Energy Function Research Articles

Point-Sim: A Lightweight Network for 3D Point Cloud Classification

Analyzing point clouds with neural networks is a current research hotspot. In order to analyze the 3D geometric features of point clouds, most neural networks improve the network performance by adding local geometric operators and trainable parameters. However, deep learning usually requires a large amount of computational resources for training and inference, which poses challenges to hardware devices and energy consumption. Therefore, some researches have started to try to use a nonparametric approach to extract features. Point-NN combines nonparametric modules to build a nonparametric network for 3D point cloud analysis, and the nonparametric components include operations such as trigonometric embedding, farthest point sampling (FPS), k-nearest neighbor (k-NN), and pooling. However, Point-NN has some blindness in feature embedding using the trigonometric function during feature extraction. To eliminate this blindness as much as possible, we utilize a nonparametric energy function-based attention mechanism (ResSimAM). The embedded features are enhanced by calculating the energy of the features by the energy function, and then the ResSimAM is used to enhance the weights of the embedded features by the energy to enhance the features without adding any parameters to the original network; Point-NN needs to compute the similarity between each feature at the naive feature similarity matching stage; however, the magnitude difference of the features in vector space during the feature extraction stage may affect the final matching result. We use the Squash operation to squeeze the features. This nonlinear operation can make the features squeeze to a certain range without changing the original direction in the vector space, thus eliminating the effect of feature magnitude, and we can ultimately better complete the naive feature matching in the vector space. We inserted these modules into the network and build a nonparametric network, Point-Sim, which performs well in 3D classification tasks. Based on this, we extend the lightweight neural network Point-SimP by adding some trainable parameters for the point cloud classification task, which requires only 0.8 M parameters for high performance analysis. Experimental results demonstrate the effectiveness of our proposed algorithm in the point cloud shape classification task. The corresponding results on ModelNet40 and ScanObjectNN are 83.9% and 66.3% for 0 M parameters—without any training—and 93.3% and 86.6% for 0.8 M parameters. The Point-SimP reaches a test speed of 962 samples per second on the ModelNet40 dataset. The experimental results show that our proposed method effectively improves the performance on point cloud classification networks.

Research on adaptive coupling trajectory tracking anti-swing control strategy for three-dimensional double-pendulum overhead crane

In the transportation process of a three-dimensional double-pendulum overhead crane, the system is significantly influenced by the pronounced coupling introduced by the double-pendulum effect, posing a considerable challenge for the development of effective anti-swing control strategies. Moreover, uncertainties in certain system parameters and errors in trolley positioning contribute to the complexity of anti-swing control strategy design. To address these practical issues, a control strategy is proposed: First, an S-shaped transport trajectory with minimal positioning error is introduced, incorporating more system parameters into the coupling signal design to enhance system coupling. Based on this, an error-coupled trajectory signal is introduced. Second, the error-coupled trajectory signal is integrated into the energy function, and leveraging adaptive principles, an adaptive coupled trajectory tracking anti-swing control strategy is proposed to estimate uncertain system parameters online. Subsequently, the asymptotic stability of the equilibrium point of the closed-loop system is verified using the Lyapunov techniques and the Barbalat lemma. Finally, through simulations and experiments, it is demonstrated that the proposed control strategy not only ensures precise positioning of the trolley and bridge but also effectively suppresses oscillations of the hook and load, exhibiting excellent control performance. Even in scenarios where system parameters undergo changes or external disturbances are introduced, the proposed control strategy exhibits strong robustness and holds significant practical potential.

Extended improved multiparameter exponential‐type potential energy function for diatomic molecules

AbstractBased on the improved multiparameter exponential‐type potential (IMPET), an extended IMPET (EIMPET) potential has been suggested to reduce the discrepancy between the calculated and experimental values, improving the accuracy near the equilibrium and in the asymptotic region. The average fitting deviation δav and Root‐Mean‐Square (RMS) error is used to evaluate the performance of the potential which generates potential energy curves (PECs) and vibrational energy levels for ClF‐A(3Π1), CP‐X2Σ+, ICl‐X1Σ+, IBr‐X1Σ+, CO‐X1Σ+, and 6Li2‐X1Σ+ molecules that agree better with the Rydberg‐Klein‐Rees (RKR) data than Morse, Huxley‐Murrell (HM) and IMPET potentials. This work provides a reference for the construction and improvement of potential energy functions for diatomic molecules.

Discrete Laplacian on Graphs, Dirichlet Problem, and Minimization of Energy


 
 
 This report is primarily interested in discrete Laplacian on graphs and Dirichlet prob- lem. We investigate two cases of graphs, their Dirichlet problems, and the method of finding solutions to such problems by minimization of their corresponding energy functionals. In the case of an infinite, locally-finite, undirected, simple graph, we find that the minimiza- tion of its energy functional yields a solution to its Dirichlet or boundary-value problem. In the case of finite, directed, locally-finite decision tree, we propose a type of such tree specified by a chosen vertex with no predecessors and by a chosen boundary set of vertices that have no successors. We then further consider a special case of such construction where all the paths of the tree terminate at the same level. We find that the minimization of such special case’s energy functional yields a solution to its Dirichlet problem only when some additional conditions on and close to the boundary (level of termination) are met.
 
 

An improvement method for pancreas CT segmentation using superpixel-based active contour.

Pancreas is one of the most challenging organs for CT image automatic segmentation due to its complex shapes and fuzzy edges. It is simple and universal to use the traditional segmentation method as a post-processor of deep learning method for segmentation accuracy improvement. As the most suitable traditional segmentation method for pancreatic segmentation, the active contour model(ACM), still suffers from the problems of weak boundary leakage and slow contour evolution speed. Therefore, a convenient post-processor for any deep learning methods using superpixel-based active contour model(SbACM) is proposed to improve the segmentation accuracy. Firstly, the superpixels with strong adhesion to edges are used to guide the design of narrowband and energy function. A multi-scale evolution strategy is also proposed to reduce the weak boundary leakage and comprehensively improve the evolution speed. Secondly, using the original image and the coarse segmentation results obtained from deep learning methods as inputs, the proposed SbACM method is used as a post-processor for fine segmentation. Finally, the pancreatic segmentation public dataset TCIA from the National Institutes of Health(NIH, USA) is used for evaluation, and the Wilcoxon Test confirmed that the improvement of proposed method is statistically significant. The results show that: (1) The superpixel-based narrowband shape and dynamic edge energy of the proposed SbACM work for boundary leakage reduction, as well as the multi-scale evolution strategy and dynamic narrowband width for the evolution speed improvement; (2)As a post-processor, SbACM can increase the Dice similarity coefficients(DSC) of five typical UNet-based models, including UNet, SS-UNet, PBR UNet, ResDSN, and nnUNet, 2.35% in average and 9.04% in maximum. (3)Based on the best backbone nnUNet, the proposed post-processor performs better than either adding edge awareness or adding edge loss in segmentation enhancement without increasing the complexity and training time of deep learning models.

Examining Lurasidone Efficacy in Patients with Schizophrenia Spectrum Illness and Concurrent Alcohol and Substance Use Disorder: A Prospective, Multicentric, Real-World Investigation

Background: Dual disorders (DD) entail the coexistence of a substance use disorder (SUD) and another mental health condition, often within psychotic and affective disorders. This study aims to evaluate lurasidone, an innovative atypical antipsychotic, in individuals diagnosed with schizophrenia spectrum disorder and concurrent comorbidities of alcohol use disorder/substance use disorder (AUD/SUD). Methods: A cohort of 23 subjects diagnosed with schizophrenia spectrum disorder and comorbid AUD/SUD underwent psychometric assessments at baseline (T0) and one-month (T1) post-lurasidone initiation. Results: Lurasidone exhibited significant reductions in psychopathological burden, evidenced by decreased total PANSS scores (Z = 2.574, p = 0.011). Positive symptoms, substance craving (VAS Craving; Z = 3.202, p = 0.001), and aggressivity (MOAS scale; Z = 2.000, p = 0.050) were notably reduced. Clinical Global Impression (CGI) scores significantly improved (Z = 2.934, p = 0.003). Quality of life enhancements were observed in SF-36 subscales (energy, emotional well-being, and social functioning) (p < 0.05) and Q-LES-Q-SF scale (Z = −2.341, p = 0.021). A safety analysis indicated lurasidone’s good tolerability, with only 8.7% reporting discontinuation due to side effects. Conclusions: This study offers initial evidence supporting lurasidone’s efficacy and safety in dual diagnoses, highlighting positive effects on psychopathology, substance craving, and quality of life. These findings emphasize the need for tailored, comprehensive treatment strategies in managing the complexities of this patient population.

Effects of low-frequency voltage on nonlinear standing wave excitation, plasma uniformity, and ion dynamics in dual-frequency asymmetric capacitive discharges

Abstract It is known that in very-high-frequency capacitively coupled plasmas (VHF CCPs), the higher harmonics generated by nonlinear sheath motion can enhance the standing wave effect (SWE), which can lead to center-peaked plasma density profiles. In this work, an improved nonlinear electromagnetic model incorporating a transmission line model, an electron momentum balance model, a bulk plasma model, a collisionless nonlinear numerical sheath model, and an ion Monte-Carlo collision (MCC) model is developed to study the effects of low-frequency (LF) voltage VL on the nonlinear standing wave excitation, plasma uniformity, and ion energy and angular distribution functions (IEDFs and IADFs) in dual-frequency (DF) asymmetric capacitive argon discharges at relatively low pressure of 3 Pa. The plasma diffusion in the radial direction and ion dynamics within the LF oscillating sheath are self-consistently considered. The LF voltage VL at 2 MHz varies from 0 to 700 V while the high-frequency (HF) voltage VH at 60 MHz is fixed at 100 V. Simulation results indicate that without the addition of an LF source (i.e. VL = 0 V), there are a considerable number of high-order harmonics with short wavelengths, leading to significant SWE and central peak in the radial plasma density profile. Nevertheless, the high-order harmonic excitations tend to be weakened and merely occur around the phase of the full LF sheath collapse due to a shorter characteristic damping time of the surface waves as VL increases. This, combined with increased surface wavelengths of both the driving frequency and the higher harmonics at a higher VL, leads to suppressed standing waves and improved plasma uniformity. Meanwhile, the simulations show that both the low and the high energy peaks of IEDF move towards higher energies, and the energy peak separation width ΔE becomes wider with the increase of VL. The IEDF at the radial center of the powered electrode exhibits a broader ΔE than that at the edge. For the IADF, an increased VL results in more ions incident on the electrode with a smaller deflection angle. Because of a thinner sheath and a higher sheath voltage at the electrode center, the peak value of IADF at the electrode center is greater than that at the edge.

Natural frequencies and stability for Z-shaped fluid-conveying pipes with constrained-layer damping – Semi-analytic modeling with experimental validation

As the common fluid-conveying component, pipe structures are susceptible to excessive vibration due to internal fluid and external excitation. Constrained-layer damping (CLD) is commonly applied to suppress structural vibrations. However, effective modeling and vibration suppression analysis for Z-shaped fluid-conveying pipes are lacking to explore novel dynamic behaviors. This paper presents a dynamic model for analyzing the three-dimensional vibration of Z-shaped fluid-conveying pipes treated by partial CLD based on the semi-analytical method. Utilizing Timoshenko beam theory and calculating the centrifugal, Coriolis, and inertial force of fluids, the energy of straight and curved fluid-conveying pipes treated by CLD is derived. Based on the Lagrangian energy function, the equation of motion is obtained by using the Rayleigh-Ritz method. The improved artificial spring set is deployed to model the interaction of pipe sections and the non-uniform constraint distribution of clamps. Modal parameters are obtained by solving characteristic equations. The proposed method is verified by comparisons with references and modal experiment results. Frequency parameters, damping effects, and stability are analyzed under various fluid, geometric, and CLD parameters. In conclusion, a novel dynamic model is provided for analyzing the vibration and stability of the Z-shaped fluid-conveying pipe treated by CLD, which lays a foundation for vibration suppression research of fluid-conveying pipe systems.

The IAEA electronic stopping power database: Modernization, review, and analysis of the existing experimental data

We review the electronic stopping power data within the IAEA database, assessing the abundance and scarcity of available data as a function of energy and collisional systems. Our analysis includes an examination of the experimental values, their evolution in time, the dispersion among data, and trends of the more recent measurements. Additionally, we provide comparisons with SRIM-2013 calculations for select cases of interest. Notably, we identify sparsely measured systems and energy regions, emphasizing the pressing need for new, reliable data and independent theoretical predictions.

Dynamics in a light-sensitive neuron with two capacitive variables

Involvement of two capacitive variables into neuron models provides better description of the cell membrane property and then the diversity effect of electromagnetic field inner and outer of the cell membrane can be estimated in clear way. Specific electric components can be combined to build equivalent neural circuits for reproducing similar neural activities under some self-adaptive control schemes. A phototube converts external light into electric stimuli and the injected energy is encoded to excite the cell membranes for presenting suitable firing patterns. Two capacitors are connected via a linear resistor for mimicking the energy exchange and changes of membrane potentials. Combination of memristor into an additive branch circuit of the neural circuit can estimate the effect of electromagnetic induction and energy absorption. The energy function H for this light-sensitive and memristive neuron is calculated in theoretical way, and the average energy function 〈H〉 can predict the occurrence of stochastic resonance, which can be confirmed by estimating the distribution of signal to noise ratios. The firing mode is relative to the energy value of the neuron, and a control law is suggested to control the mode transition in neural activities in an adaptive way.

CTGGAN: Controllable Text Generation with Generative Adversarial Network

Controllable Text Generation (CTG) aims to modify the output of a Language Model (LM) to meet specific constraints. For example, in a customer service conversation, responses from the agent should ideally be soothing and address the user’s dissatisfaction or complaints. This imposes significant demands on controlling language model output. However, demerits exist among traditional methods. Promoting and fine-tuning language models exhibit the “hallucination” phenomenon and cannot guarantee complete adherence to constraints. Conditional language models (CLM), which map control codes into LM representations or latent space, require training the modified language models from scratch and a high amount of customized dataset is demanded. Decoding-time methods employ Bayesian Rules to modify the output of the LM or model constraints as a combination of energy functions and update the output along the low-energy direction. Both methods are confronted with the efficiency sampling problem. Moreover, there are no methods that consider the relation between constraints weights and the contexts, as is essential in actual applications such as customer service scenarios. To alleviate the problems mentioned above, we propose Controllable Text Generation with Generative Adversarial Networks (CTGGAN), which utilizes a language model with logits bias as the Generator to produce constrained text and employs the Discriminator with learnable constraint weight combinations to score and update the generation. We evaluate the method in the text completion task and Chinese customer service dialogues scenario, and our method shows superior performance in metrics such as PPL and Dist-3. In addition, CTGGAN also exhibits efficient decoding compared to other methods.

Confinement of N-Body Systems and Non-integer Dimensions

The squeezing process of a three-dimensional quantum system by use of an external deformed one-body oscillator potential can also be described by the d-method, without external field and where the dimension can take non-integer values. In this work we first generalize both methods to N particles and any transition between dimensions below 3. Once this is done, the use of harmonic oscillator interactions between the particles allows complete analytic solutions of both methods, and a direct comparison between them is possible. Assuming that both methods describe the same process, leading to the same ground state energy and wave function, an analytic equivalence between the methods arises. The equivalence between both methods and the validity of the derived analytic relation between them is first tested for two identical bosons and for squeezing transitions from 3 to 2 and 1 dimensions, as well as from 2 to 1 dimension. We also investigate the symmetric squeezing from 3 to 1 dimensions of a system made of three identical bosons. We have in all the cases found that the derived analytic relations between the two methods work very well. This fact permits to relate both methods also for large squeezing scenarios, where the brute force numerical calculation with the external field is too much demanding from the numerical point of view, especially for systems with more than two particles.

Inferring effective couplings with restricted Boltzmann machines

Generative models offer a direct way of modeling complex data. Energy-based models attempt to encode the statistical correlations observed in the data at the level of the Boltzmann weight associated with an energy function in the form of a neural network. We address here the challenge of understanding the physical interpretation of such models. In this study, we propose a simple solution by implementing a direct mapping between the Restricted Boltzmann Machine and an effective Ising spin Hamiltonian. This mapping includes interactions of all possible orders, going beyond the conventional pairwise interactions typically considered in the inverse Ising (or Boltzmann Machine) approach, and allowing the description of complex datasets. Earlier works attempted to achieve this goal, but the proposed mappings were inaccurate for inference applications, did not properly treat the complexity of the problem, or did not provide precise prescriptions for practical application. To validate our method, we performed several controlled inverse numerical experiments in which we trained the RBMs using equilibrium samples of predefined models with local external fields, 2-body and 3-body interactions in different sparse topologies. The results demonstrate the effectiveness of our proposed approach in learning the correct interaction network and pave the way for its application in modeling interesting binary variable datasets. We also evaluate the quality of the inferred model based on different training methods.

Measuring the acoustic reflex through the tympanic membrane.

The acoustic reflex is the active response of the middle ear to loud sounds, altering the mechanical transfer function of the acoustic energy into the inner ear. Our goal is to observe the effect of the acoustic reflex on the tympanic membrane by identifying a significant nonlinear increase in membrane oscillations. By using interferometric spectrally encoded endoscopy we record the membrane oscillations over time in response to a loud, 200-ms-long acoustic stimulus. A gradual reflex activation is measured between approximately 40 and 100 ms, manifested as a linear 42% increase in the umbo oscillation amplitude. The measured oscillations correlate well with those expected from a mechanical model of a damped harmonic oscillator, and the results of this work demonstrate the potential of interferometric spectrally encoded endoscopy to observe unique dynamical processes in the tympanic membrane and in the middle ear.

On the Energy Dependence of the PL of RE Ions in LuBO3:RE (RE = Ce, Eu, Gd, or Tb)

LuBO3 crystallizes in the calcite type (CaCO3) structure and is a widely applied inorganic host for luminescent materials and scintillators. Even though many scientific works have been published concerning the optical properties of rare-earth-doped LuBO3, so far, no study of the emission spectra as function of the excitation energy of such orthoborates has been conducted. Therefore, this work elaborates on the photoluminescence of RE-doped LuBO3 with RE = Ce3+, Eu3+, Gd3+, or Tb3+, while an emphasize is laid on the energy dependence of these four luminescent compounds. The reflection, emission, and excitation spectra were additionally recorded, and the particle size distribution was determined. Calcite type LuBO3 was successfully obtained, and the characteristic RE luminescence was observed. Furthermore, the Gd3+-doped sample showed no scintillation due to its monovalence and a sensitizer is required to transfer the respective energy to the Gd3+-activator.

Combining Rosetta Sequence Design with Protein Language Model Predictions Using Evolutionary Scale Modeling (ESM) as Restraint.

Computational protein sequence design has the ambitious goal of modifying existing or creating new proteins; however, designing stable and functional proteins is challenging without predictability of protein dynamics and allostery. Informing protein design methods with evolutionary information limits the mutational space to more native-like sequences and results in increased stability while maintaining functions. Recently, language models, trained on millions of protein sequences, have shown impressive performance in predicting the effects of mutations. Assessing Rosetta-designed sequences with a language model showed scores that were worse than those of their original sequence. To inform Rosetta design protocols with language model predictions, we added a new metric to restrain the energy function during design using the Evolutionary Scale Modeling (ESM) model. The resulting sequences have better language model scores and similar sequence recovery, with only a minor decrease in the fitness as assessed by Rosetta energy. In conclusion, our work combines the strength of recent machine learning approaches with the Rosetta protein design toolbox.

Asymptotic behavior in a two-species chemotaxis system with Lotka-Volterra type competition and two signals

This paper deals with the study of asymptotic behavior of the following two-species chemotaxis system with Lotka-Volterra type competition and two signals{∂tu1=Δu1−∇⋅(χ11(υ1)u1∇υ1)−∇⋅(χ12(υ2)u1∇υ2)+μ1u1(1−u1−a1u2),x∈Ω,t>0,∂tu2=Δu2−∇⋅(χ21(υ1)u2∇υ1)−∇⋅(χ22(υ2)u2∇υ2)+μ2u2(1−u2−a2u1),x∈Ω,t>0,∂tυ1=Δυ1−λ1υ1+α11u1+α12u2,x∈Ω,t>0,∂tυ2=Δυ2−λ2υ2+α21u1+α22u2,x∈Ω,t>0 under homogeneous Neumann boundary conditions in a bounded domain Ω⊂Rn, where parameters μi≥0,ai≥0,λi>0, αij>0(i,j=1,2) and χij(i,j=1,2) are the chemotactic sensitivity functions. The boundedness and global existence of solutions to this system have been studied [22]. By constructing appropriate energy function, the purpose of this paper is to prove the asymptotic behavior for the cases•If a1,a2∈(0,1) and μ1,μ2 are suitably large, then the solution (u1,u2,υ1,υ2) is exponentially converges to (1−a11−a1a2,1−a21−a1a2,α11(1−a1)+α12(1−a2)λ1(1−a1a2),α21(1−a1)+α22(1−a2)λ2(1−a1a2)) as t→∞.•If a1>1>a2>0 and μ1,μ2 are suitably large, then the solution (u1,u2,υ1,υ2) is exponentially converges to (0,1,α12λ1,α22λ2) as t→∞.•If a1=1>a2>0 and μ1,μ2 are suitably large, then the solution (u1,u2,υ1,υ2) is algebraically converges to (0,1,α12λ1,α22λ2) as t→∞.

Partially deorbitalized meta-GGA

Mejia-Rodriguez and Trickey recently proposed a procedure for removing the explicit dependence of meta-GGA exchange-correlation energy functionals Exc on the kinetic energy density τ. We present a simple modification to this approach in which the exact Kohn-Sham τ is used as input for Exc but the functional derivative of τ with respect to the density ρ, required to calculate the potential term ∫d3r′δExc∕δτ(r′)∣ρ⋅δτ(r′)∕δρ(r), is evaluated using an approximate kinetic energy density functional. This ‘half-way’ strategy ensures that the Kohn-Sham potential is a local multiplicative function (as opposed to the non-local potential of a generalized Kohn-Sham approach) while preserving the inherent non-locality of the functional itself. Electronic structure codes can be easily modified to use the new method. We validate it by quantifying the accuracy of the predicted lattice parameters, bulk moduli, magnetic moments and cohesive energies of a large set of periodic solids. An unanticipated benefit of this method is to gauge the quality of approximate kinetic energy functionals by checking if the self-consistent solution is indeed at the variational minimum.

Timing performance of the CMS High Granularity Calorimeter prototype

This paper describes the experience with the calibration, reconstruction and evaluation of the timing capabilities of the CMS HGCAL prototype in the beam tests in 2018. The calibration procedure includes multiple steps and corrections ranging from tens of nanoseconds to a few hundred picoseconds. The timing performance is studied using signals from positron beam particles with energies between 20 GeV and 300 GeV. The performance is studied as a function of particle energy against an external timing reference as well as standalone by comparing the two different halves of the prototype. The timing resolution is found to be 60 ps for single-channel measurements and better than 20 ps for full showers at the highest energies, setting excellent perspectives for the HGCAL calorimeter performance at the HL-LHC.

A novel method for efficiently measuring the non-proportionality of scintillators between light output and alpha particle energies from 1.8 MeV to 5.2 MeV

It is known that scintillators exhibit non-proportional behavior between light output and the energy of gamma photons or beta particles. However, the non-proportionality between light output in scintillators and the energy of alpha particles has not been extensively measured, likely due to the challenges associated with preparing alpha particles with varying energies. To address this issue, we propose a novel method to modulate the energy of alpha particles using an americium-241 (Am-241) source covered with different numbers of Mylar films. By irradiating various scintillators, including GAGG, GGAG, YAP(Ce), and plastic scintillator, with alpha particles of different energies, we measured and evaluated the non-proportional response of these scintillators. We then compared the measured response as a function of incident energy to a simulation, which assumes a proportional response to evaluate the non-proportionality. For all the scintillators tested, non-proportionality was observed; the light output per MeV at 1.8 MeV ranged from 0.60 to 0.81 of the values observed at 5.2 MeV. The non-proportional response was largest for plastic scintillator (0.60) and smallest for GAGG (0.81). We conclude that the proposed method could be an efficient means of measuring the non-proportionality of scintillators between light output and alpha particle energies