Miracle-dz/UniFAR-FTUs huggingface dataset:
Tractable loss function and color image generation of multinary restricted Boltzmann machine

The restricted Boltzmann machine (RBM) is a representative generative model based on the concept of statistical mechanics. In spite of the strong merit of interpretability, unavailability of backpropagation makes it less competitive than other generative models. Here we derive differentiable loss functions for both binary and multinary RBMs. Then we demonstrate their learnability and performance by generating colored face images.
Modeling and Simulation of Biological Anaerobic Treatment of Sludge Resulted from Wastewater Treatment

The paper presents a kinetic model of anaerobic digestion based on mass balance of substrate, microorganisms and methane production. The model is simulated using a customized Simulink model and the value of maximum specific growth rate of microorganisms is calibrated to fit the theoretical results with experimental ones regarding methane production. The experimental data were obtained during a three months measurements campaign in 2010 at Wastewater Treatment Plant Constanța South, Romania. Key-Words: wastewater treatment, anaerobic digestion, modeling, simulation
Hybrid quantum-classical algorithms for approximate graph coloring

We show how to apply the recursive quantum approximate optimization algorithm (RQAOA) to MAX-k-CUT, the problem of finding an approximate k-vertex coloring of a graph. We compare this proposal to the best known classical and hybrid classical-quantum algorithms. First, we show that the standard (non-recursive) QAOA fails to solve this optimization problem for most regular bipartite graphs at any constant level p: the approximation ratio achieved by QAOA is hardly better than assigning colors to vertices at random. Second, we construct an efficient classical simulation algorithm which simulates level-1 QAOA and level-1 RQAOA for arbitrary graphs. In particular, these hybrid algorithms give rise to efficient classical algorithms, and no benefit arising from the use of quantum mechanics is to be expected. Nevertheless, they provide a suitable testbed for assessing the potential benefit of hybrid algorithm: We use the simulation algorithm to perform large-scale simulation of level-1 QAOA and RQAOA with up to 300 qutrits applied to ensembles of randomly generated 3-colorable constant-degree graphs. We find that level-1 RQAOA is surprisingly competitive: for the ensembles considered, its approximation ratios are often higher than those achieved by the best known generic classical algorithm based on rounding an SDP relaxation. This suggests the intriguing possibility that higher-level RQAOA may be a potentially useful algorithm for NISQ devices.
The Determinants of Cash Holdings around Different Regions of the World

This paper analyzes cash holding determinants around different regions of the world including Asia Pacific, Europe, Africa, North America, Middle East and South America. The sample consists of 5,957 companies from 47 countries grouped into six regions for the period from 2007 to 2016. Panel Generalized Method of Moments is applied to account for endogeneity in the independent variables. The study is conducted in two phases: in the first stage, model is run for all the companies where results of the analysis show that size, financial strength, investments and cash flows are positively and significantly related to cash holdings for the companies in the sample while leverage, dividend, intangibles, market to book ratio, profitability and net working capital have significant negative effect on cash holdings. In the second phase, model is run separately for each region. The results show deviations across the regions which indicate differences of institutional frameworks in different countries present in different regions of the world. The study is of immense importance for the international managers and academic researchers in analyzing cash holding determinants in different regions having varying institutional and governance settings.
Fragile, Yet Resilient: Adaptive Decline in a Collaboration Network of Firms

The dynamics of collaboration networks of firms follow a life cycle of growth and decline. That does not imply they also become less resilient. Instead, declining collaboration networks may still have the ability to mitigate shocks from firms leaving and to recover from these losses by adapting to new partners. To demonstrate this, we analyze 21.500 R&D collaborations of 14.500 firms in six different industrial sectors over 25 years. We calculate time-dependent probabilities of firms leaving the network and simulate drop-out cascades to determine the expected dynamics of decline. We then show that deviations from these expectations result from the adaptivity of the network, which mitigates the decline. These deviations can be used as a measure of network resilience.
A Noisy Opinion Formation Model with Two Opposing Mass Media

Processes of individual attitude formation and their macroscopic consequences have become an intriguing research topic, and agent-based models of opinion formation have been proposed to understand this phenomenon. This study conducted an agent-based simulation and examined the role of mass media in a noisy opinion formation process, where opinion heterogeneity is preserved by a weak intensity of assimilation and errors accompanying opinion modifications. In a computational model, agents conformed to their neighbours' opinions in social networks. In addition, each agent tended to be influenced by one of a two external agents with fixed opinions, that is, mass media that take opposite positions on an opinion spectrum. The simulation results demonstrated that a small probability of interactions with mass media reduces opinion heterogeneity even with extreme mass media position values. However, a large frequency of interactions with mass media increases opinion heterogeneity. Accordingly, intermediate assimilation strength achieves the least heterogeneous opinion distribution. The influence of mass media dampens the effects of network topology. Our simulation implies that mass media can play qualitatively different roles depending on their positions and intensity of influence.
Finite-difference modeling of faults and fractures

For the purposes of seismic propagation, a slip fault may be regarded as a surface across which the displacement caused by a seismic wave is discontinuous while the stress traction remains continuous. The simplest assumption is that this slip and the stress traction are linearly related. Such a linear slip interface condition is easily modeled when the fault is parallel to the finite-difference grid, but is more difficult to do for arbitrary nonplanar fault surfaces. To handle such situations we introduce equivalent medium theory to model material behavior in the cells of the finite-difference grid intersected by the fault. Virtually identical results were obtained from modeling the fault by (1) an explicit slip interface condition (fault parallel to the grid) and (2) using the equivalent medium theory when the finite-difference grid was rotated relative to the fault and receiver array. No additional computation time is needed except for the preprocessing required to find the relevant cells and their associated moduli. The formulation is sufficiently general to include faults in and between arbitrary anisotropic materials with slip properties that vary as a function of position.
Grafit: Learning fine-grained image representations with coarse labels

This paper tackles the problem of learning a finer representation than the one provided by training labels. This enables fine-grained category retrieval of images in a collection annotated with coarse labels only.Our network is learned with a nearest-neighbor classifier objective, and an instance loss inspired by self-supervised learning. By jointly leveraging the coarse labels and the underlying fine-grained latent space, it significantly improves the accuracy of category-level retrieval methods.Our strategy outperforms all competing methods for retrieving or classifying images at a finer granularity than that available at train time. It also improves the accuracy for transfer learning tasks to fine-grained datasets.
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment

We evaluate the out-of-distribution (OOD) detection performance of self-supervised learning (SSL) techniques with a new evaluation framework. Unlike the previous evaluation methods, the proposed framework adjusts the distance of OOD samples from the in-distribution samples. We evaluate an extensive combination of OOD detection algorithms on three different implementations of the proposed framework using simulated samples, images, and text. SSL methods consistently demonstrated the improved OOD detection performance in all evaluation settings.
FBWave: Efficient and Scalable Neural Vocoders for Streaming Text-To-Speech on the Edge

Nowadays more and more applications can benefit from edge-based text-to-speech (TTS). However, most existing TTS models are too computationally expensive and are not flexible enough to be deployed on the diverse variety of edge devices with their equally diverse computational capacities. To address this, we propose FBWave, a family of efficient and scalable neural vocoders that can achieve optimal performance-efficiency trade-offs for different edge devices. FBWave is a hybrid flow-based generative model that combines the advantages of autoregressive and non-autoregressive models. It produces high quality audio and supports streaming during inference while remaining highly computationally efficient. Our experiments show that FBWave can achieve similar audio quality to WaveRNN while reducing MACs by 40x. More efficient variants of FBWave can achieve up to 109x fewer MACs while still delivering acceptable audio quality. Audio demos are available at https://bichenwu09.github. io/vocoder_demos.
Noncoplanar multiple-
Q
 spin textures by itinerant frustration: Effects of single-ion anisotropy and bond-dependent anisotropy

We theoretically investigate multiple-$Q$ spin textures, which are composed of superpositions of spin density waves with different wave numbers, for an effective spin model of centrosymmetric itinerant magnets. Our focus is on the interplay between biquadratic interactions arising from the spin-charge coupling and magnetic anisotropy caused by the spin-orbit coupling. Taking into account two types of the magnetic anisotropy, single-ion anisotropy and bond-dependent anisotropy, we elucidate magnetic phase diagrams for an archetypal triangular-lattice system in the absence and presence of an external magnetic field. In the case of the single-ion anisotropy, we find a plethora of multiple-$Q$ instabilities depending on the strength and the sign of the anisotropy (easy plane or easy axis), including a skyrmion crystal with topological number of two. In an external magnetic field, we find that a skyrmion crystal with topological number of one is stabilized by the in-plane (out-of-plane) magnetic field under the easy-plane (easy-axis) anisotropy. We also examine the stability of the field-induced skyrmion crystal by rotating the field direction. As a biproduct, we show that a meron crystal with the skyrmion number of one half appears in the presence of the biquadratic interaction and the easy-axis anisotropy. Meanwhile, we find that the bond-dependent anisotropy also stabilizes both types of skyrmion crystals. We show that, however, for the skyrmion crystal with topological number of one, Bloch- and Neel-type skyrmion crystals are selectively realized depending on the sign of the bond-dependent anisotropy. Moreover, we find yet another multiple-$Q$ states, including a meron crystal and a double meron crystal with the skyrmion number of one. The systematic investigation will provide a reference to complex magnetic textures in centrosymmetric magnetic metals.
Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos with Bidirectional Temporal Deep Learning Tracking Algorithm

Oblique back-illumination capillaroscopy has recently been introduced as a method for highquality, non-invasive blood cell imaging in human capillaries. To make this technique practical for clinical blood cell counting, solutions for automatic processing of acquired videos are needed. Here, we take the first step towards this goal, by introducing a deep learning multi-cell tracking model, named CycleTrack, which achieves accurate blood cell counting from capillaroscopic videos. CycleTrack combines two simple online tracking models, SORT and CenterTrack, and is tailored to features of capillary blood cell flow. Blood cells are tracked by displacement vectors in two opposing temporal directions (forwardand backwardtracking) between consecutive frames. This approach yields accurate tracking despite rapidly moving and deforming blood cells. The proposed model outperforms other baseline trackers, achieving 65. 57% Multiple Object Tracking Accuracy and 73. 95% ID F1 score on test videos. Compared to manual blood cell counting, CycleTrack achieves 96. 58 ± 2. 43% cell counting accuracy among 8 test videos with 1000 frames each compared to 93. 45% and 77. 02% accuracy for independent CenterTrack and SORT almost without additional time expense. It takes 800s to track and count approximately 8000 blood cells from 9,600 frames captured in a typical one-minute video. Moreover, the blood cell velocity measured by CycleTrack demonstrates a consistent, pulsatile pattern within the physiological range of heart rate. Lastly, we discuss future improvements for the CycleTrack framework, which would enable clinical translation of the oblique back-illumination microscope towards a real-time and non-invasive point-of-care blood cell counting and analyzing technology.
Portfolio Choice with Model Misspecification: A Foundation for Alpha and Beta Portfolios

Our objective is to investigate the effect of model misspecification on mean-variance portfolios and to show how asset-pricing theory and asymptotic analysis (for large number of assets) can be used to provide powerful solutions to mitigate misspecification. The starting point of our analysis is the Arbitrage Pricing Theory (APT). We extend the APT to show that it can capture not just small pricing errors that are independent of factors, but also large pricing errors from mismeasured or missing factors. Our key insight is that, instead of treating misspecification directly in the mean-variance portfolio, it is better to first decompose the portfolio into components that correspond to the two components of returns in the APT: a ``beta'' portfolio that depends on factor risk premia and an ``alpha'' portfolio that depends only on pricing errors. For the beta portfolio, we treat misspecification using asymptotic analysis: as the number of assets increases, we show that the weights of the alpha portfolio dominate those of the beta portfolio, leading to an expression for mean-variance portfolio weights that is immune to beta misspecification. For the alpha portfolio, we treat misspecification by imposing the APT restriction on alphas, which serves both as an identification condition and a shrinkage constraint. Finally, we demonstrate that our approach achieves an out-of-sample Sharpe ratio that is more than double that of the equally weighted portfolio.
Variational analysis of driven-dissipative bosonic fields

We present a method to perform a variational analysis of the quantum master equation for driven-disspative bosonic fields with arbitrary large occupation numbers. Our approach combines the P representation of the density matrix and the variational principle for open quantum system. We benchmark the method by comparing it to wave-function Monte-Carlo simulations and the solution of the Maxwell-Bloch equation for the Jaynes-Cummings model. Furthermore, we study a model describing Rydberg polaritons in a cavity field and introduce an additional set of variational paramaters to describe correlations between different modes.
Upper bound for the Gromov width of flag manifolds

The Darboux theorem in symplectic geometry states that around any point of a symplectic manifold there is a system of local coordinates such that the symplectic manifold looks locally like R2n with its canonical symplectic form. A natural and fundamental problem in symplectic geometry is to know how far we can extend symplectically these coordinates in the symplectic manifold. This is how the concept of Gromov’s width arises. The Gromov width of a symplectic manifold (M2n, ω) is defined as
Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondence

We propose a novel Deformed Implicit Field (DIF) representation for modeling 3D shapes of a category and generating dense correspondences among shapes. With DIF, a 3D shape is represented by a template implicit field shared across the category, together with a 3D deformation field and a correction field dedicated for each shape instance. Shape correspondences can be easily established using their deformation fields. Our neural network, dubbed DIFNet, jointly learns a shape latent space and these fields for 3D objects belonging to a category without using any correspondence or part label. The learned DIF-Net can also provides reliable correspondence uncertainty measurement reflecting shape structure discrepancy. Experiments show that DIF-Net not only produces high-fidelity 3D shapes but also builds high-quality dense correspondences across different shapes. We also demonstrate several applications such as texture transfer and shape editing, where our method achieves compelling results that cannot be achieved by previous methods. 1
Outcome indistinguishability

Prediction algorithms assign numbers to individuals that are popularly understood as individual “probabilities”—what is the probability of 5-year survival after cancer diagnosis? —and which increasingly form the basis for life-altering decisions. Drawing on an understanding of computational indistinguishability developed in complexity theory and cryptography, we introduce Outcome Indistinguishability. Predictors that are Outcome Indistinguishable (OI) yield a generative model for outcomes that cannot be efficiently refuted on the basis of the real-life observations produced by . We investigate a hierarchy of OI definitions, whose stringency increases with the degree to which distinguishers may access the predictor in question. Our findings reveal that OI behaves qualitatively differently than previously studied notions of indistinguishability. First, we provide constructions at all levels of the hierarchy. Then, leveraging recently-developed machinery for proving average-case fine-grained hardness, we obtain lower bounds on the complexity of the more stringent forms of OI. This hardness result provides the first scientific grounds for the political argument that, when inspecting algorithmic risk prediction instruments, auditors should be granted oracle access to the algorithm, not simply historical predictions.
Effect of Rapamycin on Maternal Aggression in Rats

Rapamycin which is an inhibitor of mammalian target of rapamycin (mTOR), has effects as antineoplastic, retarding aging, anti-inflammatory and neuroprotective. Aim of this study is to investigate the effects of rapamycin on maternal aggression in rats. In this study 63 Wistar female rats were used. The animals were divided into 3 groups: the solvent (DMSO) group, the 5 mg/kg rapamycin group, and the 10 mg/ kg rapamycin group. For behavioral testing the resident-intruder paradigm was used. The groups were compared in terms of the latency to the first aggressive behavior, the number of attacks, the total duration of aggressive behaviors and the intensity of attacks. When the groups were compared in terms of the latency to the first aggressive behavior, it was found that 5 and 10 mg/kg rapamycin groups were significantly prolonged latencies compared to the control group. When were evaluated the number of aggressive behaviors, total duration of aggressive behaviors and average severity of attacks it was found that 5 mg/kg rapamycin group’s values were significantly lower than the control groups. These results show that acute administration of rapamycin, especially in 5 mg/kg dose of rapamycin prolongs the latency of maternal aggression, and decreased the number of attacks, the intensity of attacks and the total duration of aggressive behaviors in rats. Therefore rapamycin may have potential for use as a sedative drug, however it is necessary to conduct further studies.
Curative effects of combination therapy with 4in-1 osteosynthesis and surgical techniques against congenital pseudarthrosis of the tibia in children

Congenital pseudarthrosis of the tibia (CPT) is a rare disease with a variable history. The treatment of CPT remains especially controversial and difficult. In this study, we investigated the outcomes of 4-in-1 osteosynthesis and combined surgical techniques for CPT, and hypothesized that 4-in-1 osteosynthesis and combined surgical techniques could affect the healing of CPT in children patients. Forty cases of CPT treated with 4-in-1 osteosynthesis and combined surgical techniques during March 2012 to March 2016 were collected. The surgical methods included autogenous iliac bone graft, resection of pseudarthrosis of the tibia and surrounding affected tissues, intramedullary rod and Ilizarov external fixation, union of the tibia and fibula during which 4-in-1 osteosynthesis and wrapped autogenous iliac bone graft were performed simultaneously. After the surgery, lengthening with external fixation was paid attention and patients were instructed to do functional exercises. Ohnishi X-ray grading standard and Malhotra ankle valgus grading method were used for healing evaluation. Lateral distal tibial angle (LDTA), interior distal tibial angle (ADTA) and tibial length were measured. Follow-up was performed during which the patients received X-ray examination every 3 months. Among the 40 cases, 26 cases showed early healing (65.0%), 22 cases showed ankle valgus (55.0%) and 17 cases suffered from refracture (42.5%). The patients with Crawford IV or a family history of neurofibromatosis showed comparatively higher ankle valgus rate and refracture rate. Tibial length inequality was observed in 21 cases (52.5%). In different groups, the incidence of tibial length inequality was 33.3%~100.0%, with an average length of 0.41-1.98 cm. LDTA value was ranged from 79.7° to 96.1° and LDTA value from 78.5° to 90.9°. The ratio of cross-sectional area of healing region was 1.8-3.9 and the average value was 2.7. The patients with Crawford IV or neurofibromatosis showed decreased cross-sectional area of healing region. Taken together, we concluded that the 4-in-1 and combined with the surgical techniques could enlarge the cross-sectional area of healing region of CPT and decrease the refracture rate in children with CPT.
Skill Transfer via Partially Amortized Hierarchical Planning

To quickly solve new tasks in complex environments, intelligent agents need to build up reusable knowledge. For example, a learned world model captures knowledge about the environment that applies to new tasks. Similarly, skills capture general behaviors that can apply to new tasks. In this paper, we investigate how these two approaches can be integrated into a single reinforcement learning agent. Specifically, we leverage the idea of partial amortization for fast adaptation at test time. For this, actions are produced by a policy that is learned over time while the skills it conditions on are chosen using online planning. We demonstrate the benefits of our design decisions across a suite of challenging locomotion tasks and demonstrate improved sample efficiency in single tasks as well as in transfer from one task to another, as compared to competitive baselines. Videos are available at: this https URL