Miracle-dz/UniFAR-FTUs huggingface dataset:
Schrödinger's FP: Dynamic Adaptation of Floating-Point Containers for Deep Learning Training

We introduce a software-hardware co-design approach to reduce memory traffic and footprint during training with BFloat16 or FP32 boosting energy efficiency and execution time performance. We introduce methods to dynamically adjust the size and format of the floating-point containers used to store activations and weights during training. The different value distributions lead us to different approaches for exponents and mantissas. Gecko exploits the favourable exponent distribution with a loss-less delta encoding approach to reduce the total exponent footprint by up to $58\%$ in comparison to a 32 bit floating point baseline. To content with the noisy mantissa distributions, we present two lossy methods to eliminate as many as possible least significant bits while not affecting accuracy. Quantum Mantissa, is a machine learning-first mantissa compression method that taps on training's gradient descent algorithm to also learn minimal mantissa bitlengths on a per-layer granularity, and obtain up to $92\%$ reduction in total mantissa footprint. Alternatively, BitChop observes changes in the loss function during training to adjust mantissa bit-length network-wide yielding a reduction of $81\%$ in footprint. Schr\"{o}dinger's FP implements hardware encoders/decoders that guided by Gecko/Quantum Mantissa or Gecko/BitChop transparently encode/decode values when transferring to/from off-chip memory boosting energy efficiency and reducing execution time.
Epistasis and Adaptation on Fitness Landscapes

Epistasis occurs when the effect of a mutation depends on its carrier's genetic background. Despite increasing evidence that epistasis for fitness is common, its role during evolution is contentious. Fitness landscapes, which are mappings of genotype or phenotype to fitness, capture the full extent and complexity of epistasis. Fitness landscape theory has shown how epistasis affects the course and the outcome of evolution. Moreover, by measuring the competitive fitness of sets of tens to thousands of connected genotypes, empirical fitness landscapes have shown that epistasis is frequent and depends on the fitness measure, the choice of mutations for the landscape, and the environment in which it was measured. In this article, I review fitness landscape theory and experiments and their implications for the role of epistasis in adaptation. I discuss theoretical expectations in the light of empirical fitness landscapes and highlight open challenges and future directions toward integrating theory and data and incorporating ecological factors. Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 53 is November 2022. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Stay Safe under Panic: Affine Rust Programming with Multiparty Session Types

Communicating systems comprise diverse software components across networks. To ensure their robustness, modern programming languages such as Rust provide both strongly typed channels, whose usage is guaranteed to be affine (at most once), and cancellation operations over binary channels. For coordinating components to correctly communicate and synchronise with each other, we use the structuring mechanism from multiparty session types, extending it with affine communication channels and implicit/explicit cancellation mechanisms. This new typing discipline, affine multiparty session types (AMPST), ensures cancellation termination of multiple, independently running components and guarantees that communication will not get stuck due to error or abrupt termination. Guided by AMPST, we implemented an automated generation tool (MultiCrusty) of Rust APIs associated with cancellation termination algorithms, by which the Rust compiler auto-detects unsafe programs. Our evaluation shows that MultiCrusty provides an efficient mechanism for communication, synchronisation and propagation of the notifications of cancellation for arbitrary processes. We have implemented several usecases, including popular application protocols (OAuth, SMTP), and protocols with exception handling patterns (circuit breaker, distributed logging).
SERIES EXPANSIONS FOR ANY REAL POWER OF THE SINC FUNCTION AND A CLOSED-FORM FORMULA FOR PARTIAL BELL POLYNOMIALS OF THE SINC FUNCTION IN TERMS OF WEIGHTED STIRLING NUMBERS OF THE SECOND KIND

In the paper, with the help of the Fa´a di Bruno formula and in terms of the (weighted) Stirling numbers of the second kind, the author derives two series expansions for any positive integer power of the sinc function, ﬁnds out a closed-form formula for partial Bell polynomials with relation to derivatives of the sinc function, establishes series expansions for any real powers of the sinc and hyperbolic sinc functions.
High-Dimensional Lattice Planning with Optimal Motion Primitives

—Lattice-based planning techniques simplify the motion planning problem for autonomous vehicles by limiting available motions to a pre-computed set of primitives . These primitives are then combined online to generate more complex maneuvers. A set of motion primitives t -span a lattice if, given a real number t ≥ 1 , any conﬁguration in the lattice can be reached via a sequence of motion primitives whose cost is no more than a factor of t from optimal. Computing a minimal t -spanning set balances a trade-off between computed motion quality and motion planning performance. In this work, we formulate this problem for an arbitrary lattice as a mixed integer linear program. We also propose an A*-based algorithm to solve the motion planning problem using these primitives. Finally, we present an algorithm that removes the excessive oscillations from planned motions – a common problem in lattice-based planning. Our method is validated for autonomous driving in both parking lot and highway scenarios.
Epileptic Seizure Classification Using Combined Labels and a Genetic Algorithm

Epilepsy affects 50 million people worldwide and is one of the most common serious neurological disorders. Seizure detection and classification is a valuable tool for diagnosing and maintaining the condition. An automated classification algorithm will allow for accurate diagnosis. Utilising the Temple University Hospital (TUH) Seizure Corpus, six seizure types are compared; absence, complex partial, myoclonic, simple partial, tonic and tonicclonic models. This study proposes a method that utilises unique features with a novel parallel classifier — Parallel Genetic Naive Bayes (NB) Seizure Classifier (PGNBSC). The PGNBSC algorithm searches through the features and by reclassifying the data each time, the algorithm will create a matrix for optimum search criteria. Ictal states from the EEGs are segmented into 1.8 s windows, where the epochs are then further decomposed into 13 different features from the first intrinsic mode function (IMF). The features are compared using an original NB classifier in the first model. This is improved upon in a second model by using a genetic algorithm (Binary Grey Wolf Optimisation, Option 1) with a NB classifier. The third model uses a combination of the simple partial and complex partial seizures to provide the highest classification accuracy for each of the six seizures amongst the three models (20%, 53%, and 85% for first, second, and third model, respectively).
Scaling Up Maximal k-plex Enumeration

Finding all maximal k-plexes on networks is a fundamental research problem in graph analysis due to many important applications, such as community detection, biological graph analysis, and so on. A k-plex is a subgraph in which every vertex is adjacent to all but at most k vertices within the subgraph. In this paper, we study the problem of enumerating all large maximal k-plexes of a graph and develop several new and efficient techniques to solve the problem. Specifically, we first propose several novel upper-bounding techniques to prune unnecessary computations during the enumeration procedure. We show that the proposed upper bounds can be computed in linear time. Then, we develop a new branch-and-bound algorithm with a carefully-designed pivot re-selection strategy to enumerate all k-plexes, which outputs all k-plexes in O(n2? k n) time theoretically, where n is the number of vertices of the graph and ? k is strictly smaller than 2. In addition, a parallel version of the proposed algorithm is further developed to scale up to process large real-world graphs. Finally, extensive experimental results show that the proposed sequential algorithm can achieve up to 2× to 100× speedup over the state-of-the-art sequential algorithms on most benchmark graphs. The results also demonstrate the high scalability of the proposed parallel algorithm. For example, on a large real-world graph with more than 200 million edges, our parallel algorithm can finish the computation within two minutes, while the state-of-the-art parallel algorithm cannot terminate within 24 hours.
The extraction of a two-resistor/two-capacitor model for common IC packages and their implementation in CFD

When attempting to characterize the thermal performance of an IC component in a numerically efficient manner, the simplest useful compact model is the two-resistor model, created by combining junction-to-case and junction-to-board resistances. Such compact models have several advantages, and are therefore among the most widely used in the electronics industry. However, an overwhelming number of these models are designed for capturing steady-state behavior only. While many investigators have proposed elegant theoretical formulations to address the issue of compact transient models, most of these either cannot, or have not, been implemented in any of the widely used commercial CFD tools. This study proposes a simple technique to extract a two-resistor/two-capacitor (2RC) model from a detailed model using analytical and computational techniques. The ability of the 2RC model to predict the junction and case temperature responses to a step power excitation is investigated by applying the technique to a common package, the Plastic Ball Grid Array (PBGA). The resultant 2RC networks are implemented in a commercial CFD tool and appropriate conclusions are drawn from the results.
Creating an immersive virtual reality application for ski jumping

Virtual reality(VR) has been successfully applied to a broad range of training domains. VR provides an opportunity to train in a safe and controlled environment, with support for accurate performance measurement. In this report I present a prototype of a VR application for ski jumping. The goal for this project is to explore if such an application can be used to teach the basics of ski jumping, and if it can be used as a training tool for athletes. The development process is thoroughly described in the report. Through user testing and an expert evaluation it was concluded that the application has potential to be used as a tool for practicing the timing of the jump. Improvements for the prototype is proposed, which should enable the prototype to also evaluate the jumping technique.
Determinants of Racial/Ethnic Disparities in Incidence of Diabetes Determinants of Racial/Ethnic Disparities in Incidence of Diabetes in Postmenopausal Women in the U.S.: The Women's Health in Postmenopausal Women in the U.S.: The Women's Health Initiative 1993-2009 Initiative 1993-2009

OBJECTIVE examine determinants of racial/ethnic differences in diabetes incidence among postmenopausal women participating in the Women ’ s Health Initiative. RESEARCH DESIGN AND METHODS d Data on race/ethnicity, baseline diabetes prevalence, and incident diabetes were obtained from 158,833 women recruited from 1993 – 1998 and followed through August 2009. The relationship between race/ethnicity, other potential risk factors, and the risk of incident diabetes was estimated using Cox proportional hazards models from which hazard ratios (HRs) and 95% CIs were computed. RESULTS 84.1%non-Hispanicwhite,9.2%non-Hispanicblack,4.1%Hispanic,and2.6%Asian.Afteranaverageof10. 4yearsoffollow-up,comparedwithwhitesandadjustingforpotentialconfounders,theHRsforincidentdiabeteswere1.55forblacks(95%CI1.47 – 1.63),1.67forHispanics(1.54 – 1.81),and1.86for Asians(1.68 – 2.06). Whites, blacks, and Hispanics with all factors (i.e., weight, physicalactivity,dietary quality, and smoking) in the low-risk category had 60, 69, and 63% lower risk for incident diabetes. Although contributions of different risk factors varied slightly by race/ethnicity, most ﬁ ndings were similar across groups, and women who had both a healthy weight and were in the highest tertile of physical activity had less than one-third the risk of diabetes compared with obese and inactive women. CONCLUSIONS d Despite large racial/ethnic differences in diabetes incidence, most variability could be attributed to lifestyle factors. Our ﬁ ndings show that the majority of diabetes cases are preventable, and risk reduction strategies can be effectively applied to all racial/ethnic groups.
BAG: A Designer-Oriented Framework for the Development of AMS Circuit Generators

Author(s): Crossley, John Wiley | Advisor(s): Alon, Elad | Abstract: The recent trend in embedding multiple applications into a single System-on-Chip (SoC) has resulted in an increase in the number of Analog/Mixed-Signal (AMS) components integrated per die. Although the AMS components typically occupy a small fraction of the whole IC, they often require the longest design time because typical AMS design flows require substantial manual intervention from the designer throughout the design process. It would thus be desirable to automate the design of AMS circuits and foster their reuse across multiple SoCs and technology generations, to shorten time-to-market of new products and to free analog designers from performing repetitive tasks. In this thesis, we present the Berkeley Analog Generator (BAG) framework, an integrated framework for the development of generators of AMS circuits.Generators are parameterized design procedures that produce sized schematics and correct layouts optimized to meet a set of input specifications. BAG extends previous work by implementing interfaces to integrate all steps of the design flow into a single environment and by providing helper classes - at the schematic and particularly at the layout level - to aid the designer in developing truly parameterized and technology-independent AMS circuit generators. The BAG framework simplifies and helps codify common tasks in the AMS design flow including technology characterization, schematic and testbench translation, simulator interfacing, physical verification and extraction, and layout creation. BAG addresses one of the most labor-intensive tasks, layout, by providing template-based extensible layout generators for different styles of circuits to help designers create their own parameterized layout generators. In order to demonstrate the completeness of the BAG framework, the development process of several generators for an integrated switched-capacitor (SC) regulator and its associated subcircuits are presented as a case study and the top level SC regulator generator is used to create three instances of a SC voltage regulator targeting different power densities, absolute output power, and aspect ratios.
Testing Differential Networks with Applications to Detection of Testing Differential Networks with Applications to Detection of Gene-Gene Interactions Gene-Gene Interactions

Abstract Model organisms and human studies have led to increasing empirical evidence that interactions among genes contribute broadly to genetic variation of complex traits. In the presence of gene-by-gene interactions, the dimensionality of the feature space becomes extremely high relative to the sample size. This imposes a significant methodological challenge in identifying gene-by-gene interactions. In the present paper, through a Gaussian graphical model framework, we translate the problem of identifying gene-by-gene interactions associated with a binary trait
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time

Recently, the seminal algorithms AlphaGo and AlphaZero have started a new era in game learning and deep reinforcement learning. While the achievements of AlphaGo and AlphaZero—playing Go and other complex games at super human level—are truly impressive, these architectures have the drawback that they require high computational resources. Many researchers are looking for methods that are similar to AlphaZero, but have lower computational demands and are thus more easily reproducible. In this article, we pick an important element of AlphaZero—the Monte Carlo Tree Search (MCTS) planning stage—and combine it with temporal difference (TD) learning agents. We wrap MCTS for the first time around TD n-tuple networks and we use this wrapping only at test time to create versatile agents that keep at the same time the computational demands low. We apply this new architecture to several complex games (Othello, ConnectFour, Rubik's Cube) and show the advantages achieved with this AlphaZero-inspired MCTS wrapper. In particular, we present results that this agent is the first one trained on standard hardware (no GPU or TPU) to beat the very strong Othello program Edax up to and including level 7 (where most other learning-from-scratch algorithms could only defeat Edax up to level 2).
A Solution to the Kermack and McKendrick Integro-Differential Equations

In this manuscript, we derive a closed form solution to the full Kermack and McKendrick integro-differential equations (Kermack and McKendrick 1927) which we call the KMES. The KMES can be cast in the form of a step function response to the input of new infections; and that response is the time series of the total infections. We demonstrate the veracity of the KMES using independent data from the Covid 19 pandemic and derive many previously unknown and useful analytical expressions for diagnosing and managing an epidemic. These include new expressions for the viral load, the final size, the effective reproduction number, and the time to the peak in infections. Since the publication of the Kermack and McKendrick seminal paper (1927), thousands of authors have utilized the Susceptible, Infected, and Recovered (SIR) approximations; expressions which are putatively derived from the integro-differential equations, to model epidemic dynamics. Implicit in the use of the SIR approximation are the beliefs that there is no closed form solution to the more complex integro-differential equations, that the approximation adequately reproduces the dynamics of the integro-differential equations, and that herd immunity always exists. However, as we explicate in this manuscript, the KMES demonstrates that the SIR models are not adequate representations of the integro-differential equations, and herd immunity is not guaranteed. Our conclusion is that the KMES obsoletes the need for the SIR approximations; and provides a new level of understanding of epidemic dynamics.
A measurement-based behavioral model for I/Q RF modulators

This article describes a nonlinear behavioral modeling technique for application to I/Q modulated communication channels. The model data was gathered by independent measurements of an I/Q modulator and amplifier, using vector signal generators and analyzers. Concatenating the models in simulation and verifying by measurement validated the technique. The method was applied to a W-CDMA transmit chain. The resulting error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR) levels predicted by simulation closely matched the measurements.
Low-loss dispersion engineered wide-band, multi-band, and reconfigurable anisotropic metamaterials and Bezier metasurfaces

For more than a decade, metamaterials have generated significant interest due to their theoretical and later experimentally demonstrated properties that are not observed in nature; yet, many of these designs are plagued by very limited bandwidth and/or high losses due to the dispersion characteristics of metamaterials. While many applications exist where a narrow band response is acceptable, examples of dual-band and multi-band responses within a communication bandwidth are limited. Furthermore, the ability to implement reconfigurability becomes complicated due to the lack of high performance switch technologies. In this dissertation, a new technique for constructing and synthesizing broadband metasurfaces is presented. A synthesis technique using Bézier surfaces is subsequently shown to not only outperform known optimization techniques but to produce results with bandwidths far exceeding those found in the literature. Additionally, a composite metamaterial geometry is introduced that facilitates a dual band response with a tunable frequency ratio within usable bands. The design also facilitates reconfigurablity. To mitigate the loss and bandwidth concerns of existing RF switch technologies a new technology is introduced and characterized a chalcogenide glass phase change material enabled bistable switch. The superior efficiency and bandwidth are subsequently demonstrated by a novel quad state frequency selective surface, which once again boasts multiband reconfigurability within a communication band.
Use of a Chemotherapy Toxicity Prediction Tool to Decrease Risks for Hospitalization in Older Patients

Objectives Performance status (PS) scales such as the Eastern Cooperative Oncology Group (ECOG) PS and the Karnofsky Performance Index have limited utility in selecting therapies and predicting related adverse events in older patients with cancer. In July 2016, medical oncologists at our institution adopted the Cancer and Aging Research Group toxicity prediction score (CARG), a toxicity prediction tool, to identify patients who are “fit” for chemotherapy versus those who are “frail” and may experience severe complications. Methods Our retrospective review included referrals of beneficiaries 75 years of age and older who received standard systemic therapy and patients of the same age whose treatment was modified due to CARG. We compared the score’s utilization six months before and after its incorporation and then assessed how its application impacted admissions, emergency department (ED) visits, and medical management. Results Thirty-eight patients with a mean age of 81 years met the inclusion criteria. Their diagnoses included gastrointestinal (37%), lung (21%), hematologic (18%), breast (10.5%), genitourinary (3%), and other (10.5%) malignancies. CARG was documented for 12.5% of systemic therapy recipients before its adoption and 41% of recipients after adoption. Its use was limited by the reliance on physicians to perform scoring during time-constrained patient encounters. Patients had fewer mean inpatient admissions (0.7 versus 2.3), admission days (4.3 versus 8), and ED visits (1.1 versus 2.5) when management was modified based on the score. Conclusion CARG assessment may facilitate a safer and more tailored approach to cancer care in older patients than conventional PS scales alone. Its integration into patient screening would increase its application and better define its potential predictive capacity to decrease risks for hospitalization.