Miracle-dz/UniFAR-FTUs huggingface dataset:
Confluence for untyped proof nets via parallel cut elimination

A rewriting system is confluent (or Church–Rosser) if, whenever two distinct sequences of reduction steps can be applied to the same term, there is a term that is reachable from both results, by applying (possibly empty) sequences of additional reduction steps. Confluence simplifies the study of the convergence of programs (represented by the terms in the rewriting system) because it ensures that the ordering in which the reductions are chosen does not make a di ff erence to the result: of any two possible redexes in a term, the reduction of one instead of the other will never preclude the fact of eventually reaching the same result (if any). In some sense, confluence assures modularity of computation. The Curry-Howard correspondence [2, 6] relates proofs to programs, and cut elimination (a proof rewriting technique to show the coherence of a proof system) to program execution. Thus, confluence of cut elimination procedure assumed a computational value. It is well-known that intuitionistic sequent calculus LJ enjoys confluence while classical sequent calculus LK does not. In 1987 Girard [5] introduced linear logic ( LL ): a refinement of LJ and LK allowing a finer analysis of the use of hypotheses / resources in proofs / programs via the introduction of two dual modalities, the exponentials ! and ? : in LL , a formula of the form ! A (representing a resource available at will in cut elimination) can be proved only if each hypotheses is of the form ? B . Girard also gave a graphical syntax to represent LL proofs
Research and Development in Forensic Science for Criminal Justice Purposes

The quantification of human decomposition is a difficult issue within forensic anthropology. However, many attempts have been made to both standardize and score decomposition with the intent of estimating a post-mortem interval (PMI) with known error ranges (for example, Megyesi et al., 2005). Yet, the Megyesi et al. method (2005), aka the total body score (TBS), has several limitations. First, many regions of the body are grouped together and scored as one unit, which diminishes the fidelity in recording the often asymmetric and highly variable decomposition processes. Second, the method does not address how to handle scavenged remains, which greatly accelerates decomposition processes. Third, the method was developed from photographs with a combination of known and estimated PMIs, thus introducing unnecessary statistical noise. The proposed research will enhance the quantification of variability in decomposition by breaking down the body into 16 discrete regions, with limb antimeres scored separately. Ten individuals will be placed in the same environment at the same time at the Anthropological Research Facility (ARF) at the University of Tennessee. Separate trials will be conducted in different seasons to capture the variability of decomposition in different seasons. A modified trait list will be developed to record the presence or absence of discrete traits in the field that are later tied to a quasi-continuous weighted scoring system to minimize bias while collecting data. Two independent observers will score the same subjects each day. The associated scores will be input into a digitized homunculus into a geographic information system (GIS), ArcGIS (ESRI, 2011), each day for each individual in the study sample. At the end of each trial, the scores for each individual will be summed for each region of the body to create a heat map that will show individual regional variability in decomposition process. Further, the scores for each of the subjects in each trail will be summed upon collection and a heat map generated to show the overarching trends in decomposition. Hot spot analysis will then be performed on the combined heat maps to identify statistically significant hot (first to decompose) and cold (last to decompose) spots. Linear regression models will be created to estimate PMI. The created models will then be validated on various holdout specimens at the ARF. The proposed research will provide more
AutoSurf: Automated Expert-Guided Meshing With Graph Neural Networks and Conformal Predictions

Computational Fluid Dynamics (CFD) is widely used in different engineering fields, but accurate simulations are dependent upon proper meshing of the simulation domain. While highly refined meshes may ensure precision, they come with high computational costs. Similarly, adaptive remeshing techniques require multiple simulations and come at a great computational cost. This means that the meshing process is reliant upon expert knowledge and years of experience. Automating mesh generation can save significant time and effort and lead to a faster and more efficient design process. This paper presents a machine learning-based scheme that utilizes Graph Neural Networks (GNN) and expert guidance to automatically generate CFD meshes for aircraft models. In this work, we introduce a new 3D segmentation algorithm that outperforms two state-of-the-art models, Point-Net++ and PointMLP, for surface classification. We also present a novel approach to project predictions from 3D mesh segmentation models to CAD surfaces using the conformal predictions method, which provides marginal statistical guarantees and robust uncertainty quantification and handling. We demonstrate that the addition of conformal predictions effectively enables the model to avoid under-refinement, hence failure, in CFD meshing even for weak and less accurate models. Finally, we demonstrate the efficacy of our approach through a real-world case study that demonstrates that our automatically generated mesh is comparable in quality to expert-generated meshes, and enables the solver to converge and produce accurate results. The code and data for this project is made publicly available at https://github.com/ahnobari/AutoSurf.
Network Design in MRO Inventory for Oil & Gas Company

To mitigate operational disruptions, oil and gas companies maintain high levels of Maintenance, Repair, and Operations (MRO) inventory. However, our sponsor company was found to have twice the nonmoving inventory value compared to its competitors, prompting an interest to reduce inventory holding costs — the cost associated with the storage of inventory, such as cost of capital, annual warehouse fees, annual taxes, and annual warehouse costs. This study aims to reduce such costs by segmenting 19,153 MRO SKUs based on their demand characteristics and building a Mixed Linear Integer Programming (MILP) model to redesign the network of warehouses and plants. By eliminating the 1:1 relationship between warehouses and plants, the warehouses can serve more than one plant and the sponsor can avoid individual inventory management for each plant. Through our MILP model, we investigated different levels of consolidation though scenario analysis. In the most conservative scenario without inventory systems integration, the new network design resulted in a 12% reduction in warehouse and transportation costs and a 22% reduction in safety stock holding costs. While full inventory system and legal entities integration led to 27% savings in both. Capstone Advisor: James Blayney Rice Title: Deputy Director, Center for Transportation and Logistics
From space-time landslide susceptibility to landslide risk forecast

: The literature on landslide susceptibility is rich with examples that span a large number of 8 topics. However, the component that pertains to the extension of the susceptibility framework toward 9 space-time modeling is largely unexplored. This statement is even more valid when looking at the 10 landslide risk context, where hardly any scientific contribution investigates risk dynamics in space and 11 time. This manuscript proposes a modeling protocol where a dynamic landslide susceptibility is obtained 12 via a binomial Generalized Additive Model whose inventories span nine years (from 2013 to 2021). To 13 perform the analyses, the data cube is organized with a mapping unit made of slope units (26,333) 14 repeated over an annual temporal unit (for a total of 236,997). This phase already features a number of 15 interesting modeling experiments that have hardly appeared in the landslide literature (e.g., variable 16 interaction plots). However, the main innovative effort is in the subsequent phase of the protocol we 17 propose, for we used climate projections of the main trigger (rainfall) to obtain future estimates of yearly 18 susceptibility patterns. These are also combined with the projection of urban settlements and associated 19 populations to obtain a dynamic risk model (under the assumption that vulnerability = 1). Overall, this is 20 a unique example of such a modeling routine and a potential standard to be followed for administrations 21 to make informed decisions on future urban development.
Scale-up of $\Lambda_3$: Massive gravity with a higher strong interaction scale

Pure massive gravity is strongly coupled at a certain low scale, known as Lambda_3. I show that the theory can be embedded into another one, with new light degrees of freedom, to increase the strong scale to a significantly larger value. Certain universal aspects of the proposed mechanism are discussed, notably that the coupling of the longitudinal mode to a stress-tensor is suppressed, thus making the linear theory consistent with the fifth-force exclusion. An example of the embedding theory studied in detail is 5D AdS massive gravity, with a large cosmological constant. In this example the 4D strong scale can be increased by 19 orders of magnitude. Holographic duality then suggests that the strong scale of the 4D massive gravity can be increased by coupling it to a 4D non-local CFT, endowed with a UV cutoff; however, the 5D classical gravity picture appears to be more tractable.
When Probabilities Are Not Enough — A Framework for Causal Explanations of Student Success Models

Student success and drop-out predictions have gained increased attention in recent years, connected to the hope that by identifying struggling students, it is possible to intervene and provide early help and design programs based on patterns discovered by the models. Though by now many models exist achieving remarkable accuracy-values, models outputting simple probabilities are not enough to achieve these ambitious goals. In this paper, we argue that they can be a first exploratory step of a pipeline aiming to be capable of reaching the mentioned goals. By using Explainable Artificial Intelligence (XAI) methods, such as SHAP and LIME, we can understand what features matter for the model and make the assumption that features important for successful models are also important in real life. By then additionally connecting this with an analysis of counterfactuals and a theory-driven causal analysis, we can begin to reasonably understand not just if a student will struggle but why and provide fitting help. We evaluate the pipeline on an artificial dataset to show that it can, indeed, recover complex causal mechanisms and on a real-life dataset showing the method’s applicability. We further argue that collaborations with social scientists are mutually beneficial in this area but also discuss the potential negative effects of personal intervention systems and call for careful designs.
SegaNet: An Advanced IoT Cloud Gateway for Performant and Priority-Oriented Message Delivery

With the tremendous growth of IoT, the role of IoT cloud gateways in facilitating communication between IoT devices and the cloud has become more important than ever before. Most previous studies have focused on developing interoperability between IoT and cloud to accommodate various radio protocols. However, they have often neglected the performance aspect of the IoT cloud gateway, leaving users with limited options: either purchasing multiple gateways or connecting only a small number of IoT devices. Through our comprehensive measurements and analysis, we identified five key issues in IoT cloud gateways related to high latency, CPU bottlenecks, inefficient network stacks on ARM, substantial encryption overhead, and the lack of priority support. To address these issues, we propose a new IoT cloud gateway - SegaNet. We carefully design with 1) multiple agents management, 2) efficient TLS encryption, and 3) priority-oriented message delivery. Our prototype evaluation shows up to 16.7 × lower latency and 4.5 × lower CPU consumption than gateways of the existing IoT-cloud ecosystem.
Investigation of a novel numerical scheme for high-pressure supercritical ﬂuids turbulence

High-pressure supercritical turbulence simulations are strongly susceptible to numerical instabilities. The multi-scale nature of the ﬂow, in conjunction with the nonlinear thermodynamics and the strong density gradients across the pseudo-boiling line can trig-ger spurious pressure oscillations and unbounded ampliﬁcation of aliasing errors. A wide variety of regularization approaches have been traditionally utilized by the community, including upwind-biased schemes, artiﬁcial dissipation, and/or high-order ﬁltering, where stability is achieved at the expense of suppressing part of the turbulent energy spectrum. In this work, a novel numerical scheme based on the paradigm of physics-compatible discretizations is investigated. In particular, the proposed method discretely enforces kinetic-energy conservation (by convection) as well as preservation of pressure equilibrium; the former is achieved using proper splitting of the convective terms, whereas the latter is obtained by directly evolving an equation for pressure. The simultaneous enforcement of these two properties can lead to stable and physically consistent scale-resolving simulations of supercritical turbulence without the need for any form of artiﬁcial stabilization. The novel method is preliminarly assessed on two benchmark cases, with numerical results supporting the theoretical ﬁndings.
Optimisation of a compact cold-atoms interferometer for gravimetry

The work presented in this thesis focusses on the development of a transportable atom-interferometry experiment and a compact fibre laser system towards precision measurements of gravitational acceleration. Interference fringes are shown with clouds of cold 87Rb atoms using co-propagating laser beams to drive stimulated Raman transitions. This is demonstrated both inside and outside of laboratory environments for which an integrated and transportable experiment is constructed. Further improvements are presented that enable the generation of clouds containing 1.7 · 108 atoms at a rate of 2.5 Hz and having a temperature of (7 ± 1) µK. This is largely due to the development of a compact laser system based on all-fibre coupled components. It is demonstrated that the laser system designed here can achieve fast frequency sweeps over 1.8 GHz within 2 ms, making it widely applicable in compact atom-interferometry experiments with rubidium atoms. This is shown by creating a Mach–Zehnder type interferometer with counter-propagating Raman beams, thus enabling measurements of gravitational acceleration. Since the laser system uses only two lasers and one fibre amplifier, a significant reduction in size is achieved, as well as a decrease in the total power consumption of the overall experiment by a third to (162 ± 7) W.
Analysis of Vowel Articulation and Perception Using Acoustic Parameters

Speech is the most basic and reliable form of communication. In signal processing, speech signal carrying the message is represented in the form of an acoustic waveform. Having an insight into acoustics of speech contributes to production and perception robustness. One of the best means to study this is by exploiting the acoustic correlates of speech sounds. In this thesis, work mainly centers on introduction of acoustic correlates of vowels and their application in speech production, perception and analysis. Study of acoustic correlates of speech sounds brings out a rigorous introduction to acoustics of speech. Vowels are sonorants and the most audible sounds of speech. All the three works in this thesis mainly focus on studies related to vowel sounds and use formants as the basic spectral cue. Minor work has been extended to nasals in the first study. Formant bandwidth, one of the crucial acoustic correlates, was extracted for oral and nasal tracts using vowel and nasal articulations respectively. The exploitation of acoustic correlates of vowels against speech performance among different categories of gender was conducted in the second study. Here, the computed formants are utilized to construct vowel space area used for the comparison. Also, the role of source-filter was analyzed in perception of vowels. This study too incorporated formants in the vocal tract response of the LP method. Studying the acoustic correlates correlates of speech sounds would greatly enhance the knowledge on acoustical aspects of speech useful in Acoustic Phonetics, Auditory Phonetics, Speech Analysis, Speech Synthesis, Speech Signal Processing, to understand and evaluate speech signal.
High catalyst performance of two-dimensional Ti3B3N3S6 for hydrogen evolution reaction

Single-atom catalysts (SACs) for the hydrogen evolution reaction (HER) is an efficient electrochemical pathway to produce the green production. However, the development of HER process is hampered by the lack of high-performance electrocatalysts. In this work, we proposed a new π-d conjugated structure of the Ti3B3N3S6 monolayer as the single-atom catalysts for the HER process by using the density functional theory (DFT) calculations. The calculated results show that the Ti atom is active site of the Ti3B3N3S6 monolayer with the high catalytic activity (ΔGH = –0.14 eV) for HER. The electronic properties of the Ti3B3N3S6 monolayer were explored by the electron localization function (ELF), Bader charge analysis and the polarized density of states (PDOS) density analysis. The Ti3B3N3S6 monolayer can promote the electronic transfer during the HER process, which indicates taht the Ti3B3N3S6 monolayer can is considered to investigate the catalytic activity for HER. The Gibbs free energy of H atoms adsorption on the Ti3B3N3S6 monolayer is –0.14 eV. Furthermore, the origin of high catalytic activity for the Ti3B3N3S6 monolayer was explored by the analysis the PDOS of the H adsorption on the Ti3B3N3S6 monolayer. Therefore, our work propose a new and high catalytic single-atom catalyst for the HER.
sRDMA: A General and Low-Overhead Scheduler for RDMA

Remote Direct Memory Access (RDMA) has been widely deployed in data centers to improve application performance. However, the characteristic of RDMA to deliver messages in order cannot meet the emerging requirements of applications for scheduling messages within an RDMA connection, making RDMA unable to be fully utilized. Some works try to schedule the data to be transferred in specific applications before delivering to RDMA, or distribute messages to different connections. However, these approaches tightly couple scheduling logic with application logic and may result in high scheduling overhead. In this paper, we propose sRDMA, a general and low-overhead scheduler working in user-space RDMA driver. sRDMA allows the application to express the expected transfer order to RDMA hardware via work requests (WRs). With priority information in WRs, sRDMA slices and schedules WRs to achieve desired order of message transfer and reduce blocking impact of large messages in the same RDMA connection. Our experiments show that sRDMA can improve the performance of applications, e.g., TensorFlow, by up to , and sRDMA has negligible overhead in terms of CPU and flow throughput.
Making connections: Compartmentalization in pre -calculus students' understanding of functions

Students develop knowledge constructs that they build into concepts through their experiences. Students demonstrate compartmentalization when they understand a construct or concept within one representation, but not another, or when they do not connect mathematically related ideas. For instance, a student may understand f(x) to mean plug x into the function within a symbolic representation, but the same student may understand f(x) to mean f times x within a tabular representation. A student with these understandings has a compartmentalized understanding of function notation. A two-month study was conducted with a class of pre-calculus students enrolled in a parochial high school. The class was observed and a subset of students (n = 7) were given a series of tasks in an interview setting in order to determine their understanding of functions and in particular periodicity within the three representations: equations, graphs, and tables. The researcher studied compartmentalization in the students' understanding. Three of the seven students showed compartmentalization. All three had a compartmentalized understanding of function notation within the tabular representation. In addition, two had compartmentalization within representations in their understanding of periodicity. Students with compartmentalization in their understanding, had the greatest difficulty in solving the interview tasks. Furthermore, those students who could not translate between representations had an automatic compartmentalization in their understanding and lacked flexibility in problem-solving. All seven of the students preferred the symbolic representation. The students used this representation overwhelmingly in their classwork and homework. Six of the seven students attempted to find equations for the functions in the interview tasks before trying any other solution strategy. However, only one student was able to solve the interview tasks in this representation. Some interesting conceptions of periodicity emerged in the students' understandings. The students used symmetry, familiarity, and continuity to determine whether a function was periodic. The students did not work from a conventional definition of period. Instead, they constructed their own definition of periodicity by generalizing sinusoids and other familiar functions. The generalizations that the students made were often inconsistent with the conventional definitions. These unconventional understandings imply that they need experiences with more than just sinusoids.
Feature‐Preserving Offset Mesh Generation from Topology‐Adapted Octrees

We introduce a reliable method to generate offset meshes from input triangle meshes or triangle soups. Our method proceeds in two steps. The first step performs a Dual Contouring method on the offset surface, operating on an adaptive octree that is refined in areas where the offset topology is complex. Our approach substantially reduces memory consumption and runtime compared to isosurfacing methods operating on uniform grids. The second step improves the output Dual Contouring mesh with an offset‐aware remeshing algorithm to reduce the normal deviation between the mesh facets and the exact offset. This remeshing process reconstructs concave sharp features and approximates smooth shapes in convex areas up to a user‐defined precision. We show the effectiveness and versatility of our method by applying it to a wide range of input meshes. We also benchmark our method on the Thingi10k dataset: watertight and topologically 2‐manifold offset meshes are obtained for 100% of the cases.