kaczmarz algorithm for relatively large relaxation parameters associated to these substructures. If the system is consistent, then the algorithm converges to the solution of minimal norm; however, if the system is inconsistent, then the algorithm converges to an approximated least-squares solution that is dependent on the parameters and the network topology. We show that the relaxation parameters may be larger than the standard upper-bound in literature in this context and provide numerical experiments to support our results.