Miracle-dz/UniFAR-FTUs huggingface dataset:
Green Production as a Factor of Survival for Innovative Startups: Evidence from Italy

Many studies have analyzed empirically the determinants of survival for innovative startup companies using data about the characteristics of entrepreneurs and management or focusing on firm- and industry-specific variables. However, no attempts have been made so far to assess the role of the environmental sustainability of the production process. Based on data describing the characteristics of the Italian innovative startups in the period 2009–2018, this article studies the differences in survival between green and non-green companies. We show that, while controlling for other confounding factors, startups characterized by a green production process tend to survive longer than their counterparts. In particular, we estimate that a green innovative startup is more than twice as likely to survive than a non-green one. This evidence may support the idea that environmental sustainability can help economic development.
Embedding Culture and Grit in the Technology Acceptance Model (TAM) for Higher Education

The implementors of learning technologies within education environments often follow strategies that assume the educational environment within which they are being introduced is culturally neutral. A comprehensive literature review including 150 papers on educational technology challenges was undertaken. The purpose of this review is explore different contextual challenges to the adoption of educational technology in the higher education sector. The cultural factors that define the key stakeholders (e.g., teachers, lectures, students and support staff) are often ignored when the implementation processes are undertaken. Furthermore, it is often assumed that the personnel responsible for the implementation are also culturally neutral and do not possess any attributes unique to their culture. It has been shown that cultural factors may significantly influence the implementation of learning technologies and to design strategies that fail to consider factors may limit their efficiency and effectiveness. The challenges are interrelated and based on the findings, this review proposes a conceptual framework by integrating culture and grit into the Technology Acceptance Model(TAM) for implementing educational technology in higher education. The framework will be useful to guide both practice and research.
Master-Auxiliary: an efficient aggregation strategy for video anomaly detection

The aim of surveillance video anomaly detection is to detect events that rarely or never happened in a specified scene. Different detectors can detect different anomalies. This paper proposes an efficient strategy to aggregate multiple detectors together. At first, the aggregation strategy chooses one detector as master detector, and sets the other detectors as auxiliary detectors. Then, the aggregation strategy extracts credible information from auxiliary detectors, which includes credible abnormal (Cred-a) frames and credible normal (Cred-n) frames, and counts their Cred-a and Cred-n frequencies. Finally, the aggregation strategy utilizes the Cred-a and Cred-n frequencies to calculate soft weights in a voting manner, and uses the soft weights to assist the master detector. Experiments are carried out on multiple datasets. Compared with existing aggregation strategies, the proposed strategy achieves state-of-the-art performance.
Misalignment resilient diffractive optical networks

Abstract As an optical machine learning framework, Diffractive Deep Neural Networks (D2NN) take advantage of data-driven training methods used in deep learning to devise light–matter interaction in 3D for performing a desired statistical inference task. Multi-layer optical object recognition platforms designed with this diffractive framework have been shown to generalize to unseen image data achieving, e.g., >98% blind inference accuracy for hand-written digit classification. The multi-layer structure of diffractive networks offers significant advantages in terms of their diffraction efficiency, inference capability and optical signal contrast. However, the use of multiple diffractive layers also brings practical challenges for the fabrication and alignment of these diffractive systems for accurate optical inference. Here, we introduce and experimentally demonstrate a new training scheme that significantly increases the robustness of diffractive networks against 3D misalignments and fabrication tolerances in the physical implementation of a trained diffractive network. By modeling the undesired layer-to-layer misalignments in 3D as continuous random variables in the optical forward model, diffractive networks are trained to maintain their inference accuracy over a large range of misalignments; we term this diffractive network design as vaccinated D2NN (v-D2NN). We further extend this vaccination strategy to the training of diffractive networks that use differential detectors at the output plane as well as to jointly-trained hybrid (optical-electronic) networks to reveal that all of these diffractive designs improve their resilience to misalignments by taking into account possible 3D fabrication variations and displacements during their training phase.
A high-resolution view of the jets in 3C 465

We present new high-resolution and high-sensitivity studies of the jets in the WAT source 3C 465, using deep transverse-resolved radio observations from e-MERLIN, and with complementary observations from the Very Large Array (VLA). We derive a lower limit βj = (νj/c) ≳ 0.5 for the jet speed, and an upper limit $\theta _{\rm j}\, \lesssim$ 61° for the jet angle to the line of sight. The jet spectral index (α, defined in the sense S ∝ να) is fairly constant (<αjet > = − 0.7), and spectral flattening within 4.4 kpc of the core coincides with bright knots and is consistent with the site of X-ray particle acceleration at the base of the radio jet found in previous studies. There is little difference between the spectra of the two hotspot components, plausibly indicating that electron populations of the same properties are injected there. The north-west (NW) and south-east (SE) plumes are approximately homologous structures, with variations in mass injection and propagation in external pressure and density gradients in the two regions plausibly accounting for the slightly steeper spectrum in the NW plume, <αNWp> = −1.43 compared with the SE plume, <αSEp> = −1.38. Our synchrotron lifetime model supports plausible reacceleration of particles within the plume materials. Overall, our results show that the first-order Fermi process at mildly relativistic and non-relativistic shocks is the most likely acceleration mechanism at play in 3C 465 and distinguish differences between the acceleration at $\beta _{\rm j}\, \gt $ 0.5 and $\beta _{\rm j}\, \lt $ 0.5. The former case can accelerate electrons to higher Lorentz factors.
Gamma-ray counterparts of 2WHSP high-synchrotron-peaked BL Lac objects as possible signatures of ultra-high-energy cosmic ray emission

We present a search for high-energy $\gamma$-ray emission from 566 Active Galactic Nuclei at redshift $z > 0.2$, from the 2WHSP catalog of high-synchrotron peaked BL Lac objects with eight years of Fermi-LAT data. We focus on a redshift range where electromagnetic cascade emission induced by ultra-high-energy cosmic rays can be distinguished from leptonic emission based on the spectral properties of the sources. Our analysis leads to the detection of 160 sources above $\approx$ $5\sigma$ (TS $\geq 25$) in the 1 - 300 GeV energy range. By discriminating significant sources based on their $\gamma$-ray fluxes, variability properties, and photon index in the Fermi-LAT energy range, and modeling the expected hadronic signal in the TeV regime, we select a list of promising sources as potential candidate ultra-high-energy cosmic-ray emitters for follow-up observations by Imaging Atmospheric Cherenkov Telescopes.
Stochastic approximation of lamplighter metrics

We observe that embeddings into random metrics can be fruitfully used to study the L1$L_1$ ‐embeddability of lamplighter graphs or groups, and more generally lamplighter metric spaces. Once this connection has been established, several new upper bound estimates on the L1$L_1$ ‐distortion of lamplighter metrics follow from known related estimates about stochastic embeddings into dominating tree‐metrics. For instance, every lamplighter metric on an n$n$ ‐point metric space embeds bi‐Lipschitzly into L1$L_1$ with distortion O(logn)$O(\log n)$ . In particular, for every finite group G$G$ the lamplighter group H=Z2≀G$H=\mathbb {Z}_2\wr G$ bi‐Lipschitzly embeds into L1$L_1$ with distortion O(loglog|H|)$O(\log \log |H|)$ . In the case where the ground space in the lamplighter construction is a graph with some topological restrictions, better distortion estimates can be achieved. Finally, we discuss how a coarse embedding into L1$L_1$ of the lamplighter group over the d$d$ ‐dimensional infinite lattice Zd$\mathbb {Z}^d$ can be constructed from bi‐Lipschitz embeddings of the lamplighter graphs over finite d$d$ ‐dimensional grids, and we include a remark on Lipschitz free spaces over finite metric spaces.
Non-commutative birational maps satisfying Zamolodchikov equation, and Desargues lattices

We present new solutions of the functional Zamolodchikov tetrahedron equation in terms of birational maps in totally non-commutative variables. All the maps originate from Desargues lattices, which provide geometric realization of solutions to the non-Abelian Hirota-Miwa system. The first map is derived using the original Hirota's gauge for the corresponding linear problem, and the second one from its affine (non-homogeneous) description. We provide also an interpretation of the maps within the local Yang-Baxter equation approach. We exploit decomposition of the second map into two simpler maps which, as we show, satisfy the pentagonal condition. We provide also geometric meaning of the matching ten-term condition between the pentagonal maps. The generic description of Desargues lattices in homogeneous coordinates allows to define another solution of the Zamolodchikov equation, but with functional parameter which should be adjusted in a particular way. Its ultra-local reduction produces a birational quantum map (with two central parameters) with Zamolodchikov property, which preserves Weyl commutation relations. In the classical limit our construction gives the corresponding Poisson map satisfying the Zamolodchikov condition.
Bayesian model predictive control: Efficient model exploration and regret bounds using posterior sampling

Tight performance specifications in combination with operational constraints make model predictive control (MPC) the method of choice in various industries. As the performance of an MPC controller depends on a sufficiently accurate objective and prediction model of the process, a significant effort in the MPC design procedure is dedicated to modeling and identification. Driven by the increasing amount of available system data and advances in the field of machine learning, data-driven MPC techniques have been developed to facilitate the MPC controller design. While these methods are able to leverage available data, they typically do not provide principled mechanisms to automatically trade off exploitation of available data and exploration to improve and update the objective and prediction model. To this end, we present a learning-based MPC formulation using posterior sampling techniques, which provides finite-time regret bounds on the learning performance while being simple to implement using off-the-shelf MPC software and algorithms. The performance analysis of the method is based on posterior sampling theory and its practical efficiency is illustrated using a numerical example of a highly nonlinear dynamical car-trailer system.
SoK: Arms Race in Adversarial Malware Detection

Malicious software (malware) is a major cyber threat that shall be tackled with Machine Learning (ML) techniques because millions of new malware examples are injected into cyberspace on a daily basis. However, ML is known to be vulnerable to attacks known as adversarial examples. In this SoK paper, we systematize the field of Adversarial Malware Detection (AMD) through the lens of a unified framework of assumptions, attacks, defenses and security properties. This not only guides us to map attacks and defenses into some partial order structures, but also allows us to clearly describe the attack-defense arms race in the AMD context. In addition to manually drawing insights, we also propose using ML to draw insights from the systematized representation of the literature. Examples of the insights are: knowing the defender's feature set is critical to the attacker's success; attack tactic (as a core part of the threat model) largely determines what security property of a malware detector can be broke; there is currently no silver bullet defense against evasion attacks or poisoning attacks; defense tactic largely determines what security properties can be achieved by a malware detector; knowing attacker's manipulation set is critical to defender's success; ML is an effective method for insights learning in SoK studies. These insights shed light on future research directions.
Geometric algorithms for predicting resilience and recovering damage in neural networks

Biological neural networks have evolved to maintain performance despite significant circuit damage. To survive damage, biological network architectures have both intrinsic resilience to component loss and also activate recovery programs that adjust network weights through plasticity to stabilize performance. Despite the importance of resilience in technology applications, the resilience of artificial neural networks is poorly understood, and autonomous recovery algorithms have yet to be developed. In this paper, we establish a mathematical framework to analyze the resilience of artificial neural networks through the lens of differential geometry. Our geometric language provides natural algorithms that identify local vulnerabilities in trained networks as well as recovery algorithms that dynamically adjust networks to compensate for damage. We reveal striking vulnerabilities in commonly used image analysis networks, like MLP's and CNN's trained on MNIST and CIFAR10 respectively. We also uncover high-performance recovery paths that enable the same networks to dynamically re-adjust their parameters to compensate for damage. Broadly, our work provides procedures that endow artificial systems with resilience and rapid-recovery routines to enhance their integration with IoT devices as well as enable their deployment for critical applications.
RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images

Recent methods for people detection in overhead, fisheye images either use radially-aligned bounding boxes to represent people, assuming people always appear along image radius or require significant pre-/post-processing which radically increases computational complexity. In this work, we develop an end-to-end rotation-aware people detection method, named RAPiD, that detects people using arbitrarily-oriented bounding boxes. Our fully-convolutional neural network directly regresses the angle of each bounding box using a periodic loss function, which accounts for angle periodicities. We have also created a new dataset1 with spatio-temporal annotations of rotated bounding boxes, for people detection as well as other vision tasks in overhead fisheye videos. We show that our simple, yet effective method outperforms state-of-the-art results on three fisheye-image datasets. The source code for RAPiD is publicly available2.
Proper Learning, Helly Number, and an Optimal SVM Bound

The classical PAC sample complexity bounds are stated for any Empirical Risk Minimizer (ERM) and contain an extra logarithmic factor $\log(1/{\epsilon})$ which is known to be necessary for ERM in general. It has been recently shown by Hanneke (2016) that the optimal sample complexity of PAC learning for any VC class C is achieved by a particular improper learning algorithm, which outputs a specific majority-vote of hypotheses in C. This leaves the question of when this bound can be achieved by proper learning algorithms, which are restricted to always output a hypothesis from C. In this paper we aim to characterize the classes for which the optimal sample complexity can be achieved by a proper learning algorithm. We identify that these classes can be characterized by the dual Helly number, which is a combinatorial parameter that arises in discrete geometry and abstract convexity. In particular, under general conditions on C, we show that the dual Helly number is bounded if and only if there is a proper learner that obtains the optimal joint dependence on $\epsilon$ and $\delta$. As further implications of our techniques we resolve a long-standing open problem posed by Vapnik and Chervonenkis (1974) on the performance of the Support Vector Machine by proving that the sample complexity of SVM in the realizable case is $\Theta((n/{\epsilon})+(1/{\epsilon})\log(1/{\delta}))$, where $n$ is the dimension. This gives the first optimal PAC bound for Halfspaces achieved by a proper learning algorithm, and moreover is computationally efficient.
Exploring the Best Loss Function for DNN-Based Low-latency Speech Enhancement with Temporal Convolutional Networks

Recently, deep neural networks (DNNs) have been successfully used for speech enhancement, and DNN-based speech enhancement is becoming an attractive research area. While time-frequency masking based on the short-time Fourier transform (STFT) has been widely used for DNN-based speech enhancement over the last years, time domain methods such as the time-domain audio separation network (TasNet) have also been proposed. The most suitable method depends on the scale of the dataset and the type of task. In this paper, we explore the best speech enhancement algorithm on two different datasets. We propose a STFT-based method and a loss function using problem-agnostic speech encoder (PASE) features to improve subjective quality for the smaller dataset. Our proposed methods are effective on the Voice Bank + DEMAND dataset and compare favorably to other state-of-the-art methods. We also implement a low-latency version of TasNet, which we submitted to the DNS Challenge and made public by open-sourcing it. Our model achieves excellent performance on the DNS Challenge dataset.
Efficient Offline Monitoring of Linear Temporal Logic with Bit Vectors

A bitmap is a data structure designed to compactly represent sets of integers; it provides very fast operations for querying and manipulating such sets, exploiting bit-level parallelism. In this paper, we describe a technique for the offline verification of arbitrary expressions of Linear Temporal Logic using bitmap manipulation. An event trace is first preprocessed and transformed into a set of bitmaps. The LTL expression is then evaluated through a recursive procedure manipulating these bitmaps. Experimental results show that, for complex LTL formulas containing almost 20 operators, event traces can be evaluated at a throughput of millions of events per second.
Better Late than Never; Scaling Computations in Blockchain by Delaying Transactions

Proof-of-Work~(PoW) based blockchains typically allocate only a tiny fraction (e.g., less than 1% for Ethereum) of the average interarrival time~($\mathbb{I}$) between blocks for validating transactions. A trivial increase in validation time~($\tau$) introduces the popularly known Verifier's Dilemma, and as we demonstrate, causes more forking and increases unfairness. Large $\tau$ also reduces the tolerance for safety against a Byzantine adversary. Solutions that offload validation to a set of non-chain nodes (a.k.a. off-chain approaches) suffer from trust issues that are non-trivial to resolve. In this paper, we present Tuxedo, the first on-chain protocol to theoretically scale $\tau/\mathbb{I} \approx 1$ in PoW blockchains. The key innovation in Tuxedo is to separate the consensus on the ordering of transactions from their execution. We achieve this by allowing miners to delay validation of transactions in a block by up to $\zeta$ blocks, where $\zeta$ is a system parameter. We perform security analysis of Tuxedo considering all possible adversarial strategies in a synchronous network with end-to-end delay $\Delta$ and demonstrate that Tuxedo achieves security equivalent to known results for longest chain PoW Nakamoto consensus. Additionally, we also suggest a principled approach for practical choices of parameter $\zeta$ as per the application requirement. Our prototype implementation of Tuxedo atop Ethereum demonstrates that it can scale $\tau$ without suffering the harmful effects of naive scaling in existing blockchains.
Radula castlei sp. nov. und Anmerkungen zur Gattung Radula

Radula (sect. Cladoradula) castlei from Venezuela is described and figured. Additions are made to Castle's list of Radula species. The names of Radula sections as adapted by Castle are revised and the majority validated by Latin diagnoses and designation of a type species. Es ist ein besonderes Ereignis fiir die Lebermooskunde, daB seit Castle (1969) die erste der wirklich groBen Lebermoosgattungen-niimlich Radula Dum. -in einer monographischen Bearbeitung dieses Jahrhunderts mit eingehenden Beschreibungen, genauen Abbildungen und umfassender Dokumentation vollstandig vorliegt. Diese au3ergewShnliche Leistung ist das Werk eines Mannes-Hempstead Castle's. Ein Lebensalter-genau 32 Jahre-sind verstrichen von der ersten Lieferung (Castle 1937) bis zur letzten (Castle 1969). Wohl sind in diesem Jahrhundert auch Monographien anderer Lebermoosgattungen erschienen wie die von K. Miiller (1905) iiber Scapania (65 Arten), Evans (1934) iiber Acromastigum (28 Arten) und Jovet-Ast (1953-1954) iiber Colura (50 Arten). Aber Radula iibertrifft mit 213 von Castle anerkannten Arten diese Gattungen bei weitem. Sicherlich werden durch weitere Materialien einige der von Castle anerkannten Arten eingezogen werden miissen, zumal von manchen bisher nur sehr spiirliche Belege zuganglich sind. Aber dabei ist groBte Vorsicht geboten, da Castle durch jahrzehntelange Beschiiftigung mit Radula seinen Blick fiir die oft recht unscheinbaren Artmerkmale dieser Gattung auBerordentlich gescharft hat. Da3 aber auch noch manch ausgezeichnete neue Radula auf ihre Beschreibung wartet, zeigt R. castlei sp. nov., zu der Castle (1969 in litt.) bemerkte: "The Venezuelan plant is most distinctive and, in all probability, a valid and undescribed species. " Radula (Sekt. Cladoradula) castlei Grolle, sp. nov. (Fig. 1-5) R. boryanae (Web.) Nees probabiliter affinis, sed differt: (1) Folia flaccida (non subrigida); (2) carina folii caulini 1,5-1,6x longior quam latitudo caulis, leviter incurva (non brevior, valde incurva); (3) lobulus folii caulini late subrectangulatus (non rotundatus); (4) folii caulini appendiculum lobuli lateraliter inciso-bilobatum (non rotundatum); (5) cellulae foliorum trigonis nullis (non distinctis). VENEZUELA. ESTADO MERIDA: E1 Valle nordlich Merida, Gehalz bei etwa 2600 m, Oberwinkler & Poelt HV69-52 (JE--Holotypus. Isotypen in NY und Hb. Poelt, Berlin). Diizisch (nur $ bekannt), olivgriin. SproBe 2-5 cm lang, einfach bis doppelt gefiedert, ausschlieBlich terminale Verzweigung vom Radula-Typ, Fiederung ziemlich 1 Sektion Biologie, 69 Jena, DDR. This content downloaded from 157.55. 39.105 on Fri, 07 Oct 2016 05:00:34 UTC All use subject to http://about.jstor. org/terms 1970] GROLLE: RADULA 663