Miracle-dz/UniFAR-FTUs huggingface dataset:
Connecting the Nodes: An interactive perspective on innovative microenterprises in a mature industry

The emergent nature of the innovation process has always rendered it an elusive subject of study. Nonetheless, academics remained undeterred in their attempts to articulate the innovation process in academics terms as attested by the growing amount of research on the topic. Existing theories for explaining innovation (or the lack of innovation) center on empirical samples from large firms in the high-tech sectors such as electronics, software and information technology. This partly explains why popular concepts of innovation processes are associated with firms having characteristics such as strong science and technology components or the ability to conduct research and development (R&D) activities which should lead to commercialization. What these concepts do not explain well is how small and medium sized enterprises (SMEs), in particular microenterprises, which are increasingly credited with contributing to innovative output, are acquiring and converting resources that they do not possess. Microenterprises (also known as small businesses, start-ups, family owned-businesses) possess similar characteristics to their SME counterparts when innovating through interacting in networks to access external resources as a way to make up for their lack of resources. However, the challenges faced by microenterprises can differ in scope from those of the general population of SMEs, in particular by having an emphasis on the importance of external actor bonds, resources ties and activity links during their innovation process. This thesis examined the interaction aspect of innovating microenterprises that are seen to be renewing the mature industry landscape. Set in the context of the food industry which have been viewed as traditional and having low levels of innovation, the innovation process of these Swedish microenterprises are examined through how they address the barriers to innovation each in their own way, utilizing and developing capacities through interaction in the network. Findings suggest that recommendations for microenterprises to build up the networks to gain access to external resources should be accompanied by an awareness of the types and quality of external resources and an on-going evaluation of the capacities that microenterprises have and continue to develop during the innovation process. This involves considering a strategic combination of actor bonds, resources ties and activities links that will connect the synergy between the capacities of both past and present nodes in the network to help overcome barriers in the innovation process for microenterprises. (Less)
Laser cutting and etching textiles and apparel design: an experimental study on the implementation and documentation of laser cutting and etching in the apparel design creative process

Textile cutting is a standard pre-production stage in the apparel manufacturing process that has developed from a manual to a fully automated procedure in recent decades. Laser cutting technology has improved the efficiency and sustainability of cutting pattern markers on a large cutting scale, as well as the ability to cut intricate internal patterns and shapes on a smaller cutting scale. CO2 laser technology has been adopted by industry and academia professionals at an exponential rate, but standard material testing for cutting and etching different textiles has been minimally documented. There were three primary objectives that comprise the purpose of this research which were to: (a) perform material testing to document and create a user manual that explains the parameters needed to laser cut and etch various textiles; (b) use Rogers’ Diffusion of Innovation theory to examine “perceived attributes of an innovation” (2003, pp. 1516), which are characteristics that help determine the rate of adoption, to survey apparel design students in order to determine the possible diffusion of laser cutting and the user manual in apparel design educational settings; and (c) apply knowledge from material testing to create a fashion ensemble that portrays the capabilities of laser cutting and etching textiles in apparel design. To meet the objectives, the researcher first tested how 127 textiles from the Basic Swatch Kit provided by Textile Fabric Consultants, Inc. responded to the laser cutter to develop a user manual for students, educators and industry professionals. Second, the researcher used Rogers’ Diffusion of Innovation theory to analyze laser cutting and etching in the apparel industry. A sample of 26 undergraduate apparel design students participated in a survey in which the results demonstrated that laser cutting is viewed as a means to enhance their design projects and
On the exactness for polynomial optimization strengthened with Fritz John conditions

We utilize the same technique as in [arXiv:2205.04254 (2022)] to provide some representations of polynomials non-negative on a basic semialgebraic set, deﬁned by polynomial inequalities, under more general conditions. Based on each representation, we obtain semideﬁnite programs which return a sequence of values that ﬁnitely converges to the optimal value of a given polynomial optimization problem under generic assump-tion. Consequently, we can compute exactly the minimal value of any polynomial over a basic convex semi-algebraic set which is deﬁned by the inequalities of concave polynomials.
Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities

The Past Extragradient (PEG) [Popov, 1980] method, also known as the Optimistic Gradient method, has known a recent gain in interest in the optimization community with the emergence of variational inequality formulations for machine learning. Recently, in the unconstrained case, Golowich et al. [2020] proved that a $O(1/N)$ last-iterate convergence rate in terms of the squared norm of the operator can be achieved for Lipschitz and monotone operators with a Lipschitz Jacobian. In this work, by introducing a novel analysis through potential functions, we show that (i) this $O(1/N)$ last-iterate convergence can be achieved without any assumption on the Jacobian of the operator, and (ii) it can be extended to the constrained case, which was not derived before even under Lipschitzness of the Jacobian. The proof is significantly different from the one known from Golowich et al. [2020], and its discovery was computer-aided. Those results close the open question of the last iterate convergence of PEG for monotone variational inequalities.
A Note on the Chernoff Bound for Random Variables in the Unit Interval

The Chernoﬀ bound is a well-known tool for obtaining a high probability bound on the expectation of a Bernoulli random variable in terms of its sample average. This bound is commonly used in statistical learning theory to upper bound the generalisation risk of a hypothesis in terms of its empirical risk on held-out data, for the case of a binary-valued loss function. However, the extension of this bound to the case of random variables taking values in the unit interval is less well known in the community. In this note we provide a proof of this extension for convenience and future reference.
A conceptual model of the diurnal warm layer for idealized atmospheric simulations with interactive SST

The diurnal variability of sea surface temperature (SST) may play an important role for cloud organization above the tropical ocean, with implications for precipitation extremes, storminess, and climate sensitivity. Recent cloud-resolving simulations demonstrate how imposed diurnal SST oscillations can strongly, and delicately, impact mesoscale convective organization. In spite of this nuanced interaction, many idealized modeling studies of tropical convection either assume a constant, homogeneous SST or, in case of a responsive sea surface, represent the upper ocean by a slab with fixed thickness. Here we show that slab ocean models with constant heat capacity fail to capture the wind-dependence of observed diurnal sea surface warming. To alleviate this shortcoming, we present a simple, yet explicitly depth-resolved model of upper-ocean temperature dynamics under atmospheric forcing. Our modular scheme describes turbulent mixing as diffusion with a wind-dependent diffusivity, in addition to a bulk mixing term and heat fluxes entering as sources and sinks. Using observational data, we apply Bayesian inference to calibrate the model. In contrast with a slab model, our model captures the exponential reduction of the diurnal warming amplitude with increasing wind speed. Further, our model performs comparably to a more elaborately parameterized diurnal warm layer model. Formulated as a single partial differential equation with three key tuning parameters, the model is suitable as an interactive numerical boundary condition for idealized atmospheric simulations.
How Do Spitzer IRAC Fluxes Compare to HST CALSPEC?

An accurate tabulation of stellar brightness in physical units is essential for a multitude of scientific endeavors. The HST/CALSPEC database of flux standards contains many stars with spectral coverage in the 0.115–1 μm range, with some extensions to longer wavelengths of 1.7 or 2.5 μm. Modeled flux distributions to 32 μm for calibration of JWST complement the shorter-wavelength HST measurements. Understanding the differences between IRAC observations and CALSPEC models is important for science that uses IR fluxes from multiple instruments, including JWST. The absolute flux of Spitzer IRAC photometry at 3.6–8 μm agrees with CALSPEC synthetic photometry to 1% for the three prime HST standards: G191B2B, GD153, and GD71. For a set of 17–22 A-star standards, the average IRAC difference rises from agreement at 3.6 μm to 3.4% ± 0.1% brighter than CALSPEC at 8 μm. For a smaller set of G-type stars, the average of the IRAC photometry falls below CALSPEC by as much as 3.7% ± 0.3% for IRAC1, while one G-type star, P330E, is consistent with the A-star ensemble of IRAC/CALSPEC ratios.
Vision Transformer Adapter for Dense Predictions

This work investigates a simple yet powerful dense prediction task adapter for Vision Transformer (ViT). Unlike recently advanced variants that incorporate vision-specific inductive biases into their architectures, the plain ViT suffers inferior performance on dense predictions due to weak prior assumptions. To address this issue, we propose the ViT-Adapter, which allows plain ViT to achieve comparable performance to vision-specific transformers. Specifically, the backbone in our framework is a plain ViT that can learn powerful representations from large-scale multi-modal data. When transferring to downstream tasks, a pre-training-free adapter is used to introduce the image-related inductive biases into the model, making it suitable for these tasks. We verify ViT-Adapter on multiple dense prediction tasks, including object detection, instance segmentation, and semantic segmentation. Notably, without using extra detection data, our ViT-Adapter-L yields state-of-the-art 60.9 box AP and 53.0 mask AP on COCO test-dev. We hope that the ViT-Adapter could serve as an alternative for vision-specific transformers and facilitate future research. The code and models will be released at https://github.com/czczup/ViT-Adapter.
A prototyping method for the re-design of intensive perennial systems: the case of vineyards in France

The results of our re-design and experimentation of grapevine agrosystem, as well as on the other crops (Lancon et al., 2007 and Wery & Langeveld, 2010) show promising perspectives of the prototyping method to achieve high goals for performance and innovation. The complexity of the grapevine agrosystem (ie the number of technical interventions and their potential interactions) requires a strong systemic approach at the interface between the technical and biophysical dimensions of cropping systems (Rapidel et al., 2009). The approach must implement agro-ecological processes to greatly limit inputs. It also required a high innovation and significant changes in the grapevine agrosystem genetics, structure and management. Our results point out the need to re-design grapevine systems from the crop plantation with new varieties, new training systems and with intercrops aiming to improve ecosystem services and maintain a very high level of sustainability criteria.
REMuS-GNN: A Rotation-Equivariant Model for Simulating Continuum Dynamics

Numerical simulation is an essential tool in many areas of science and engineering, but its performance often limits application in practice or when used to explore large parameter spaces. On the other hand, surrogate deep learning models, while accelerating simulations, often exhibit poor accuracy and ability to generalise. In order to improve these two factors, we introduce REMuS-GNN, a rotation-equivariant multi-scale model for simulating continuum dynamical systems encompassing a range of length scales. REMuS-GNN is designed to predict an output vector field from an input vector field on a physical domain discretised into an unstructured set of nodes. Equivariance to rotations of the domain is a desirable inductive bias that allows the network to learn the underlying physics more efficiently, leading to improved accuracy and generalisation compared with similar architectures that lack such symmetry. We demonstrate and evaluate this method on the incompressible flow around elliptical cylinders.
A Safe Flight Approach of the UAV in the Electrical Line Inspection

Abstract In recent years, the UAV (Unmanned Aerial Vehicle) inspection for the electrical line has received increasing attentions due to the advantages of low costs, easiness to control and flexibility. The UAV can inspect the electrical tower independently and automatically by planning the flight path. But during the inspection along the path, the UAV is easily impacted by gust wind due to its light weight and small size, which always leads to the crash into the electrical tower. Thus, in this paper, a safe flight approach (SFA) is proposed to make the flight be safer during the inspection. The main contributions include: firstly, the piecewise linear interpolation method is proposed to fit the distribution curve of the electrical towers based on the GPS coordinates of the electrical towers; secondly, the no-fly zone on the both sides of the distribution curve are created, and a security distance formula (SDF) is raised to decide the width of the no-fly zone; thirdly, a gust wind formula (GWF) is proposed to improve the artificial potential field approach, which can contribute to the path planning of the UAV; finally, a flight path of the UAV can be planned using the SFA to make the UAV avoid colliding with the electric tower. The proposed approach is tested on the experiment to demonstrate its effectiveness.
Nonperturbative renormalization of quantum thermodynamics from weak to strong couplings

By solving the exact master equation of open quantum systems, we formulate the quantum thermodynamics from weak to strong couplings. The open quantum systems exchange matters, energies and information with their reservoirs through quantum particles tunnelings that are described by the generalized Fano-Anderson Hamiltonians. We ﬁnd that the exact solution of the reduced density matrix of these systems approaches a Gibbs-type state in the steady-state limit for the systems in arbitrary initial states as well as for both the weak and strong system-reservoir coupling strengths. When the couplings become strong, thermodynamic quantities of the system must be renormalized. The renormalization eﬀects are obtained nonperturbatively after exactly traced over all reservoir states through the coherent state path integrals. The renormalized system Hamiltonian is characterized by the renormalized system energy levels and interactions, corresponding to the quantum work done by the system. The renormalized temperature is introduced to characterize the entropy production counting the heat transfer between the system and the reservoir. We further ﬁnd that only with the renormalized system Hamiltonian and other renormalized thermodynamic quantities, can the exact steady state of the system be expressed as the standard Gibbs state. Consequently, the corresponding exact steady-state particle occupations in the renormalized system energy levels obey the Bose-Einstein and the Fermi-Dirac distributions for bosonic and fermionic systems, respectively. In the very weak system-reservoir coupling limit, the renormalized system Hamiltonian and the renormalized temperature are reduced to the original bare Hamiltonian of the system and the initial temperature of the reservoir. Thus, the conventional statistical mechanics and thermodynamics are thereby rigorously deduced from quantum dynamical evolution. In the last, this nonperturbative renormalization method is also extended to general interacting open quantum systems.
Sharp asymptotics on the compression of two-layer neural networks

In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M <N nodes. More precisely, we consider the setting in which the weights of the target network are i.i. d. sub-Gaussian, and we minimize the population L<inf>2</inf> loss between the outputs of the target and of the compressed network, under the assumption of Gaussian inputs. By using tools from high-dimensional probability, we show that this non-convex problem can be simplified when the target network is sufficiently over-parameterized, and provide the error rate of this approximation as a function of the input dimension and N. In this mean-field limit, the simplified objective, as well as the optimal weights of the compressed network, does not depend on the realization of the target network, but only on expected scaling factors. Furthermore, for networks with ReLU activation, we conjecture that the optimum of the simplified optimization problem is achieved by taking weights on the Equiangular Tight Frame (ETF), while the scaling of the weights and the orientation of the ETF depend on the parameters of the target network. Numerical evidence is provided to support this conjecture.
An explicit comparison of anticyclotomic p-adic L-functions for hida families

The aim of this note is to compare several anticyclotomic $p$-adic $L$-functions for modular forms and $p$-adic families of ordinary modular forms, which have been defined and studied from different perspectives by Skinner-Urban, Hida, Perin-Riou, Bertolini-Darmon, Vatsal, Chida-Hsieh, Longo-Vigni, Castella-Longo and Castella-Kim-Longo. The main result of this paper is a comparison between the central critical twist of the two-variable anticyclotomic $p$-adic $L$-function obtained as specialisation of the three-variable $p$-adic $L$-function of Skinner-Urban and the two-variable $p$-adic $L$-function introduced by one of the authors on collaboration with Vigni by means of $p$-adic families of Gross points.
Measuring Alignment Bias in Neural Seq2seq Semantic Parsers

Prior to deep learning the semantic parsing community has been interested in understanding and modeling the range of possible word alignments between natural language sentences and their corresponding meaning representations. Sequence-to-sequence models changed the research landscape suggesting that we no longer need to worry about alignments since they can be learned automatically by means of an attention mechanism. More recently, researchers have started to question such premise. In this work we investigate whether seq2seq models can handle both simple and complex alignments. To answer this question we augment the popular Geo semantic parsing dataset with alignment annotations and create Geo-Aligned. We then study the performance of standard seq2seq models on the examples that can be aligned monotonically versus examples that require more complex alignments. Our empirical study shows that performance is significantly better over monotonic alignments.
EXPLICIT MULTIPLICATION TABLES OF CAYLEY-DICKSON ALGEBRAS

A long-standing unresolved issue in the Cayley-Dickson algebra of dimension 2 n is that there lacks of an explicit multiplication table although it can be constructed via inductive construction. In this article we solve this open problem. We show that the Cayley-Dickson algebra is a twisted group algebra with an explicit twist function σ ( A, B ) such that e A e B = ( − 1) σ ( A,B ) e A ⊕ B . The same approach also yields similar results for split Cayley-Dickson algebras.
Moving Smart Contracts - A Privacy Preserving Method for Off-Chain Data Trust

Blockchains provide environments where parties can interact transparently and securely peer-to-peer without needing a trusted third party. Parties can trust the integrity and correctness of transactions and the verifiable execution of binary code on the blockchain (smart contracts) inside the system. Including information from outside of the blockchain remains challenging. A challenge is data privacy. In a public system, shared data becomes public and, coming from a single source, often lacks credibility. A private system gives the parties control over their data and sources but trades in positive aspects as transparency. Often, not the data itself is the most critical information but the result of a computation performed on it. An example is research data certification. To keep data private but still prove data provenance, researchers can store a hash value of that data on the blockchain. This hash value is either calculated locally on private data without the chance for validation or is calculated on the blockchain, meaning that data must be published and stored on the blockchain -- a problem of the overall data amount stored on and distributed with the ledger. A system we called moving smart contracts bypasses this problem: Data remain local, but trusted nodes can access them and execute trusted smart contract code stored on the blockchain. This method avoids the system-wide distribution of research data and makes it accessible and verifiable with trusted software.
F3B: A Low-Latency Commit-and-Reveal Architecture to Mitigate Blockchain Front-Running

Front-running attacks, which benefit from advanced knowledge of pending transactions, have proliferated in the cryptocurrency space since the emergence of decentralized finance. Front-running causes devastating losses to honest participants—estimated at $280M each month—and endangers the fairness of the ecosystem. We present Flash Freezing Flash Boys (F3B), a blockchain architecture to address front-running attacks by relying on a commit-and-reveal scheme where the contents of transactions are encrypted and later revealed by a decentralized secret-management committee once the underlying consensus layer has committed the transaction. F3B mitigates front-running attacks because an adversary can no longer read the content of a transaction before commitment, thus preventing the adversary from benefiting from advance knowledge of pending transactions. We design F3B to be agnostic to the underlying consensus algorithm and compatible with legacy smart contracts by addressing front-running at the blockchain architecture level. Unlike existing commit-and-reveal approaches, F3B only requires writing data onto the underlying blockchain once, estab-lishing a significant overhead reduction. An exploration of F3B shows that with a secret-management committee consisting of 8 and 128 members, F3B presents between 0.1 and 1.8 seconds of transaction-processing latency, respectively.