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The maximum edge colouring problem considers the maximum colour assignment to edges of a graph under the condition that every vertex has at most a fixed number of distinct coloured edges incident on it. If that fixed number is $q$ we call the colouring a maximum edge $q$-colouring. The problem models a non-overlapping frequency channel assignment question on wireless networks. The problem has also been studied from a purely combinatorial perspective in the graph theory literature. We study the question when the input graph is sparse. We show the problem remains $NP$-hard on $1$-apex graphs. We also show that there exists $PTAS$ for the problem on minor-free graphs. The $PTAS$ is based on a recently developed Baker game technique for proper minor-closed classes, thus avoiding the need to use any involved structural results. This further pushes the Baker game technique beyond the problems expressible in the first-order logic., Comment: 10 pages, to appear in the proceedings of WG 2023

A resource leak occurs when a program fails to free some finite resource after it is no longer needed. Such leaks are a significant cause of real-world crashes and performance problems. Recent work proposed an approach to prevent resource leaks based on checking resource management specifications. A resource management specification expresses how the program allocates resources, passes them around, and releases them; it also tracks the ownership relationship between objects and resources, and aliasing relationships between objects. While this specify-and-verify approach has several advantages compared to prior techniques, the need to manually write annotations presents a significant barrier to its practical adoption. This paper presents a novel technique to automatically infer a resource management specification for a program, broadening the applicability of specify-and-check verification for resource leaks. Inference in this domain is challenging because resource management specifications differ significantly in nature from the types that most inference techniques target. Further, for practical effectiveness, we desire a technique that can infer the resource management specification intended by the developer, even in cases when the code does not fully adhere to that specification. We address these challenges through a set of inference rules carefully designed to capture real-world coding patterns, yielding an effective fixed-point-based inference algorithm. We have implemented our inference algorithm in two different systems, targeting programs written in Java and C#. In an experimental evaluation, our technique inferred 85.5% of the annotations that programmers had written manually for the benchmarks. Further, the verifier issued nearly the same rate of false alarms with the manually-written and automatically-inferred annotations.

Language models of code (LMs) work well when the surrounding code provides sufficient context. This is not true when it becomes necessary to use types, functionality or APIs defined elsewhere in the repository or a linked library, especially those not seen during training. LMs suffer from limited awareness of such global context and end up hallucinating. Integrated development environments (IDEs) assist developers in understanding repository context using static analysis. We extend this assistance, enjoyed by developers, to LMs. We propose monitor-guided decoding (MGD) where a monitor uses static analysis to guide the decoding. We construct a repository-level dataset PragmaticCode for method-completion in Java and evaluate MGD on it. On models of varying parameter scale, by monitoring for type-consistent object dereferences, MGD consistently improves compilation rates and agreement with ground truth. Further, LMs with fewer parameters, when augmented with MGD, can outperform larger LMs. With MGD, SantaCoder-1.1B achieves better compilation rate and next-identifier match than the much larger text-davinci-003 model. We also conduct a generalizability study to evaluate the ability of MGD to generalize to multiple programming languages (Java, C# and Rust), coding scenarios (e.g., correct number of arguments to method calls), and to enforce richer semantic constraints (e.g., stateful API protocols). Our data and implementation are available at https://github.com/microsoft/monitors4codegen ., Comment: Accepted to NeurIPS 2023 and to appear as "Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context" at https://neurips.cc/virtual/2023/poster/70362 . Contents: 11 pages, 15 additional pages of appendix, 13 figures, 3 tables

An $(n,m)$-graph is characterised by having $n$ types of arcs and $m$ types of edges. A homomorphism of an $(n,m)$-graph $G$ to an $(n,m)$-graph $H$, is a vertex mapping that preserves adjacency, direction, and type. The $(n,m)$-chromatic number of $G$, denoted by $\chi_{n,m}(G)$, is the minimum value of $|V(H)|$ such that there exists a homomorphism of $G$ to $H$. The theory of homomorphisms of $(n,m)$-graphs have connections with graph theoretic concepts like harmonious coloring, nowhere-zero flows; with other mathematical topics like binary predicate logic, Coxeter groups; and has application to the Query Evaluation Problem (QEP) in graph database. In this article, we show that the arboricity of $G$ is bounded by a function of $\chi_{n,m}(G)$ but not the other way around. Additionally, we show that the acyclic chromatic number of $G$ is bounded by a function of $\chi_{n,m}(G)$, a result already known in the reverse direction. Furthermore, we prove that the $(n,m)$-chromatic number for the family of graphs with a maximum average degree less than $2+ \frac{2}{4(2n+m)-1}$, including the subfamily of planar graphs with girth at least $8(2n+m)$, equals $2(2n+m)+1$. This improves upon previous findings, which proved the $(n,m)$-chromatic number for planar graphs with girth at least $10(2n+m)-4$ is $2(2n+m)+1$. It is established that the $(n,m)$-chromatic number for the family $\mathcal{T}_2$ of partial $2$-trees is both bounded below and above by quadratic functions of $(2n+m)$, with the lower bound being tight when $(2n+m)=2$. We prove $14 \leq \chi_{(0,3)}(\mathcal{T}_2) \leq 15$ and $14 \leq \chi_{(1,1)}(\mathcal{T}_2) \leq 21$ which improves both known lower bounds and the former upper bound. Moreover, for the latter upper bound, to the best of our knowledge we provide the first theoretical proof., Comment: 18 pages

Conventional methods of measuring entanglement in a two-qubit photonic mixed state require the detection of both qubits. We generalize a recently introduced method which does not require the detection of both qubits, by extending it to cover a wider class of entangled states. Specifically, we present a detailed theory that shows how to measure entanglement in a family of two-qubit mixed states - obtained by generalizing Werner states - without detecting one of the qubits. Our method is interferometric and does not require any coincidence measurement or postselection. We also perform a quantitative analysis of anticipated experimental imperfections to show that the method is experimentally implementable and resistant to experimental losses., Comment: 11 pages main paper, 5 pages supplementary material, 4 figures

In this paper, we investigate the theoretical properties of stochastic gradient descent (SGD) for statistical inference in the context of nonconvex optimization problems, which have been relatively unexplored compared to convex settings. Our study is the first to establish provable inferential procedures using the SGD estimator for general nonconvex objective functions, which may contain multiple local minima. We propose two novel online inferential procedures that combine SGD and the multiplier bootstrap technique. The first procedure employs a consistent covariance matrix estimator, and we establish its error convergence rate. The second procedure approximates the limit distribution using bootstrap SGD estimators, yielding asymptotically valid bootstrap confidence intervals. We validate the effectiveness of both approaches through numerical experiments. Furthermore, our analysis yields an intermediate result: the in-expectation error convergence rate for the original SGD estimator in nonconvex settings, which is comparable to existing results for convex problems. We believe this novel finding holds independent interest and enriches the literature on optimization and statistical inference.

We investigate the nonequilibrium refrigeration of one and two-qubit systems in a squeezed thermal bath. We characterize the performance of one and two-qubit refrigerators in the presence of squeezed heat baths, in terms of their coefficients of performance, cooling rates, and figures of merit. Our results show that the performance of the refrigerators is strongly influenced by the squeezing parameter and the number of qubits. The performance of the two-qubit refrigerator is found to be better than that of the one-qubit refrigerator under the same operating conditions. Our findings suggest that a squeezed thermal bath can be a promising resource for the design of efficient quantum refrigerators in the non-equilibrium regime., Comment: 17 pages, 10 figures

Wordle is a single-player word-based game where the objective is to guess the 5-letter word in a maximum of 6 tries. The game was released to the public in October 2021 and has since gained popularity with people competing against each other to maintain daily streaks and guess the word in a minimum number of tries. There have been works using probabilistic and reinforcement learning based approaches to solve the game. Our work aims to formulate and analyze deterministic algorithms that can solve the game and minimize the number of turns required to guess the word and do so for any generalized setting of the game. As a simplifying assumption, for our analysis of all the algorithms we present, we assume that all letters will be unique in any word which is part of our vocabulary. We propose two algorithms to play Wordle - one a greedy based approach, and other based on Cliques. The Greedy approach is applicable for both hard and easy modes of Wordle, while the Clique formation based approach only works on the Easy mode. We present our analysis on both approaches one by one, next.

Speech processing techniques are useful for analyzing speech and language development in children with Autism Spectrum Disorder (ASD), who are often varied and delayed in acquiring these skills. Early identification and intervention are crucial, but traditional assessment methodologies such as caregiver reports are not adequate for the requisite behavioral phenotyping. Natural Language Sample (NLS) analysis has gained attention as a promising complement. Researchers have developed benchmarks for spoken language capabilities in children with ASD, obtainable through the analysis of NLS. This paper proposes applications of speech processing technologies in support of automated assessment of children's spoken language development by classification between child and adult speech and between speech and nonverbal vocalization in NLS, with respective F1 macro scores of 82.6% and 67.8%, underscoring the potential for accurate and scalable tools for ASD research and clinical use., Comment: Accepted to Interspeech 2023, 5 pages

Empirical best prediction (EBP) is a well-known method for producing reliable proportion estimates when the primary data source provides only small or no sample from finite populations. There are potential challenges in implementing existing EBP methodology such as limited auxiliary variables in the frame (not adequate for building a reasonable working predictive model) or unable to accurately link the sample to the finite population frame due to absence of identifiers. In this paper, we propose a new data linkage approach where the finite population frame is replaced by a big probability sample, having a large set of auxiliary variables but not the outcome binary variable of interest. We fit an assumed model on the small probability sample and then impute the outcome variable for all units of the big sample to obtain standard weighted proportions. We develop a new adjusted maximum likelihood (ML) method so that the estimate of model variance doesn't fall on the boundary, which is otherwise encountered in commonly used ML method. We also propose an estimator of the mean squared prediction error using a parametric bootstrap method and address computational issues by developing an efficient Expectation Maximization algorithm. The proposed methodology is illustrated in the context of election projection for small areas.

We here investigate bulk-viscosity driven quasi de-Sitter inflation, that is, the period of accelerated expansion in the early universe during which $-\dot{H}\ll H^2$, with $H(t)$ being the Hubble expansion rate. We do so in the framework of a causal theory of relativistic hydrodynamics that takes into account non-equilibrium effects associated to bulk viscosity that may be present as the early universe undergoes an accelerated expansion. In this framework, the existence of a quasi de-Sitter universe emerges as a natural consequence of the presence of bulk viscosity, without requiring to introduce additional scalar fields. As a result, the equation of state, determined by numerically solving the generalized momentum-conservation equation involving bulk-viscosity pressure turns out to be time-dependent. The transition timescale characterising its departure from an exact de-Sitter phase is intricately related to the magnitude of the bulk viscosity. We examine the properties of the new equation of state, as well as the transition timescale in presence of bulk-viscosity pressure. In addition, we construct a fluid description of inflation and demonstrated that, in the context of the causal formalism, it is equivalent to the scalar field theory of inflation. Our analysis also shows that the slow-roll conditions are realised in the bulk-viscosity supported model of inflation. Finally, we examine the viability of our model by computing the inflationary observables, namely, the spectral index and the tensor-to-scalar ratio of the curvature perturbations, and compare them with a number of different observations finding good agreement in most cases., Comment: 28 pages, fixed typos, some references modified, all results unchanged, matches the published version, This article belongs to the Special Issue Exact Solutions in Modern Cosmology with Symmetry/Asymmetry

Neutrino mixing is caused by the fact that neutrino flavors are not eigenstates of the free Hamiltonian. This causes oscillations among different neutrino flavors. When neutrinos pass through a medium, weak interactions produce different effective masses for neutrinos of different flavors, leading to a modification of the mixing parameters. In curved spacetime there is an additional contribution to neutrino Hamiltonian from a torsion-induced four-fermion interaction, which also causes neutrino mixing while propagating through fermionic matter. We provide an outline of the calculation of this effect on neutrino oscillation., Comment: Based on talk delivered by Riya Barick at the International Conference on Neutrinos and Dark Matter (NuDM-2022), Sharm El-Sheikh, Egypt, 25-28 September, 2022

The heavy-tailed behavior of the generalized extreme-value distribution makes it a popular choice for modeling extreme events such as floods, droughts, heatwaves, wildfires, etc. However, estimating the distribution's parameters using conventional maximum likelihood methods can be computationally intensive, even for moderate-sized datasets. To overcome this limitation, we propose a computationally efficient, likelihood-free estimation method utilizing a neural network. Through an extensive simulation study, we demonstrate that the proposed neural network-based method provides Generalized Extreme Value (GEV) distribution parameter estimates with comparable accuracy to the conventional maximum likelihood method but with a significant computational speedup. To account for estimation uncertainty, we utilize parametric bootstrapping, which is inherent in the trained network. Finally, we apply this method to 1000-year annual maximum temperature data from the Community Climate System Model version 3 (CCSM3) across North America for three atmospheric concentrations: 289 ppm $\mathrm{CO}_2$ (pre-industrial), 700 ppm $\mathrm{CO}_2$ (future conditions), and 1400 ppm $\mathrm{CO}_2$, and compare the results with those obtained using the maximum likelihood approach., Comment: 19 pages, 6 figures

The Rossby mode (r-mode) perturbations in pulsars as a steady gravitational wave (GW) sources have been explored. The time evolution and the intensity of the emitted GWs in terms of the strain tensor amplitude have been estimated with the approximation of slow rotation adopting the equation of state derived using the Skyrme effective interaction with NRAPR parameter set. The core of the neutron star has been considered to be $\beta$-equilibrated nuclear matter composed of neutrons, protons, electrons and muons, which is surrounded by a solid crust. Calculations have been made for the critical frequencies, the evolution of frequencies and frequency change rates with time as well as the fiducial viscous and gravitational timescales, across a broad spectrum of pulsar masses. Our findings reveal that the r-mode instability region is associated with rotating young and hot pulsars. Furthermore, it is noteworthy that pulsars with low $L$ value emit gravitational radiation and fall within the r-mode instability region if the primary dissipative mechanism is shear viscosity along the crust-core interface boundary layer. The r-mode perturbation amplitude increases because of GW emissions, in contrast to other non-radial perturbations which transport to infinity the star's angular momentum. Thus the presence of these stellar perturbations implies a non-negative rate of change in transfer of rotational angular momentum. This observation suggests that for a glitching pulsar, the GW emission intensity evolves increasingly with time till the angular frequency diminishes to a value that is below a crucial threshold, after which the compact star ceases to emit radiation., Comment: 15 pages including 15 figures and 2 tables

Citations in scientific papers not only help us trace the intellectual lineage but also are a useful indicator of the scientific significance of the work. Citation intents prove beneficial as they specify the role of the citation in a given context. In this paper, we present CitePrompt, a framework which uses the hitherto unexplored approach of prompt-based learning for citation intent classification. We argue that with the proper choice of the pretrained language model, the prompt template, and the prompt verbalizer, we can not only get results that are better than or comparable to those obtained with the state-of-the-art methods but also do it with much less exterior information about the scientific document. We report state-of-the-art results on the ACL-ARC dataset, and also show significant improvement on the SciCite dataset over all baseline models except one. As suitably large labelled datasets for citation intent classification can be quite hard to find, in a first, we propose the conversion of this task to the few-shot and zero-shot settings. For the ACL-ARC dataset, we report a 53.86% F1 score for the zero-shot setting, which improves to 63.61% and 66.99% for the 5-shot and 10-shot settings, respectively., Comment: Selected for publication at ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES 2023

The area of stochastic heat engines using active particles has attracted a lot of attention recently. They have been shown to exhibit advantages over engines using passive particles. In this work, we use multiple self-propelling particles undergoing Vicsek-like aligning interaction as our working system. The particles are confined in a two-dimensional circular trap. The interplay between the confinement and the activity of the particles induces clustering. These clusters change their locations relative to the walls of the trap, when the wall steepness is varied with time. In this work we demonstrate that changing the steepness of the wall and the activity of the particles time-periodically can cause the system to act as an engine. In this setup, we study the variations in extracted work with the activity, rotational diffusion, and the Vicsek radius of individual particles. We also comment on the complications involved in the definition of the engine efficiency in accordance with the usual prescription of stochastic thermodynamics., Comment: 13 pages, 8 figures

Bosons in periodic potentials with very strong local interactions, known as the constrained bosons often exhibit interesting physical behavior. We investigate the ground state properties of a two-leg Bose-Hubbard ladder by imposing three-body constraint in one leg and hardcore constraint in the other. By using the cluster-mean-field theory approximation and the density matrix renormalization group method, we show that at unit filling, for strong two-body attraction among the three-body constrained bosons, the system becomes a gapped pair-Mott insulator where all the bosons form strong bound pairs and occupy the leg with three-body constraint. With increase in hopping strength this pair-Mott insulator phase undergoes a phase transition to the gapless superfluid phase for equal leg and rung hopping strengths. However, when the rung hopping is stronger compared to the leg hopping, we obtain a crossover to another gapped phase which is called the rung-Mott insulator phase where the bosons prefer to delocalize on the rungs than the legs. By moving away from unit filling, the system remains in the superfluid phase except for a small region below the gapped phase where a pair superfluid phase is stabilized in the regime of strong attractive interaction. We further extend our studies by considering three-body constraint on both the legs and find that the crossover from the gapped to gapped phase does not occur rather the system undergoes a transition from a pair-rung-Mott insulator phase to the superfluid phase at unit filling. Moreover, in this case we find the signature of the pair superfluid phase on either sides of this gapped phase., Comment: 12 pages, 16 figures

Haldane model is a celebrated tight binding toy model in a 2D honeycomb lattice that exhibits quantized Hall conductance in the absence of an external magnetic field. In our work, we deform the bands of the Haldane model smoothly by varying one of its three nearest neighbour hopping amplitudes ($t_1$), while keeping the other two ($t$) fixed. This breaks the $C_3$ symmetry of the Hamiltonian, while the $M_x*T$ symmetry is preserved. The symmetry breaking causes the Dirac cones to shift from the K and the K' points in the Brillouin zone (BZ) to an intermediate M point. This is evident from the Berry curvature plots which show a similar shift in the corresponding values as a function of $\frac{t_1}{t}$. We observe two different topological phases, one being a topological insulator (TI) phase and the other is a higher order topological insulator (HOTI). The Chern number ($C$) remains perfectly quantized at a value of $C=1$ for the TI phase and goes to zero in the HOTI phase. Furthermore the evolution of the Wannier charge center (WCC) as the band is deformed shows a jump in the TI phase indicating a non-trivial bulk. We also study the HOTI phase and diagonalize the real space Hamiltonian on a rhombic supercell to show the presence of in-gap zero energy corner modes. The polarization of the system, namely $p_x$ and $p_y$, are evaluated, along the $x$ and the $y$ directions respectively. We see that both $p_x$ and $p_y$ are quantized in the HOTI phase owing to the presence of the inversion symmetry of the system., Comment: 8 Pages, 11 Figures

Recent works on the concatenation of two simple heat engines have shown that it may lead to non-monotonic variations in the efficiency and power with parameters like driving amplitudes and asymmetries in cycle periods. Motivated by this study, we investigate the effect of the concatenation between two stochastic heat engines where colloidal particles have been trapped in harmonic potentials. The stiffness parameters of each engine are varied cyclically, but with different cycle periods, with a common thermal bath that acts as a sink for the first engine but as a source for the second. We consider two types of protocols, first where the trap strength undergoes sudden jumps, and the second where it varies linearly with time. In both we find several non-trivial effects, like the the non-monotonic functional dependence of the engine outputs on several parameters used in the setup. For a protocol that varies linearly with time, the concatenation leads to enhanced output power as compared to a single effective engine, in a suitable range of parameters. It has been shown that the output from the combined system shows a peak with respect to the asymmetry in cycle times of the engines that have been concatenated. A general relation of the efficiency of an arbitrary number of concatenated engines driven quasistatically has been provided., Comment: 24 pages, 17 figures

Large language models (LLMs), such as OpenAI's Codex, have demonstrated their potential to generate code from natural language descriptions across a wide range of programming tasks. Several benchmarks have recently emerged to evaluate the ability of LLMs to generate functionally correct code from natural language intent with respect to a set of hidden test cases. This has enabled the research community to identify significant and reproducible advancements in LLM capabilities. However, there is currently a lack of benchmark datasets for assessing the ability of LLMs to generate functionally correct code edits based on natural language descriptions of intended changes. This paper aims to address this gap by motivating the problem NL2Fix of translating natural language descriptions of code changes (namely bug fixes described in Issue reports in repositories) into correct code fixes. To this end, we introduce Defects4J-NL2Fix, a dataset of 283 Java programs from the popular Defects4J dataset augmented with high-level descriptions of bug fixes, and empirically evaluate the performance of several state-of-the-art LLMs for the this task. Results show that these LLMS together are capable of generating plausible fixes for 64.6% of the bugs, and the best LLM-based technique can achieve up to 21.20% top-1 and 35.68% top-5 accuracy on this benchmark.

We have calculated $\beta^-$ decay rates to the continuum and bound states of some fully ionized atoms in the stellar s-process environment having free electron density and temperature in the range $n_e = 10^{26} $ cm$^{-3} - 10^{27} $ cm$^{-3}$ and $T = 10^8$ K - $5 \times 10^{8}$ K, respectively. The presence of bare atoms in these particular situations has been confirmed by solving Saha ionization equation taking into account the ionization potential depression (IPD). At these temperatures, low lying excited energy levels of parent nuclei may have thermal equilibrium population and those excited levels may also decay via $\beta^-$ emission. The Nuclear Matrix Element (NME) of all the transitions of the set of 15 nuclei is calculated using nuclear shell-model. These NME are then used to calculate the comparative half-life ($ft_{1/2}$) of the transitions. Calculated terrestrial half-lives of the $\beta^-$ decays are in good agreement with the experimental results in most of the cases. Decay to bound and continuum states of bare atoms from ground/isomeric levels and excited nuclear levels have been calculated separately. The ratio of bound state to continuum state decay rates as a function of IPD modified $Q$-value reveals that bound state $\beta^-$ decay rate may compete and even dominate for $Q$-value $<$ 100 keV. The importance of the bound state $\beta^-$ decay in stellar situations has been shown explicitly. We have calculated total $\beta^-$ decay rates (bound state plus continuum state) taking into account IPD corrected neutral atom $Q$-value as a function of density and temperature. We have also presented results for the stellar $\beta^-$ half-lives and compared the ratio of neutral atom to bare atom half-lives for different density and temperature combinations. These results may be useful for s-process nucleosynthesis calculations., Comment: 36 pages, 10 figures, 7 tables

Dropout is a widely used regularization trick to resolve the overfitting issue in large feedforward neural networks trained on a small dataset, which performs poorly on the held-out test subset. Although the effectiveness of this regularization trick has been extensively studied for convolutional neural networks, there is a lack of analysis of it for unsupervised models and in particular, VAE-based neural topic models. In this paper, we have analyzed the consequences of dropout in the encoder as well as in the decoder of the VAE architecture in three widely used neural topic models, namely, contextualized topic model (CTM), ProdLDA, and embedded topic model (ETM) using four publicly available datasets. We characterize the dropout effect on these models in terms of the quality and predictive performance of the generated topics., Comment: Accepted at EACL 2023

Topic modeling has emerged as a dominant method for exploring large document collections. Recent approaches to topic modeling use large contextualized language models and variational autoencoders. In this paper, we propose a negative sampling mechanism for a contextualized topic model to improve the quality of the generated topics. In particular, during model training, we perturb the generated document-topic vector and use a triplet loss to encourage the document reconstructed from the correct document-topic vector to be similar to the input document and dissimilar to the document reconstructed from the perturbed vector. Experiments for different topic counts on three publicly available benchmark datasets show that in most cases, our approach leads to an increase in topic coherence over that of the baselines. Our model also achieves very high topic diversity., Comment: Accepted at 19th International Conference on Natural Language Processing (ICON 2022)

Josephson tunnel junctions exhibit a supercurrent typically proportional to the sine of the superconducting phase difference $\phi$. In general, a term proportional to $\cos(\phi)$ is also present, alongside microscopic electronic retardation effects. We show that voltage pulses sharply varying in time prompt a significant impact of the $\cos(\phi)$ term. Its interplay with the $\sin(\phi)$ term results in a nonequilibrium fractional Josephson effect (NFJE) $\sim\sin(\phi/2)$ in the presence of bound states close to zero frequency. Our microscopic analysis reveals that the interference of non-equilibrium virtual quasiparticle excitations is responsible for this phenomenon. We also analyse this phenomenon for topological Josephson junctions with Majorana bound states. Remarkably, the NFJE is independent of the ground state fermion parity unlike its equilibrium counterpart.

We briefly review a number of major features of the approach to quantum measurement theory based on environment-induced decoherence of the measuring apparatus, and summarize our observations in the form of a couple of general principles that, unlike the wave function collapse hypothesis, emerge as ones consistent with the unitary Schr\"odinger evolution of wave functions. We conclude with a few observations of a philosophical nature, to the effect that that quantum theory does not purport to describe reality but constitutes an {\it interpretation} of our phenomenal reality within a context -- one where the Planck scale is not crossed. Beyond the Planck scale, a radically new interpretation of reality is likely to emerge., Comment: 12 pages