Miracle-dz/UniFAR-FTUs huggingface dataset:
Dimension and depth inequalities over complete intersections

For a pair of ﬁnitely generated modules M and N over a codimension c complete intersection ring R with ℓ ( M ⊗ R N ) ﬁnite, we pay special attention to the inequality dim M + dim N ≤ dim R + c . In particular, we develop an extension of Hochster’s theta invariant whose nonvanishing detects equality. In addition, we consider a parallel theory where dimension and codimension are replaced by depth and complexity, respectively.
Some notes on the impact of Lagrange's memoir"On the construction of geographical maps"

These are notes on the impact of Lagrange's memoir on the construction of geographical maps. We mention the relations of some ideas and questions introduced in this memoir with other notions that appeared later in the works of several mathematicians, including in particular Chebyshev (19th c.) and Darboux (19th-20th c.), two mathematicians who were particularly interested in geography.
The ReSWARM Microgravity Flight Experiments: Planning, Control, and Model Estimation for On-Orbit Close Proximity Operations

On-orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on-orbit assembly, repair, and astronaut assistance. Of these scenarios, on-orbit assembly is an enabling technology that will allow large space structures to be built in-situ, using smaller building block modules. However, robotic on-orbit assembly involves a number of technical hurdles such as changing system models. For instance, grappled modules moved by a free-flying"assembler"robot can cause significant shifts in system inertial properties, which has cascading impacts on motion planning and control portions of the autonomy stack. Further, on-orbit assembly and other scenarios require collision-avoiding motion planning, particularly when operating in a"construction site"scenario of multiple assembler robots and structures. These complicating factors, relevant to many autonomous microgravity robotics use cases, are tackled in the ReSWARM flight experiments as a set of tests on the International Space Station using NASA's Astrobee robots. RElative Satellite sWarming and Robotic Maneuvering, or ReSWARM, demonstrates multiple key technologies for close proximity operations and on-orbit assembly: (1) global long-horizon planning, accomplished using offline and online sampling-based planner options that consider the system dynamics; (2) on-orbit reconfiguration model learning, using the recently-proposed RATTLE information-aware planning framework; and (3) robust control tools to provide low-level control robustness using current system knowledge. These approaches are detailed individually and in an"on-orbit assembly scenario"of multi-waypoint tracking on-orbit. Additionally, detail is provided discussing the practicalities of hardware implementation and unique aspects of working with Astrobee in microgravity.
Nearly odd-order and nearly real finite groups

Two families of groups close to groups of odd order, and two families of groups close to real groups will be described. The first two are the family of finite groups in which all real irreducible ordinary characters are linear, and the family of all finite groups with the dual condition on the conjugacy classes, namely, groups in which all real conjugacy classes are contained in the center of the group. We will see that each group in one of these families is a direct product of a group of odd order with a 2-group. The families close to real groups are the family of finite groups in which every non-real irreducible ordinary character is linear, and dually, the family of all finite groups in which every non-real conjugacy class is contained in the center. For a group G in the first family we show that the collection of real elements R(G) is a normal subgroupG has a normal π-complement where π = π (∣G: R(G)∣), and a Hall π-subgroup which is either abelian or a nearly real 2-group. A group is in the second fa.
Can tests for treatment group equality be improved?: The bootstrap and trimmed means conjecture

Westfall & Young's (1993) results suggest that by combining bootstrap methods with methods based on trimmed means researchers can obtain better Type I error control when testing for treatment group equality. Thus, the methods due to Alexander & Govern (1994), Box (1954), and Welch (1951) were compared under conditions of non-normality and variance heterogeneity when group sizes were unequal, conditions known to produce liberal rates of error. We found that, typically, in one-way designs better Type I error control can indeed be obtained when the bootstrap method is used in conjunction with test statistics based on trimmed means.
Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binarie

Binary reverse engineering is used to understand and analyse programs for which the source code is unavailable. Decompilers can help, transforming opaque binaries into a more readable source code-like representation. Still, reverse engineering is difficult and costly, involving considering effort in labelling code with helpful summaries. While the automated summarisation of decompiled code can help reverse engineers understand and analyse binaries, current work mainly focuses on summarising source code, and no suitable dataset exists for this task. In this work, we extend large pre-trained language models of source code to summarise de-compiled binary functions. Further-more, we investigate the impact of input and data properties on the performance of such models. Our approach consists of two main components; the data and the model. We first build CAPYBARA, a dataset of 214K decompiled function-documentation pairs across various compiler optimisations. We extend CAPYBARA further by removing identifiers, and deduplicating the data. Next, we fine-tune the CodeT5 base model with CAPYBARA to create BinT5. BinT5 achieves the state-of-the-art BLEU-4 score of 60.83, 58.82 and, 44.21 for summarising source, decompiled, and obfuscated decompiled code, respectively. This indicates that these models can be extended to decompiled binaries successfully. Finally, we found that the performance of BinT5 is not heavily dependent on the dataset size and compiler optimisation level. We recommend future research to further investigate transferring knowledge when working with less expressive input formats such as stripped binaries.
On-Sky Performance of New 90 GHz Detectors for the Cosmology Large Angular Scale Surveyor (CLASS)

The Cosmology Large Angular Scale Surveyor (CLASS) is a polarization-sensitive telescope array located at an altitude of 5,200 m in the Chilean Atacama Desert and designed to measure the polarized Cosmic Microwave Background (CMB) over large angular scales. The CLASS array is currently observing with three telescopes covering four frequency bands: one at 40 GHz (Q); one at 90 GHz (W1); and one dichroic system at 150/220 GHz (HF). During the austral winter of 2022, we upgraded the first 90 GHz telescope (W1) by replacing four of the seven focal plane modules. These new modules contain detector wafers with an updated design, aimed at improving the optical efficiency and detector stability. We present a description of the design changes and measurements of on-sky optical efficiencies derived from observations of Jupiter.
New Lower Bounds against Homogeneous Non-Commutative Circuits

We give several new lower bounds on size of homogeneous non-commutative circuits. We present an explicit homogeneous bivariate polynomial of degree $d$ which requires homogeneous non-commutative circuit of size $\Omega(d/\log d)$. For an $n$-variate polynomial with $n>1$, the result can be improved to $\Omega(nd)$, if $d\leq n$, or $\Omega(nd \frac{\log n}{\log d})$, if $d\geq n$. Under the same assumptions, we also give a quadratic lower bound for the ordered version of the central symmetric polynomial.
Data-Driven Model Identification via Hyperparameter Optimization for Autonomous Racing Systems

—In this letter, we propose a model identiﬁcation method via hyperparameter optimization (MIHO). Our method is able to identify the parameters of the parametric models in a data-driven manner. We utilize MIHO for the dynamics parameters of the AV-21, the full-scaled autonomous race vehicle, and integrate them into our model-based planning and control systems. In experiments, the models with the optimized parameters demonstrate the generalization ability of the vehicle dynamics model. We further conduct extensive ﬁeld tests to validate our model-based system. The tests show that our race systems leverage the learned model dynamics well and successfully perform obstacle avoidance and high-speed driving over 200km / h at the Indianapolis Motor Speedway and Las Vegas Motor Speedway. The source code for MIHO and videos of the tests are available at https://github.com/hynkis/MIHO.
Lessons Learned Applying Deep Learning Approaches to Forecasting Complex Seasonal Behavior

Deep learning methods have gained popularity in recent years through the media and the relative ease of implementation through open source packages such as Keras. We investigate the applicability of popular recurrent neural networks in forecasting call center volumes at a large financial services company. These series are highly complex with seasonal patterns - between hours of the day, day of the week, and time of the year - in addition to autocorrelation between individual observations. Though we investigate the financial services industry, the recommendations for modeling cyclical nonlinear behavior generalize across all sectors. We explore the optimization of parameter settings and convergence criteria for Elman (simple), Long Short-Term Memory (LTSM), and Gated Recurrent Unit (GRU) RNNs from a practical point of view. A designed experiment using actual call center data across m any different “skills” (income call streams) compares performance measured by validation error rates of the best observed RNN configurations against other modern and classical forecasting techniques. We summarize the utility of and considerations required for using deep learning methods in forecasting.
Quantum energy inequalities along stationary worldlines

Quantum energy inequalities (QEIs) are lower bounds on the averaged energy density of a quantum field. They have been proved for various field theories in general curved spacetimes but the explicit lower bound is not easily calculated in closed form. In this paper we study QEIs for the massless minimally coupled scalar field in four-dimensional Minkowski spacetime along stationary worldlines—curves whose velocity evolves under a 1-parameter Lorentz subgroup—and find closed expressions for the QEI bound, in terms of curvature invariants of the worldline. Our general results are illustrated by specific computations for the six prototypical stationary worldlines. When the averaging period is taken to infinity, the QEI bound is consistent with a constant energy density along the worldline. For inertial and uniformly linearly accelerated worldlines, this constant value is attained by the Minkowski and Rindler vacuums respectively. It is an open question as to whether the bounds for other stationary worldlines are attained by other states of interest.
Curious convergence with hypostatic hybrid equilibrium models

The paper describes an unexpected type of convergence behaviour occurring for a single, variable degree, primitive-type equilibrium element. For this element the number of independent stress fields is less than the number of independent boundary displacement variables that do not correspond to rigid element modes of displacement. This leads to the conclusion that the element is hypostatic and that, in the absence of self-stressing modes, no convergence can occur. Such ‘conventional’ counting procedures do not, however, reveal the whole picture, and numerical determination of the rank of the coefficient matrix of the equilibrium equations shows that, in practice, self-stressing modes can and do exist in a model which would conventionally be described as hypostatic. The rank deficiency in the coefficient matrix is shown to be due to the fact that, upon transformation, independent stress fields do not necessarily lead to independent boundary tractions. Generalization to conventionally iso- and hyperstatic models demonstrates that, whenever the coefficient matrix is rank-deficient, spurious kinematic modes coexist with self-stressing modes. The problem which reveals the curious convergence characteristics for the primitive-type element is resolved using a macro-type element, and it is seen that, with the larger degree of hyperstaticity available to this element, strictly monotonic convergence characteristics are observed. © 1997 John Wiley & Sons, Ltd.
Task-sequencing Simulator: Integrated Machine Learning to Execution Simulation for Robot Manipulation

—A task-sequencing simulator in robotics manip- ulation to integrate simulation-for-learning and simulation-for-execution is introduced. Unlike existing machine-learning simulation where a non-decomposed simulation is used to simulate a training scenario, the task-sequencing simulator runs a composed simulation using building blocks. This way, the simulation-for-learning is structured similarly to a multi-step simulation-for-execution. To compose both learning and execution scenarios, a uniﬁed trainable-and-composable description of blocks called a concept model is proposed and used. Using the simulator design and concept models, a reusable simulator for learning different tasks, a common-ground system for learning-to-execution, simulation-to-real is achieved and shown.
IRNet: Iterative Refinement Network for Noisy Partial Label Learning

Partial label learning (PLL) is a typical weakly supervised learning, where each sample is associated with a set of candidate labels. The basic assumption of PLL is that the ground-truth label must reside in the candidate set. However, this assumption may not be satisfied due to the unprofessional judgment of the annotators, thus limiting the practical application of PLL. In this paper, we relax this assumption and focus on a more general problem, noisy PLL, where the ground-truth label may not exist in the candidate set. To address this challenging problem, we propose a novel framework called"Iterative Refinement Network (IRNet)". It aims to purify the noisy samples by two key modules, i.e., noisy sample detection and label correction. Ideally, we can convert noisy PLL into traditional PLL if all noisy samples are corrected. To guarantee the performance of these modules, we start with warm-up training and exploit data augmentation to reduce prediction errors. Through theoretical analysis, we prove that IRNet is able to reduce the noise level of the dataset and eventually approximate the Bayes optimal classifier. Experimental results on multiple benchmark datasets demonstrate the effectiveness of our method. IRNet is superior to existing state-of-the-art approaches on noisy PLL.
Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks

We investigate gradient descent training of wide neural networks and the corresponding implicit bias in function space. For univariate regression, we show that the solution of training a width-$n$ shallow ReLU network is within $n^{- 1/2}$ of the function which fits the training data and whose difference from the initial function has the smallest 2-norm of the second derivative weighted by a curvature penalty that depends on the probability distribution that is used to initialize the network parameters. We compute the curvature penalty function explicitly for various common initialization procedures. For instance, asymmetric initialization with a uniform distribution yields a constant curvature penalty, and thence the solution function is the natural cubic spline interpolation of the training data. \hj{For stochastic gradient descent we obtain the same implicit bias result. } We obtain a similar result for different activation functions. For multivariate regression we show an analogous result, whereby the second derivative is replaced by the Radon transform of a fractional Laplacian. For initialization schemes that yield a constant penalty function, the solutions are polyharmonic splines. Moreover, we show that the training trajectories are captured by trajectories of smoothing splines with decreasing regularization strength.
Predicting unstable toughness of concrete based on initial toughness criterion

The fracture processes of concrete were described by a cohesive crack model based on initial toughness criterion. The corresponding analytical method to predict the instability state was proposed. In this model, the initial toughness was adopted as the crack propagation criterion and the weight function method was used to calculate the stress intensity factor and the crack opening displacement caused by the cohesive stress. The unstable toughness can be easily obtained using the proposed method without measuring parameters at the critical state that was necessary in traditional methods. The proposed method was verified by existing experimental data of wedge splitting specimens with different grades of concrete and the sensitivity of the results on the tensile softening curve was discussed. The results demonstrate that the proposed method can well predict the peak load, the critical effective crack length, and the unstable toughness of concrete specimens. Moreover, the calculated unstable toughness is not sensitive to the tensile softening curve.
Principes physiques des lasers à semiconducteurs

Resume. Les principes physiques des lasers a semiconducteurs sont passes en revue. Apres quelques rappels fondamentaux de physique des solides concernant les etats electroniques dans les semiconducteurs et les concepts de fonction de Bloch ou de bande interdite, la maniere dont un semiconducteurpeutemettredelalumiereseraexaminee.Lajonction p-n est le dispositif de base permettant d’obtenir l’inversion de population necessaire au gain d’emission stimulee. Nous exposons les principes et les equations fondamentales regissant le transport electronique dans ces structures. L’equation de seuil des lasers a semiconducteurs est ensuite determinee et les differentes ameliorations par rapport a la jonction pn de base (le laser a double heterostructure, le laser a puits quantique) sont exposees dans une perspective historique. Enfin, la diversite des diodes lasers est illustree par quelques variations : lasers de puissance, lasers a cavite verticale emettant par la surface. La synthese est suivie d’une tentative de mise en perspective des avancees futures des lasers a semiconducteurs.
Target identification with Bayesian networks

Tracking algorithms can tell fairly reliable where target is heading. That is enough in civilian aviation, but in defence applications it might be not. Target's type and its hostility are at least as important. Normally, identification of type or friend or foe cannot be determined from target's kinematic information. To identify a target we also need other information. Every plane type has its own specialities e.g. we know that certain type has two engines which affects directly to heat of exhaust fumes. This kind of speciality is generally referred as an attribute information. Because attribute information is type depended, it must be modelled by an expert, who has beforehand knowledge of the target's causality relations. One of the best theories to get expert's knowledge into a tracking system is Bayesian networks. Bayesian networks is a model that describes relationships between attributes. In this paper we concentrate to identification problem. Question is how comprehension of the target's type changes with time when observations are corrupted by noise. We illustrate theory of Bayesian networks and explain its place in racking system. Finally we analyze performance of Bayesian networks in case where the problem is to identify targets from noisy data set.
The Haas Effect - Lateral extent and perceptual weighting of localization cues

The Haas effect is a well-known manifestation of the precedence effect. Originally, Haas measured the echo threshold as a function of the primary auditory event and its single reflection being equally loud. What is not well known is the lateral position of the lead/lag pair as a function of inter-stimulus interval and level difference between lead and lag. How robust the Haas effect is in the localization dominance region was investigated, adjusting the level difference between lead and lag, and using 200 ms band-passed noise presented dichotically over headphones. In addition, the onset and offset cues were removed for half the trials and left intact for the other half to investigate the roles of onset and offset cues versus ongoing cues. Lateral displacement of the auditory event was recorded with an acoustic pointer. Analysis of these results help to reveal the perceptual weighting of localization cues in the Haas effect.
Early Diagnosis of Oral Cancer and Lesions in Fanconi Anemia Patients: A Prospective and Longitudinal Study Using Saliva and Plasma

Fanconi anemia (FA) patients display an exacerbated risk of oral squamous cell carcinoma (OSCC) and precursor lesions at young ages, mainly at the oral cavity. As patients have defects in DNA repair mechanisms, standard-of-care treatments to OSCC such as radiotherapy and chemotherapy give rise to severe toxicities. New methods for early diagnosis are urgently necessary to allow treatments in early disease stages and achieve better clinical outcomes. We have conducted a prospective, longitudinal study whereby liquid biopsies from sixteen lesion/tumor-free patients were analyzed for the presence of mutations in cancer genes. DNA from saliva and plasma were sequentially collected and deep-sequenced, and the clinical evolution followed during a median time of around 2 years. In 9/16 FA patients we detected mutations in cancer genes (mainly TP53) with molecular allele frequencies (MAF) down to 0.07 %. Importantly, all patients having mutations and clinical follow-up data after mutation detection (n=6) developed oral precursor lesions or OSCC. Lead-time between mutation detection and tumor diagnosis ranged from 23 to 630 days. Strikingly, FA patients without mutations display significantly lower risk of developing precursor lesions or OSCC. Therefore, our diagnostic approach could help to stratify FA patients into risk groups, which would allow closer surveillance for OSCC or precursor lesions.
CFD Modeling of Waste Heat Recovery System to Dry Sand Mold at G-7 Trading and Industry PLC, Ethiopia

As the energy crisis's impact spreads, energy consumption has emerged as the fundamental impediment to industry's long-term viability. Utilizing and recovering various sources of waste heat can considerably reduce an organization's energy use. Energy conservation is crucial not just for fuel use, but also for the conservation and protection of the global ecosystem. As a result, it is necessary to make a concerted effort to save energy generated by waste heat. This paper aims to develop flue gas as a substitute for wood burning for drying mold at foundry shop of G-7 trading and industrial PLC. Flue gas temperature and velocity were measured at various locations throughout this study, and an analytical computation was done on the flue gas supply system and mold drying chamber. To show what happens within the hot flue gas supply duct and mould drying chamber, Computational Fluid Dynamics (CFD) Ansys software was used. The business benefits by eliminating the usage of wood as a fuel and making the process more cost-effective and environmentally friendly by capturing hot flue gas from the top section of the furnace or chimney and supplying it through duct to the drying chamber to dry the mold. As a result, the negative impact of deforestation will be mitigated, and a favorable working environment will be established.