Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning high-dimensional mixed graphical models with missing values

Current biomedical instrumentation enables monitoring an increasing amount of mixed discrete and continuous variables. This results in multivariate samples amenable for their analysis using mixed graphical models. The dimension of these data sets, with a number of variablespmuch larger than the number of observationsn, precludes the direct application of classical learning algorithms and specific procedures that work under thepnsetting are required for that purpose. Yet, a further obstacle to the wide application of these pro- cedures in the biomedical field arises from the fact that missing observations often occur in clinical and genotype data. The high-dimension ofpimpedes approaching the problem by simple complete-case analysis and increases substantially the computational burden if we want to use multiply-imputed data sets. Here we show that using limited-order correla- tions to learn mixed graphical models from data withpnenables a straightforward and effective application of complete-case analysis to the missing data problem. More impor- tantly, because complete-case analysis is only appropriate under the restrictive assumption that data are missing completely at random, we adapt an expectation-maximization al- gorithm to the limited-order correlation framework and demonstrate its better suitability under the less stringent assumption of data being missing at random.
Current populations of Colobus vellerosus (Geoffory, 1834) & Cercopithecus lowei (Thomas, 1923) and land-use, land cover changes in Boabeng-Fiema Monkey Sanctuary, Ghana

Background and Research aim: This study evaluated the density of two primate species Colobus vellerosus and Cercopithecus lowei and the change in land-use types in Boabeng-Fiema Monkey Sanctuary in Ghana, from 2007 to 2019. Method: Total counts of individual monkeys were done in all six patches of forest in the Sanctuary in 2019. Using Landsat imagery, land-cover maps of the study area were examined to evaluate the change that has occurred over a nine-year period between 2010 and 2019. Results: A total of 602 individuals of C. vellerosus were counted in 34 groups (0.58 group/ha). Group locations were: 15 at Boabeng (0.12 groups/ha), five at Fiema (0.08 group/ha), three at Bomini (0.09 group/ha), four at Busuyna (0.13 group/ha), three at Bonte (0.06 group/ha), and four at Akrudwa (0.11 group/ha). C. lowei was only encountered at Boabeng and Fiema, with a total of 351 individuals distributed in 26 groups. In 2010, forest covered a land area of 1,540.08 ha, and it was estimated to have increased to 2,643.12 ha in 2019. Farmlands covered 5,069.07 ha in 2010, and in 2019 were estimated to cover 4,155.03 ha. Built-up areas in 2010 covered an area of 433.89 ha, and in 2019 had declined to 244.89 ha. Conclusion: The monkey populations have increased and spread to occupy all patches in the Monkey Sanctuary. On LULCC, 72% increase, 18% reduction, and 44% reduction in forest cover, farmland and built-up areas were observed respectively. Implications for conservation: There is a blend of traditional and conventional conservation efforts contributing to the increase in primate population, the occupancy of previously ‘empty’ forest patches and change in areas of land-use types.
Cyclodextrins and Their Polymers Affect Human Serum Albumin’s Interaction with Drugs Used in the Treatment of Pulmonary Infections

Respiratory infectious diseases have challenged medical communities and researchers. Ceftriaxone, meropenem and levofloxacin are widely used for bacterial infection treatment, although they possess severe side effects. To overcome this, we propose cyclodextrin (CD) and CD-based polymers as a drug delivery system for the drugs under consideration. CD polymers demonstrate higher binding affinity for levofloxacin (Ka ≈ 105 M) compared to drug–CD complexes. CDs slightly alter the drugs’ affinity for human serum albumin (HSA), whereas CD polymers increase the drugs’ binding affinity up to 100 times. The most significant effect was observed for more the hydrophilic drugs ceftriaxone and meropenem. The drug’s encapsulation in CD carriers leads to a decrease in the degree of change in the protein’s secondary structure. The drug–CD carrier–HSA complexes demonstrate satisfying antibacterial activity in vitro, and even a high binding affinity does not decrease the drug’s microbiological properties after 24 h. The proposed carriers are promising for a drug form with a prolonged drug release.
Genetic structure and host associations of the noctuid moth Callopistria exotica, a potential biological control agent for Lygodium microphyllum

ABSTRACT Lygodium microphyllum is a damaging environmental weed that continues to spread rapidly across Florida. The natural enemies released so far have had some impact on L. microphyllum, but this weed continues to grow densely and spread, so new biological control agents are required. Native range surveys of L. microphyllum identified a noctuid moth, Callopistria exotica, commonly feeding on this fern. We collected C. exotica feeding on Lygodium in Australia, China, India, Indonesia, Japan, Malaysia, Singapore, and Thailand, and sequenced three genes (COI, 18S, 28S) to assess genetic structure across its geographic distribution. We identified a unique C. exotica genetic lineage in Japan, feeding on Lygodium japonicum. This lineage may represent a distinct species, and could potentially be well-suited to control L. japonicum, which has also developed into a damaging weed across Florida. Furthermore, we found genetic structuring within the moth samples collected from L. microphyllum, with three distinct mitochondrial lineages identified. We recommend treating these distinct C. exotica lineages independently from one another for the purposes of host range testing, and suggest further tests to clarify their species status relative to one another. Our study illustrates how the genetic screening of native range survey data can provide important preliminary information that helps develop hypotheses to direct additional surveys, agent selection, and host range testing.
Multivariate matching methods that are equal percent bias reducing

Matched sampling is a method of data collection designed to reduce bias and variability due to specific matching variables. Although often used to control for bias in studies in which randomization is practically impossible, there is virtually no statistical literature devoted to investigating the ability of matched sampling to control bias in the common case of many matching variables. An obvious problem in studying the multivariate matching situation is the variety of sampling plans, underlying distributions, and intuitively reasonable matching methods. This article considers one class of multivariate matching methods which yield the same percent reduction in expected bias for each of the matching variables. The primary result is the derivation of the expression for the maximum attainable percent reduction in bias given fixed distributions and fixed sample sizes. An examination of trends in this maximum leads to a procedure for estimating minimum ratios of sample sizes needed to obtain well-matched samples.
Development of a Structural Equation Model to Examine the Relationships between Genetic Polymorphisms and Cardiovascular Risk Factors

Genome-wide association studies (GWASs) have been used to discover genetic polymorphisms that affect cardiovascular diseases (CVDs). Structural equation modelling (SEM) has been identified as a robust multivariate analysis tool. However, there is a paucity of research that has conducted SEM in African populations. The purpose of this study was to create a model that may be used to examine the relationships between genetic polymorphisms and their respective cardiovascular risk (CVR) factors. The procedure involved three steps. Firstly, the creation of latent variables and the hypothesis model. Next, confirmatory factor analysis (CFA) to examine the relationships between the latent variables, SNPs, dyslipidemia and metabolic syndrome, with their respective indicators. Then finally, model fitting using JASP statistical software v.0. 16.4. 0. The indicators for the SNPs and dyslipidemia all indicated significant factor loadings, −0.96 to 0.91 (p = <0.001) and 0.92 to 0.96 (p ≤ 0.001), respectively. The indicators for metabolic syndrome also had significant coefficients of 0.20 (p = 0.673), 0.36 (p = 0.645) and 0.15 (p = 0.576), but they were not statistically significant. There were no significant relationships observed between the SNPs, dyslipidemia and metabolic syndrome. The SEM produced an acceptable model according to the fit indices.
On the Modification of the Ffowcs Williams-Hawkings Integration for Jet Noise Prediction

This paper proposes a modification on the end-cap treatment for the Ffowcs Williams and Hawkings (FW-H) equation with permeable control surface, aiming at jet-noise simulations by the use of time-accurate numerical methods. It is a known issue that a closed end cap affects the accuracy of the permeable FW-H equation, as turbulent jet flow passes through the control surface. This is essentially an outcome of the neglected volumetric quadrupole term. Based on our previous study of the quadrupole correction, evaluated at an end-cap surface for uniform mean flow, we derive a mathematical extension to nonuniform convection velocity. By introducing locally frozen assumption for convecting vortices, a modification factor is obtained as a convergent series if the deviation of convection velocity from mean flow is subsonic. The derived formulation is applied to a simple subsonic round jet at ReD = 1 × 10 and Ma = 0.9. Although the present jet-flow simulation fails in accurately reproducing mixing-layer transition behind a nozzle exit, which leads to a significant overestimate of far-field noise, our modification on the FW-H formulation suppresses the spurious noise due to closed end-cap surface, as well as a more empirical, disk averaging approach proposed by Shur et al. (2005)
Lightweight Read Reference Voltage Calibration Strategy for Improving 3-D TLC NAND Flash Memory Reliability

Flash memory has gradually become the dominant storage device in the consumer market and data centers since the storage capacity increases and production costs decline. Unfortunately, as the bit density of flash memory increased, the severity of reliability issues has escalated. Read retry is necessary to recover high bit-error-rate flash data; however, read retry requires the flash to perform sequential read operations, which significantly increases read latency. To improve the flash memory read performance, a reliable adaptive optimization strategy for flash memory read reference voltage (RRV) is urgently needed. In this paper, we performed a full range of error characterization tests on three-dimensional (3-D) triple-level cell (TLC) flash memory, focusing on the effects of retention leakage, P/E wear, and interlayer variation on the threshold voltage. Finally, a lightweight flash memory RRV calibration strategy was constructed. The experiment results show that the predicted RRV using the proposed strategy is very close to the actual optimal RRV, reducing the raw bit error rate (RBER) of the same model of flash memory by up to 93.2%.
UNSAT Solver Synthesis via Monte Carlo Forest Search

We introduce Monte Carlo Forest Search (MCFS), a class of reinforcement learning (RL) algorithms for learning policies in {tree MDPs}, for which policy execution involves traversing an exponential-sized tree. Examples of such problems include proving unsatisfiability of a SAT formula; counting the number of solutions of a satisfiable SAT formula; and finding the optimal solution to a mixed-integer program. MCFS algorithms can be seen as extensions of Monte Carlo Tree Search (MCTS) to cases where, rather than finding a good path (solution) within a tree, the problem is to find a small tree within a forest of candidate trees. We instantiate and evaluate our ideas in an algorithm that we dub Knuth Synthesis, an MCFS algorithm that learns DPLL branching policies for solving the Boolean satisfiability (SAT) problem, with the objective of achieving good average-case performance on a given distribution of unsatisfiable problem instances. Knuth Synthesis leverages two key ideas to avoid the prohibitive costs of policy evaluations in an exponentially-sized tree. First, we estimate tree size by randomly sampling paths and measuring their lengths, drawing on an unbiased approximation due to Knuth (1975). Second, we query a strong solver at a user-defined depth rather than learning a policy across the whole tree, to focus our policy search on early decisions that offer the greatest potential for reducing tree size. We matched or improved performance over a strong baseline on three well-known SAT distributions (R3SAT, sgen, satfc).
On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis

Bilevel optimization reveals the inner structure of otherwise oblique optimization problems, such as hyperparameter tuning, neural architecture search, and meta-learning. A common goal in bilevel optimization is to minimize a hyper-objective that implicitly depends on the solution set of the lower-level function. Although this hyper-objective approach is widely used, its theoretical properties have not been thoroughly investigated in cases where \textit{the lower-level functions lack strong convexity}. In this work, we first provide hardness results to show that the goal of finding stationary points of the hyper-objective for nonconvex-convex bilevel optimization can be intractable for zero-respecting algorithms. Then we study a class of tractable nonconvex-nonconvex bilevel problems when the lower-level function satisfies the Polyak-{\L}ojasiewicz (PL) condition. We show a simple first-order algorithm can achieve better complexity bounds of $\tilde{\mathcal{O}}(\epsilon^{-2})$, $\tilde{\mathcal{O}}(\epsilon^{-4})$ and $\tilde{\mathcal{O}}(\epsilon^{-6})$ in the deterministic, partially stochastic, and fully stochastic setting respectively. The complexities in the first two cases are optimal up to logarithmic factors.
On the Circuit Diameter Conjecture for Counterexamples to the Hirsch Conjecture

Circuit diameters of polyhedra are a fundamental tool for studying the complexity of circuit augmentation schemes for linear programming and for finding lower bounds on combinatorial diameters. The main open problem in this area is the circuit diameter conjecture, the analogue of the Hirsch conjecture in the circuit setting. A natural question is whether the well-known counterexamples to the Hirsch conjecture carry over. Previously, Stephen and Yusun showed that the Klee-Walkup counterexample to the unbounded Hirsch conjecture does not transfer to the circuit setting. Our main contribution is to show that the original counterexamples for the other variants, for bounded polytopes and using monotone walks, also do not transfer. Our results rely on new observations on structural properties of these counterexamples. To resolve the bounded case, we exploit the geometry of certain $2$-faces of the polytopes underlying all known bounded Hirsch counterexamples in Santos' work. For Todd's monotone Hirsch counterexample, we provide two alternative approaches. The first one uses sign-compatible circuit walks, and the second one uses the observation that Todd's polytope is anti-blocking. Along the way, we enumerate all linear programs over the polytope and find four new orientations that contradict the monotone Hirsch conjecture, while the remaining $7107$ satisfy the bound.
Dimensional reduction of dynamical systems by machine learning: automatic generation of the optimum extensive variables and their time-evolution map

A framework is proposed to generate a phenomenological model that extracts the essence of a dynamical system (DS) with large degrees of freedom using machine learning. For a given microscopic DS, the optimum transformation to a small number of macroscopic variables, which is expected to be extensive, and the rule of time evolution that the variables obey are simultaneously identified. The utility of this method is demonstrated through its application to the nonequilibrium relaxation of the three-state Potts model.
Localization under consistent assumptions over dynamics

Accurate maps are a prerequisite for virtually all autonomous vehicle tasks. Most state-of-the-art maps assume a static world, and therefore dynamic objects are filtered out of the measurements. However, this division ignores movable but non-moving, i.e. semi-static, objects, which are usually recorded in the map and treated as static objects, violating the static world assumption, causing error in the localization. In this paper, we present a method for modeling moving and movable objects for matching the map and the measurements consistently. This reduces the error resulting from inconsistent categorization and treatment of non-static measurements. A semantic segmentation network is used to categorize the measurements into static and semi-static classes, and a background subtraction-based filtering method is used to remove dynamic measurements. Experimental comparison against a state-of-the-art baseline solution using real-world data from Oxford Radar RobotCar data set shows that consistent assumptions over dynamics increase localization accuracy.
NLP Reproducibility For All: Understanding Experiences of Beginners

As natural language processing (NLP) has recently seen an unprecedented level of excitement, and more people are eager to enter the field, it is unclear whether current research reproducibility efforts are sufficient for this group of beginners to apply the latest developments. To understand their needs, we conducted a study with 93 students in an introductory NLP course, where students reproduced the results of recent NLP papers. Surprisingly, we find that their programming skill and comprehension of research papers have a limited impact on their effort spent completing the exercise. Instead, we find accessibility efforts by research authors to be the key to success, including complete documentation, better coding practice, and easier access to data files. Going forward, we recommend that NLP researchers pay close attention to these simple aspects of open-sourcing their work, and use insights from beginners’ feedback to provide actionable ideas on how to better support them.
ChatGPT: A Study on its Utility for Ubiquitous Software Engineering Tasks

ChatGPT (Chat Generative Pre-trained Transformer) is a chatbot launched by OpenAI on November 30, 2022. OpenAI's GPT-3 family of large language models serve as the foundation for ChatGPT. ChatGPT is fine-tuned with both supervised and reinforcement learning techniques and has received widespread attention for its articulate responses across diverse domains of knowledge. In this study, we explore how ChatGPT can be used to help with common software engineering tasks. Many of the ubiquitous tasks covering the breadth of software engineering such as ambiguity resolution in software requirements, method name suggestion, test case prioritization, code review, log summarization can potentially be performed using ChatGPT. In this study, we explore fifteen common software engineering tasks using ChatGPT. We juxtapose and analyze ChatGPT's answers with the respective state of the art outputs (where available) and/or human expert ground truth. Our experiments suggest that for many tasks, ChatGPT does perform credibly and the response from it is detailed and often better than the human expert output or the state of the art output. However, for a few other tasks, ChatGPT in its present form provides incorrect answers and hence is not suited for such tasks.
A Multi-Scale Attentive Transformer for Multi-Instrument Symbolic Music Generation

Recently, multi-instrument music generation has become a hot topic. Different from single-instrument generation, multi-instrument generation needs to consider inter-track harmony besides intra-track coherence. This is usually achieved by composing note segments from different instruments into a signal sequence. This composition could be on different scales, such as note, bar, or track. Most existing work focuses on a particular scale, leading to a shortage in modeling music with diverse temporal and track dependencies. This paper proposes a multi-scale attentive Transformer model to improve the quality of multi-instrument generation. We first employ multiple Transformer decoders to learn multi-instrument representations of different scales and then design an attentive mechanism to fuse the multi-scale information. Experiments conducted on SOD and LMD datasets show that our model improves both quantitative and qualitative performance compared to models based on single-scale information. The source code and some generated samples can be found at https://github.com/HaRry-qaq/MSAT.
Detecting Errors in a Numerical Response via any Regression Model

Noise plagues many numerical datasets, where the recorded values in the data may fail to match the true underlying values due to reasons including: erroneous sensors, data entry/processing mistakes, or imperfect human estimates. We consider general regression settings with covariates and a potentially corrupted response whose observed values may contain errors. By accounting for various uncertainties, we introduced veracity scores that distinguish between genuine errors and natural data fluctuations, conditioned on the available covariate information in the dataset. We propose a simple yet efficient filtering procedure for eliminating potential errors, and establish theoretical guarantees for our method. We also contribute a new error detection benchmark involving 5 regression datasets with real-world numerical errors (for which the true values are also known). In this benchmark and additional simulation studies, our method identifies incorrect values with better precision/recall than other approaches.
Model-Based Simulation for Optimising Smart Reply

Smart Reply (SR) systems present a user with a set of replies, of which one can be selected in place of having to type out a response. To perform well at this task, a system should be able to effectively present the user with a diverse set of options, to maximise the chance that at least one of them conveys the user’s desired response. This is a significant challenge, due to the lack of datasets containing sets of responses to learn from. Resultantly, previous work has focused largely on post-hoc diversification, rather than explicitly learning to predict sets of responses. Motivated by this problem, we present a novel method SimSR, that employs model-based simulation to discover high-value response sets, through simulating possible user responses with a learned world model. Unlike previous approaches, this allows our method to directly optimise the end-goal of SR–maximising the relevance of at least one of the predicted replies. Empirically on two public datasets, when compared to SoTA baselines, our method achieves up to 21% and 18% improvement in ROUGE score and Self-ROUGE score respectively.