Miracle-dz/UniFAR-FTUs huggingface dataset:
A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs

By enabling correct differentiation in stochastic computation graphs (SCGs), the infinitely differentiable Monte-Carlo estimator (DiCE) can generate correct estimates for the higher order gradients that arise in, e.g., multi-agent reinforcement learning and meta-learning. However, the baseline term in DiCE that serves as a control variate for reducing variance applies only to first order gradient estimation, limiting the utility of higherorder gradient estimates. To improve the sample efficiency of DiCE, we propose a new baseline term for higher order gradient estimation. This term may be easily included in the objective, and produces unbiased variance-reduced estimators under (automatic) differentiation, without affecting the estimate of the objective itself or of the first order gradient estimate. It reuses the same baseline function (e.g., the state-value function in reinforcement learning) already used for the first order baseline. We provide theoretical analysis and numerical evaluations of this new baseline, which demonstrate that it can dramatically reduce the variance of DiCE’s second order gradient estimators and also show empirically that it reduces the variance of third and fourth order gradients. This computational tool can be easily used to estimate higher order gradients with unprecedented efficiency and simplicity wherever automatic differentiation is utilised, and it has the potential to unlock applications of higher order gradients in reinforcement learning and meta-learning. Equal contribution Man AHL, London, UK. Work done at the University of Oxford. Department of Computer Science, University of Oxford, Oxford, UK Department of Computer Science, University College London, London, UK School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA. Correspondence to: Jakob Foerster , Jingkai Mao . Proceedings of the 36 th International Conference on Machine Learning, Long Beach, California, PMLR 97, 2019. Copyright 2019 by the author(s).
Just a Little Bit ” : A Small Amount of Side Channel Can Go a Long

We apply the FLUSH+RELOAD side-channel attack based on cache hits/misses to extract a small amount of data from OpenSSL ECDSA signature requests. We then apply a “standard” lattice technique to extract the private key, but unlike previous attacks we are able to make use of the side-channel information from almost all of the observed executions. This means we obtain private key recovery by observing a relatively small number of executions, and by expending a relatively small amount of post-processing via lattice reduction. We demonstrate our analysis via experiments using the curve secp256k1 used in the Bitcoin protocol. In particular we show that with as little as 200 signatures we are able to achieve a reasonable level of success in recovering the secret key for a 256-bit curve. This is significantly better than prior methods of applying lattice reduction techniques to similar side channel information.
Combining Unsupervised and Supervised Statistical Learning Methods for POLINA VASILJEVA

In this thesis we revisit the challenging problem of forecasting currency exchange rate. We combine machine learning methods such as agglomerative hierarchical clustering and random forest to construct a two-step approach for predicting movements in currency exchange prices of the Swedish krona and the US dollar. We use a data set with over 200 predictors comprised of different financial and macro-economic time series and their transformations. We perform forecasting for one week ahead with different parameterizations and find a hit rate of on average 53%, with some of the parameterizations yielding hit rates as high as 60%. However, there is no clear indicator that there exists a combination of the methods and parameters that outperforms all of the tested cases. In addition, our results indicate that the two-step approach is sensitive to changes in the training set. This work has been conducted at the Third Swedish National Pension Fund (AP3) and KTH Royal Institute of Technology.
Performance Measures for the Analysis of Rural Public Transit in Alabama

As rural public transit systems are vital to the livelihood of rural Americans, improving the operations of these systems is the focus of this work. The use of performance measures to evaluate operation is essential to maintain growth and avoid becoming stagnant. The main goal of this study was to examine existing performance measures (PM) and modify them to allow for comparison of performance among rural transit agencies in Alabama. The tasks presented in this paper are a review of performance measures, data collection, and data analysis for agencies in Alabama. The report concludes that performance measures can be developed that balance external factors in the analysis and allow for a fair comparison of agencies.
Clinicopathological analysis of HOXD4 expression in diffuse gliomas and its correlation with IDH mutations and 1p/19q co-deletion

Backgrounds HOX (homologous box) is known as the dominant gene of vertebrate growth and cell differentiation. Abnormal expression of HOX gene in various tumors has attracted the attention of scholars. As a component of HOX clusters, HOXD4 plays a controversial role in the tumorigenesis of central nervous system. Results The data demonstrated that and the results demonstrated that HOXD4 was overexpressed in glioma tissues compared to that of normal brain tissues. patients with high HOXD4 expression had a significant shorter survival than those with low HOXD4 expression in total glioma cohort (p<0.001), WHO Grade II cohort (p=0.003) and Grade III cohort (p<0.001), but not in Grade IV cohort when OS (overall survival) was analyzed (p=0.216). The findings were confirmed by the large-scale omics data analysis including lower-grade glioma (LGG) and glioblastoma multiforme (GBM) in TCGA (the cancer genome atlas) and CGGA (Chinese glioma genome atlas). Moreover, it was revealed that the expression of HOXD4 have a significant impact on the OS of Grade IV glioma with IDH wild-type and 1p/19q intact according to TCGA data. Methods Clinicopathological analysis of HOXD4 expression in 453 glioma patients was performed in the current study. Expression of HOXD4 was evaluated by qPCR and immunohistochemical (IHC) staining. Univariate and multivariate analysis were conducted to investigate the prognostic role of HOXD4 in glioma patients. Conclusions Expression of HOXD4 was closely related to the clinical outcomes of patients with gliomas, and HOXD4 may be a potential prognostic biomarker of gliomas.
Design and implementation of high-speed BCP processor for OBS core node based on FPGA

The burst control packet (BCP) process time in the OBS core node is very important for the OBS networks. Although several OBS testbeds have been reported, the BCP process time in core node is still not fast enough. In this article, a new high-speed OBS core node BCP processor based on FPGA is proposed. In the processor, a new valid wavelength scheduling algorithm is created. Not only does it have high bandwidth utilization in scheduling valid wavelength, but also the scheduling time is less. The performance is that a BCP just needs only 180ns for processing when system clock is 200MHz, about 50ns used to schedule valid wavelength and 130ns for other issues in core node.
Reflections on the current status of the national sickle cell disease program in the United States.

Some clouds of concern now appear on the horizon for the national sickle cell disease program. There is flagging general attention by the black population and a dilution of interest in and visibility of the sickle cell problem brought about by political maneuvering to bring the program under the legislative umbrella of many other genetic diseases (which occur predominantly in Caucasians). In addition, the federal program has recently phased-out six comprehensive sickle cell centers and imposed budgetary cutbacks in the remaining centers. The victims of this disease, the black population in general, and the researchers and investigators who seek ways to bring this disease under control need reassurance from the current national administration that the sickle cell program will not be permitted to die a slow death from financial attrition, attenuation of interest, and skillful neglect leading to the phasing-out of another "minority project. " The national sickle cell program, in the relatively short span of six years, has made significant and notable progress not only in research endeavor but also in improved patient care and community-wide education. In this context, certainly, the positive aspects of the national sickle cell disease program continue to far outweigh any negative ones. (1)
Evaluation of Association between Serum HS-CRP and Lipid Profile in Type 2 Diabetes Mellitus

High sensitive C-reactive protein (hs-CRP) is an established marker of low grade systemic inflammation. A chronic lowgrade inflammatory condition has been proposed to underline increased risk for atherosclerotic disease, including renal dysfunction and cardiovascular disease suggesting a possible link between the high incidence of macrovascular complications and diabetes. Insulin resistance results in high concentrations of serum lipids in diabetics due to increased mobilization of free fatty acids from fat depots. Because serum hs-CRP and serum lipid profile reflect closely related component of the same disease process, a strong relationship between these variables may be anticipated. We selected 50 patients of Type-2 diabetes mellitus and 50 normal healthy individuals to evaluate this association. Serum estimations showed a significant increase (p< 0.001) in hs-CRP, Total cholesterol (TC), Triglycerides (TG), Low Density Lipoprotein (LDL) and Very Low Density Lipoprotein (VLDL) in diabetics when compared to controls. In contrast there was significant decrease (p< 0.001) in HDL-C in diabetics. The statistical analysis depicted a non significant correlation between serum hs-CRP and total cholesterol ( p= 0.909, r=0.017), serum triglyceride (p=0.294, r=0.151) and serum HDL (p=0.724, r=-0.051) in type 2 diabetic patients. This finding may therefore, suggest that hs-CRP act as an independent marker for cardiovascular risk factor.
Chemical and biochemical approaches in the study of histone methylation and demethylation

Histone methylation represents one of the most critical epigenetic events in DNA function regulation in eukaryotic organisms. Classic molecular biology and genetics tools provide significant knowledge about mechanisms and physiological roles of histone methyltransferases and demethylases in various cellular processes. In addition to this stream line, development and application of chemistry and chemistry‐related techniques are increasingly involved in biological study, and offer information otherwise difficult to obtain by standard molecular biology methods. Herein, we review recent achievements and progress in developing and applying chemical and biochemical approaches in the study of histone methylation, including chromatin immunoprecipitation, chemical ligation, mass spectrometry, biochemical methylation and demethylation assays, and inhibitor development. These technological advances allow histone methylation to be studied from genome‐wide level to molecular and atomic levels. With ChIP technology, information can be obtained about precise mapping of histone methylation patterns at specific promoters, genes, or other genomic regions. MS is particularly useful in detecting and analyzing methylation marks in histone and nonhistone protein substrates. Chemical approaches that permit site‐specific incorporation of methyl groups into histone proteins greatly facilitate the investigation of biological impacts of methylation at individual modification sites. Discovery and design of selective organic inhibitors of histone methyltransferases and demethylases provide chemical probes to interrogate methylation‐mediated cellular pathways. Overall, these chemistry‐related technological advances have greatly improved our understanding of the biological functions of histone methylation in normal physiology and diseased states, and also are of great potential to translate basic epigenetics research into diagnostic and therapeutic applications in the clinic. © 2010 Wiley Periodicals, Inc. Med Res Rev., 32, No. 4, 815‐867, 2012
H3K9ac and HDAC2 Activity Are Involved in the Expression of Monocarboxylate Transporter 1 in Oligodendrocyte

Recently, it is reported that monocarboxylate transporter 1 (MCT1) plays crucial role in oligodendrocyte differentiation and myelination. We found that MCT1 is strongly expressed in oligodendrocyte but weakly expressed in oligodendrocyte precursors (OPCs), and the underlying mechanisms remain elusive. Histone deacetylases (HDACs) activity is required for induction of oligodendrocyte differentiation and maturation. We asked whether HDACs are involved in the regulation of MCT1 expression. This work revealed that the acetylation level of histone H3K9 (H3K9ac) was much higher in mct1 gene (Slc16a1) promoter in OPCs than that in oligodendrocyte. H3K9ac regulates MCT1 expression was confirmed by HDAC acetyltransferase inhibitors trichostatin A and curcumin. Of note, there was a negative correlation between H3K9ac and MCT1 expression in oligodendrocyte. Further, we found that the levels of HDAC1, 2, and 3 protein in oligodendrocyte were obviously higher than those in OPCs. However, specific knockdown of HDAC2 but not HDAC1 and HDAC3 significantly decreased the expression of MCT1 in oligodendrocyte. Conversely, overexpression of HDAC2 remarkably enhanced the expression of MCT1. The results imply that HDAC2 is involved in H3K9ac modification which regulates the expression of MCT1 during the development of oligodendrocyte.
Quantum supercharger library: Hyper‐parallelism of the Hartree–Fock method

We present here a set of algorithms that completely rewrites the Hartree–Fock (HF) computations common to many legacy electronic structure packages (such as GAMESS‐US, GAMESS‐UK, and NWChem) into a massively parallel compute scheme that takes advantage of hardware accelerators such as Graphical Processing Units (GPUs). The HF compute algorithm is core to a library of routines that we name the Quantum Supercharger Library (QSL). We briefly evaluate the QSL's performance and report that it accelerates a HF 6‐31G Self‐Consistent Field (SCF) computation by up to 20 times for medium sized molecules (such as a buckyball) when compared with mature Central Processing Unit algorithms available in the legacy codes in regular use by researchers. It achieves this acceleration by massive parallelization of the one‐ and two‐electron integrals and optimization of the SCF and Direct Inversion in the Iterative Subspace routines through the use of GPU linear algebra libraries. © 2015 Wiley Periodicals, Inc.
Gambling with Health A Method to Test Theories of Decision under Risk in the Domain of Health

In order to know how to efficiently allocate resources in health care we need a measure of its outcome, health. There is an ongoing debate on how the values of different health states are best measured. The dominating approach is to elicit utilities from decisions made by the public, in an experimental setting. In order to elicit valid utilities a theory of how decisions are made is needed. Traditionally the expected utility theory has been used but recently the prospect theory has been shown much interest. In this thesis a method is developed to test which one of these two theories is most suitable to use in the domain of health. The general idea behind the method is that each respondent constructs her own scale for rating health states. The method is also applied in a small scale experiment. It is concluded that the method seems applicable. Furthermore, the experiment indicates that probability weighting is plausible while the other features of the prospect theory are not clearly supported. The thesis ends with a brief discussion about how the method could be used in future surveys.
Asymmetric Data Acquisition System for an Endoscopic PET-US Detector

According to current prognosis studies of pancreatic cancer, survival rate nowadays is still as low as 6% mainly due to late detections. Taking into account the location of the disease within the body and making use of the level of miniaturization in radiation detectors that can be achieved at the present time, EndoTOFPET-US collaboration aims at the development of a multimodal imaging technique for endoscopic pancreas exams that combines the benefits of high resolution metabolic information from time-of- flight (TOF) positron emission tomography (PET) with anatomical information from ultrasound (US). A system with such capabilities calls for an application-specific high-performance data acquisition system (DAQ) able to control and readout data from different detectors. The system is composed of two novel detectors: a PET head extension for a commercial US endoscope placed internally close to the region-of-interest (ROI) and a PET plate placed over the patient's abdomen in coincidence with the PET head. These two detectors will send asymmetric data streams that need to be handled by the DAQ system. The approach chosen to cope with these needs goes through the implementation of a DAQ capable of performing multi-level triggering and which is distributed across two different on-detector electronics and the off-detector electronics placed inside the reconstruction workstation. This manuscript provides an overview on the design of this innovative DAQ system and, based on results obtained by means of final prototypes of the two detectors and DAQ, we conclude that a distributed multi-level triggering DAQ system is suitable for endoscopic PET detectors and it shows potential for its application in different scenarios with asymmetric sources of data.
Work-in-progress: a unified framework for throughput analysis of synchronous data flow graphs under memory constraints

Streaming applications are often modeled with Synchronous data flow graphs (SDFGs). A proper analysis of the models is helpful to predict the performance of a system. In this paper, we focus on the throughput analysis of memory-constrained SDFGs (MC SDFGs), which needs to choose a memory abstraction that decides when the space of consumed data is released and when the required space is claimed. Di↵erent memory abstractions may lead to di↵erent achievable throughputs. The existing techniques, however, consider only a certain abstraction. If a model is implemented according to other abstractions, the analysis result may not truly evaluate the performance of the system. In this paper, we present a unified framework for throughput analysis of MC SDFGs for di↵erence abstractions, aiming to provide evaluations matching up to the corresponding implementations.
The Optimal Supply of Public and Private Liquidity

We develop a theory of optimal government debt in which publicly-issued and privately-issued safe assets are substitutes. While government bonds are backed by future tax revenues, privately-issued safe assets are backed by the future repayment of pools of defaultable private loans. We find that a higher supply of public debt crowds out privately-issued safe assets less than one for one and reduces the interest spread between borrowing and deposit rates. Our main result is that the optimal level of public debt does not fully crowd out private lending and maintains a positive interest spread. Moreover, the optimal level of public debt is higher the more severe are financial frictions.
TRANSFER LEARNING IN NATURAL LANGUAGE PRO-

Pre-trained word embeddings are the primary method for transfer learning in several Natural Language Processing (NLP) tasks. Recent works have focused on using unsupervised techniques such as language modeling to obtain these embeddings. In contrast, this work focuses on extracting representations from multiple pre-trained supervised models, which enriches word embeddings with task and domain specific knowledge. Experiments performed in cross-task, cross-domain and cross-lingual settings indicate that such supervised embeddings are helpful, especially in the low-resource setting, but the extent of gains is dependent on the nature of the task and domain.
Adoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review

It is essential to increase the productivity of agricultural and farming processes to improve yields and cost-effectiveness with new technology such as the Internet of Things (IoT). In particular, IoT can make agricultural and farming industry processes more efficient by reducing human intervention through automation. In this study, the aim to analyze recently developed IoT applications in the agriculture and farming industries to provide an overview of sensor data collections, technologies, and sub-verticals such as water management and crop management. In this review, data is extracted from 60 peer-reviewed scientific publications (2016-2018) with a focus on IoT sub-verticals and sensor data collection for measurements to make accurate decisions. Our results from the reported studies show water management is the highest sub-vertical (28.08%) followed by crop management (14.60%) then smart farming (10.11%). From the data collection, livestock management and irrigation management resulted in the same percentage (5.61%). In regard to sensor data collection, the highest result was for the measurement of environmental temperature (24.87%) and environmental humidity (19.79%). There are also some other sensor data regarding soil moisture (15.73%) and soil pH (7.61%). Research indicates that of the technologies used in IoT application development, Wi-Fi is the most frequently used (30.27%) followed by mobile technology (21.10%). As per our review of the research, we can conclude that the agricultural sector (76.1%) is researched considerably more than compared to the farming sector (23.8%). This study should be used as a reference for members of the agricultural industry to improve and develop the use of IoT to enhance agricultural production efficiencies. This study also provides recommendations for future research to include IoT systems' scalability, heterogeneity aspects, IoT system architecture, data analysis methods, size or scale of the observed land or agricultural domain, IoT security and threat solutions/protocols, operational technology, data storage, cloud platform, and power supplies.