Miracle-dz/UniFAR-FTUs huggingface dataset:
Markov Decision Process Design: A Framework for Integrating Strategic and Operational Decisions

We consider the problem of optimally designing a system for repeated use under uncertainty. We develop a modeling framework that integrates design and operational phases, which are represented by a mixed-integer program and discounted-cost infinite-horizon Markov decision processes, respectively. We seek to simultaneously minimize the design costs and the subsequent expected operational costs. This problem setting arises naturally in several application areas, as we illustrate through examples. We derive a bilevel mixed-integer linear programming formulation for the problem and perform a computational study to demonstrate that realistic instances can be solved numerically.
Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks

We analyze the dynamics of finite width effects in wide but finite feature learning neural networks. Starting from a dynamical mean field theory description of infinite width deep neural network kernel and prediction dynamics, we provide a characterization of the $\mathcal{O}(1/\sqrt{\text{width}})$ fluctuations of the DMFT order parameters over random initializations of the network weights. Our results, while perturbative in width, unlike prior analyses, are non-perturbative in the strength of feature learning. In the lazy limit of network training, all kernels are random but static in time and the prediction variance has a universal form. However, in the rich, feature learning regime, the fluctuations of the kernels and predictions are dynamically coupled with a variance that can be computed self-consistently. In two layer networks, we show how feature learning can dynamically reduce the variance of the final tangent kernel and final network predictions. We also show how initialization variance can slow down online learning in wide but finite networks. In deeper networks, kernel variance can dramatically accumulate through subsequent layers at large feature learning strengths, but feature learning continues to improve the signal-to-noise ratio of the feature kernels. In discrete time, we demonstrate that large learning rate phenomena such as edge of stability effects can be well captured by infinite width dynamics and that initialization variance can decrease dynamically. For CNNs trained on CIFAR-10, we empirically find significant corrections to both the bias and variance of network dynamics due to finite width.
TinyDet: Accurate Small Object Detection in Lightweight Generic Detectors

Small object detection requires the detection head to scan a large number of positions on image feature maps, which is extremely hard for computation- and energy-efficient lightweight generic detectors. To accurately detect small objects with limited computation, we propose a two-stage lightweight detection framework with extremely low computation complexity, termed as TinyDet. It enables high-resolution feature maps for dense anchoring to better cover small objects, proposes a sparsely-connected convolution for computation reduction, enhances the early stage features in the backbone, and addresses the feature misalignment problem for accurate small object detection. On the COCO benchmark, our TinyDet-M achieves 30.3 AP and 13.5 AP^s with only 991 MFLOPs, which is the first detector that has an AP over 30 with less than 1 GFLOPs; besides, TinyDet-S and TinyDet-L achieve promising performance under different computation limitation.
Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4

Harnessing logical reasoning ability is a comprehensive natural language understanding endeavor. With the release of Generative Pretrained Transformer 4 (GPT-4), highlighted as"advanced"at reasoning tasks, we are eager to learn the GPT-4 performance on various logical reasoning tasks. This report analyses multiple logical reasoning datasets, with popular benchmarks like LogiQA and ReClor, and newly-released datasets like AR-LSAT. We test the multi-choice reading comprehension and natural language inference tasks with benchmarks requiring logical reasoning. We further construct a logical reasoning out-of-distribution dataset to investigate the robustness of ChatGPT and GPT-4. We also make a performance comparison between ChatGPT and GPT-4. Experiment results show that ChatGPT performs significantly better than the RoBERTa fine-tuning method on most logical reasoning benchmarks. With early access to the GPT-4 API we are able to conduct intense experiments on the GPT-4 model. The results show GPT-4 yields even higher performance on most logical reasoning datasets. Among benchmarks, ChatGPT and GPT-4 do relatively well on well-known datasets like LogiQA and ReClor. However, the performance drops significantly when handling newly released and out-of-distribution datasets. Logical reasoning remains challenging for ChatGPT and GPT-4, especially on out-of-distribution and natural language inference datasets. We release the prompt-style logical reasoning datasets as a benchmark suite and name it LogiEval.
F-RDW: Redirected Walking with Forecasting Future Position

In order to serve better VR experiences to users, existing predictive methods of Redirected Walking (RDW) exploit future information to reduce the number of reset occurrences. However, such methods often impose a precondition during deployment, either in the virtual environment's layout or the user's walking direction, which constrains its universal applications. To tackle this challenge, we propose a mechanism F-RDW that is twofold: (1) forecasts the future information of a user in the virtual space without any assumptions by using the conventional method, and (2) fuse this information while maneuvering existing RDW methods. The backbone of the first step is an LSTM-based model that ingests the user's spatial and eye-tracking data to predict the user's future position in the virtual space, and the following step feeds those predicted values into existing RDW methods (such as MPCRed, S2C, TAPF, and ARC) while respecting their internal mechanism in applicable ways. The results of our simulation test and user study demonstrate the significance of future information when using RDW in small physical spaces or complex environments. We prove that the proposed mechanism significantly reduces the number of resets and increases the traveled distance between resets, hence augmenting the redirection performance of all RDW methods explored in this work. Our project and dataset are available at https://github.com/YonseiCGnA-VR/F-RDW.
Do Most Students Need in-Person Lectures? A Study of a Large Statistics Class

Over 1100 students over four semesters were given the option of taking an introductory undergraduate statistics class either by in-person attendance in lectures or by taking exactly the same class (same instructor, recorded lectures, homework, blind grading, website, etc.) without the in-person lectures. Roughly, equal numbers of students chose each option. The online students did slightly better on computer-graded exams. The causal effect of choosing only online lectures was estimated by adjusting for measured confounders using four standard methods. The four nearly identical point estimates remained positive but were small and not statistically significant at the 95% confidence level. Sensitivity analysis indicated that unmeasured confounding was unlikely to be large but might plausibly reduce the point estimate to zero. No statistically significant differences were found in preliminary comparisons of effects on females/males, U.S./non-U.S. citizens, freshmen/non-freshman, and lower-scoring/higher-scoring math ACT groups.
Meiotic Recognition of Evolutionarily Diverged Homologs: Chromosomal Hybrid Sterility Revisited

Abstract Hybrid sterility (HS) is an early postzygotic reproductive isolation mechanism observed in all sexually reproducing species. Infertility of hybrids prevents gene flow between incipient species and leads to speciation. While Drosophila studies have focused almost exclusively on the genic control of HS, two other model species, Mus musculus and budding yeast, provided the first experimental evidence of hybrid sterility governed by the nongenic effects of DNA sequence divergence. Here, we propose that the nongenic effect of increasing DNA divergence between closely related species may impair mutual recognition of homologous chromosomes and disrupt their synapsis. Unsynapsed or mispaired homologs can induce early meiotic arrest, or their random segregation can cause aneuploidy of spermatids and sperm cells. Impaired recognition of homologs may thus act as a universal chromosomal checkpoint contributing to the complexity of genetic control of HS. Chromosomal HS controlled by the Prdm9 gene in mice and HS driven by the mismatch repair machinery in yeast are currently the most advanced examples of chromosomal homology search-based HS. More focus on the cellular and molecular phenotypes of meiosis will be needed to further validate the role of homolog recognition in hybrid sterility and speciation.
Palynology and mycology as biological evidence in a homicide case

Criminal cases are studied from several disciplines to link a suspect with a criminal act. In this case, a man was reported missing in a coastal area in Buenos Aires Province, Argentina. The victim's relatives pointed to a possible suspect, and the local police carried out the investigation. We contributed to this research by applying palynological and mycological techniques. Palynomorphs and fungal spores offer valuable trace evidence, as they can be easily transferred between objects and crime scenes due to their minute size and persist on them for a long time. The victim was found 25 days later, lying on sandy soil, which partially covered the body, 35 km from where the suspect was arrested. Comparative samples were collected from the crime scene and the suspect's home and belongings (clothes, footwear, and seized vehicle). The palynological associations obtained from the crime scene and the defendant's belongings were dominated by diatoms and acritarchs (Acantomorphitae), all elements of marine origin, and a high CFU number of Bipolaris cynodontis, which allowed the defendant's clothing to be related to the place of corpse discovery. Soil from the defendant's home had an entirely continental composition, and the fungal biota was characteristic of prairie areas which were different from those of the crime scene.
Passive collection of ticks in New Hampshire reveals species-specific patterns of distribution and activity

Ticks and tick-borne diseases are increasing in the United States, including New Hampshire (NH). We report on the findings of an ongoing free crowdsourcing program spanning four years within NH. The date of tick's submission was recorded along with species, sex, stage, location they were collected (translated into latitude and longitude), the activity the individual was doing when the tick was found, and host species. A total of 14,252 ticks belonging to subclass Acari, family Ixodidae and genera Ixodes, Dermacentor, Amblyomma, and Haemaphysalis was recorded from the period 2018–2021 throughout NH. A total of 2,787 Ixodes scapularis and 1,041 Dermacentor variabilis, were tested for the presence of Borrelia sp. (Spirochaetales: Spirochaetaceae), B. burgdorferi sensu lato, B. miyamotoi, B. mayonii, Babesia microti (Piroplasmida: Babesiidae), Anaplasma phagocytophilum (Rickettsiales: Anaplasmataceae), Francisella tularensis (Thiotrichales: Francisellaceae), and Rickettsia rickettsii (Rickettsiales: Rickettsiaceae) by PCR. For the I. scapularis ticks tested, the pathogen prevalence was 37% B. burgdorferi s.l. 1% B. miyamotoi, 6% A. phagocytophilum, and 5% Ba. microti. Only one D. variabilis resulted positive to F. tularensis. We created state-wide maps informing the differences of ticks as detailed by administrative divisions. Dermacentor variabilis peaked in June and I. scapularis peaked in May and October. The most reported activity by people with tick encounters was while walking/hiking, and the least was biking. Using the reported distribution of both species of ticks, we modeled their climate suitability in the target territory. In NH, I. scapularis and D. variabilis have distinct patterns of emergence, abundance, and distribution. Tick prevention is important especially during April–August when both tick species are abundant and active.
Structure of exhaust jets produced by magnetic reconnection localized in the out‐of‐plane direction

Three‐dimensional electromagnetic particle‐in‐cell simulations are used to investigate the structure of exhaust jets produced by magnetic reconnection localized in the out‐of‐plane direction. The localized reconnection is produced by periodically blocking the cross‐tail current density, a procedure that has effects analogous to those produced by the assumption of a region of anomalous resistivity in fluid treatments of reconnection. The width of the blocking region is varied between 4 and 24di, where di is the ion inertial length. After an initial displacement in the electron‐drift direction, the jet front undergoes a marked expansion in the ion‐drift direction, reaching a total cross‐tail width of 15–20di regardless of the initial width. The jet front breaks up into small‐scale finger structures of the order of 1–2di in width, which appears to be due to the action of the ballooning/interchange instability. Ahead of the front, the ion pressure Pixx is increased due to reflection of ions from the moving front and the penetration of high‐speed ions in the jet through the front. The ion temperature Tixx exhibits a minimum within the front, while the electron temperature is enhanced in the front. The properties of the reconnection‐generated fronts are compared and contrasted with those of interchange heads produced by a decreasing entropy profile.
Antimalarial Drug Resistance Profiling of Plasmodium falciparum Infections in India Using Next-Generation Sequencing

Background: Tracking the emergence and spread of antimalarial drug resistance has become critical to sustaining progress towards the control and eventual elimination of malaria in South Asia, especially India. Methods: An amplicon sequencing protocol was used for high-throughput molecular surveillance of antimalarial drug resistance in a total of 158 isolates at three sites in India: Chennai, Nadiad and Rourkela. Five genes of the Plasmodium falciparum implicated in antimalarial resistance were investigated here; Pfcrt for chloroquine resistance, Pfdhfr for pyrimethamine resistance, Pfdhps for sulfadoxine resistance, Pfk13 for artemisinin resistance and Pfmdr1 for resistance to multiple antimalarials. Results: Mutations in the propeller domain of PfK13 were observed in two samples only, however these mutations are not validated for artemisinin resistance. A high proportion of parasites from the P. falciparum dominant site Rourkela showed wild-type Pfcrt and Pfdhfr haplotypes, while mutant Pfcrt and Pfdhfr haplotypes were fixed at the P. vivax dominant sites Chennai and Nadiad. The wild-type PfDHPS haplotype was predominant across all study sites. Finally, we observed the largest proportion of suspected multi-clonal infections at Rourkela, which has the highest transmission of P. falciparum among our study sites. Conclusion: This is the first simultaneous high-throughput next generation sequencing of five complete P. falciparum genes from infected patients in India.
Tensor Products of Operator Spaces II

Abstract Together with Vern Paulsen we were able to show that the elementary theory of tensor norms of Banach spaces carries over to operator spaces. We suggested that the Grothendieck tensor norm program, which was of course enormously important in the development of Banach space theory, be carried out for operator spaces. Some of this has been done by the authors mentioned above, and by Effros and Ruan. We give alternative developments of some of this work, and otherwise continue the tensor norm program.
What do riders say and where? The detection and analysis of eyewitness transit tweets

Abstract Information shared on social media by transit system customers is often candid, localized, and includes in the moment information about emerging events or issues. Twitter provides an unfiltered and timestamped feed of information that can be aggregated to generate valuable insights. Our research aims to identify passenger-related transit incidents from a public Twitter feed. Detecting these incidents in real time enables transit agencies to immediately respond to them by dispatching security, safety, or maintenance crews or by rapidly replying to requests made to the agency that are urgent in nature. We leverage natural language processing to extract latent information from identified eyewitness tweets about transit, focusing on location details, topic classification, and sentiment analysis. We outline an approach to developing a useful corpus of transit-focused tweets, detecting in the moment events, classifying these tweets into topics, and detecting locations where possible. We then demonstrate the approach through an application to Calgary Transit in Calgary, Canada. The results demonstrate that key incidents can be identified and prioritized for an agency.
Typha fiber reinforced polyester composites: tensile properties and statistical analysis

Abstract This article presents the study of the tensile characteristics of the Typha fiber reinforced polyester composites. Composites have been developed by hand lay-up technique with different Typha fibers treated conditions (sea water, alkali and combined treatment), different volume fraction and reinforcement origin (Leaf or Stem fibers) and structure (mixture of fibers or nonwoven structure). The experimental results indicated an improvement in tensile strength with increasing fiber content. Thus, a maximum tensile strength was observed and was equal to 24.58 and 25.07 MPa, respectively, for a fiber content of 12.6% of the leaf fibers and 10.3% of the typha stalk fibers. Mercerization has successfully modified the structure of fibers and improved the performance of Typha fiber composites by promoting better fiber to resin bonding. Statistical analysis shows that the optimum solution was obtained by using a bulk structure having a rate of 12.6% for the extracted fibers from the chemically treated stems.
Robotic gaze and human views: A systematic exploration of robotic gaze aversion and its effects on human behaviors and attitudes

Similar to human–human interaction (HHI), gaze is an important modality in conversational human–robot interaction (HRI) settings. Previously, human-inspired gaze parameters have been used to implement gaze behavior for humanoid robots in conversational settings and improve user experience (UX). Other robotic gaze implementations disregard social aspects of gaze behavior and pursue a technical goal (e.g., face tracking). However, it is unclear how deviating from human-inspired gaze parameters affects the UX. In this study, we use eye-tracking, interaction duration, and self-reported attitudinal measures to study the impact of non-human inspired gaze timings on the UX of the participants in a conversational setting. We show the results for systematically varying the gaze aversion ratio (GAR) of a humanoid robot over a broad parameter range from almost always gazing at the human conversation partner to almost always averting the gaze. The main results reveal that on a behavioral level, a low GAR leads to shorter interaction durations and that human participants change their GAR to mimic the robot. However, they do not copy the robotic gaze behavior strictly. Additionally, in the lowest gaze aversion setting, participants do not gaze back as much as expected, which indicates a user aversion to the robot gaze behavior. However, participants do not report different attitudes toward the robot for different GARs during the interaction. In summary, the urge of humans in conversational settings with a humanoid robot to adapt to the perceived GAR is stronger than the urge of intimacy regulation through gaze aversion, and a high mutual gaze is not always a sign of high comfort, as suggested earlier. This result can be used as a justification to deviate from human-inspired gaze parameters when necessary for specific robot behavior implementations.
A highly sensitive colorimetric approach based on tris (bipyridine) Ruthenium (II/III) mediator for the enzymatic detection of phenylalanine

The accurate monitoring of phenylalanine concentration plays a prominent role in the treatment of phenylketonuria (PKU). In this study, we present an enzymatic assay based on Phenylalanine Dehydrogenase/NAD+ and tris (bipyridine) Ruthenium (II/III) as a colorimetric mediator for the detection of Phenylalanine concentration. The amount of amino acid was quantitatively recognized by optical absorption measurements at 452 nm through the conversion of Ru (byp)3 3+ to Ru (byp)3 2+, which is induced by the neoformed NADH. A detection limit of 0.33 µM, a limit of quantification of 1.01 µM, and a sensitivity of 36.6 a.u nM−1 were obtained. The proposed method was successfully tested using biological specimens from patients affected by hyperphenylalaninemia. The proposed enzymatic assay showed a high selectivity, making it a promising alternative for the development of versatile assays for the detection of phenylalanine in diluted serums.
Long-term effects of lumacaftor/ivacaftor on paranasal sinus abnormalities in children with cystic fibrosis detected with magnetic resonance imaging

Introduction: Chronic rhinosinusitis (CRS) usually presents with nasal congestion, rhinorrhea and anosmia impacts quality of life in cystic fibrosis (CF). Especially mucopyoceles pathognomonic for CRS in CF may cause complications such as spread of infection. Previous studies using magnetic resonance imaging (MRI) demonstrated early onset and progression of CRS from infancy to school age in patients with CF, and mid-term improvements of CRS in preschool and school-age children with CF treated with lumacaftor/ivacaftor for at least 2 months. However, long-term data on treatment effects on paranasal sinus abnomalities in preschool and school-age children with CF are lacking. Methods: 39 children with CF homozygous for F508del (mean age at baseline MRI 5.9 ± 3.0 years, range 1-12 years) underwent MRI before (MRI1) and about 7 months after starting lumacaftor/ivacaftor and then annually (median 3 follow-up MRI, range 1–4) (MRI2-4). MRI were evaluated using the previously evaluated CRS-MRI score with excellent inter-reader agreement. For intraindividual analysis ANOVA mixed-effects analysis including Geisser-Greenhouse correction and Fisher’s exact test, and for interindividual group analysis Mann-Whitney test were used. Results: The CRS-MRI sum score at baseline was similar in children starting lumacaftor/ivacaftor in school age and children starting therapy at preschool age (34.6 ± 5.2 vs.32.9 ± 7.8, p = 0.847). Mucopyoceles were the dominant abnormality in both, especially in maxillary sinus (65% and 55%, respectively). In children starting therapy in school age the CRS-MRI sum score decreased longitudinally from MRI1 to MRI2 (−2.1 ± 3.5, p < 0.05), MRI3 (−3.0 ± 3.7, p < 0.01) and MRI4 (−3.6 ± 4.7, p < 0.01), mainly due to a decrease in the mucopyoceles subscore (−1.0 ± 1.5, p = 0.059; −1.2 ± 2.0, p < 0.05; −1.6 ± 1.8, p < 0.01; and −2.6 ± 2.8, p = 0.417, respectively). In children starting lumacaftor/ivacaftor in preschool age, the CRS-MRI sum score remained stable under therapy over all three follow-up MRI (0.6 ± 3.3, p = 0.520; 2.4 ± 7.6, p = 0.994; 2.1 ± 10.5, p > 0.999 and −0.5 ± 0.5, p = 0.740; respectively). Conclusion: Longitudinal paranasal sinus MRI shows improvements in paranasal sinus abnormalities in children with CF starting lumacaftor/ivacaftor therapy at school age. Further, MRI detects a prevention of an increase in paranasal sinus abnormalities in children with CF starting lumacaftor/ivacaftor therapy at preschool age. Our data support the role of MRI for comprehensive non-invasive therapy and disease monitoring of paranasal sinus abnormalities in children with CF.