Miracle-dz/UniFAR-FTUs huggingface dataset:
The association between tolerance for ambiguity and fear of negative evaluation: A study of engineering technology capstone courses

Dubikovsky, Sergey I. Ph.D., Purdue University, December 2016. The Association between Tolerance for Ambiguity and Fear of Negative Evaluation: A Study of Engineering Technology Capstone Courses. Major Professor: Matthew Ohland. For many students in engineering and engineering technology programs in the US, senior capstone design courses are mandatory for graduation. Also, the same courses are required by accreditation bodies such as ABET, Inc. and others. The students must form a team, define a problem, and find a feasible technical solution to address this problem. In other words, students must demonstrate the knowledge and skills acquired during their studies at the college or university level. In reality, however, there are many additional non-technical, so-called “soft” skills, present in such projects. The most prominent example is that the majority of the listed steps do not have a single “correct” resolution. Instead, there is an array of solutions, many of which could be successfully used to achieve the final result. This situation creates roadblocks and anxiety during the projects.
Integral Global Optimality Conditions and an Algorithm for Multiobjective Problems

Abstract In this work, we propose integral global optimality conditions for multiobjective problems not necessarily differentiable. The integral characterization, already known for single objective problems, are extended to multiobjective problems by weighted sum and Chebyshev weighted scalarizations. Using this last scalarization, we propose an algorithm for obtaining an approximation of the weak Pareto front whose effectiveness is illustrated by solving a collection of multiobjective test problems.
A Theory of Natural Intelligence

Introduction: In contrast to current AI technology, natural intelligence -- the kind of autonomous intelligence that is realized in the brains of animals and humans to attain in their natural environment goals defined by a repertoire of innate behavioral schemata -- is far superior in terms of learning speed, generalization capabilities, autonomy and creativity. How are these strengths, by what means are ideas and imagination produced in natural neural networks? Methods: Reviewing the literature, we put forward the argument that both our natural environment and the brain are of low complexity, that is, require for their generation very little information and are consequently both highly structured. We further argue that the structures of brain and natural environment are closely related. Results: We propose that the structural regularity of the brain takes the form of net fragments (self-organized network patterns) and that these serve as the powerful inductive bias that enables the brain to learn quickly, generalize from few examples and bridge the gap between abstractly defined general goals and concrete situations. Conclusions: Our results have important bearings on open problems in artificial neural network research.
Formation of primordial black holes after Starobinsky inflation

In this paper, we adapted the Appleby–Battye–Starobinsky model of [Formula: see text] gravity towards describing double cosmological inflation and formation of primordial black holes with masses up to [Formula: see text] g in the single-field model. We found that it is possible to get an enhancement of the power spectrum of scalar curvature perturbations to the level beyond the Hawking (black hole evaporation) limit of [Formula: see text] g, so that the primordial black holes resulting from gravitational collapse of those large primordial perturbations can survive in the present universe and form part of cold dark matter. Our results agree with the current measurements of cosmic microwave background radiation within [Formula: see text] but require fine-tuning of the parameters.
Qubits from the classical collision entropy

An orthodox formulation of quantum mechanics relies on a set of postulates in Hilbert space supplemented with rules to connect it with classical mechanics such as quantisation techniques, correspondence principle, etc. Here we deduce a qubit and its dynamics straightforwardly from a discrete deterministic dynamics and conservation of the classical collision entropy. No Hilbert space is required although it can be inferred from this approach if necessary.
Star-specific Key-homomorphic PRFs from Linear Regression and Extremal Set Theory

We introduce a novel method to derandomize the learning with errors (LWE) problem by generating deterministic yet sufficiently independent LWE instances, that are constructed via special linear regression models. We also introduce star-specific key-homomorphic (SSKH) pseudorandom functions (PRFs), which are defined by the respective sets of parties that construct them. We use our derandomized variant of LWE to construct a SSKH PRF family. The sets of parties constructing SSKH PRFs are arranged as star graphs with possibly shared vertices, i.e., some pair of sets have non-empty intersections. We reduce the security of our SSKH PRF family to the hardness of LWE. To establish the maximum number of SSKH PRFs that can be constructed — by a set of parties — in the presence of passive/active and external/internal adversaries, we prove several bounds on the size of maximally cover-free at most t-intersecting k-uniform family of sets H, where the three properties are defined as: (i) k-uniform: ∀A ∈ H : |A| = k, (ii) at most t-intersecting: ∀A,B ∈ H, B 6= A : |A ∩B| ≤ t, (iii) maximally cover-free: ∀A ∈ H : A 6⊆ ⋃
Measuring Context-Word Biases in Lexical Semantic Datasets

State-of-the-art pretrained contextualized models (PCM) eg. BERT use tasks such as WiC and WSD to evaluate their word-in-context representations. This inherently assumes that performance in these tasks reflect how well a model represents the coupled word and context semantics. We question this assumption by presenting the first quantitative analysis on the context-word interaction being tested in major contextual lexical semantic tasks. To achieve this, we run probing baselines on masked input, and propose measures to calculate and visualize the degree of context or word biases in existing datasets. The analysis was performed on both models and humans. Our findings demonstrate that models are usually not being tested for word-in-context semantics in the same way as humans are in these tasks, which helps us better understand the model-human gap. Specifically, to PCMs, most existing datasets fall into the extreme ends (the retrieval-based tasks exhibit strong target word bias while WiC-style tasks and WSD show strong context bias); In comparison, humans are less biased and achieve much better performance when both word and context are available than with masked input. We recommend our framework for understanding and controlling these biases for model interpretation and future task design.
TNS: Terrain Traversability Mapping and Navigation System for Autonomous Excavators

We present a terrain traversability mapping and navigation system (TNS) for autonomous excavator applications in an unstructured environment. We use an efficient approach to extract terrain features from RGB images and 3D point clouds and incorporate them into a global map for planning and navigation. Our system can adapt to changing environments and update the terrain information in real-time. Moreover, we present a novel dataset, the Complex Worksite Terrain (CWT) dataset, which consists of RGB images from construction sites with seven categories based on navigability. Our novel algorithms improve the mapping accuracy over previous SOTA methods by 4.17-30.48% and reduce MSE on the traversability map by 13.8-71.4%. We have combined our mapping approach with planning and control modules in an autonomous excavator navigation system and observe 49.3% improvement in the overall success rate. Based on TNS, we demonstrate the first autonomous excavator that can navigate through unstructured environments consisting of deep pits, steep hills, rock piles, and other complex terrain features.
Scalable compression for machine and human vision tasks via multi-branch shared module

Abstract. The existing image compression methods are mainly aimed at human perception tasks instead of machine vision tasks. Rich features learned by the shallow and deep layers of pre-trained visual geometry group (VGG)-net can serve human perception and machine vision tasks, respectively. To improve the machine analysis capabilities of the human-targeted compression method, we propose a scalable image compression framework at low bit-rates. Specifically, the scalable compression framework is composed of a base layer (BL) and an enhancement layer (EL), which utilize the correlation between the above features to perform machine analysis and human perception tasks, respectively. For effectively utilizing the above two types of features, a multi-branch shared module that utilizes the complementarity of multi-branch convolution kernels to retain compact information for the above features to support BL and EL tasks is proposed. In addition, for further improving the accuracy of machine analysis, the machine-vision importance map is introduced in BL; it adaptively utilizes the spatial and channel information from the deepest layer of VGG-net to guide local bit allocation. When the bit-rate is limited to 0.2 bpp, the average recognition accuracy (Top-1) of the BL of proposed method is 5.2%, 13.4%, 6.0%, and 7.4% higher than that of BPG, Webp, Mentzer, and NIC, respectively, on the ILSVRC2012 verification dataset. Meanwhile, the EL provides good visual experience.
Interface instabilities in Faraday waves of two-layer liquids with free surface

Abstract For a fluid system with an interface between two-layer liquids and a free surface, there exist two series of natural frequencies. Under vertical excitations, the interface and the free surface can be excited separately or simultaneously. Laboratory experiments were conducted to excite the first- and second-mode Faraday waves on the free surface, which forced the in-phase motion of the interface. The experimental data were used to validate the extended numerical model NEWTANK. The secondary surface instability in the form of period tripling breaking occurred on the free surface when second-mode surface resonant response was large. Further numerical investigations were conducted to study the resonance of interface and in addition to the primary Faraday instability, the secondary interface instability occurred due to Rayleigh–Taylor (RT) instability. The analysis revealed that this was due to the contribution from the combined effects of vertical fluid acceleration and external excitation, which made the upper layer liquid essentially ‘heavier’ than the lower layer at certain instances, even when the amplitude of acceleration of the external excitation was lower than that of gravity at all times. A dimensionless parameter ‘normalised effective gravitational acceleration’ (NEGA) was proposed to quantify the occurrence criterion of RT instability in a dynamic fluid system. Furthermore, simultaneous resonances at both the interface and the free surface were studied and reverse flows could be formed across the interface as the upper Faraday waves and lower Faraday waves moved in opposite directions, which generated the secondary interface instability of Kelvin–Helmholtz (KH) type. When KH instability took place, more energy was transferred to higher frequency modes as time evolved.
Inducing optical self-pulsation by electrically tuning graphene on a silicon microring

Abstract A mechanism for self-pulsation in a proposed graphene-on-silicon microring device is studied. The relevant nonlinear effects of two photon absorption, Kerr effect, saturable absorption, free carrier absorption, and dispersion are included in a coupled mode theory framework. We look at the electrical tunability of absorption and the Kerr effect in graphene. We show that the microring can switch from a stable rest state to a self-pulsation state by electrically tuning the graphene under constant illumination. This switching is indicative of a supercritical Hopf bifurcation since the frequency of the pulses is approximately constant at 7 GHz and the amplitudes initial grow with increasing Fermi level. The CMOS compatibility of graphene and the opto-electronic mechanism allows this to device to be fairly easily integrated with other silicon photonic devices.
Towards a Portable Drug Discovery Pipeline with SYCL 2020

The outcome of the drug discovery process is a molecule that has strong interaction with the target protein. Domain experts expect a beneficial effect from this interaction. The virtual screening is one of the early stages of the process and it aims at finding promising molecules to forward to later stages. We perform this task in-silico to evaluate a very large chemical library in a short time frame. This activity typically comprises two compute-intensive tasks: a docking function that predicts the displacement of atoms, and a scoring function, which estimates the interaction strength [6] Dompé Farmaceutici led the development of LiGen [1, 2, 3], a molecular docking platform targeting High-Performance Computing systems. LiGen has been used for the discovery of novel treatments in the fight against viral infections and multidrug-resistant bacteria [4]. The LiGen processing pipeline includes two main components, ligen-dock and ligen-score, originally developed in OpenACC, refactored to CUDA using non-portable target-specific optimizations [7]. In this talk, we discuss the challenges of making the LiGen docking pipeline portable among different accelerators and GPUs by porting the original codebase from CUDA to SYCL. The code has been refactored by removing critical CUDA semantics with portable ones, and by exploiting several features from the SYCL 2020 standard [5], including sub-groups, group algorithms, and Unified Shared Memory. For comparison, we have developed two versions based on, respectively, accessor and USM-based memory accesses. Particular efforts have been spent on kernel tuning, in particular to optimize those kernels with high register pressure. The final SYCL code base, comprising more than 20 SYCL kernels, has been evaluated on several architectures including NVIDIA V100, NVIDIA A100, AMD MI100 as well as Intel Xeon, and by using both HipSYCL and Intel DPC++ compiler. In terms of performance portability, the SYCL implementation achieves similar performance compared to the CUDA native version on NVIDIA V100 and AMD M100, with minimal modification needed.
A Comparison of SYCL, OpenCL, CUDA, and OpenMP for Massively Parallel Support Vector Machine Classification on Multi-Vendor Hardware

In scientific computing and Artificial Intelligence (AI), which both rely on massively parallel tasks, frameworks like the Compute Unified Device Architecture (CUDA) and the Open Computing Language (OpenCL) are widely used to harvest the computational power of accelerator cards, in particular of Graphics Processing Units (GPUs). A few years ago, GPUs from NVIDIA were used almost exclusively for these tasks but meanwhile, AMD and Intel are increasing their shares of the GPUs market. This introduces many new challenges for code development, as the prevailing CUDA code can only run on NVIDIA hardware and must be adapted or even completely rewritten to run on GPUs from AMD or Intel. In this paper, we compare the different competing programming frameworks OpenMP, CUDA, OpenCL, and SYCL, paying special attention to the two SYCL implementations hipSYCL and DPC++. Thereby, we investigate the different frameworks with respect to their usability, performance, and performance portability on a variety of hardware platforms from different vendors, i.e., GPUs from NVIDIA, AMD, and Intel and Central Processing Units (CPUs) from AMD and Intel. Besides discussing the runtimes of these frameworks on the different hardware platforms, we also focus our comparison on the differences between the nd_range kernel formulation and the SYCL specific hierarchical kernels. Our Parallel Least Squares Support Vector Machine (PLSSVM) library implements backends for the four previously mentioned programming frameworks for a Least Squares Support Vector Machines (LS-SVMs). At its example, we show which of the frameworks is best suited for a standard workload that is frequently employed in scientific computing and AI, depending on the target hardware: The most computationally intensive part of our PLSSVM library is solving a system of linear equations using the Conjugate Gradient (CG) method. Specifically, we parallelize the implicit matrix-vector multiplication inside the CG method, a workload common in many scientific codes. The PLSSVM code, utility scripts, and documentation are all available on GitHub: https://github.com/SC-SGS/PLSSVM.
STIFT: A Spatio-Temporal Integrated Folding Tree for Efficient Reductions in Flexible DNN Accelerators

Increasing deployment of Deep Neural Networks (DNNs) recently fueled interest in the development of specific accelerator architectures capable of meeting their stringent performance and energy consumption requirements. DNN accelerators can be organized around three separate NoCs, namely distribution, multiplier, and reduction networks (or DN, MN, and RN, respectively) between the global buffer(s) and the compute units (multipliers/adders). Among them, the RN, used to generate and reduce the partial sums produced during DNN processing, is a first-order driver of the area and energy efficiency of the accelerator. RNs can be orchestrated to exploit a Temporal, Spatial or Spatio-Temporal reduction dataflow. Among these, Spatio-Temporal reduction is the one that has shown superior performance. However, as we demonstrate in this work, a state-of-the-art implementation of the Spatio-Temporal reduction dataflow, based on the addition of Accumulators (Ac) to the RN (i.e., RN+Ac strategy), can result into significant area and energy expenses. To cope with this important issue, we propose STIFT (that stands for Spatio-Temporal Integrated Folding Tree) that implements the Spatio-Temporal reduction dataflow entirely on the RN hardware substrate (i.e., without the need for the extra accumulators). STIFT results into significant area and power savings regarding the more complex RN+Ac strategy, at the same time its performance advantage is preserved.
Simulación y guíado de aeronaves utilizando programación orientada a objetos

Flight simulators have become nowadays a fundamental tool for both pilot training and other applications such as the test of new mission plans and the development of guidance and control systems. The latter is very useful these days, especially when its applied to unmanned aerial vehicles (UAVs). The mission simulation of that kind of aircrafts could become a fundamental element for the development of unmanned aircrafts, specially in companies or institutions that do not have the required budget for an intensive flight test program. The ability of simulating a complete mission allows the engineers to find errors in the mission concept as well as errors due to the guidance and control philosophies applied to the vehicle, all that in a very simple and cost-effective way. It must be noted that an adequate mission planning procedure and the design of a guidance, navigation and control system of an aircraft are extremely complex subjects, which require an in-depth study in order to guarantee the operational security requirements. The scope of this work consists on the design and implementation of a graphical application which includes concepts of both aircraft simulation and their guidance and control. Additionally, a number of navigation instruments has been implemented in the simulator’s cockpit, which will lead to a more realistic vision of the simulation, as well as familiarize the reader with different concepts related to aircraft instrumentation. The main objective of this work is to offer a modular and easily expandable that can be used for educational purposes in the fields of software development for aeronautical applications. Due to the breadth of the subjects seen in this project, the depth of their implementation in this project has been different for each of them. However, all the aspects of this project have been treated equally from the points of view of documentation, study of the state of the art and the design of their systems and components. MASTER UNIVERSITARIO EN INGENIERÍA AERONÁUTICA Simulación y Guiado de Aeronaves utilizando Programación Orientada a Objetos
Characterization of the DNA binding domain of StbA, a key protein of a new type of DNA segregation system

Low-copy-number plasmids require sophisticated genetic devices to achieve efficient segregation of plasmid copies during cell division. Plasmid R388 uses a unique segregation mechanism, based on StbA, a small multifunctional protein. StbA is the key protein in a segregation system not involving a plasmid-encoded NTPase partner, it regulates the expression of several plasmid operons, and it is the main regulator of plasmid conjugation. The mechanisms by which StbA, together with the centromere-like sequence stbS, achieves segregation, is largely uncharacterized. To better understand the molecular basis of R388 segregation, we determined the crystal structure of the conserved N-terminal domain of StbA to 1.9 Å resolution. It folds into an HTH DNA-binding motif, structurally related to that of the PadR subfamily II of transcriptional regulators. StbA is organized in two domains. Its N-terminal domain carries the specific stbS DNA binding activity. A truncated version of StbA, deleted of its C-terminal domain, displays only partial activities in vivo, indicating that the non-conserved C-terminal domain is required for efficient segregation and subcellular plasmid positioning. The structure of StbA DNA-binding domain also provides some insight into how StbA monomers cooperate to repress transcription by binding to the stbDR and to form the segregation complex with stbS.
Distributed and gradual microstructure changes track the emergence of behavioural benefit from memory reactivation

Memory traces develop gradually and link to neural plasticity. Memory reactivation during sleep is crucial for consolidation, but its precise impact on plasticity and contribution to long-term memory storage remains unclear. We used multimodal diffusion-weighted imaging to track the location and timescale of microstructural changes following Targeted Memory Reactivation (TMR) of a motor task. This showed continuous microstructure plasticity in precuneus across 10 days post-TMR, paralleling the gradual development of behavioural benefit. Both early (0 - 24 h post-TMR) and late (24 h - 10 days post-TMR) microstructural changes in striatum and sensorimotor cortex were associated with the emergence of behavioural effects of TMR at day 20. Furthermore, the baseline microstructural architecture of sensorimotor cortex predicted TMR susceptibility. These findings demonstrate that repeated reactivation of memory traces during sleep engenders microstructural plasticity which continues days after the stimulation night and is associated with the emergence of memory benefits at the behavioural level.