cssi/SciSciGPT-SciSciNet huggingface dataset:
Solving Empirical Risk Minimization in the Current Matrix Multiplication Time

Many convex problems in machine learning and computer science share the same form: \begin{align*} \min_{x} \sum_{i} f_i( A_i x + b_i), \end{align*} where $f_i$ are convex functions on $\mathbb{R}^{n_i}$ with constant $n_i$, $A_i \in \mathbb{R}^{n_i \times d}$, $b_i \in \mathbb{R}^{n_i}$ and $\sum_i n_i = n$. This problem generalizes linear programming and includes many problems in empirical risk minimization. In this paper, we give an algorithm that runs in time \begin{align*} O^* ( ( n^{\omega} + n^{2.5 - \alpha/2} + n^{2+ 1/6} ) \log (n / \delta) ) \end{align*} where $\omega$ is the exponent of matrix multiplication, $\alpha$ is the dual exponent of matrix multiplication, and $\delta$ is the relative accuracy. Note that the runtime has only a log dependence on the condition numbers or other data dependent parameters and these are captured in $\delta$. For the current bound $\omega \sim 2.38$ [Vassilevska Williams'12, Le Gall'14] and $\alpha \sim 0.31$ [Le Gall, Urrutia'18], our runtime $O^* ( n^{\omega} \log (n / \delta))$ matches the current best for solving a dense least squares regression problem, a special case of the problem we consider. Very recently, [Alman'18] proved that all the current known techniques can not give a better $\omega$ below $2.168$ which is larger than our $2+1/6$. Our result generalizes the very recent result of solving linear programs in the current matrix multiplication time [Cohen, Lee, Song'19] to a more broad class of problems. Our algorithm proposes two concepts which are different from [Cohen, Lee, Song'19] : $\bullet$ We give a robust deterministic central path method, whereas the previous one is a stochastic central path which updates weights by a random sparse vector. $\bullet$ We propose an efficient data-structure to maintain the central path of interior point methods even when the weights update vector is dense.
The differential contribution of pacemaker neurons to synaptic transmission in the pyloric network of the Jonah crab, Cancer borealis

The pacemaker-driven pyloric circuit of the Jonah crab stomatogastric nervous system is a well-studied model system for exploring circuit dynamics and neuromodulation, yet the understanding of the ...
High-resolution 3D seismic acquisition at the Tomakomai CO2 storage project, offshore Hokkaido, Japan

Monitoring injected CO2 is an important part of assuring permanence of long term storage to mitigate atmospheric emissions. Three-dimensional (3D) seismic has been shown to be an effective technology for visualizing and measuring subsurface geology and fluids. In this study, we demonstrate the successful acquisition, processing and initial interpretation of a high-resolution 3D (HR3D) seismic survey in Tomakomai, offshore Japan, above an active offshore CO2 injection site. The successful deployment of this novel marine seismic monitoring technology validates HR3D as an appropriate characterization and monitoring tool for large-demonstration and commercial-scale offshore Carbon Capture and Storage (CCS) sites.
Counting Roots of a Polynomial in a Convex Compact Region by Means of Winding Number Calculation via Sampling

In this paper, we propose a novel efficient algorithm for calculating winding numbers, aiming at counting the number of roots of a given polynomial in a convex region on the complex plane. This algorithm can be used for counting and exclusion tests in a subdivision algorithms for polynomial root-finding, and would be especially useful in application scenarios where high-precision polynomial coefficients are hard to obtain but we succeed with counting already by using polynomial evaluation with lower precision. We provide the pseudo code of the algorithm as well as a proof of its correctness.
First-year results from a research program on human factors in software engineering

For the past two years the Software Management Research Unit at General Electric has been investigating several areas of human factors in software engineering with support from Engineering Psychology Programs of the Office of Naval Research. There have been two major thrusts in this research. The first thrust investigated the effects of several modern programming practices on programmer efficiency. The second thrust investigated the prediction of programmer performance from software complexity metrics such as those proposed by Halstead and McCabe. This research program consisted of separate experiments on the understanding, modification, debugging, and construction of software, each using professional programmers. Each experiment investigated both the effects of experimentally manipulated programming practices, and the values of complexity metrics computed from the programs employed.
Pushing the Limits of Low-Resource Morphological Inflection.

Recent years have seen exceptional strides in the task of automatic morphological inflection generation. However, for a long tail of languages the necessary resources are hard to come by, and state-of-the-art neural methods that work well under higher resource settings perform poorly in the face of a paucity of data. In response, we propose a battery of improvements that greatly improve performance under such low-resource conditions. First, we present a novel two-step attention architecture for the inflection decoder. In addition, we investigate the effects of cross-lingual transfer from single and multiple languages, as well as monolingual data hallucination. The macro-averaged accuracy of our models outperforms the state-of-the-art by 15 percentage points. Also, we identify the crucial factors for success with cross-lingual transfer for morphological inflection: typological similarity and a common representation across languages.
How Secure Is Your IoT Network

The proliferation of IoT devices in smart homes, hospitals, and enterprise networks is wide-spread and continuing to increase in a superlinear manner. The question is: how can one assess the security of an IoT network in a holistic manner? In this paper, we have explored two dimensions of security assessment– using vulnerability information and attack vectors of IoT devices and their underlying components (compositional security scores) and using SIEM logs captured from the communications and operations of such devices in a network (dynamic activity metrics). These measures are used to evaluate the security of IoT devices and the overall IoT network, demonstrating the effectiveness of attack circuits as practical tools for computing security metrics (exploitability, impact, and risk to confidentiality, integrity, and availability) of the network. We decided to approach threat modeling using attack graphs. To that end, we propose the notion of attack circuits, which are generated from input/output pairs constructed from CVEs using NLP, and an attack graph composed of these circuits. Our system provides insight into possible attack paths an adversary may utilize based on their exploitability, impact, or overall risk. We have performed experiments on IoT networks to demonstrate the efficacy of the proposed techniques.
MODELING AND ARTIFICIAL INTELLIGENCE APPROACHES TO ENZYME SYSTEMS.

Modeling is a means of formulating and testing complex hypotheses. Useful modeling is now possible with biological laboratory microcomputers with which experimenters feel comfortable. Artificial intelligence (AI) is sufficiently similar to modeling that AI techniques, now becoming usable on microcomputers, are applicable to modeling. Microcomputer and AI applications to physiological system studies with multienzyme models and with kinetic models of isolated enzymes are described. Using an IBM PC microcomputer, we have been able to fit kinetic enzyme models; to extend this process to design kinetic experiments by determining the optimal conditions; and to construct an enzyme (hexokinase) kinetics data base. We have also used a PC to do most of the constructing of complex multienzyme models, initially with small simple BASIC programs; alternative methods with standard spreadsheet or data base programs have been defined. Formulating and solving differential equations in appropriate representational languages, and sensitivity analysis, are soon likely to be feasible with PCs. Much of the modeling process can be stated in terms of AI expert systems, using sets of rules for fitting and evaluating models and designing further experiments. AI techniques also permit critiquing and evaluating the data, experiments, and hypotheses being modeled, and can be extended to supervise the calculations involved.
A Dynamic Escape Problem of Molecular Motors

Animal skeletal muscle exhibits very interesting behavior at near-stall forces (when the muscle is loaded so strongly that it can barely contract). Near this physical limit, the myosin II proteins may be unable to reach advantageous actin binding sites through simple attractive forces. It has been shown that the advantageous utilization of thermal agitation is a likely source for an increased force-production capacity and reach in myosin-V (a processing motor protein), and here we explore the dynamics of a molecular motor without hand-over-hand motion including Brownian motion to show how local elastic energy well boundaries may be overcome. We revisit a spatially two-dimensional mechanical model to illustrate how thermal agitation can be harvested for useful mechanical work in molecular machinery inspired by this biomechanical phenomenon without rate functions or empirically inspired spatial potential functions. Additionally, the model accommodates variable lattice spacing, and it paves the way for a full three-dimensional model of cross-bridge interactions where myosin II may be azimuthally misaligned with actin binding sites. With potential energy sources based entirely on realizable components, this model lends itself to the design of artificial, molecular-scale motors.
Sci-Hi-C: A single-cell Hi-C method for mapping 3D genome organization in large number of single cells.

Abstract The highly dynamic nature of chromosome conformation and three-dimensional (3D) genome organization leads to cell-to-cell variability in chromatin interactions within a cell population, even if the cells of the population appear to be functionally homogeneous. Hence, although Hi-C is a powerful tool for mapping 3D genome organization, this heterogeneity of chromosome higher order structure among individual cells limits the interpretive power of population based bulk Hi-C assays. Moreover, single-cell studies have the potential to enable the identification and characterization of rare cell populations or cell subtypes in a heterogeneous population. However, it may require surveying relatively large numbers of single cells to achieve statistically meaningful observations in single-cell studies. By applying combinatorial cellular indexing to chromosome conformation capture, we developed single-cell combinatorial indexed Hi-C (sci-Hi-C), a high throughput method that enables mapping chromatin interactomes in large number of single cells. We demonstrated the use of sci-Hi-C data to separate cells by karytoypic and cell-cycle state differences and to identify cellular variability in mammalian chromosomal conformation. Here, we provide a detailed description of method design and step-by-step working protocols for sci-Hi-C.
Factorizable Module Algebras

The aim of this paper is to introduce and study a large class of $\mathfrak{g}$-module algebras which we call factorizable by generalizing the Gauss factorization of (square or rectangular) matrices. This class includes coordinate algebras of corresponding reductive groups $G$, their parabolic subgroups, basic affine spaces and many others. It turns out that tensor products of factorizable algebras are also factorizable and it is easy to create a factorizable algebra out of virtually any $\mathfrak{g}$-module algebra. We also have quantum versions of all these constructions in the category of $U_q(\mathfrak{g})$-module algebras. Quite surprisingly, our quantum factorizable algebras are naturally acted on by the quantized enveloping algebra $U_q(\mathfrak{g}^*)$ of the dual Lie bialgebra $\mathfrak{g}^*$ of $\mathfrak{g}$.
Can Piracy Increase Innovation? The Software Industry’s Response to Online File Sharing

Existing theory predicts two possible and opposing competitive responses by firms to digital piracy, resulting in an ambiguous impact on innovation. We resolve this tension by using a natural experiment stemming from a shock to file-sharing technology, which led to a sudden increase in piracy in the early 2000's, to analyze the impact of piracy on innovation in the software industry. Using difference-in-differences and instrumental-variables estimators to compare a treatment group of consumer-focused software firms with a matched control group of enterprise-focused software firms, we find that piracy led to an increase in subsequent R&D spending, copyrights, trademarks, and patents for large, incumbent firms. However, we find that firms decreased their rate of new product releases following the rise in piracy. We conclude that piracy pushes firms to develop new products that are superior to those available as pirated substitutes, but this innovation may reach the public at a slower pace. Our findings have implications for managers pursuing value appropriation in knowledge-based industries while faced with the threat of digital piracy.
Sliding toward nutrition malpractice: time to reconsider and redeploy.

Today much of the international development community takes seriously the need for better nutrition. A few years ago that was not the case. Nutrition is now widely understood not only as a welfare good, but as an investment that directly influences productivity of the labor force and the school force and as a key factor in development. Increasingly, development economists acknowledge that something has to be done about malnutrition. Further, seldom is structural adjustment any longer instituted without prior concern for the effects on nutrition. This is particularly significant for the disadvantaged countries ofthe world, where the nutrition arithmetic has barely changed in recent decades. Malnutrition is still staggering, and in some places, particularly sub-Saharan Africa, getting worse (1). We of the international nutrition community, I am embarrassed to say, have made a disappointingly small dent in this problem, even though in the past 20 years something on the order of$ 1 .6 billion in US government-sponsored research alone has been directed to malnutrition. We do have a great deal to show in scientific and technological advances, but very little to show in terms ofmarked declines in malnutrition in the world. To the extent there has been improvement, it has occurred not so much because of our decades of work but largely because of rising incomes in parts ofAsia. True, some direct nutrition programs have been successful in Chile, Tanzania (Iringa), Thailand, and India (Tamil Nadu), but how many other actions can we point to that have brought about improved nutrition to sizable populations? Yet we have in hand, for instance, vitamin A technology to prevent the deaths ofas many as 2.5 million children each year (2), though we are nowhere close to achieving this goal. Another micronutrient-iodine-provides an even starker case. Ever since Ramalingaswami’s (3, 4) classic study of salt fortification in North India in the 1960s, we have known what has to be done. We understand the etiology and consequences of iodine deficiency; we know who suffers from it and where they are and we have available very-low-cost technologies to correct it. Still, more than 200 million people have goiter and probably several times that number have subclinical iodine deficiency, and we now know that even mild and moderate deficiency affects cognitive capacities. Such performance can only be called nutrition malpractice. Salt can also be fortified with iron to fight the one billion cases of iron-deficiency anemia in developing countries. Moreover, for some years the idea of fortifying salt with a combination of iron and iodine making possible a major reduction of two important public health problems with one shake, has been discussed. But where are the people trying to make it happen? Is it not something of a scandal that we have done so little in applying our scientific knowledge?
Optimal spatial-dynamic management to minimize the damages caused by aquatic invasive species

Invasive species have been recognized as a leading threat to biodiversity. In particular, lakes are especially affected by species invasions because they are closed systems sensitive to disruption. Accurately controlling the spread of invasive species requires solving a complex spatial-dynamic optimization problem. In this work we propose a novel framework for determining the optimal management strategy to maximize the value of a lake system net of damages from invasive species, including an endogenous diffusion mechanism for the spread of invasive species through boaters’ trips between lakes. The proposed method includes a combined global iterative process which determines the optimal number of trips to each lake in each season and the spatial-dynamic optimal boat ramp fee.
Towards Annotating and Creating Sub-Sentence Summary Highlights

Highlighting is a powerful tool to pick out important content and emphasize. Creating summary highlights at the sub-sentence level is particularly desirable, because sub-sentences are more concise than whole sentences. They are also better suited than individual words and phrases that can potentially lead to disfluent, fragmented summaries. In this paper we seek to generate summary highlights by annotating summary-worthy sub-sentences and teaching classifiers to do the same. We frame the task as jointly selecting important sentences and identifying a single most informative textual unit from each sentence. This formulation dramatically reduces the task complexity involved in sentence compression. Our study provides new benchmarks and baselines for generating highlights at the sub-sentence level.
Chromatic matching in a plasma undulator

The principle of chromatic matching, i.e., using two laser modes of different geometric mode numbers and different colors so that they copropagate at the same group velocity, is proposed in order to realize the plasma undulator concept. Chromatic matching allows one to overcome group velocity slippage, whereby lower-order modes outrun higher-order ones, allowing for extended interaction lengths. The dephasing limit can be mitigated by using a special tapering of the plasma channel such that the electron bunch propagates in phase with the laser mode in addition to maintaining a constant undulator frequency. Furthermore, controlled dephasing is proposed as a means to induce chirp in the generated X-ray spectrum.
Gene Knock-Ins in Drosophila Using Homology-Independent Insertion of Universal Donor Plasmids.

Targeted genomic knock-ins are a valuable tool to probe gene function. However, knock-in methods involving homology-directed repair (HDR) can be laborious. Here, we adapt the mammalian CRISPaint [clustered regularly interspaced short palindromic repeat (CRISPR)-assisted insertion tagging] homology-independent knock-in method for Drosophila melanogaster, which uses CRISPR/Cas9 and nonhomologous end joining to insert “universal” donor plasmids into the genome. Using this method in cultured S2R+ cells, we efficiently tagged four endogenous proteins with the bright fluorescent protein mNeonGreen, thereby demonstrating that an existing collection of CRISPaint universal donor plasmids is compatible with insect cells. In addition, we inserted the transgenesis marker 3xP3-red fluorescent protein into seven genes in the fly germ line, producing heritable loss-of-function alleles that were isolated by simple fluorescence screening. Unlike in cultured cells, insertions/deletions always occurred at the genomic insertion site, which prevents predictably matching the insert coding frame to the target gene. Despite this effect, we were able to isolate T2A-Gal4 insertions in four genes that serve as in vivo expression reporters. Therefore, homology-independent insertion in Drosophila is a fast and simple alternative to HDR that will enable researchers to dissect gene function.
Genomics Testing and Personalized Medicine in the Preoperative Setting.

Pharmacogenomics (PGx) is the study of how individuals' personal genotypes may affect their responses to various pharmacologic agents. The application of PGx principles in perioperative medicine is fairly novel. Challenges in executing PGx programs into health care systems include physician buy-in and integration into usual clinical workflow, including the electronic health record. This article discusses the current evidence highlighting the potential of PGx with various drug categories (including opioids, nonopioid analgesics, sedatives, β-blockers, antiemetics, and anticoagulants) used in the perioperative process and the challenges of integrating PGx into a health care system and relevant workflows.
Discharge patterns accompanying sustained activation of motor units in speech musculature

Abstract: Discharge patterns were studied in motor units (MUs) of facial, mandibular and lingual musculature during periods of steady discharge produced by three subjects, using a motor unit training paradigm. Recordings were made with concentric bipolar electrodes at relatively low force levels. An attempt was made to test hypotheses derived from Kernell's studies of cat hindlimb motoneurones which show that neurons with shorter afterhyperpolarization (AHP) durations are recruited later and have greater sensitivity to input changes. It was assumed that AHP durations of the motoneurones of steadily discharging human MUs correspond to their mean interspike intervals at a transition point beyond which any decrease in mean ISI results in negative correlations between successive ISIs. Transitions to negative correlations were observed in all muscles, at mean ISIs ranging from 35–55 ms. Clear transitions were also observed in some individual MUs, but not in enough to determine whether later recruited MUs possessed shorter AHP durations. Later recruited units did possess greater sensitivity to input changes. Variability of MUs between and within muscles was also evaluated.
DiabDeep: Pervasive Diabetes Diagnosis Based on Wearable Medical Sensors and Efficient Neural Networks

Diabetes impacts the quality of life of millions of people around the globe. However, diabetes diagnosis is still an arduous process, given that this disease develops and gets treated outside the clinic. The emergence of wearable medical sensors (WMSs) and machine learning points to a potential way forward to address this challenge. WMSs enable a continuous, yet user-transparent, mechanism to collect and analyze physiological signals. However, disease diagnosis based on WMS data and its effective deployment on resource-constrained edge devices remain challenging due to inefficient feature extraction and vast computation cost. To address these problems, we propose a framework called DiabDeep that combines efficient neural networks (called DiabNNs) with off-the-shelf WMSs for pervasive diabetes diagnosis. DiabDeep bypasses the feature extraction stage and acts directly on WMS data. It enables both an (i) accurate inference on the server, e.g., a desktop, and (ii) efficient inference on an edge device, e.g., a smartphone, to obtain a balance between accuracy and efficiency based on varying resource budgets and design goals. On the resource-rich server, we stack sparsely connected layers to deliver high accuracy. On the resource-scarce edge device, we use a hidden-layer long short-term memory based recurrent layer to substantially cut down on computation and storage costs while incurring only a minor accuracy loss. At the core of our system lies a grow-and-prune training flow: it leverages gradient-based growth and magnitude-based pruning algorithms to enable DiabNNs to learn both weights and connections, while improving accuracy and efficiency. We demonstrate the effectiveness of DiabDeep through a detailed analysis of data collected from 52 participants. For server (edge) side inference, we achieve a 96.3 percent (95.3 percent) accuracy in classifying diabetics against healthy individuals, and a 95.7 percent (94.6 percent) accuracy in distinguishing among type-1 diabetic, type-2 diabetic, and healthy individuals. Against conventional baselines, such as support vector machines with linear and radial basis function kernels, k-nearest neighbor, random forest, and linear ridge classifiers, DiabNNs achieve higher accuracy, while reducing the model size (floating-point operations) by up to 454.5× (8.9×). Therefore, the system can be viewed as pervasive and efficient, yet very accurate.
HIV-1 Rev interacts with HERV-K RcREs present in the human genome and promotes export of unspliced HERV-K proviral RNA.

The HERV-K (HML-2) viruses are the youngest of the human endogenous retroviruses. They are present as several almost complete proviral copies and numerous fragments in the human genome. Many HERV-K proviruses express a regulatory protein Rec, which binds to an element present in HERV-K mRNAs called the RcRE. This interaction is necessary for the nucleo-cytoplasmic export and expression of HERV-K mRNAs that retain introns and plays a role analogous to that of Rev and the RRE in HIV replication. There are over 900 HERV-K RcREs distributed throughout the human genome. Thus, it was of interest to determine if Rev could functionally interact with selected RcRE elements that map either to HERV-K proviruses or human gene regions. This interaction would have the potential to alter the expression of both HERV-K mRNAs and cellular mRNAs during HIV-1 infection. In this study we employed a combination of RNAseq, bioinformatics and cell-based functional assays. Potential RcREs were identified through a number of bioinformatic approaches. They were then tested for their ability to promote export and translation of a reporter mRNA with a retained intron in conjunction with Rev or Rec. Some of the selected elements functioned well with either Rev, Rec or both, whereas some showed little or no function. Rev function on individual RcREs varied and was also dependent on the Rev sequence. We also performed RNAseq on total and cytoplasmic RNA isolated from SupT1 cells expressing HIV Rev, with or without Tat, or HERV-K Rec. Proviral mRNA from three HERV-K loci (4p16.1b, 22q11.23 and most significantly 3q12.3) accumulated in the cytoplasm in the presence of Rev or Tat and Rev, but not Rec. Consistent with this, the 3′ RcRE from 3q12.3 functioned well with HIV-Rev in our reporter assay. In contrast, this RcRE showed little or no function with Rec. The HIV Rev protein can functionally interact with many RcREs present in the human genome, depending on the RcRE sequence, as well as the Rev sequence. This leads to export of some of the HERV-K proviral mRNAs and also has the potential to change the expression of non-viral genes.
Asymmetric budget constraints in a first-price auction

Abstract I solve a first-price auction for two bidders with asymmetric budget distributions and known valuations for one object. I show that in any equilibrium, the expected utilities and bid distributions of both bidders are unique. If budgets are sufficiently low, the bidders will bid their entire budget in any equilibrium. For sufficiently high budgets, mass points in the equilibrium strategies arise. A less restrictive budget distribution could make both bidders strictly worse off. If the budget distribution of one bidder is dominated by the budget distribution of the other bidder in the reverse-hazard-rate order, the weaker bidder will bid more aggressively than the stronger bidder. In contrast to existing results for symmetric budget distributions, with asymmetric budget distributions, a second-price auction can yield a strictly higher revenue than a first-price auction. Under an additional assumption, I derive the unique equilibrium utilities and bid distributions of both bidders in an all-pay auction.
Chatting at Church: Information Diffusion through Religious Networks

This paper shows the causal relationship between mutual religious association and the formation of social ties. We analyze dyadic relationships and show that joint attendance at a religious institu...
A Guide and Toolbox to Replicability and Open Science in Entomology

The ability to replicate scientific experiments is a cornerstone of the scientific method. Sharing ideas, workflows, data, and protocols facilitates testing the generalizability of results, increases the speed that science progresses, and enhances quality control of published work. Fields of science such as medicine, the social sciences, and the physical sciences have embraced practices designed to increase replicability. Granting agencies, for example, may require data management plans and journals may require data and code availability statements along with the deposition of data and code in publicly available repositories. While many tools commonly used in replicable workflows such as distributed version control systems (e.g., 'git') or script programming languages for data cleaning and analysis may have a steep learning curve, their adoption can increase individual efficiency and facilitate collaborations both within entomology and across disciplines. The open science movement is developing within the discipline of entomology, but practitioners of these concepts or those desiring to work more collaboratively across disciplines may be unsure where or how to embrace these initiatives. This article is meant to introduce some of the tools entomologists can incorporate into their workflows to increase the replicability and openness of their work. We describe these tools and others, recommend additional resources for learning more about these tools, and discuss the benefits to both individuals and the scientific community and potential drawbacks associated with implementing a replicable workflow.
STARE2: Detecting Fast Radio Bursts in the Milky Way

There are several unexplored regions of the short-duration radio transient phase space. One such unexplored region is the luminosity gap between giant pulses (from pulsars) and cosmologically located fast radio bursts (FRBs). The Survey for Transient Astronomical Radio Emission 2 (STARE2) is a search for such transients out to 7 Mpc. STARE2 has a field of view of 3.6 steradians and is sensitive to 1 millisecond transients above ~300 kJy. With a two-station system we have detected and localized a solar burst, demonstrating that the pilot system is capable of detecting short duration radio transients. We found no convincing non-solar transients with duration between 65 μs and 34 ms in 200 days of observing, limiting with 95% confidence the all-sky rate of transients above ~300 kJy to <40 sky−1 yr−1. If the luminosity function of FRBs could be extrapolated down to 300 kJy for a distance of 10 kpc, then one would expect the rate to be ~2 sky−1 yr−1.
Coding Against Deletions in Oblivious and Online Models

We consider binary error correcting codes when errors are deletions. A basic challenge concerning deletion codes is determining ${p}_{0}^{(adv)}$ , the zero-rate threshold of adversarial deletions , defined to be the supremum of all $p$ for which there exists a code family with rate bounded away from 0 capable of correcting a fraction $p$ of adversarial deletions. A recent construction of deletion-correcting codes shows that ${p}_{0}^{(adv)} \ge \sqrt {2}-1$ , and the trivial upper bound, ${p}_{0}^{(adv)}\le {1}/{2}$ , is the best known. Perhaps surprisingly, we do not know whether or not ${p}_{0}^{(adv)} = 1/2$ . In this work, to gain further insight into deletion codes, we explore two related error models: oblivious deletions and online deletions, which are in between random and adversarial deletions in power. In the oblivious model, the channel can inflict an arbitrary pattern of ${pn}$ deletions, picked without knowledge of the codeword. We prove the existence of binary codes of positive rate that can correct any fraction ${p} of oblivious deletions, establishing that the associated zero-rate threshold ${p}_{0}^{(obliv)}$ equals 1. For online deletions, where the channel decides whether to delete bit ${x}_{{i}}$ based only on knowledge of bits ${x}_{1}{x}_{2} {\dots }{x}_{{i}}$ , define the deterministic zero-rate threshold for online deletions ${p}_{0}^{(on,{d})}$ to be the supremum of $p$ for which there exist deterministic codes against an online channel causing ${pn}$ deletions with low average probability of error. That is, the probability that a randomly chosen codeword is decoded incorrectly is small. We prove ${p}_{0}^{(adv)}={1}/{2}$ if and only if ${p}_{0}^{(on,{d})}={1}/{2}$ .
Purinergic Receptors: Elucidating the Role of these Immune Mediators in HIV-1 Fusion

Purinergic receptors are inflammatory mediators activated by extracellular nucleotides released by dying or injured cells. Several studies have described an important role for these receptors in HIV-1 entry, particularly regarding their activity on HIV-1 viral membrane fusion. Several reports identify purinergic receptor antagonists that inhibit HIV-1 membrane fusion; these drugs are suspected to act through antagonizing Env-chemokine receptor interactions. They also appear to abrogate activity of downstream mediators that potentiate activation of the NLRP3 inflammasome pathway. Here we review the literature on purinergic receptors, the drugs that inhibit their function, and the evidence implicating these receptors in HIV-1 entry.
Computing minimal persistent cycles: polynomial and hard cases

Persistent cycles, especially the minimal ones, are useful geometric features functioning as augmentations for the intervals in the purely topological persistence diagrams (also termed as barcodes). In our earlier work, we showed that computing minimal 1-dimensional persistent cycles (persistent 1-cycles) for finite intervals is NP-hard while the same for infinite intervals is polynomially tractable. In this paper, we address this problem for general dimensions with Z2 coefficients. In addition to proving that it is NP-hard to compute minimal persistent d-cycles (d > 1) for both types of intervals given arbitrary simplicial complexes, we identify two interesting cases which are polynomially tractable. These two cases assume the complex to be a certain generalization of manifolds which we term as weak pseudomanifolds. For finite intervals from the dth persistence diagram of a weak (d + 1)-pseudomanifold, we utilize the fact that persistent cycles of such intervals are null-homologous and reduce the problem to a minimal cut problem. Since the same problem for infinite intervals is NP-hard, we further assume the weak (d + 1)-pseudomanifold to be embedded in Rd+1 so that the complex has a natural dual graph structure and the problem reduces to a minimal cut problem. Experiments with both algorithms on scientific data indicate that the minimal persistent cycles capture various significant features of the data.
Real-Time Multiobjective Microgrid Power Management Using Distributed Optimization in an Agent-Based Bargaining Framework

In this paper, we propose a multi-objective power management procedure for microgrids (MGs). Through this procedure the power management problem is modeled as a bargaining game among different agents with different sets of objective functions. Nash bargaining solution (NBS) is employed to find the solution of the bargaining game. NBS lies on the Pareto-front of the power management problem. Moreover, it introduces a unique and fair balance among the objective functions of different agents and removes the need to track the whole Pareto-front in real-time. Distributed gradient algorithm is applied to find the NBS through a modular distributed decision framework without using a central control unit. In this way, the problem of data privacy of different parties within the MG is addressed. The proposed methodology has been tested through simulations on islanded and grid-connected MGs under different pricing scenarios (fixed versus time-of-use pricing).
Simple Rho GTPase Dynamics Generate a Complex Regulatory Landscape Associated with Cell Shape

Abstract Migratory cells exhibit a variety of morphologically distinct responses to their environments that manifest in their cell shape. Some protrude uniformly to increase substrate contacts, others are broadly contractile, some polarize to facilitate migration, and yet others exhibit mixtures of these responses. Prior studies have identified a discrete collection of shapes that the majority of cells display and demonstrated that activity levels of the cytoskeletal regulators Rac1 and RhoA GTPase regulate those shapes. Here, we use computational modeling to assess whether known GTPase dynamics can give rise to a sufficient diversity of spatial signaling states to explain the observed shapes. Results show that the combination of autoactivation and mutually antagonistic cross talk between GTPases, along with the conservative membrane binding, generates a wide array of distinct homogeneous and polarized regulatory phenotypes that arise for fixed model parameters. From a theoretical perspective, these results demonstrate that simple GTPase dynamics can generate complex multistability in which six distinct stable steady states (three homogeneous and three polarized) coexist for a fixed set of parameters, each of which naturally maps to an observed morphology. From a biological perspective, although we do not explicitly model the cytoskeleton or resulting cell morphologies, these results, along with prior literature linking GTPase activity to cell morphology, support the hypothesis that GTPase signaling dynamics can generate the broad morphological characteristics observed in many migratory cell populations. Further, the observed diversity may be the result of cells populating a complex morphological landscape generated by GTPase regulation rather than being the result of intrinsic cell-cell variation. These results demonstrate that Rho GTPases may have a central role in regulating the broad characteristics of cell shape (e.g., expansive, contractile, polarized, etc.) and that shape heterogeneity may be (at least partly) a reflection of the rich signaling dynamics regulating the cytoskeleton rather than intrinsic cell heterogeneity.
Protein velocity and acceleration from single-cell multiomics experiments

The simultaneous quantification of protein and RNA makes possible the inference of past, present, and future cell states from single experimental snapshots. To enable such temporal analysis from multimodal single-cell experiments, we introduce an extension of the RNA velocity method that leverages estimates of unprocessed transcript and protein abundances to extrapolate cell states. We apply the model to six datasets and demonstrate consistency among cell landscapes and phase portraits. The analysis software is available as the protaccel Python package.
Spectral and matrix factorization methods for consistent community detection in multi-layer networks

We consider the problem of estimating a consensus community structure by combining information from multiple layers of a multi-layer network using methods based on the spectral clustering or a low-rank matrix factorization. As a general theme, these “intermediate fusion” methods involve obtaining a low column rank matrix by optimizing an objective function and then using the columns of the matrix for clustering. However, the theoretical properties of these methods remain largely unexplored. In the absence of statistical guarantees on the objective functions, it is difficult to determine if the algorithms optimizing the objectives will return good community structures. We investigate the consistency properties of the global optimizer of some of these objective functions under the multi-layer stochastic blockmodel. For this purpose, we derive several new asymptotic results showing consistency of the intermediate fusion techniques along with the spectral clustering of mean adjacency matrix under a high dimensional setup, where the number of nodes, the number of layers and the number of communities of the multi-layer graph grow. Our numerical study shows that the intermediate fusion techniques outperform late fusion methods, namely spectral clustering on aggregate spectral kernel and module allegiance matrix in sparse networks, while they outperform the spectral clustering of mean adjacency matrix in multi-layer networks that contain layers with both homophilic and heterophilic communities.
Deep Transfer Learning for Thermal Dynamics Modeling in Smart Buildings

Thermal dynamics modeling has been a critical issue in building heating, ventilation, and air-conditioning (HVAC) systems, which can significantly affect the control and maintenance strategies. Due to the uniqueness of each specific building, traditional thermal dynamics modeling approaches heavily depending on physics knowledge cannot generalize well. This study proposes a deep supervised domain adaptation (DSDA) method for thermal dynamics modeling of building indoor temperature evolution and energy consumption. A long short term memory network based Sequence to Sequence scheme is pre-trained based on a large amount of data collected from a building and then adapted to another building which has a limited amount of data by applying the model fine-tuning. We use four publicly available datasets: SML and AHU for temperature evolution, long-term datasets from two different commercial buildings, termed as Building 1 and Building 2 for energy consumption. We show that the deep supervised domain adaptation is effective to adapt the pre-trained model from one building to another building and has better predictive performance than learning from scratch with only a limited amount of data.
Safety Augmented Value Estimation From Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks

Reinforcement learning (RL) for robotics is challenging due to the difficulty in hand-engineering a dense cost function, which can lead to unintended behavior, and dynamical uncertainty, which makes exploration and constraint satisfaction challenging. We address these issues with a new model-based reinforcement learning algorithm, Safety Augmented Value Estimation from Demonstrations (SAVED), which uses supervision that only identifies task completion and a modest set of suboptimal demonstrations to constrain exploration and learn efficiently while handling complex constraints. We then compare SAVED with 3 state-of-the-art model-based and model-free RL algorithms on 6 standard simulation benchmarks involving navigation and manipulation and a physical knot-tying task on the da Vinci surgical robot. Results suggest that SAVED outperforms prior methods in terms of success rate, constraint satisfaction, and sample efficiency, making it feasible to safely learn a control policy directly on a real robot in less than an hour. For tasks on the robot, baselines succeed less than $\text{5}\%$ of the time while SAVED has a success rate of over $\text{75}\%$ in the first 50 training iterations. Code and supplementary material is available at https://tinyurl.com/saved-rl .
{ABC}: A Simple Explicit Congestion Controller for Wireless Networks

We propose Accel-Brake Control (ABC), a simple and deployable explicit congestion control protocol for network paths with time-varying wireless links. ABC routers mark each packet with an "accelerate" or "brake", which causes senders to slightly increase or decrease their congestion windows. Routers use this feedback to quickly guide senders towards a desired target rate. ABC requires no changes to header formats or user devices, but achieves better performance than XCP. ABC is also incrementally deployable; it operates correctly when the bottleneck is a non-ABC router, and can coexist with non-ABC traffic sharing the same bottleneck link. We evaluate ABC using a Wi-Fi implementation and trace-driven emulation of cellular links. ABC achieves 30-40% higher throughput than Cubic+Codel for similar delays, and 2.2X lower delays than BBR on a Wi-Fi path. On cellular network paths, ABC achieves 50% higher throughput than Cubic+Codel.
Sensitivity Analysis for Markov Decision Process Congestion Games

We consider a non-atomic congestion game where each decision maker performs selfish optimization over states of a common MDP. The decision makers optimize for their own expected cost, and influence each other through congestion effects on the state-action costs. We analyze the sensitivity of MDP congestion game equilibria to uncertainty and perturbations in the state-action costs by applying an implicit function type analysis. The occurrence of a stochastic Braess paradox is defined and analyzed based on sensitivity of game equilibria and demonstrated in simulation. We further analyze how the introduction of stochastic dynamics affects the magnitude of Braess paradox in comparison to deterministic dynamics.
Semi-dense stereo correspondence with dense features

We present a new feature based algorithm for stereo correspondence. Most of the previous feature based methods match sparse features like edge pixels, producing only sparse disparity maps. Our algorithm detects and matches dense features between the left and right images of a stereo pair, producing a semi-dense disparity map. Our dense feature is defined with respect to both images of a stereo pair, and it is computed during the stereo matching process, not a preprocessing step. In essence, a dense feature is a connected set of pixels in the left image and a corresponding set of pixels in the right image such that the intensity edges on the boundary of these sets are stronger than their matching error (which is basically the difference in intensities between corresponding boundary pixels). Our algorithm produces accurate semi-dense disparity maps, leaving featureless regions in the scene unmatched It is robust, requires little parameter tuning, can handle brightness differences between images, and is fast (linear complexity).
Competition × drought interactions change phenotypic plasticity and the direction of selection on Arabidopsis traits

Populations often exhibit genetic diversity in traits involved in responses to abiotic stressors, but what maintains this diversity is unclear. Arabidopsis thaliana exhibits high within-population variation in drought response. One hypothesis is that competition, varying at small scales, promotes diversity in resource use strategies. However, little is known about natural variation in competition effects on Arabidopsis physiology. We imposed drought and competition treatments on diverse genotypes. We measured resource economics traits, physiology, and fitness to characterize plasticity and selection in response to treatments. Plastic responses to competition differed depending on moisture availability. We observed genotype-drought-competition interactions for relative fitness: competition had little effect on relative fitness under well-watered conditions, whereas competition caused rank changes in fitness under drought. Early flowering was always selected. Higher δ13 C was selected only in the harshest treatment (drought and competition). Competitive context significantly changed the direction of selection on aboveground biomass and inflorescence height in well-watered environments. Our results highlight how local biotic conditions modify abiotic selection, in some cases promoting diversity in abiotic stress response. The ability of populations to adapt to environmental change may thus depend on small-scale biotic heterogeneity.