cssi/SciSciGPT-SciSciNet huggingface dataset:
NoisyCUR: An Algorithm for Two-Cost Budgeted Matrix Completion

Matrix completion is a ubiquitous tool in machine learning and data analysis. Most work in this area has focused on the number of observations necessary to obtain an accurate low-rank approximation. In practice, however, the cost of observations is an important limiting factor, and experimentalists may have on hand multiple modes of observation with differing noise-vs-cost trade-offs. This paper considers matrix completion subject to such constraints: a budget is imposed and the experimentalist’s goal is to allocate this budget between two sampling modalities in order to recover an accurate low-rank approximation. Specifically, we consider that it is possible to obtain low noise, high cost observations of individual entries or high noise, low cost observations of entire columns. We introduce a regression-based completion algorithm for this setting and experimentally verify the performance of our approach on both synthetic and real data sets. When the budget is low, our algorithm outperforms standard completion algorithms. When the budget is high, our algorithm has comparable error to standard nuclear norm completion algorithms and requires much less computational effort.
Essential moral sources of ethical standards in child and youth care work

It is proposed that an ethical framework for child and youth care practice should take into account the differences between descriptive ethical inquiry on the one hand and normative and analytical ethical inquiry on the other. This will help us avoid the error of deriving our ethical principles from our practices, when in fact what we need is a moral criterion originating outside our practice that is not based on efficiency. Mattingly (1995) suggests this in recommending that we “Develop an ethical vision.” This ethical vision should take into account the domains of morality proposed by Taylor (1989), including respect for human life, issues of what makes a rich, meaningful life, and ideas about dignity. Doing so may provide a moral foundation not just for a code of ethics, but a moral framework for evaluating the entire range of our practice with children and youth.
Characterization and computation of feasible trajectories for an articulated probe with a variable-length end segment

Abstract An articulated probe is modeled in the plane as two line segments, ab and bc, joined at b, with ab being very long, and bc of some small length r. We investigate a trajectory planning problem involving the articulated two-segment probe where the length r of bc can be customized. Consider a set P of simple polygonal obstacles with a total of n vertices, a target point t located in the free space such that t cannot see to infinity, and a circle S centered at t enclosing P. The probe initially resides outside S, with ab and bc being collinear, and is restricted to the following sequence of moves: a straight line insertion of abc into S followed by a rotation of bc around b. The goal is to compute a feasible obstacle-avoiding trajectory for the probe so that, after the sequence of moves, c coincides with t. We prove that, for n line segment obstacles, the smallest length r for which there exists a feasible probe trajectory can be found in O ( n 2 + ϵ ) time using O ( n 2 + ϵ ) space, for any constant ϵ > 0 . Furthermore, we prove that all values r for which a feasible probe trajectory exists form O ( n 2 ) intervals, and can be computed in O ( n 5 / 2 ) time using O ( n 2 + ϵ ) space. We also show that, for a given r, the feasible trajectory space of the articulated probe can be characterized by a simple arrangement of complexity O ( n 2 ) , which can be constructed in O ( n 2 ) time. To obtain our solutions, we design efficient data structures for a number of interesting variants of geometric intersection and emptiness query problems.
An Integrate-and-Fire Spiking Neural Network Model Simulating Artificially Induced Cortical Plasticity.

We describe an integrate-and-fire (IF) spiking neural network that incorporates spike-timing-dependent plasticity (STDP) and simulates the experimental outcomes of four different conditioning protocols that produce cortical plasticity. The original conditioning experiments were performed in freely moving non-human primates (NHPs) with an autonomous head-fixed bidirectional brain-computer interface (BCI). Three protocols involved closed-loop stimulation triggered from (1) spike activity of single cortical neurons, (2) electromyographic (EMG) activity from forearm muscles, and (3) cycles of spontaneous cortical beta activity. A fourth protocol involved open-loop delivery of pairs of stimuli at neighboring cortical sites. The IF network that replicates the experimental results consists of 360 units with simulated membrane potentials produced by synaptic inputs and triggering a spike when reaching threshold. The 240 cortical units produce either excitatory or inhibitory postsynaptic potentials (PSPs) in their target units. In addition to the experimentally observed conditioning effects, the model also allows computation of underlying network behavior not originally documented. Furthermore, the model makes predictions about outcomes from protocols not yet investigated, including spike-triggered inhibition, γ-triggered stimulation and disynaptic conditioning. The success of the simulations suggests that a simple voltage-based IF model incorporating STDP can capture the essential mechanisms mediating targeted plasticity with closed-loop stimulation. Significance Statement Previous experiments have shown that the synaptic connections between neurons in cerebral cortex can be modified by different activity-dependent stimulation methods. Understanding the mechanisms that mediate these changes is key to designing effective protocols for producing targeted plasticity in clinical and basic science applications. We describe a simple but powerful neural network model that replicates a wide range of experimental results on targeted cortical plasticity. The model consists of integrate-and-fire (IF) spiking units and incorporates spike-timing-dependent plasticity (STDP). It provides important insights into the mechanisms underlying induction of plasticity with bidirectional brain-computer interfaces (BCIs). The model also predicts the outcomes of several novel and untested conditioning protocols.
Multiple time-scales of decision-making in the hippocampus and prefrontal cortex.

The prefrontal cortex and hippocampus are crucial for memory-guided decision-making. Neural activity in the hippocampus exhibits place-cell sequences at multiple timescales, including slow behavioral sequences (~seconds) and fast theta sequences (~100-200 ms) within theta oscillation cycles. How prefrontal ensembles interact with hippocampal sequences to support decision-making is unclear. Here, we examined simultaneous hippocampal and prefrontal ensemble activity in rats during learning of a spatial working-memory decision task. We found clear theta sequences in prefrontal cortex, nested within its behavioral sequences. In both regions, behavioral sequences maintained representations of current choices during navigation. In contrast, hippocampal theta sequences encoded alternatives for deliberation, and were coordinated with prefrontal theta sequences that predicted upcoming choices. During error trials, these representations were preserved to guide ongoing behavior, whereas replay sequences during inter-trial periods were impaired prior to navigation. These results establish cooperative interaction between hippocampal and prefrontal sequences at multiple timescales for memory-guided decision-making.
DiscretizationNet: A machine-learning based solver for Navier–Stokes equations using finite volume discretization

Abstract Over the last few decades, existing Partial Differential Equation (PDE) solvers have demonstrated a tremendous success in solving complex, non-linear PDEs. Although accurate, these PDE solvers are computationally costly. With the advances in Machine Learning (ML) technologies, there has been a significant increase in the research of using ML to solve PDEs. The goal of this work is to develop an ML-based PDE solver, that couples’ important characteristics of existing PDE solvers with ML technologies. The two solver characteristics that have been adopted in this work are: (1) the use of discretization-based schemes to approximate spatio-temporal partial derivatives and (2) the use of iterative algorithms to solve linearized PDEs in their discrete form. In the presence of highly non-linear, coupled PDE solutions, these strategies can be very important in achieving good accuracy, better stability and faster convergence. Our ML-solver, DiscretizationNet, employs a generative CNN-based encoder–decoder model with PDE variables as both input and output features. During training, the discretization schemes are implemented inside the computational graph to enable faster GPU computation of PDE residuals, which are used to update network weights that result into converged solutions. A novel iterative capability is implemented during the network training to improve the stability and convergence of the ML-solver. The ML-Solver is demonstrated to solve the steady, incompressible Navier–Stokes equations in 3-D for several cases such as, lid-driven cavity, flow past a cylinder and conjugate heat transfer.
UNIT: unifying tensorized instruction compilation

Because of the increasing demand for intensive computation in deep neural networks, researchers have developed both hardware and software mechanisms to reduce the compute and memory burden. A widely adopted approach is to use mixed precision data types. However, it is hard to benefit from mixed precision without hardware specialization because of the overhead of data casting. Recently, hardware vendors offer tensorized instructions specialized for mixed-precision tensor operations, such as Intel VNNI, Nvidia Tensor Core, and ARM DOT. These instructions involve a new computing idiom, which reduces multiple low precision elements into one high precision element. The lack of compilation techniques for this emerging idiom makes it hard to utilize these instructions. In practice, one approach is to use vendor-provided libraries for computationally-intensive kernels, but this is inflexible and prevents further optimizations. Another approach is to manually write hardware intrinsics, which is error-prone and difficult for programmers. Some prior works tried to address this problem by creating compilers for each instruction. This requires excessive efforts when it comes to many tensorized instructions. In this work, we develop a compiler framework, UNIT, to unify the compilation for tensorized instructions. The key to this approach is a unified semantics abstraction which makes the integration of new instructions easy, and the reuse of the analysis and transformations possible. Tensorized instructions from different platforms can be compiled via UNIT with moderate effort for favorable performance. Given a tensorized instruction and a tensor operation, UNIT automatically detects the applicability of the instruction, transforms the loop organization of the operation, and rewrites the loop body to take advantage of the tensorized instruction. According to our evaluation, UNIT is able to target various mainstream hardware platforms. The generated end-to-end inference model achieves 1.3 x speedup over Intel oneDNN on an x86 CPU, 1.75x speedup over Nvidia cuDNN on an Nvidia GPU, and 1.13x speedup over a carefully tuned TVM solution for ARM DOT on an ARM CPU.
The Democracy Principle in State Constitutions

In recent years, antidemocratic behavior has rippled across the nation. Lame-duck state legislatures have stripped popularly elected governors of their powers; extreme partisan gerrymanders have warped representative institutions; state officials have nullified popularly adopted initiatives. The federal constitution offers few resources to address these problems, and ballot-box solutions cannot work when antidemocratic actions undermine elections themselves. Commentators increasingly decry the rule of the many by the few. This Article argues that a vital response has been neglected. State constitutions embody a deep commitment to democracy. Unlike the federal constitution, they were drafted — and have been repeatedly rewritten and amended — to empower popular majorities. In text, history, and structure alike, they express a commitment to popular sovereignty, majority rule, and political equality. We shorthand this commitment the "democracy principle" and describe its development and current potential. The Article’s aims are both theoretical and practical. At the level of theory, we offer a new view of American constitutionalism, one in which the majoritarian commitment of states’ founding documents complements the anti-majoritarian tilt of the national document. Such complementarity is an unspoken premise of the familiar claim that the federal constitution may temper excesses and abuses of state majoritarianism. We focus on the other half of the equation: state constitutions may ameliorate national democratic shortcomings. At the level of practice, we show how the democracy principle can inform a number of contemporary conflicts. Reimagining recent cases concerning electoral interference, political entrenchment, and more, we argue that it is time to reclaim the state constitutional commitment to democracy.
ER-Directed TREX1 Limits cGAS Recognition of Micronuclei

Chromosomal instability in cancer results in the formation of nuclear aberrations termed micronuclei. Spontaneous loss of micronuclear envelope integrity exposes DNA to the cytoplasm, leading to chromosome fragmentation and innate immune activation. Despite connections to cancer genome evolution and anti-tumor immunity, the mechanisms underlying damage and immune sensing of micronuclear DNA are poorly understood. Here, we use a novel method for the purification of micronuclei and live-cell imaging to show that the ER-associated nuclease TREX1 inhibits cGAS sensing of micronuclei by stably associating with and degrading micronuclear DNA upon micronuclear envelope rupture. We identify a TREX1 mutation, previously associated with autoimmune disease, that untethers TREX1 from the ER, disrupts TREX1 localization to micronuclei, alleviates micronuclear DNA damage, and enhances cGAS recognition of micronuclei. Together, these results establish ER-directed resection of micronuclear DNA by TREX1 as a critical regulator of cytosolic DNA sensing in chromosomally unstable cells and provide a mechanistic basis for the importance of TREX1 ER-tethering in preventing autoimmunity.
In vitro profiling of orphan G protein coupled receptor (GPCR) constitutive activity.

Background and purpose Members of the GPCR family are targeted by a significant fraction of the available FDA-approved drugs. However, the physiological role and pharmacological properties of many GPCRs remain unknown, representing untapped potential in drug design. Of particular interest are ~100 less-studied GPCRs known as orphans because their endogenous ligands are unknown. Intriguingly, disease-causing mutations identified in patients, together with animal studies, have demonstrated that many orphan receptors play crucial physiological roles and, thus, represent attractive drug targets. Experimental approach The majority of deorphanized GPCRs demonstrate coupling to Gi/o . However, a limited number of techniques allow the detection of intrinsically small constitutive activity associated with Gi/o protein activation, which represents a significant barrier in our ability to study orphan GPCR signalling. Using luciferase reporter assays, we effectively detected constitutive Gs , Gq and G12/13 protein signalling by unliganded receptors and introducing various G protein chimeras, we provide a novel, highly sensitive tool capable of identifying Gi/o coupling in unliganded orphan GPCRs. Key results Using this approach, we measured the constitutive activity of the entire class C GPCR family that includes eight orphan receptors and a subset of 20 prototypical class A GPCR members, including 11 orphans. Excitingly, this approach illuminated the G protein coupling profile of eight orphan GPCRs (GPR22, GPR137b, GPR88, GPR156, GPR158, GPR179, GPRC5D and GPRC6A) previously linked to pathophysiological processes. Conclusion and implications We provide a new platform that could be utilized in ongoing studies in orphan receptor signalling and de-orphanization efforts.
Universal rules of life: metabolic rates, biological times and the equal fitness paradigm

Here we review and extend the equal fitness paradigm (EFP) as an important step in developing and testing a synthetic theory of ecology and evolution based on energy and metabolism. The EFP states that all organisms are equally fit at steady state, because they allocate the same quantity of energy, ~ 22.4 kJ/g/generation to the production of offspring. On the one hand, the EFP may seem tautological, because equal fitness is necessary for the origin and persistence of biodiversity. On the other hand, the EFP reflects universal laws of life: how biological metabolism - the uptake, transformation and allocation of energy - links ecological and evolutionary patterns and processes across levels of organisation from: (1) structure and function of individual organisms, (2) life history and dynamics of populations, and (3) interactions and coevolution of species in ecosystems. The physics and biology of metabolism have facilitated the evolution of millions of species with idiosyncratic anatomy, physiology, behaviour and ecology but also with many shared traits and tradeoffs that reflect the single origin and universal rules of life.
DyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural Networks

This paper presents a data-driven localization framework with high precision in time-varying complex multi-path environments, such as dense urban areas and indoors, where GPS and model-based localization techniques come short. We consider the angle-delay profile (ADP), a linear transformation of channel state information (CSI), in massive MIMO systems and show that ADPs preserve users’ motion when stacked temporally. We discuss that given a static environment, future frames of ADP time-series are predictable employing a video frame prediction algorithm. We express that a deep convolutional neural network (DCNN) can be employed to learn the background static scattering environment. To detect foreground changes in the environment, corresponding to path blockage or addition, we introduce an algorithm taking advantage of the trained DCNN. Furthermore, we present DyLoc, a data-driven framework to recover distorted ADPs due to foreground changes and to obtain precise location estimations. We evaluate the performance of DyLoc in several dynamic scenarios employing DeepMIMO dataset [1] to generate geo-tagged CSI datasets for indoor and outdoor environments. We show that previous DCNN-based techniques fail to perform with desirable accuracy in dynamic environments, while DyLoc pursues localization precisely. Moreover, simulations show that as the environment gets richer in terms of the number of multipath, DyLoc gets more robust to foreground changes.
Climate mobility and the pandemic: art–science lessons for societal resilience

Societies were underprepared for the COVID-19 pandemic. This is also the case for climate change. Art may have a greater role to play in advancing societal resilience across these different categor...
Demonstration of a MOT in a sub-millimeter membrane hole.

We demonstrate the generation of a cold-atom ensemble within a sub-millimeter diameter hole in a transparent membrane, a so-called “membrane MOT”. With a sub-Doppler cooling process, the atoms trapped by the membrane MOT are cooled down to 10 $$\upmu$$ K. The atom number inside the unbridged/bridged membrane hole is about $$10^4$$ to $$10^5$$ , and the $$1/e^2$$ -diameter of the MOT cloud is about 180 $$\upmu$$ m for a 400 $$\upmu$$ m-diameter membrane hole. Such a membrane device can, in principle, efficiently load cold atoms into the evanescent-field optical trap generated by the suspended membrane waveguide for strong atom-light interaction and provide the capability of sufficient heat dissipation at the waveguide. This represents a key step toward the photonic atom trap integrated platform (ATIP).
Regulatory Triage in a Volatile Political Era

Policymakers and administrators periodically revise or jettison rules, enforcement priorities, and agency structures for a variety of reasons. This is particularly the case when presidential administrations change, as evidenced, for example, by the transition from President Carter to President Reagan. As the current U.S. presidency undergoes one of the most monumental political changes in recent history, proposals to dramatically cut and revoke public laws — not just revise them — have taken center stage. Health care, immigration, and environmental regulations are among areas slated to receive the most attention, and executive action in these areas has already commenced. President Trump has also indicated a desire to severely slash funding for agencies like the Environmental Protection Agency. There is a threat that as this regulatory reform rapidly progresses, typical regulatory capture problems will prevail. Substantial regulatory changes could proceed at the expense of dispersed interests that collectively have much to lose from certain changes — such as important public health and environmental protections — but individually lack the motivation or re­sources to overcome organizational transaction costs. This Piece lumps the process of reviewing and prioritizing regu­lations into a broad term called “regulatory triage” and argues that this is an important time for deploying this type of system. Changes to rules and the agencies that write and enforce them must progress in a manner that takes into account the values of a diverse set of stakeholders, including the individuals who could be the most negatively impacted by the loss of regulatory protections. Although process alone cannot adequately address this threat, this Piece argues that agencies should build from previous lessons regarding comparative regulatory assessment and form stakeholder groups tasked with prioritizing the preservation of rules, agency offices, and enforce­ment efforts that address the greatest societal problems. Agencies, in turn, should take into account the recommendations of these groups. Any regulatory triage approach must proceed with great caution, however. The last thing on the minds of most agency officials facing further cuts to an already understaffed and underfinanced office is a lengthy process that will only consume more time and money, taking away valuable resources from the most pressing problems.
Analytical characterization and design space exploration for optimization of CNNs

Moving data through the memory hierarchy is a fundamental bottleneck that can limit the performance of core algorithms of machine learning, such as convolutional neural networks (CNNs). Loop-level optimization, including loop tiling and loop permutation, are fundamental transformations to reduce data movement. However, the search space for finding the best loop-level optimization configuration is explosively large. This paper develops an analytical modeling approach for finding the best loop-level optimization configuration for CNNs on multi-core CPUs. Experimental evaluation shows that this approach achieves comparable or better performance than state-of-the-art libraries and auto-tuning based optimizers for CNNs.
Spatial Shrinkage Via the Product Independent Gaussian Process Prior

We study the problem of sparse signal detection on a spatial domain. We propose a novel approach to model continuous signals that are sparse and piecewise-smooth as the product of independent Gauss...
Changing Tides: Public Attitudes on Climate Migration

Little existing work studies public perceptions of climate-induced migration. We redress this gap, drawing on diverse literatures in political science and social psychology. We argue that climate m...
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model

We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint partitions, but the ranking of items within a partition is unknown. Given $N$ items with $M$ partitions, calculating the likelihood of data with partitioned preference under the PL model has a time complexity of $O(N+S!)$, where $S$ is the maximum size of the top $M-1$ partitions. This computational challenge restrains most existing PL-based listwise LTR methods to a special case of partitioned preference, top-$K$ ranking, where the exact order of the top $K$ items is known. In this paper, we exploit a random utility model formulation of the PL model, and propose an efficient numerical integration approach for calculating the likelihood and its gradients with a time complexity $O(N+S^3)$. We demonstrate that the proposed method outperforms well-known LTR baselines and remains scalable through both simulation experiments and applications to real-world eXtreme Multi-Label classification tasks.
Risk Factors For Infection And Health Impacts Of The Covid-19 Pandemic In People With Autoimmune Diseases.

BACKGROUND: People with autoimmune or inflammatory conditions taking immunomodulatory/suppressive medications may have higher risk of novel coronavirus disease 2019 (COVID-19). Chronic disease care has also changed for many patients, with uncertain downstream consequences. METHODS: We included participants with autoimmune or inflammatory conditions followed by specialists at Johns Hopkins. Participants completed periodic surveys querying comorbidities, disease-modifying medications, exposures, COVID-19 testing and outcomes, social behaviors, and disruptions to healthcare. We assessed whether COVID-19 risk is higher among those on immunomodulating or suppressive agents and characterized pandemic-associated changes to care and mental health. RESULTS: 265 (5.6%) developed COVID-19 over 9 months of follow-up (April-December 2020). Patient characteristics (age, race, comorbidity, medications) were associated with differences in social distancing behaviors during the pandemic. Glucocorticoid exposure was associated with higher odds of COVID-19 in models incorporating behavior and other potential confounders (OR: 1.43; 95%CI: 1.08, 1.89). Other medication classes were not associated with COVID-19 risk. Diabetes (OR: 1.72; 95%CI: 1.08, 2.73), cardiovascular disease (OR: 1.68; 95%CI: 1.24, 2.28), and kidney disease (OR: 1.76; 95%CI: 1.04, 2.97) were associated with higher odds of COVID-19. Of the 2156 reporting pre-pandemic utilization of infusion, mental health or rehabilitative services, 975 (45.2%) reported disruptions therein, which disproportionately affected individuals experiencing changes to employment or income. CONCLUSIONS: Glucocorticoid exposure may increase risk of COVID-19 in people with autoimmune or inflammatory conditions. Disruption to healthcare and related services was common. Those with pandemic-related reduced income may be most vulnerable to care disruptions.
Monetizing Propaganda: How Far-right Extremists Earn Money by Video Streaming

Video streaming platforms such as Youtube, Twitch, and DLive allow users to live-stream video content for viewers who can optionally express their appreciation through monetary donations. DLive is one of the smaller and lesser-known streaming platforms, and historically has had fewer content moderation practices. It has thus become a popular place for violent extremists and other clandestine groups to earn money and propagandize. What is the financial structure of the DLive streaming ecosystem and how much money is changing hands? In the past it has been difficult to understand how far-right extremists fundraise via podcasts and video streams because of the secretive nature of the activity and because of the difficulty of getting data from social media platforms. This paper describes a novel experiment to collect and analyze data from DLive's publicly available ledgers of transactions in order to understand the financial structure of the clandestine, extreme far-right video streaming community. The main findings of this paper are, first, that the majority of donors are using micropayments in varying frequencies, but a small handful of donors spend large amounts of money to finance their favorite streamers. Next, the timing of donations to high-profile far-right streamers follows a fairly predictable pattern that is closely tied to a broadcast schedule. Finally, the far-right video streaming financial landscape is divided into separate cliques which exhibit very little crossover in terms of sizable donations. This work will be important to technology companies, policymakers, and researchers who are trying to understand how niche social media services, including video platforms, are being exploited by extremists to propagandize and fundraise.
What is the optimal timing of intracytoplasmic sperm injection (ICSI) after EGG retrieval? A randomized controlled trial.

To determine if oocyte denudation and ICSI at 36.5 versus 39 h post HCG and/or Lupron trigger (2.5 h versus 5 h post-oocyte retrieval) influences fertilization and blastulation rates in good prognosis couples We performed a prospective, randomized controlled trial of 12 patients undergoing IVF with ICSI at an academic fertility center, resulting in 206 MII oocytes analyzed. At time of retrieval, patients with more than 10 oocytes retrieved had their oocytes randomized into two groups—one group for oocyte denudation and ICSI at 36.5 h post HCG and/or Lupron trigger and the other group for these procedures at 39 h post HCG and/or Lupron trigger (2.5 and 5 h after oocyte retrieval). Primary outcomes were fertilization and blastulation rates. No difference was observed in fertilization rate, total blastulation rate, or day of blastulation based on timing of denudation and ICSI (all p > 0.05). Multiple regression analyses for fertilization and blastulation controlling for age and BMI revealed no difference in fertilization based on time of ICSI (p = 0.38, 0.71, respectively). A conditional logistic regression to account for multiple oocytes derived from each patient also found no difference in fertilization or blastulation based on timing of ICSI, even when controlling for age and BMI (p = 0.47, 0.59, respectively). In good prognosis couples, we observed no difference in fertilization or blastulation rates based on timing of ICSI within the currently accepted 2- to 6-h window post-retrieval based on a 34-h trigger. The oocyte appears to have a physiological tolerance for fertilization during this window of time, and variability in the timing of ICSI during this window is unlikely to have an impact on cycle outcome.
Concave elliptic equations and generalized Khovanskii–Teissier inequalities

We explain a general construction through which concave elliptic operators on complex manifolds give rise to concave functions on cohomology. In particular, this leads to generalized versions of the Khovanskii-Teissier inequalities.
MOCCASIN: a method for correcting for known and unknown confounders in RNA splicing analysis.

The effects of confounding factors on gene expression analysis have been extensively studied following the introduction of high-throughput microarrays and subsequently RNA sequencing. In contrast, there is a lack of equivalent analysis and tools for RNA splicing. Here we first assess the effect of confounders on both expression and splicing quantifications in two large public RNA-Seq datasets (TARGET, ENCODE). We show quantification of splicing variations are affected at least as much as those of gene expression, revealing unwanted sources of variations in both datasets. Next, we develop MOCCASIN, a method to correct the effect of both known and unknown confounders on RNA splicing quantification and demonstrate MOCCASIN’s effectiveness on both synthetic and real data. Code, synthetic and corrected datasets are all made available as resources. Confounding factors on gene expression analysis can be analyzed by several existing tools. Here the authors develop an algorithm called MOCCASIN to correct the effect of known and unknown confounders on RNA splicing quantification.
Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm

Sparse adversarial attacks can fool deep neural networks (DNNs) by only perturbing a few pixels (regularized by l_0 norm). Recent efforts combine it with another l_infty imperceptible on the perturbation magnitudes. The resultant sparse and imperceptible attacks are practically relevant, and indicate an even higher vulnerability of DNNs that we usually imagined. However, such attacks are more challenging to generate due to the optimization difficulty by coupling the l_0 regularizer and box constraints with a non-convex objective. In this paper, we address this challenge by proposing a homotopy algorithm, to jointly tackle the sparsity and the perturbation bound in one unified framework. Each iteration, the main step of our algorithm is to optimize an l_0-regularized adversarial loss, by leveraging the nonmonotone Accelerated Proximal Gradient Method (nmAPG) for nonconvex programming; it is followed by an l_0 change control step, and an optional post-attack step designed to escape bad local minima. We also extend the algorithm to handling the structural sparsity regularizer. We extensively examine the effectiveness of our proposed homotopy attack for both targeted and non-targeted attack scenarios, on CIFAR-10 and ImageNet datasets. Compared to state-of-the-art methods, our homotopy attack leads to significantly fewer perturbations, e.g., reducing 42.91% on CIFAR-10 and 75.03% on ImageNet (average case, targeted attack), at similar maximal perturbation magnitudes, when still achieving 100% attack success rates. Our codes are available at: this https URL.
Average-tempered stable subordinators with applications

In this paper the running average of a subordinator with a tempered stable distribution is considered. We investigate a family of previously unexplored infinite-activity subordinators induced by the probability distribution of the running average process and determine their jump intensity measures. Special cases including gamma processes and inverse Gaussian processes are discussed. Then we derive easily implementable formulas for the distribution functions, cumulants, and moments, as well as provide explicit estimates for their asymptotic behaviors. Numerical experiments are conducted for illustrating the applicability and efficiency of the proposed formulas. Two important extensions of the running average process and its equi-distributed subordinator are examined with concrete applications to structural degradation modeling and financial derivatives pricing, where their advantages relative to several existing models are highlighted together with the mention of Euler discretization and compound Poisson approximation techniques.
World Trade Center responders in their own words: predicting PTSD symptom trajectories with AI-based language analyses of interviews.

BACKGROUND Oral histories from 9/11 responders to the World Trade Center (WTC) attacks provide rich narratives about distress and resilience. Artificial Intelligence (AI) models promise to detect psychopathology in natural language, but they have been evaluated primarily in non-clinical settings using social media. This study sought to test the ability of AI-based language assessments to predict PTSD symptom trajectories among responders. METHODS Participants were 124 responders whose health was monitored at the Stony Brook WTC Health and Wellness Program who completed oral history interviews about their initial WTC experiences. PTSD symptom severity was measured longitudinally using the PTSD Checklist (PCL) for up to 7 years post-interview. AI-based indicators were computed for depression, anxiety, neuroticism, and extraversion along with dictionary-based measures of linguistic and interpersonal style. Linear regression and multilevel models estimated associations of AI indicators with concurrent and subsequent PTSD symptom severity (significance adjusted by false discovery rate). RESULTS Cross-sectionally, greater depressive language (β = 0.32; p = 0.049) and first-person singular usage (β = 0.31; p = 0.049) were associated with increased symptom severity. Longitudinally, anxious language predicted future worsening in PCL scores (β = 0.30; p = 0.049), whereas first-person plural usage (β = -0.36; p = 0.014) and longer words usage (β = -0.35; p = 0.014) predicted improvement. CONCLUSIONS This is the first study to demonstrate the value of AI in understanding PTSD in a vulnerable population. Future studies should extend this application to other trauma exposures and to other demographic groups, especially under-represented minorities.
Habitat heterogeneity affects the thermal ecology of an endangered lizard

Global climate change is already contributing to the extirpation of numerous species worldwide, and sensitive species will continue to face challenges associated with rising temperatures throughout this century and beyond. It is especially important to evaluate the thermal ecology of endangered ectotherm species now so that mitigation measures can be taken as early as possible. A recent study of the thermal ecology of the federally endangered Blunt-nosed Leopard Lizard (Gambelia sila) suggested that they face major activity restrictions due to thermal constraints in their desert habitat, but that large shade-providing shrubs act as thermal buffers to allow them to maintain surface activity without overheating. We replicated this study and also included a population of G. sila with no access to large shrubs to facilitate comparison of the thermal ecology of G. sila populations in shrubless and shrubbed sites. We found that G. sila without access to shrubs spent more time sheltering inside rodent burrows than lizards with access to shrubs, especially during the hot summer months. Lizards from a shrubbed site had higher midday body temperatures and therefore poorer thermoregulatory accuracy than G. sila from a shrubless site, suggesting that greater surface activity may represent a thermoregulatory trade-off for G. sila. Lizards at both sites are currently constrained from using open, sunny microhabitats for much of the day during their short active seasons, and our projections suggest that climate change will exacerbate these restrictions and force G. sila to use rodent burrows for shelter even more than they do now, especially at sites without access to shrubs. The continued management of shrubs and of burrowing rodents at G. sila sites is therefore essential to the survival of this endangered species.
Throughput-fairness tradeoffs in mobility platforms

This paper studies the problem of allocating tasks from different customers to vehicles in mobility platforms, which are used for applications like food and package delivery, ridesharing, and mobile sensing. A mobility platform should allocate tasks to vehicles and schedule them in order to optimize both throughput and fairness across customers. However, existing approaches to scheduling tasks in mobility platforms ignore fairness. We introduce Mobius, a system that uses guided optimization to achieve both high throughput and fairness across customers. Mobius supports spatiotemporally diverse and dynamic customer demands. It provides a principled method to navigate inherent tradeoffs between fairness and throughput caused by shared mobility. Our evaluation demonstrates these properties, along with the versatility and scalability of Mobius, using traces gathered from ridesharing and aerial sensing applications. Our ridesharing case study shows that Mobius can schedule more than 16,000 tasks across 40 customers and 200 vehicles in an online manner.
Materials and structures for hypersonic vehicles

Hypersonic vehicles are envisioned to require, in addition to carbon-carbon and ceramic-matrix composities for leading edges heated to above 2000 F, such 600 to 1800 F operating temperature materials as advanced Ti alloys, nickel aluminides, and metal-matrix composited; These possess the necessary low density and high strength and stiffness. The primary design drivers are maximum vehicle heating rate, total heat load, flight envelope, propulsion system type, mission life requirements and liquid hydrogen containment systems. Attention is presently given to aspects of these materials and structures requiring more intensive development.
RNN‐based counterfactual prediction, with an application to homestead policy and public schooling

This paper proposes a method for estimating the effect of a policy intervention on an outcome over time. We train recurrent neural networks (RNNs) on the history of control unit outcomes to learn a useful representation for predicting future outcomes. The learned representation of control units is then applied to the treated units for predicting counterfactual outcomes. RNNs are specifically structured to exploit temporal dependencies in panel data and are able to learn negative and non‐linear interactions between control unit outcomes. We apply the method to the problem of estimating the long‐run impact of US homestead policy on public school spending.
Reliable generation of glial enriched progenitors from human fibroblast-derived iPSCs.

White matter stroke (WMS) occurs as small infarcts in deep penetrating blood vessels in the brain and affects the regions of the brain that carry connections, termed the subcortical white matter. WMS progresses over years and has devastating clinical consequences. Unlike large grey matter strokes, WMS disrupts the axonal architecture of the brain and depletes astrocytes, oligodendrocyte lineage cells, axons and myelinating cells, resulting in abnormalities of gait and executive function. An astrocytic cell-based therapy is positioned as a strong therapeutic candidate after WMS. In this study we report, the reliable generation of a novel stem cell-based therapeutic product, glial enriched progenitors (GEPs) derived from human induced pluripotent stem cells (hiPSCs). By transient treatment of hiPSC derived neural progenitors (hiPSC-NPCs) with the small molecule deferoxamine, a prolyl hydroxylase inhibitor, for three days hiPSC-NPCs become permanently biased towards an astrocytic fate, producing hiPSC-GEPs. In preparation for clinical application, we have developed qualification assays to ensure identity, safety, purity, and viability of the cells prior to manufacture. Using tailored q-RT-PCR-based assays, we have demonstrated the lack of pluripotency in our final therapeutic candidate cells (hiPSC-GEPs) and we have identified the unique genetic profile of hiPSC-GEPs that is clearly distinct from the parent lines, hiPSCs and iPSC-NPCs. After completion of the viability assay, we have stablished the therapeutic window of use for hiPSC-GEPs in future clinical applications (7 h). Lastly, we were able to reliably and consistently produce a safe therapeutic final product negative for contamination by any human or murine viral pathogens, selected bacteria, common laboratory mycoplasmas, growth of any aerobes, anaerobes, yeast, or fungi and 100 times less endotoxin levels than the maximum acceptable value. This study demonstrates the reliable and safe generation of patient derived hiPSC-GEPs that are clinically ready as a cell-based therapeutic approach for WMS.
Adaptive Semi-Persistent Scheduling for Enhanced On-road Safety in Decentralized V2X Networks

Decentralized vehicle-to-everything (V2X) networks (i.e., Mode-4 C-V2X and Mode 2a NR-V2X), rely on periodic Basic Safety Messages (BSMs) to disseminate time-sensitive information (e.g., vehicle position) and has the potential to improve on-road safety. For BSM scheduling, decentralized V2X networks utilize sensing-based semi-persistent scheduling (SPS), where vehicles sense radio resources and select suitable resources for BSM transmissions at prespecified periodic intervals termed as Resource Reservation Interval (RRI). In this paper, we show that such a BSM scheduling (with a fixed RRI) suffers from severe under- and over-utilization of radio resources under varying vehicle traffic scenarios; which severely compromises timely dissemination of BSMs, which in turn leads to increased collision risks. To address this, we extend SPS to accommodate an adaptive RRI, termed as SPS++. Specifically, SPS++ allows each vehicle - (i) to dynamically adjust RRI based on the channel resource availability (by accounting for various vehicle traffic scenarios), and then, (ii) select suitable transmission opportunities for timely BSM transmissions at the chosen RRI. Our experiments based on Mode-4 C-V2X standard implemented using the ns-3 simulator show that SPS++ outperforms SPS by at least 50% in terms of improved on-road safety performance, in all considered simulation scenarios.
Deep Learning–Based Scene Simplification for Bionic Vision

Retinal degenerative diseases cause profound visual impairment in more than 10 million people worldwide, and retinal prostheses are being developed to restore vision to these individuals. Analogous to cochlear implants, these devices electrically stimulate surviving retinal cells to evoke visual percepts (phosphenes). However, the quality of current prosthetic vision is still rudimentary. Rather than aiming to restore “natural” vision, there is potential merit in borrowing state-of-the-art computer vision algorithms as image processing techniques to maximize the usefulness of prosthetic vision. Here we combine deep learning–based scene simplification strategies with a psychophysically validated computational model of the retina to generate realistic predictions of simulated prosthetic vision, and measure their ability to support scene understanding of sighted subjects (virtual patients) in a variety of outdoor scenarios. We show that object segmentation may better support scene understanding than models based on visual saliency and monocular depth estimation. In addition, we highlight the importance of basing theoretical predictions on biologically realistic models of phosphene shape. Overall, this work has the potential to drastically improve the utility of prosthetic vision for people blinded from retinal degenerative diseases.
Low-Depth Mechanisms for Quantum Optimization

By gaining intuition about the mechanisms by which quantum optimization algorithms solve problems, approaches are identified that substantially improve the performance of quantum algorithms for this task.
Ultra High Power Cooling Solution for 3D-ICs

A direct silicon water cooling solution using fusion bonded silicon lid is proposed. It is successfully demonstrated as an effective cooling solution with total power >2600 W on a single SoC, equivalent to power density of 4.8 W/mm2. Low temperature logic chip to silicon lid fusion bonding, with trench/grid cooling structure cutting into silicon lid enables minimal thermal resistance between active device and cooling water and best cooling efficiency. Direct water cooling on logic chip silicon backside has also been demonstrated with power density better than 7 W/mm2.