Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Network Analysis and its application in Collaborative E-learning

The rise in use of the internet has enabled the collection of various interactive data. Social Network data, has created numerous opportunities for analyzing diﬀerent trends among the users. Moreover, creation of exponential amounts of data and continuous improvement in machine learning algorithms have helped identify latent user behaviour. Social Network Analysis (SNA) can have phenomenal impact in the ﬁeld of collaborative E-learning (e.g., user analysis and content recommendation) . In this paper, we review some of the SNA techniques and algorithms. We also take an example of Vialogues, a collaborative E-learning platform and look at how diﬀerent SNA techniques can be used to analyze Vialogues data.
Non-adiabatic-like accelerated expansion of the late universe in entropic cosmology

In `entropic cosmology', instead of a cosmological constant $\Lambda$, an extra driving term is added to the Friedmann equation and the acceleration equation, taking into account the entropy and the temperature on the horizon of the universe. By means of the modified Friedmann and acceleration equations, we examine a non-adiabatic-like accelerated expansion of the universe in entropic cosmology. In this study, we consider a homogeneous, isotropic, and spatially flat universe, focusing on the single-fluid (single-component) dominated universe at late-times. To examine the properties of the late universe, we solve the modified Friedmann and acceleration equations, neglecting high-order corrections for the early universe. We derive the continuity (conservation) equation from the first law of thermodynamics, assuming non-adiabatic expansion caused by the entropy and temperature on the horizon. Using the continuity equation, we formulate the generalized Friedmann and acceleration equations, and propose a simple model. Through the luminosity distance, it is demonstrated that the simple model agrees well with both the observed accelerated expansion of the universe and a fine-tuned standard $\Lambda$CDM (lambda cold dark matter) model. However, we find that the increase of the entropy for the simple model is likely uniform, while the increase of the entropy for the standard $\Lambda$CDM model tends to be gradually slow especially after the present time. In other words, the simple model predicts that the present time is not a special time, unlike for the prediction of the standard $\Lambda$CDM model.
Influence of Solid Waste on Environment

One of the most important environmental troubles that growing international locations are dealing with is improper stable waste disposal and control strategies. Besides the waste, we generate at school, home, market, and different public places, they're also the ones from industries, farms, hospitals, pharmaceuticals, and different sources [1]. While a number of the garbage, refuse, sludge, trash, and garbage are biodegradable e.g., paper products, food droppings, in addition to plants like grass and twigs, and others aren't biodegradable and they consist of metals, aluminium cans, plastics, damaged computers, and vehicle parts. As they do now no longer decay easily, they pile up in landfills and refuse dumps and those convey terrific damage to the water, land, plants and people around it. The loss of green waste management practices ends in health hazards, visitor’s congestion, blockage of drainages, and obnoxious [2].
Large fluctuations in diffusion-controlled absorption

Suppose that N0 independently diffusing particles, each with diffusivity D, are initially released at x = ℓ > 0 on the semi-infinite interval 0 ⩽ x < ∞ with an absorber at x = 0. We determine the probability P(N)? > that N particles survive until time t = T. We also employ macroscopic fluctuation theory to find the most likely history of the system, conditional on there being exactly N survivors at time t = T. Depending on the basic parameter ℓ/4DT? >, very different histories can contribute to the extreme cases of N = N0 (all particles survive) and N = 0 (no survivors). For large values of ℓ/4DT? >, the leading contribution to P(N=0)? > comes from an effective point-like quasiparticle that contains all the N0 particles and moves ballistically toward the absorber until absorption occurs.
Self-Distilled Pruning of Neural Networks

Pruning aims to reduce the number of pa-001 rameters while maintaining performance close 002 to the original network. This work proposes 003 a novel self-distillation based pruning strat-004 egy, whereby the representational similarity 005 between the pruned and unpruned versions of 006 the same network is maximized. Unlike previ-007 ous approaches that treat distillation and prun-008 ing separately, we use distillation to inform the 009 pruning criteria, without requiring a separate 010 student network as in knowledge distillation. 011 We show that the proposed cross-correlation 012 objective for self-distilled pruning implicitly 013 encourages sparse solutions, naturally comple-014 menting magnitude-based pruning criteria. Ex-015 periments on the GLUE and XGLUE bench-016 marks show that self-distilled pruning increases 017 mono-and cross-lingual language model per-018 formance. Self-distilled pruned models also 019 outperform smaller Transformers with an equal 020 number of parameters and are competitive 021 against (6 times) larger distilled networks. We 022 also observe that self-distillation (1) maximizes 023 class separability, (2) increases the signal-to-024 noise ratio, and (3) converges faster after prun-025 ing steps, providing further insights into why 026 self-distilled pruning improves generalization. 027
Node Creation Method 1 : Pre-Defined Node Locations

With additional data provided for defining nodes, nodes are initially created using the node definitions provided. If the regions within the data are found to be overlapping, then a step is performed to merge overlapping regions together into one region. The remainder of the algorithm focuses on determining the interaction anchors that overlap with the nodes in the network which is necessary for defining the edges of the network. For this, both nodes and anchors are separated based on chromosome where each list is sorted by start position. For each chromosome, the corresponding list of nodes and anchors are iterated concurrently as follows: Select the first node in the sorted list and iterate over all anchors until the next anchor's start position is greater than the node's end position. Each anchor is compared with the current node as well as the next node in the list to determine whether or not the anchor overlaps with either of these nodes after extending the nodes by the extend parameter in both directions. If an anchor overlaps with both nodes with extension, another comparison is made without extension. If only one node overlaps with the anchor without extension, then the anchor is assigned to the overlapping node. If both nodes are still overlapping with the anchor, then the node that is overlapping with the anchor greater than half its size is assigned. In the case that neither node overlaps without extension, then a final comparison is made with extension again, checking if the anchor overlaps with one node (with extension) greater than half the length of the
ShrinkNAS : Single-Path One-Shot Operator Exploratory Training for Transformer with Dynamic Space Shrinking

Neural Architecture Search (NAS) for Trans-001 former has shown its growing capabilities in 002 exploiting the benefits of various Transformer 003 architecture configurations. Recent studies en-004 vision the diverse potential of introducing un-005 precedented Transformer operators (OPs, such 006 as Convolution) to its structure, yet the existing 007 methods of doing so are all time-consuming. 008 Traditionally, Single-Path One-Shot (SPOS) 009 models enable efficient search over a vast set 010 of OPs. However, existing SPOS methods 011 on Transformer focus only on dimensional 012 configurations of the vanilla Transformer OP 013 (e.g., Multi-head Attention), and did not con-014 sider introducing other OPs. This paper ex-015 plores the possibility of including OPs in the 016 Transformer-based SPOS architecture search 017 to discover better Transformer structures with 018 the high efficiency facilitated in the SPOS cat-019 egory. To achieve that, we propose Dynamic 020 Space Shrinking (DSS), a novel method that 021 resolves problems brought from newly added 022 OPs by dynamically keeping the current sam-023 ple space containing subnets with good config-024 urations and performance. We implemented 025 DSS in ShrinkNAS, the first SPOS one-shot 026 inter-OP model for Transformer. Our evalua-027 tion shows that ShrinkNAS is of much higher 028 elasticity by finding a better structure beating 029 the human-designed ones under tight constraint 030 (<10M parameters), while existing intra-OP 031 SPOS methods are not even close. 032
FRCAMB: An $f(R)$ Code for Anisotropies in the Microwave Background

An $f(R)$ gravity model is proposed to realize a late time accelerated expansion of our Universe. To test the viability of an $f(R)$ gravity model through cosmic observations, the background evolution and the Einstein-Boltzmann equation should be solved for studying the effects on the cosmic microwave background power spectrum and on the matter power spectrum. In the market, we already have the modified versions of {\bf CAMB} code, for instance {\bf EFTCAMB} and {\bf MGCAMB}. However, in these publicly available Einstein-Boltzmann codes, a specific background cosmology, for example the $\Lambda$CDM or $w$CDM, is assumed. This assumption would be non-proper for a specific $f(R)$ model where the background evolution may be different from a $\Lambda$CDM cosmology. Therefore the main task for this paper is to present a code to calculate the anisotropies in the microwave background for any $f(R)$ gravity model based on {\bf CAMB} code, i.e. {\bf FRCAMB}, where the background and perturbation evolutions are included consistently. As results, one can treat {\bf FRCAMB} as a blackbox to output the CMB power spectrum and matter power spectrum, once an $f(R)$ function, its first two derivative with respect to $R$, i.e. $f_R\equiv df/dR$, $f_{RR}\equiv d^2f/dR^2$ and the reasonable values of the model parameters are inputted properly. As by-products, one can also output the effective equation of state of $f(R)$ model, the evolution of the dimensionless energy densities and other interesting cosmological quantities.
Keeping Secrets: Multi-objective Genetic Improvement for Detecting and Reducing Information Leakage

Information leaks in software can unintentionally reveal private data, yet they are hard to detect and fix. Although several methods have been proposed to detect leakage, such as static verification-based approaches, they require specialist knowledge, and are time-consuming. Recently, we introduced HyperGI, a dynamic, hypertest-based approach that can detect and produce potential fixes for hyperproperty violations. In particular, we focused on violations of the noninterference property, as it results in information flow leakage. Our instantiation of HyperGI was able to detect and reduce leakage in three small programs. Its fitness function tried to balance information leakage and program correctness but, as we pointed out, there may be tradeoffs between keeping program semantics and reducing information leakage that require developer decisions. In this work we ask if it is possible to automatically detect and repair information leakage in more realistic programs without requiring specialist knowledge. We instantiate a multi-objective version of HyperGI in a tool, called LeakReducer, which explicitly encodes the tradeoff between program correctness and information leakage. We apply LeakReducer to six leaky programs, including the well-known Heartbleed bug. LeakReducer is able to detect leakage in all, in contrast to state-of-the-art fuzzers, detecting leakage in only two programs. Moreover, LeakReducer is able to reduce leakage in all subjects, with comparable results to previous work, while scaling to much larger software.
Data-Adaptive Autonomous Seakeeping

The safe operation of a ship in heavy weather and high sea states requires accounting for the risk of extreme ship motion responses in stochastic ocean waves. Excessive ship motions can lead to hazardous and unsafe conditions such as pure loss of stability, surf-riding and broaching. Mitigation of these risks is typically performed through selection of ship headings and speeds for the given seaway, and avoiding conditions likely to lead to severe motions. To address this challenge, a robust, onboard, fast data-adaptive model is needed to evaluate the expected ship motion responses to the ocean environment, and ship loading conditions. In this study, data-adaptive Long Short-Term Memory (LSTM) neural networks are investigated as part of a multi-fidelity approach incorporating Large Amplitude Program (LAMP), and a reduced-order model known as SimpleCode. An initial assessment of this multi-fidelity approach has focused on prediction of ship motion responses in waves. LSTM networks were trained and tested using LAMP simulations as a target, and SimpleCode simulations and wave time-series as inputs. LSTM networks were shown to improve the fidelity of SimpleCode seakeeping predictions relative to LAMP while retaining the computational efficiency of a reduced-order model. Potential areas of application for data-adaptive autonomous seakeeping include unmanned and reduced crewed vessels, as well as operator guidance for manned systems.
Study of $B_c \to KK$ decay with perturbative QCD approach

In the framework of the perturbative QCD approach, we study the charmless pure weak annihilation Bc->KK decay and find that the branching ratio BR(Bc->KK) O(10^-7). This prediction is so tiny that the Bc->KK decay might be unmeasurable at the Large Hadron Collider.
Universal single-frequency oscillations in a quantum impurity system after a local quench

Long-lived single-frequency oscillations in the local non-equilibrium dynamics of a quantum many-body system is an exceptional phenomenon. In fact, till now, it has never been observed, nor predicted, for the physically relevant case where a system is prepared to be quenched from one quantum phase to another. Here we show how the quench dynamics of the entanglement spectrum may reveal the emergence of such oscillations in a correlated quantum system with Kondo impurities. The oscillations we find are characterized by a single frequency. This frequency is independent of the amount of energy released by the local quench, and scales with the inverse system size. Importantly, the quench-independent frequency manifests itself also in local observables, such as the spin-spin correlation function of the impurities.
A new method for computing self-gravity in an isolated system

A new approximation method for inverting the Poisson's equation is presented for a continuously distributed and finite-sized source in an unbound domain. The advantage of this image multipole method arises from its ability to place the computational error close to the computational domain boundary, making the source region almost error free. It is contrasted to the modified Green's function method that has small but finite errors in the source region. Moreover, this approximation method also has a systematic way to greatly reduce the errors at the expense of somewhat greater computational efforts. Numerical examples of three-dimensional and two-dimensional cases are given to illustrate the advantage of the new method.
Response of Carbon Nanotube (CNT) Ply Subjected to a Pulsed Magnetic Field

In this study, the possible deformation of a single Carbon Nanotube (CNT) ply subjected to a pulsed magnetic field was investigated. In all tests the capacitor bank was charged to 6kJ of energy. A Photon Doppler Velocimetry (PDV) system was used to measure velocity or displacement of the CNT ply during the experiments. The resistance of the CNT ply was measured using four-point probe technique before and after the experiments. Preliminary results show that the single CNT plies do not permanently deform in response to the pulsed magnetic fields. However, they can be displaced, either by themselves a small amount (0.6mm) or by a large amount using a driver material. Also, the resistance of the CNT plies may increase or decrease depends on the lay-out (i.e., yarn) and current flow directions.
Green energy and environmental sustainability on Vernacular Architecture. The case of Ayllus of Atacama Desert and stilt houses of Chiloé

The main objective of this study is to explore the development of vernacular techniques capable of facilitate a green and sustainable architecture today’s. An ayllu in San Pedro de Atacama and a stilt house in Chiloé are selected as a comparative case, since (1) both are part of the vernacular architecture of its respective place, (3) both cases belong to extreme regions of the country, with both geographical characteristics totally opposite, the ayllu in the extreme north with a desert climate and the stilt houses in the country’s south with low temperatures and maritime-rainy climate, (4) both cases are works of green and sustainable architecture, wooden on stilt houses and adobe on ayllu. The methodology is carried out through three points, contamination levels (1), materiality (2) and energetic design and climate adaptation (3). It is concluded that the development of particular vernacular architecture, can introduce new logics to develop green energy and being closer to environmental sustainability, as has occurred in the palafitos and in the ayllu.
Asymptotic black holes

Following earlier works on the KMY model of black-hole formation and evaporation, we construct the metric for a matter sphere in gravitational collapse, with the back-reaction of pre-Hawking radiation taken into consideration. The mass distribution and collapsing velocity of the matter sphere are allowed to have an arbitrary radial dependence. We find that a generic gravitational collapse asymptote to a universal configuration which resembles a black hole but without horizon. This approach clarifies several misunderstandings about black-hole formation and evaporation, and provides a new model for black-hole-like objects in the universe.
Case report: Joint deformity associated with systemic lupus erythematosus

Typically, Jaccoud arthropathy (JA) is characterized by joint deformation without bone erosion. However, some recent studies have shown that bone erosion also occurs in JA; however, this remains controversial. To date, there have been no unified diagnostic standards for JA. Herein, we report a case of systemic lupus erythematosus complicated with JA without bone erosion.
Discovering Gluino Events at LHC-8 via Disappearing Chargino Tracks

In this note, we advocate a new method for identifying gluino pair production events at the LHC. The method is motivated by and works for theories with heavy squarks and Wino-like LSPs (with nearly degenerate LSP and chargino). Such theories are well motivated and their gluinos typically have a O(50%) branching ratio to charged Winos. Observing the track of a long lived charged Wino produced from gluino decay could give a clear identification of a gluino event. Charged Wino NLSPs produced in colliders can be long-lived enough to leave a reconstructable high pT charged track before decaying into a soft pion (or a soft lepton) and the LSP, a signature with low SM background. By supplementing the canonical gluino search strategy with a search for these stiff chargino tracks, our results suggest it will be possible to find gluinos with significantly less luminosity. In addition, we describe a procedure for obtaining a kinematic measurement of the gluino mass using the three momenta of the reconstructed chargino tracks. With measurements of the gluino mass and cross section, it will be possible to determine the gluino spin, and confirm that the excess events are indeed due to a spin 1/2 superpartner. It may also be possible to use these stiff Wino tracks to obtain an approximate measurement of the chargino mass, and therefore the LSP (dark matter) mass.
Effects of gate-first and gate-last process on interface quality of In0.53Ga0.47As metal-oxide-semiconductor capacitors using atomic-layer-deposited Al2O3 and HfO2 oxides

We have investigated the effects of gate-first and gate-last process on oxide/InGaAs interface quality using In0.53Ga0. 47As metal-oxide-semiconductor capacitors (MOSCAPs) with atomic-layer-deposited (ALD) oxides. Sequence of source/drain activation anneal in the process results in remarkable electrical and physical difference. Applying gate-last process provides significant frequency dispersion reduction and interface trap density reduction for InGaAs MOSCAPs compared to gate-first process. A large amount of In–O, Ga–O, and As–As bonds was observed on InGaAs surface after gate-first process while no detectable interface reaction after gate-last process. Electrical and physical results also show that ALD Al2O3 exhibits better interface quality on InGaAs than HfO2.
Characterizing the Implicit Bias of Regularized SGD in Rank Minimization

We study the bias of Stochastic Gradient Descent (SGD) to learn low-rank weight matrices when training deep neural networks. Our results show that training neural networks with mini-batch SGD and weight decay causes a bias towards rank minimization over the weight matrices. Specifically, we show, both theoretically and empirically, that this bias is more pronounced when using smaller batch sizes, higher learning rates, or increased weight decay. Additionally, we predict and observe empirically that weight decay is necessary to achieve this bias. Unlike previous literature, our analysis does not rely on assumptions about the data, convergence, or optimality of the weight matrices and applies to a wide range of neural network architectures of any width or depth. Finally, we empirically investigate the connection between this bias and generalization, finding that it has a marginal effect on generalization.
Efficiently Learning Recoveries from Failures Under Partial Observability

—Operating under real world conditions is challenging due to the possibility of a wide range of failures induced by partial observability. In relatively benign settings, such failures can be overcome by retrying or executing one of a small number of hand-engineered recovery strategies. By contrast, contact-rich sequential manipulation tasks, like opening doors and assembling furniture, are not amenable to exhaustive hand-engineering. To address this issue, we present a general approach for robustifying manipulation strategies in a sample-efﬁcient manner. Our approach incrementally improves robustness by ﬁrst discovering the failure modes of the current strategy via exploration in simulation and then learning additional recovery skills to handle these failures. To ensure efﬁcient learning, we propose an online algorithm Value Upper Conﬁdence Limit (Value-UCL) that selects what failure modes to prioritize and which state to recover to such that the expected performance improves maximally in every training episode. We use our approach to learn recovery skills for door-opening and evaluate them both in simulation and on a real robot with little ﬁne-tuning. Compared to open-loop execution, our experiments show that even a limited amount of recovery learning improves task success substantially from 71% to 92.4% in simulation and from 75% to 90% on a real robot.