Miracle-dz/UniFAR-FTUs huggingface dataset:
Topological quantum phase transition in the Ising-like antiferromagnetic spin chain BaCo$_2$V$_2$O$_8$

Since the seminal ideas of Berezinskii, Kosterlitz and Thouless, topological excitations are at the heart of our understanding of a whole novel class of phase transitions. In most of the cases, those transitions are controlled by a single type of topological objects. There are however some situations, still poorly understood, where two dual topological excitations fight to control the phase diagram and the transition. Finding experimental realization of such cases is thus of considerable interest. We show here that this situation occurs in BaCo$_2$V$_2$O$_8$, a spin-1/2 Ising-like quasi-one dimensional antiferromagnet when subjected to a uniform magnetic field transverse to the Ising axis. Using neutron scattering experiments, we measure a drastic modification of the quantum excitations beyond a critical value of the magnetic field. This quantum phase transition is identified, through a comparison with theoretical calculations, to be a transition between two different types of solitonic topological objects, which are captured by different components of the dynamical structure factor.
Order Reduction Techniques via Routh Approximation: A Critical Survey

ABSTRACT Routh Approximation is believed to witness tremendous usage in deriving the reduced models from their higher equivalents. Order reduction is a critical field of interest for researchers or control engineers dealing with practical and physical systems of higher order. Reason for the popularity of Routh Approximation among other reduction methodologies is its strength of computational simplicity, ease of access and the promise to retain the model stability. This paper accomplishes a survey of the available Routh Approximation algorithms in their varied configuration since five decades. Last survey report based on Routh Approximation was cited in late seventies as discussed in the paper. One prime reason for the outcome of this paper is the problem met by the authors during their own work for analysing varied configuration of Routh Approximation for order reduction. Many times, the arrangement, stated or performed, seemed to be already in existence. Thus, the paper would be helpful to the researchers working in the area of order reduction and have prime focus on the utility of Routh Approximation.
A simple proof of the discrete time geometric Pontryagin maximum principle

We establish a geometric Pontryagin maximum principle for discrete time optimal control problems on finite dimensional smooth manifolds under the following three types of constraints: a) constraints on the states pointwise in time, b) constraints on the control actions pointwise in time, c) constraints on the frequency spectrum of the optimal control trajectories. Our proof follows, in spirit, the path to establish geometric versions of the Pontryagin maximum principle on smooth manifolds indicated in [Cha11] in the context of continuous-time optimal control.
MULTI-AGENT REINFORCEMENT LEARNING

Despite the recent advancement in multi-agent reinforcement learning (MARL), the MARL agents easily overfit the training environment and perform poorly in evaluation scenarios where other agents behave differently. Obtaining generalizable policies for MARL agents is thus necessary but challenging mainly due to complex multi-agent interactions. In this work, we model the MARL problem with Markov Games and propose a simple yet effective method, called ranked policy memory (RPM), i.e., to maintain a look-up memory of policies to achieve good generalizability. The main idea of RPM is to train MARL policies via gathering massive multi-agent interaction data. In particular, we first rank each agent’s policies by its training episode return, i.e., the episode return of each agent in the training environment; we then save the ranked policies in the memory; when an episode starts, each agent can randomly select a policy from the RPM as the behavior policy. Each agent uses the behavior policy to gather multi-agent interaction data for MARL training. This innovative self-play framework guarantees the diversity of multi-agent interaction in the training data. Experimental results on Melting Pot demonstrate that RPM enables MARL agents to interact with unseen agents in multi-agent generalization evaluation scenarios and complete given tasks. It significantly boosts the performance up to 818% on average.
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic Models

Computational learning theory states that many classes of boolean formulas are learnable in polynomial time. This paper addresses the understudied subject of how, in practice, such formulas can be learned by deep neural networks. Specifically, we analyze boolean formulas associated with model-sampling benchmarks, combinatorial optimization problems, and random 3-CNFs with varying degrees of constrainedness. Our experiments indicate that: (i) neural learning generalizes better than pure rule-based systems and pure symbolic approach; (ii) relatively small and shallow neural networks are very good approximators of formulas associated with combinatorial optimization problems; (iii) smaller formulas seem harder to learn, possibly due to the fewer positive (satisfying) examples available; and (iv) interestingly, underconstrained 3-CNF formulas are more challenging to learn than overconstrained ones. Such findings pave the way for a better understanding, construction, and use of interpretable neurosymbolic AI methods.
A REMARK ON AN INTEGRAL INEQUALITY FOR THE B-OPERATORS

In this paper, an error has been pointed out in the proofs of some recent results concerning the B-operators given by Shah and Liman [Integral estimates for the family of B- operators, Operators and Matrices, Vol. 5(1),(2010), 79 - 87]. Certain sharp L_p inequalities valid for 0 < p < \infty for B-operators are also obtained.
Going Deep: The Trade and Welfare Effects of TTIP

Since July 2013, the EU and the US have been negotiating a preferential trade agreement (PTA), the Transatlantic Trade and Investment Partnership (TTIP). We use a multi-country, multi-industry Ricardian trade model with national and international input-output linkages to quantify its potential economic consequences. We structurally estimate the sectoral trade flow elasticities of trade costs and of existing PTAs. We simulate the trade, value added, and welfare effects of the TTIP, assuming that the agreement would eliminate all transatlantic tariffs and reduce non-tariff barriers as other deep PTAs have. The long-run level of real per capita income would change by 2.12% in the EU, by 2.68% in the US, and by -0.03% in the rest of the world relative to the status quo. However, there is substantial heterogeneity across the 134 geographical entities that we investigate. Gross value of EU-US trade could triple, but its value added would grow by substantially less. Moreover, trade diversion effects are more pronounced in value added trade than in gross trade. This signals a deepening of the transatlantic value chain.
A ug 2 02 3 Skin-Friction and Forced Convection from Rough and Smooth Plates

By introducing the concept of self-similar roughness, skin-friction coefficient and forced convection formulas are derived for a plate given its characteristic length, root-mean-squared (RMS) height-of-roughness, isotropic spatial period, and the fluid’s Prandtl number. Compared with this novel theory, the RMS relative error (RMSRE) of friction measurements from Pimenta, Moffat, and Kays (1975) is 4.5%. RMSRE is 3.3% and 4.4% from Bergstrom, Akinlade, and Tachie (2005) wire meshes and perforated sheets. RMSRE is 1.7% from this investigation’s convection experiments. Further analysis of self-similar roughness leads to turbulent friction coefficient and forced convection from a smooth plate. This friction formula matches measurements by Smith and Walker (1959) and Spalding and Chi (1964) with unprecedented 0.75% RMSRE. Friction data-sets by Žukauskas and Šlančiauskas (ZS) (1987) yield RMSRE between 2.5% and 5.2%. Laminar oscillations discovered by Tuck and Kouzoubov (1995) mimic flow along small periodic roughness, leading to comprehensive smooth friction and convection formulas from either critical Reynolds number or free-stream turbulence ratio. Friction measurements by Gebers (1920) yield 2.8% RMSRE. Convection data-sets by ZS yield RMSRE between 1.0% and 6.7%. Table of
Block-wise Intermediate Representation Training for Model Compression

Knowledge distillation (KD) is a popular method for reducing the computational 1 overhead of deep network inference, in which the output of a teacher model is used 2 to train a smaller, faster student model. Hint training (i.e., FitNets) extends KD by 3 regressing a student model’s intermediate representation (IR) to a teacher model’s IR. 4 In this work, we introduce bLock-wise Intermediate representation Training (LIT), a 5 novel model compression technique that extends the use of IRs in deep network com6 pression, outperforming KD and hint training. LIT has two key ideas: 1) LIT trains a 7 student of the same width (but shallower depth) as the teacher by directly comparing 8 the IRs, and 2) LIT uses the IR from the previous block in the teacher model as an 9 input to the current student block during training, avoiding unstable IRs in the student 10 network. We show that LIT provides substantial reductions in network depth without 11 loss in accuracy — for example, LIT can compress a ResNeXt-110 to a ResNeXt-20 12 (5.5×) on CIFAR10 and a VDCNN-29 to a VDCNN-9 (3.2×) on Amazon Reviews, 13 outperforming KD and hint training in network size for a given accuracy. Finally, we 14 show that LIT can effectively compress GAN generators, which are not supported 15 in the KD framework because GANs output pixels as opposed to probabilities. 16
Socio-cultural integration of Afghan refugees in Türkiye: the role of traumatic events, post-displacement stressors and mental health

Abstract Aims Socio-cultural integration of refugees has received scant attention in the academic literature. Türkiye hosts the largest number of refugees, including Afghans, as the second largest asylum-seeking group in Türkiye. There is a dearth of research into the mental health and integration of Afghan refugees in Türkiye. The aim of the present study was to investigate socio-cultural integration outcomes among Afghan refugees in Türkiye by considering the role of traumatic events and post-displacement stressors. The role of mental health in integration outcomes was further examined. Methods A cross-sectional, web-based survey study with 785 Afghan refugees in Türkiye was conducted between April and June 2021. Data were collected on socio-demographic characteristics, potentially traumatic events (PTEs) (Harvard Trauma Questionnaire), post-displacement stressors (Post-Migration Living Difficulties Checklist), mental health symptoms (Hopkins Symptoms Checklist-25), social integration- social bonds (contact with co-ethnic group members) and social bridges (contact with the host community)- and cultural integration (Vancouver Index of Acculturation). Structural equation modelling was conducted to test the hypothesized relationship between conflict and displacement-related stressors, mental health and socio-cultural integration. Results Findings showed that conflict-related traumatic events and post-displacement stressors significantly predicted higher mental health symptoms. Experiencing traumatic events significantly predicted higher levels of social bridges, adopting destination culture and lower level of maintaining heritage culture. Mental health problems predicted the relationship between stressors related to forced displacement and integration outcomes-social bridges and adopting destination culture. These findings highlight the role of mental health as an indispensable resource for socio-cultural integration. Further, conflict and displacement-related stressors are important determinants of socio-cultural integration among Afghan refugees in Türkiye. Conclusion Exposure to PTEs and post-displacement stressors were significant risk factors for the mental health and socio-cultural integration of Afghan refugees in Türkiye. These stressful experiences deteriorate refugees’ mental health, which hinders their integration into the host society.
Karyotype Evolution in Vespertilionoidea: Centromere Repositioning and Inversions in Molossidae (Chiroptera, Mammalia)

Detailed karyotype analysis including fluorescence in-situ hybridization with chromosome-specific and sub-regional probes revealed novel cytogenetic signatures for elucidating phylogenetic relationships in Vespertilionoidea. Two shared derived Robertsonian fusions characterize all members of this superfamily. Further, the majority of Molossidae species show karyotypes with 48 chromosomes, including six of seven species studied here, namely Ozimops petersi, Mormopterus jugularis, Tadarida teniotis, Mops mops, Molossus molossus and M. pretiosus. A shared derived feature of all molossid taxa studied is a variant homolog to Myotis myotis chromosome MMY22. Despite their overall similarity in karyotype composition with one large and three medium-sized bi-armed as well as 19 acrocentric to subtelocentric autosomal pairs, our detailed analysis revealed small differences which shed new light onto molossid phylogenetic relationships. Because of the retention of several ancestral chromosomal characters, O. petersi and M. jugularis represent early offshoots of the molossid tree. A unique derived paracentric inversion in the MMY6 homolog unites all other molossids studied so far. The next species to branch off is T. tadarida, followed by a group composed of M. mops and all studied New World taxa, united by a further derived inversion in the MMY5 homolog. In the light of our findings, we recommend to elevate the subspecies M. t. griseiventer to species rank, because the specimen from Venezuela with a 2n = 42 karyotype differed clearly from the nominate subspecies Molossops t. temminckii with 2n = 48. Further, karyotypes of Miniopterus and Natalus were studied. In the 2n = 46 chromosomal complement of the family Miniopteridae a large number of autapomorphic characters were found. Based on the observation of a common derived condition of the MMY22 homolog, we assume that long-winged bats (Miniopteridae) are closer related to vespertilionids than to molossids. Finally, karyotype comparison in Natalus revealed two autapomorphic characters in the 2n = 36 chromosomal complement.
Active and Natural Suction at Forward-Facing Steps for Delaying Laminar–Turbulent Transition

Two-dimensional, direct numerical simulations are used to study the impact of active and natural (passive) suction at forward-facing steps on laminar–turbulent transition. Suction is applied through a gap in front of a step that is located in a flat-plate boundary-layer flow without streamwise pressure gradient. A steady base flow is used with a Mach number of 0.6. Subsequently, Tollmien–Schlichting waves are introduced by suction and blowing at the wall, and their growth over the surface imperfection is evaluated by N factors. The investigated step heights are in the range of one to two times the local displacement thickness of the smooth flat plate. Both sharp and rounded step corners are investigated. Cases with and without suction are compared with the smooth flat plate without suction based on ΔN factors according to the eN method. Thus, it is shown that suction is capable to compensate or even overcompensate the negative impact of different steps on laminar–turbulent transition. The work concludes w.
A UGMENT YOUR BATCH : BETTER TRAINING WITH LARGER BATCHES

Recently, there is regained interest in large batch training of neural networks, both in theory and practice. New insights and methods allowed certain models to be trained using large batches with no adverse impact on performance. Most works focused on accelerating wall clock training time by modifying the learning rate schedule, without introducing accuracy degradation. We propose to use large batch training to boost accuracy and accelerate convergence by combining it with data augmentation. Our method, ”batch augmentation”, suggests using multiple instances of each sample at the same large batch. We show empirically that this simple yet effective method improves convergence and final generalization accuracy. We further suggest possible reasons for its success.
A UGMENT YOUR BATCH : BETTER TRAINING WITH LARGER BATCHES

Recently, there is regained interest in large batch training of neural networks, both of theory and practice. New insights and methods allowed certain models to be trained using large batches with no adverse impact on performance. Most works focused on accelerating wall clock training time by modifying the learning rate schedule, without introducing accuracy degradation. We propose to use large batch training to boost accuracy and accelerate convergence by combining it with data augmentation. Our method, ”batch augmentation”, suggests using multiple instances of each sample at the same large batch. We show empirically that this simple yet effective method improves convergence and final generalization accuracy. We further suggest possible reasons for its success.
How Leader’s Red Tape Interacts With Employees’ Red Tape From the Lens of the Job Demands-Resources Model

Currently, the public sector is undergoing a major digital transformation. Although this digitization is seen as a positive transformation, digital tools can also put additional job demands on employees, resulting in negative HR outcomes. An example of a job demand resulting from such digital tools is red tape. By building on the job demands-resources model, we developed a theoretical model investigating the relationship between red tape originating from digital tools and turnover intention. Our data analysis (N = 779 teachers; 91 school leaders) provides evidence for a positive indirect relationship between red tape originating from digital tools and turnover intention, through emotional exhaustion. Additionally, our results also suggest that the amount of red tape perceived by an employee’s leader will exacerbate the strength of this indirect relationship, hereby providing evidence that a factor at leader-level can exacerbate effects on a lower level.
Geometric attitude estimation for small satellites

Increasing satellite attitude requirements demand high accuracy estimation methods capable of operating under significant constraints. To meet these demands, dynamical modeling has been used as an effective alternative to ADCS rate hardware for small satellite missions. However, current dynamical models and estimators generally do not respect the Hamiltonian nature of attitude dynamics. This nature confers unique geometric properties to the system which must be preserved for accurate modeling. This thesis addresses the inconsistency by considering estimation from a geometric point of view. It proves that the nonlinear and first-order attitude equations form a single Hamiltonian system with joint structure. This motivates the first known unified geometric treatment of nonlinear and first-order Hamiltonian equations. The resulting nonlinear maps and state transition matrices are shown to offer considerable advantages, including better state accuracies and integral invariant preservation for comparable computational expense compared to standard solutions of the same order. Additionally, a new unified attitude integrator is presented that exactly preserves the Hamiltonian structure of the nonlinear/first-order attitude system. These geometric methods are employed in the Kalman Filter, and it is demonstrated that qualitative properties preserved by the geometric maps are preserved with high accuracy by the estimators. Simulations reveal distinct geometric properties of the geometric filters' state estimates and error bounds that nongeometric estimators do not possess. Substantially improved state estimates are also demonstrated. A geometric investigation of the general nonlinear estimation problem follows. Probability density functions are shown to be conserved properties of deterministic Hamiltonian systems, and appropriate geometric integrators exactly preserve the functions as they evolve in time. Based on these insights, a new iterative filter is derived which preserves qualitative properties of nonlinear dynamics, first-order dynamics, and the general estimation problem. Comparisons with a benchmark iterative filter demonstrate substantially reduced computational burden and superior convergence properties given high nonlinearity.
Dysthyroidism during immune checkpoint inhibitors is associated with improved overall survival in adult cancers: data mining of 1385 electronic patient records

Background Dysthyroidism (DT) is a common toxicity of immune checkpoint inhibitors (ICIs) and prior work suggests that dysthyroidism (DT) might be associated with ICI efficacy. Patients and methods ConSoRe, a new generation data mining solution, was used in this retrospective study, to extract data from electronic patient records of adult cancer patients treated with ICI at Institut Paoli-Calmettes (Marseille, France). Every DT was verified and only ICI-induced DT was retained. Survival analyses were performed by Kaplan-Meier method (log-rank test) and Cox model. To account for immortal time bias, a conditional landmark analysis was performed (2 months and 6 months), together with a time-varying Cox model. Results Data extraction identified 1385 patients treated with ICI between 2011 and 2021. DT was associated with improved overall survival (OS) (HR 0.46, (95% CI 0.33 to 0.65), p<0.001), with a median OS of 35.3 months in DT group vs 15.4 months in non-DT group (NDT). Survival impact of DT was consistent using a 6-month landmark analysis with a median OS of 36.7 months (95% CI 29.4 to not reported) in the DT group vs 25.5 months (95% CI 22.8 to 27.8) in the NDT group. In multivariate analysis, DT was independently associated with improved OS (HR 0.49, 95% CI 0.35 to 0.69, p=0.001). After adjustment in time-varying Cox model, this association remained significant (adjusted HR 0.64, 95% CI 0.45 to 0.90, p=0.010). Moreover, patients with DT and additional immune-related adverse event had increased OS compared with patients with isolated DT, with median OS of 38.8 months vs 21.4 months, respectively. Conclusion Data mining identified a large number of patients with ICI-induced DT, which was associated with improved OS accounting for immortal time bias.
Matching Features without Descriptors: Implicitly Matched Interest Points (IMIPs)

The extraction and matching of interest points is a prerequisite for visual pose estimation and related problems. Traditionally, matching has been achieved by assigning descriptors to interest points and matching points that have similar descriptors. In this paper, we propose a method by which interest points are instead already implicitly matched at detection time. Thanks to this, descriptors do not need to be calculated, stored, communicated, or matched any more. This is achieved by a convolutional neural network with multiple output channels. The i-th interest point is the location of the maximum of the i-th channel, and the i-th interest point in one image is implicitly matched with the i-th interest point in another image. This paper describes how to design and train such a network in a way that results in successful relative pose estimation performance with as little as 128 output channels. While the overall matching score is slightly lower than with traditional methods, the network also outputs the confidence for a specific interest point resulting in a valid match. Most importantly, the approach completely gets rid of descriptors and thus enables localization systems with a significantly smaller memory footprint and multi-agent localization systems that require significantly less bandwidth. We evaluate performance relative to state-of-the-art alternatives.