Computational Theory Research Articles

An analysis of how many undiscovered vulnerabilities remain in information systems

Vulnerability management strategy, from both organizational and public policy perspectives, hinges on an understanding of the supply of undiscovered vulnerabilities. If the number of undiscovered vulnerabilities is small enough, then a reasonable investment strategy would be to focus on finding and removing the remaining undiscovered vulnerabilities. If the number of undiscovered vulnerabilities is and will continue to be large, then a better investment strategy would be to focus on quick patch dissemination and engineering resilient systems. This paper examines a paradigm, namely that the number of undiscovered vulnerabilities is manageably small, through the lens of mathematical concepts from the theory of computing. From this perspective, we find little support for the paradigm of limited undiscovered vulnerabilities. We then briefly support the notion that these theory-based conclusions are relevant to practical computers in use today. We find no reason to believe undiscovered vulnerabilities are not essentially unlimited in practice and we examine the possible economic impacts should this be the case. Based on our analysis, we recommend vulnerability management strategy adopts an approach favoring quick patch dissemination and engineering resilient systems, while continuing good software engineering practices to reduce (but never eliminate) vulnerabilities in information systems.

Virome comparison (VC): A novel approach to comparing viromes based on virus species specificity and virome specificity diversity.

The human virome, or the viral communities distributed on or in our body, is estimated to contain about 380 trillion of viruses (individuals), which has far reaching influences on our health and diseases. Obviously, the sheer numbers of viruses alone make the comparisons of two or multiple viromes extremely challenging. In fact, the theory of computation in computer science for so-termed NP-hard problems stipulates that the problem is unsolvable when the size of virome is sufficiently large even with fastest supercomputers. Practically, one has to develop heuristic and approximate algorithms to obtain practically satisfactory solutions for NP-hard problems. Here, we extend the species-specificity and specificity-diversity framework to develop a method for virome comparison (VC). The VC method consists of a pair of metrics: virus species specificity (VS) and virome specificity diversity (VSD) and corresponding pair of random search algorithms. Specifically, the VS and VS permutation (VSP) test can detect unique virus species (US) or enriched virus species (ES) in each virome (treatment), and the VSD and VSD permutation (VSDP) test can further determine holistic differences between two viromes or their subsets (assemblages of viruses). The test with four virome data sets demonstrated that the VC method is effective, efficient, and robust.

"IMPLEMENTING DOCUMENT CLASSIFICATION IN PYTHON AND EVALUATING RESULTS"

"In our article, we will cover general information about machine learning, its main types, as well as the most important libraries for machine learning in Python. Machine learning is one of the main methods for demonstrating data science in general. In machine learning, the computational and algorithmic capabilities of data science are combined with approaches, and the result is a set of data mining approaches, mainly related to the efficiency and theory of computation. Document classification plays an important role in the archiving department, they classify pre-defined documents and store them in a digital archive. Relevance of the topic In digital archives, document classification is an important process for many document preservation organizations. In the course of the study, a technology for testing the use of deep learning algorithms is being created. Deep learning, i.e. deep structured learning, is part of a broad group of machine learning methods based on artificial neural networks. Learning cannot be controlled, partially controlled or controlled. Keywords: Machine learning, Artificial neural networks, Python, Scikit-learn, Keras, TensorFlow."

On Protocols for Monotone Feasible Interpolation

Dag-like communication protocols, a generalization of the classical tree-like communication protocols, are useful objects in the realm of proof complexity (most importantly for monotone feasible interpolation) and circuit complexity. We consider three kinds of protocols in this article ( d is the degree of a protocol): — IEQ- d -dags: feasible sets of these protocols are described by inequality which means that the feasible sets are combinatorial triangles; these protocols are also called triangle-dags in the literature, — EQ- d -dags: feasible sets are described by equality, and — c -IEQ- d -dags: feasible sets are described by a conjunction of c inequalities. Garg, Göös, Kamath, and Sokolov (Theory of Computing, 2020) mentioned all these protocols, and they noted that EQ- d -dags are a special case of c -IEQ- d -dags. The exact relationship between these types of protocols is unclear. As our main contribution, we prove the following statement: EQ-2-dags can efficiently simulate c -IEQ- d -dags when c and d are constants. This implies that EQ-2-dags are at least as strong as IEQ- d -dags and that EQ-2-dags have the same strength as c -IEQ- d -dags for c ≥ 2 (because 2-IEQ-2-dags can trivially simulate EQ-2-dags). Hrubeš and Pudlák (Information Processing Letters, 2018) proved that IEQ- d -dags over the monotone Karchmer-Wigderson relation are equivalent to monotone real circuits which implies that we have exponential lower bounds for these protocols. Lower bounds for EQ-2-dags would directly imply lower bounds for the proof system R(LIN).

Optical Band Gap Tuning, DFT Understandings, and Photocatalysis Performance of ZnO Nanoparticle-Doped Fe Compounds

Iron-doped Zinc oxide nanoparticles were produced by the sol-gel combustion method. This study aims to see how iron doping affects the structural, optical, and photocatalytic characteristics of ZnO composites. XRD examined all samples to detect the structural properties and proved that all active materials are a single hexagonal phase. The morphology and particle size were investigated by TEM. Computational Density functional theory (DFT) calculation of the band structure, density of state, and charge distributions for ZnO were investigated in comparison with ZnO dope iron. We reported the application results of ZnO doped Fe for Methylene blue dye removal under photocatalytic degradation effect. The iron concentrations affect the active material's band gap, producing higher photocatalytic performance. The acquired results could be employed to enhance the photocatalytic properties of ZnO.

Design, synthesis and investigating the interaction of novel s-triazine collector with pyrite surface: A DFT-D3+U and experimental studies

In this study the green (environmentally friendly) novel di-sodium 2,6-dithio-4-butyl-amino-1,3,5-Triazine (SDTBAT) collector also known as s-triazine molecule was designed and synthesised for use in sulphide minerals separation. The SDTBAT adsorption was investigated on pyrite mineral surface using computational density functional theory with dispersion correction and U-parameter (DFT-D3+U) as well as experimental methods. The chemical adsorption behaviour of SDTBAT collector was compared with the sodium normal butyl xanthate (SNBX) and sodium normal butyl dithiocarbamate (SNBDTC). Computationally, it was observed that the SDTBAT adsorbed through the S and N atoms, while SNBX and SBDTC adsorbed through S atoms onto pyrite Fe atoms. The adsorption energies followed the decreasing adsorption strength as: SDTBAT > SNBX > SNBDTC, which suggested that the SDTBAT had strong exothermic adsorption than xanthate and DTC. The experimental micro-flotation tests showed fast floating and higher recoveries of pyrite when using the SDTBAT collector compared to xanthate and DTC. The XPS and FTIR analysis of pyrite-SDTBAT revealed that the SDTBAT adsorbed through S and N atoms on Fe atoms. This clearly demonstrated that the SDTBAT was a potential collector to replace the xanthate collector due to its high flotation power for separation of sulphide minerals.

Flexible intentions: An Active Inference theory.

We present a normative computational theory of how the brain may support visually-guided goal-directed actions in dynamically changing environments. It extends the Active Inference theory of cortical processing according to which the brain maintains beliefs over the environmental state, and motor control signals try to fulfill the corresponding sensory predictions. We propose that the neural circuitry in the Posterior Parietal Cortex (PPC) compute flexible intentions-or motor plans from a belief over targets-to dynamically generate goal-directed actions, and we develop a computational formalization of this process. A proof-of-concept agent embodying visual and proprioceptive sensors and an actuated upper limb was tested on target-reaching tasks. The agent behaved correctly under various conditions, including static and dynamic targets, different sensory feedbacks, sensory precisions, intention gains, and movement policies; limit conditions were individuated, too. Active Inference driven by dynamic and flexible intentions can thus support goal-directed behavior in constantly changing environments, and the PPC might putatively host its core intention mechanism. More broadly, the study provides a normative computational basis for research on goal-directed behavior in end-to-end settings and further advances mechanistic theories of active biological systems.

Effective Procedures

The “somewhat vague, intuitive” notion from computability theory of an effective procedure (method) or algorithm can be fairly precisely defined, even if it does not have a purely mathematical definition—and even if (as many have asserted) for that reason, the Church–Turing thesis (that the effectively calculable functions on natural numbers are exactly the general recursive functions), cannot be proved. However, it is logically provable from the notion of an effective procedure, without reliance on any (partially) mathematical thesis or conjecture concerning effective procedures, such as the Church–Turing thesis, that the class of effective procedures is undecidable, i.e., that there is no effective procedure for ascertaining whether a given procedure is effective. The proof does not even appeal to a precise definition of ‘effective procedure’. Instead, it relies solely and entirely on a basic grasp of the intuitive notion of such a procedure. Though the result itself is not surprising, it is also not without significance. It has the consequence, for example, that the solution to a decision problem, if it is to be complete, must be accompanied by a separate argument that the proposed ascertainment procedure is, in fact, a decision procedure, i.e., effective—for example, that it invariably terminates with the correct verdict.

Automated Grading of Automata with ACL2s

Almost all Computer Science programs require students to take a course on the Theory of Computation (ToC) which covers various models of computation such as finite automata, push-down automata and Turing machines. ToC courses tend to give assignments that require paper-and-pencil solutions. Grading such assignments takes time, so students typically receive feedback for their solutions more than a week after they complete them. We present the Automatic Automata Checker (A2C), an open source library that enables one to construct executable automata using definitions that mimic those found in standard textbooks. Such constructions are easy to reason about using semantic equivalence checks, properties and test cases. Instructors can conveniently specify solutions in the form of their own constructions. A2C can check for semantic equivalence between student and instructor solutions and can immediately generate actionable feedback, which helps students better understand the material. A2C can be downloaded and used locally by students as well as integrated into Learning Management Systems (LMS) like Gradescope to automatically grade student submissions and generate feedback. A2C is based on the ACL2s interactive theorem prover, which provides advanced methods for stating, proving and disproving properties. Since feedback is automatic, A2C can be deployed at scale and integrated into massively open online courses.

Down closed-quasi-injectivity of partially ordered acts

Action of a pomonoid on partially ordered sets ([Formula: see text]-posets) has beautiful aspects in practical subjects such as automata theory, projection algebra and theoretical computer science which makes it always capture the interest of mathematicians. On the other hand, the study of different kinds of weakly injectivity (which category theory inherited from homological and commutative algebra) is an interesting subject for mathematicians. One of the important kinds of weakly injectivity is quasi-injectivity. In this paper, we study quasi-injectivity in the category of [Formula: see text]-posets with respect to special kind of order embeddings, namely, down-closed embeddings (dc-quasi-injectivity).

Neural knowledge assembly in humans and neural networks

Human understanding of the world can change rapidly when new information comes to light, such as when a plot twist occurs in a work of fiction. This flexible "knowledge assembly" requires few-shot reorganization of neural codes for relations among objects and events. However, existing computational theories are largely silent about how this could occur. Here, participants learned a transitive ordering among novel objects within two distinct contexts before exposure to new knowledge that revealed how they were linked. Blood-oxygen-level-dependent (BOLD) signals in dorsal frontoparietal cortical areas revealed that objects were rapidly and dramatically rearranged on the neural manifold after minimal exposure to linking information. We then adapt online stochastic gradient descent to permit similar rapid knowledge assembly in a neural network model.

CFD Modelling and Numerical Simulation of the Windage Characteristics of a High-Speed Gearbox Based on Negative Pressure Regulation

Windage power loss plays a leading role in the total power loss of high-speed gears, which seriously affects the transmission efficiency of gear systems and leads to high energy consumption. This paper proposes a negative pressure regulation method to reduce windage power loss. Based on the computational fluid dynamics theory, the flow field distribution and windage power loss in the gearbox under different negative pressure conditions are studied, and the effect of the negative pressure environment and speed on the windage power loss is obtained. In order to further save calculation costs, an optimization algorithm of the BP neural network based on a genetic algorithm is proposed to effectively predict the windage power loss. The results show that the high-speed airflow near the tooth’s surface will produce a large pressure moment, which is the main cause of wind resistance loss. The windage power loss increases with the increase in the negative pressure or speed of the gearbox, but the effect of speed is more obvious. The prediction results of the optimization algorithm are in good agreement with the finite element simulation data and the open literature, which can predict the best parameters for reducing windage power loss.

Numerical Study on the Effects of the Multiple Porous Medium Breakwaters on the Propagation of the Solitary Wave

Submerged breakwater, as an important marine engineering structure, can effectively absorb wave energy and is widely used in marine engineering protection. As a new type of breakwater, porous medium breakwater has a certain influence on wave propagation. However, the influence of multiple porous medium submerged breakwaters on extreme waves remains to be studied. In the study, considering effects of extreme waves generated by hurricanes or tsunamis, the influence of the solitary wave on the multiple semi-circle porous medium breakwater is systematically investigated. According to the computational fluid dynamics theory, a numerical tank is established, in which the porous medium module is coupled. The computational capability of this model is verified first. Then, depending on the model, a series of cases are carried out to study the effects of different porous medium breakwaters on the propagation of the solitary wave. The results show that when the porosity is 0.8, the force on the submerged breakwaters is the smallest, and the water level and the velocity decrease seriously. With the increase in the diameter of the submerged breakwater, the wave profile gradually becomes flat. The higher the wave height, the more serious the wave deformation after passing through the submerged breakwater.

Hierarchical predictive coding in distributed pain circuits.

Predictive coding is a computational theory on describing how the brain perceives and acts, which has been widely adopted in sensory processing and motor control. Nociceptive and pain processing involves a large and distributed network of circuits. However, it is still unknown whether this distributed network is completely decentralized or requires networkwide coordination. Multiple lines of evidence from human and animal studies have suggested that the cingulate cortex and insula cortex (cingulate-insula network) are two major hubs in mediating information from sensory afferents and spinothalamic inputs, whereas subregions of cingulate and insula cortices have distinct projections and functional roles. In this mini-review, we propose an updated hierarchical predictive coding framework for pain perception and discuss its related computational, algorithmic, and implementation issues. We suggest active inference as a generalized predictive coding algorithm, and hierarchically organized traveling waves of independent neural oscillations as a plausible brain mechanism to integrate bottom-up and top-down information across distributed pain circuits.

Polyphosphazene Based Inorganic‐Organic Hybrid Cathode Containing Pyrene Tetraone Sides for Aqueous Zinc‐Ion Batteries

AbstractAqueous rechargeable zinc‐ion batteries (ARZBs) are intriguing for electrochemical energy storage applications because of their safety and cost‐effectiveness. Regarding cathode materials, rapid development has been observed with the organic‐based cathode materials that offer much higher structural integrity upon successive (de‐)insertion of charge carries ions. Even though promising results demonstrated with organic electrodes, they still suffer from the short cycle‐life due to their discharge products solubility in electrolyte. Herein, electrochemical performance and charge storage mechanism of the synthesized polyphosphazene‐based inorganic‐organic hybrid electrode containing pyrene‐4,5,9,10‐tetraone (PTO) redox active lateral group, poly[(bis(2‐amino‐4,5,9,10‐pyrenetetraone)], abbreviated as (PPAPT), were investigated in ARZBs. The charge storage mechanism of PPAPT was examined by variousex‐situ[Fourier transform infrared spectroscopy (FTIR), X‐ray diffraction (XRD), scanning electron microscopy and energy dispersive X‐ray spectroscopy (SEM‐EDS), X‐ray photoelectron spectroscopy (XPS)] andin‐situ[pH change with bromocresol green indicator and electrochemical quartz crystal microbalance (EQCM)] characterization techniques as well as computational density functional theory (DFT) revealing that the PPAPT electrode (de‐)coordinates both zinc and proton. The electrode and its discharge product are insoluble in the electrolyte demonstrated by UV‐vis analysis and exhibited a stable cycling performance with a discharge capacity of 125.4 mAh g−1after 1000 cycles at a current density of 10 C.

Between Michigan and Rochester: Identity-based thinking is cognitively primary

This article synthesizes a large body of research in the social and cognitive sciences to develop a distinctly cognitive understanding of political identity. Building on dual-process and computational theories of mind, the article defends three claims about the mental and behavioral implications of identity in political domains: (1) identity-based thinking is people’s default (often fast, automatic, and cognitively inaccessible) way of mentally representing politics and of drawing inferences based on those representations; (2) people are not limited to identity-based thinking and can sometimes learn to override it via slow, volitional, and conscious reasoning; and (3) the cognitive complexity of identity-based thinking is in-between the levels of mental sophistication that the Michigan and Rochester Schools in political science posit. This account of political identity illuminates, inter alia, why even low-information voters can quickly identify their political allies and opponents, why even high-information politicians can misperceive their constituents, why affective polarization has been increasing in the United States in recent decades, and why many normative theories of justice advise people to override identity-based thinking.

Rapid Mixing of Glauber Dynamics up to Uniqueness via Contraction

For general antiferromagnetic 2-spin systems, including the hardcore model on weighted independent sets and the antiferromagnetic Ising model, there is an for the partition function on graphs of maximum degree when the infinite regular tree lies in the uniqueness region by Li, Lu, and Yin [Correlation Decay up to Uniqueness in Spin Systems, preprint, https://arxiv.org/abs/1111.7064, 2021]. Moreover, in the tree nonuniqueness region, Sly in [Computational transition at the uniqueness threshold, in Proceedings of the 51st Annual IEEE Symposium on Foundations of Computer Science, 2010, pp. 287–296] showed that there is no to estimate the partition function unless . The algorithmic results follow from the correlation decay approach due to Weitz [Counting independent sets up to the tree threshold, in Proceedings of the 38th Annual ACM Symposium on Theory of Computing, 2006, pp. 140–149] or the polynomial interpolation approach developed by Barvinok [Combinatorics and Complexity of Partition Functions, Springer, 2016]. However, the running time is only polynomial for constant . For the hardcore model, recent work of Anari, Liu, and Oveis Gharan [Spectral independence in high-dimensional expanders and applications to the hardcore model, in Proceedings of the 61st Annual IEEE Symposium on Foundations of Computer Science, 2020, pp. 1319–1330] establishes rapid mixing of the simple single-site Markov chain, known as the Glauber dynamics, in the tree uniqueness region. Our work simplifies their analysis of the Glauber dynamics by considering the total pairwise influence of a fixed vertex on other vertices, as opposed to the total influence of other vertices on , thereby extending their work to all 2-spin models and improving the mixing time. More important, our proof ties together the three disparate algorithmic approaches: we show that contraction of the so-called tree recursions with a suitable potential function, which is the primary technique for establishing efficiency of Weitz’s correlation decay approach and Barvinok’s polynomial interpolation approach, also establishes rapid mixing of the Glauber dynamics. We emphasize that this connection holds for all 2-spin models (both antiferromagnetic and ferromagnetic), and existing proofs for the correlation decay and polynomial interpolation approaches immediately imply rapid mixing of the Glauber dynamics. Our proof utilizes the fact that the graph partition function is a divisor of the partition function for Weitz’s self-avoiding walk tree. This fact leads to new tools for the analysis of the influence of vertices and may be of independent interest for the study of complex zeros.

Isomorphism Testing for Graphs Excluding Small Minors

We prove that there is a graph isomorphism test running in time on -vertex graphs excluding some -vertex graph as a minor. Previously known bounds were [I. N. Ponomarenko, J. Soviet Math., 55 (1991), pp. 1621–1643] and [L. Babai, Proceedings of the 48th Annual ACM Symposium on Theory of Computing, 2016, pp. 684–697]. For the algorithm we combine recent advances in the group-theoretic graph isomorphism machinery with new graph-theoretic arguments.

Understanding of Intramolecular Charge Transfer Dynamics of a Push–Pull Dimethylamino-phenylethynylphenyl-dicyanoimidazole by Steady-State and Ultrafast Spectroscopic Studies

Photophysical behaviors of D−π–A compound 2-{4-[4-(N,N-dimethylamino)phenylethynyl]phenyl-1-methyl-1H-imidazole-4,5-dicarbonitrile (DMAPPIDCN) were explored using steady-state absorption, fluorescence emission, and femtosecond time-resolved absorption and emission spectroscopic techniques at room temperature along with computational time-dependent density functional theory (TD-DFT) calculation. The spectroscopic studies were carried out in different solvents of varying polarities including binary solvent mixtures. The role of the solvent polarity, viscosity, and temperature on the relaxation mechanism of DMAPPIDCN is disclosed. The observed steady-state and time-resolved spectroscopic features were attributed to intramolecular charge transfer (ICT) dynamics. The ICT in DMAPPIDCN is rationalized to a sequential twisted motion of both N(CH3)2 and whole N,N-dimethylaminophenyl moieties around the molecular axis interconnecting the adjacent imidazolephenyl moiety leading to the TICT1 and TICT2 (σ*) states. The increased solvent polarity affected mostly the fluorescence emission spectra pointing to a significant increase in the excited state dipole moment. This result clearly reveals formation of the TICT2 (σ*) involving efficient charge transfer from the (N,N-dimethylamino)phenyl (DMAP) donor to the phenyl-1-methyl-1H-imidazole-dicarbonitrile (PIDCN) acceptor in the excited state in a polar environment. In the TICT2 (σ*) state, the planes of electron-withdrawing and electron-donating moieties are perpendicular with the angle (DMAP)C–C≡C being 141.1°. This nonplanar arrangement accounts for the observed large Stokes shift. Time-resolved fluorescence spectroscopic studies unveil the excited state relaxation processes confirming the increase in the nonradiative decay rate in aprotic medium with increase in the solvent dielectric constants. Femtosecond transient spectroscopic studies unambiguously confirmed the existence of well separated LE and TICT states and their ensuing kinetics in polar medium. In nonpolar solvents, DMAPPIDCN shows strong fluorescence which emits from the LE (ππ*) state, whereas in polar solvents, formation of two consecutive TICT states occurs from the LE (ππ*) in a sub picosecond to few picosecond time domain depending on polarity of the solvents and the non-radiative decay from the TICT states.

Cu-Doping Induced Phase Transformation in CsPbI3 Nanocrystals with Enhanced Structural Stability and Photoluminescence Quantum Yield

The synthesis of air-stable and cubic phases of all-inorganic halide perovskite nanocrystals (HPNCs) by a hot-injection approach is still challenging due to their rapid in situ phase transformations. Therefore, understanding and preventing this phase conversion by doping of cations is the key to improve the structural stability, environmental durability, and photoluminescence quantum yield. Here, the doping of divalent Cu ions at the Pb-site of cesium lead iodide (CsPbI3) is reported to achieve cubic-phase (α-phase) HPNCs with superior quantum yield and enhanced stability compared to undoped CsPbI3. Rietveld refined X-ray diffraction patterns and atomic-resolution transmission electron microscopy analyses reveal structural transformation from a mixed (γ-orthorhombic and α-cubic) phase to a cubic (α-CsPbI3) one with a smaller lattice constant at an optimal (5.6%) Cu concentration. Computational density functional theory (DFT) analysis credits the resultant structural and environmental stability for the Cu-doped sample to the charge accumulation at the dopant site that leads to increased bond strength with consequent minimization of the energy of the system to give rise to the maximum stability of the HPNCs at this dopant ratio. With the increase of Cu-doping, a red shift is observed in the absorbance and photoluminescence spectra up to a critical doping level, making the system optically tuneable. The optimally doped (5.6% Cu) CsPbI3 HPNCs exhibit stronger light emission with higher carrier lifetime and higher quantum yield (>80%), which are absolutely essential requirements in air-stable optoelectronic devices.