Negative / Null Result ReportOpen accessComputer Science
Hans-Joachim Böckenhauer, Elisabet Burjons, Martin Raszyk et al. · 2018 · arXiv
Parameterized complexity allows us to analyze the time complexity of problems with respect to a natural parameter depending on the problem. Reoptimization looks for solutions or approximations for problem instances when given solutions to neighboring instances. We try to combine both techniques, in order to better classify the complexity of problems in the parameterized setting. Specifically, we see that some problems in the class of compositional problems, which do not have polynomial kernels under standard complexity-theoretic assumptions, do have polynomial kernels under reoptimization for
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G. Maciejewski · 2022 · arXiv
Uninterrupted observations from space-borne telescopes provide the photometric precision that is required to detect shallow transits of small planets missed by ground-based surveys. We used data from the Transiting Exoplanet Survey Satellite (TESS) to search for nearby planetary companions in 12 planetary systems with hot Jupiters: HD 2685, Qatar-10, WASP-4, WASP-48, WASP-58, WASP-91, WASP-120, WASP-121, WASP-122, WASP-140, XO-6, and XO-7. We also applied the transit timing method based on homogeneously determined mid-transit times in order to search for non-transiting companions that could gr
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Ali Çivril · 2017 · arXiv
It is well known that sparse approximation problem is \textsf{NP}-hard under general dictionaries. Several algorithms have been devised and analyzed in the past decade under various assumptions on the \emph{coherence} $μ$ of the dictionary represented by an $M \times N$ matrix from which a subset of $k$ column vectors is selected. All these results assume $μ=O(k^{-1})$. This article is an attempt to bridge the big gap between the negative result of \textsf{NP}-hardness under general dictionaries and the positive results under this restrictive assumption. In particular, it suggests that the afo
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Yossi Arjevani, Ohad Shamir · 2016 · arXiv
Finite-sum optimization problems are ubiquitous in machine learning, and are commonly solved using first-order methods which rely on gradient computations. Recently, there has been growing interest in \emph{second-order} methods, which rely on both gradients and Hessians. In principle, second-order methods can require much fewer iterations than first-order methods, and hold the promise for more efficient algorithms. Although computing and manipulating Hessians is prohibitive for high-dimensional problems in general, the Hessians of individual functions in finite-sum problems can often be effic
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Guy Van den Broeck, Adnan Darwiche · 2013 · arXiv
Lifted inference algorithms exploit symmetries in probabilistic models to speed up inference. They show impressive performance when calculating unconditional probabilities in relational models, but often resort to non-lifted inference when computing conditional probabilities. The reason is that conditioning on evidence breaks many of the model's symmetries, which can preempt standard lifting techniques. Recent theoretical results show, for example, that conditioning on evidence which corresponds to binary relations is #P-hard, suggesting that no lifting is to be expected in the worst case. In
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Davin Choo, Kirankumar Shiragur · 2023 · arXiv
Recovering causal relationships from data is an important problem. Using observational data, one can typically only recover causal graphs up to a Markov equivalence class and additional assumptions or interventional data are needed for complete recovery. In this work, under some standard assumptions, we study causal graph discovery via adaptive interventions with node-dependent interventional costs. For this setting, we show that no algorithm can achieve an approximation guarantee that is asymptotically better than linear in the number of vertices with respect to the verification number; a wel
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Valerio Varricchio, Brian Paden, Dmitry Yershov et al. · 2017 · arXiv
Nearest-neighbor search dominates the asymptotic complexity of sampling-based motion planning algorithms and is often addressed with k-d tree data structures. While it is generally believed that the expected complexity of nearest-neighbor queries is $O(log(N))$ in the size of the tree, this paper reveals that when a classic k-d tree approach is used with sub-Riemannian metrics, the expected query complexity is in fact $Θ(N^p \log(N))$ for a number $p \in [0, 1)$ determined by the degree of nonholonomy of the system. These metrics arise naturally in nonholonomic mechanical systems, including cl
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Alejandro Alonso-Valero, Daniel Berzal-Rozalén, Felipe J. Llanes-Estrada et al. · 2024 · arXiv
We deploy the Hartree-Fock approximation for all-heavy quark hadrons, including quarkonium, baryons, tetraquarks, pentaquarks, dibaryons and up to the 12-body dibaryon-antidibaryon which completely fill the $1s$ orbital, in a unified manner, with the spinless LO Coulomb interaction and beyond. After treating the $c$ and $b$ quarks in various combinations, we delve a bit longer on $t$-quark bound states. We extend the negative result of Kuchiev, Flambaum and Shuryak on the 12-body topball to now include the NLO QCD potential. We find that none of the examined multitop states should have binding
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Jordan Awan, Ana Kenney, Matthew Reimherr et al. · 2019 · arXiv
The exponential mechanism is a fundamental tool of Differential Privacy (DP) due to its strong privacy guarantees and flexibility. We study its extension to settings with summaries based on infinite dimensional outputs such as with functional data analysis, shape analysis, and nonparametric statistics. We show that one can design the mechanism with respect to a specific base measure over the output space, such as a Guassian process. We provide a positive result that establishes a Central Limit Theorem for the exponential mechanism quite broadly. We also provide an apparent negative result, sho
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S. Albeverio, H. Gottschalk, J. -L. Wu · 2004 · arXiv
We construct Euclidean random fields $X$ over $\R^d$, by convoluting generalized white noise $F$ with some integral kernels $G$, as $X=G* F$. We study properties of Schwinger (or moment) functions of $X$. In particular, we give a general equivalent formulation of the cluster property in terms of truncated Schwinger functions which we then apply to the above fields. We present a partial negative result on the reflection positivity of convoluted generalized white noise. Furthermore, by representing the kernels $G_\a$ of the pseudo--differential operators $(-\D + m^2_0)^{-α}$ for $α\in (0,1)$ and
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Yechan Park, Yuya Sasaki · 2024 · arXiv
This paper addresses the challenge of estimating the Average Treatment Effect on the Treated Survivors (ATETS; Vikstrom et al., 2018) in the absence of long-term experimental data, utilizing available long-term observational data instead. We establish two theoretical results. First, it is impossible to obtain informative bounds for the ATETS with no model restriction and no auxiliary data. Second, to overturn this negative result, we explore as a promising avenue the recent econometric developments in combining experimental and observational data (e.g., Athey et al., 2020, 2019); we indeed fin
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Tian-Fu Chen, Jie-Hong R. Jiang · 2024 · arXiv
Boolean matching is an important problem in logic synthesis and verification. Despite being well-studied for conventional Boolean circuits, its treatment for reversible logic circuits remains largely, if not completely, missing. This work provides the first such study. Given two (black-box) reversible logic circuits that are promised to be matchable, we check their equivalences under various input/output negation and permutation conditions subject to the availability/unavailability of their inverse circuits. Notably, among other results, we show that the equivalence up to input negation and pe
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Michael Bekos, Stephen Kobourov, Michael Kaufmann et al. · 2014 · arXiv
Given an $n$-vertex graph $G$ and two positive integers $d,k \in \mathbb{N}$, the ($d,kn$)-differential coloring problem asks for a coloring of the vertices of $G$ (if one exists) with distinct numbers from 1 to $kn$ (treated as \emph{colors}), such that the minimum difference between the two colors of any adjacent vertices is at least $d$. While it was known that the problem of determining whether a general graph is ($2,n$)-differential colorable is NP-complete, our main contribution is a complete characterization of bipartite, planar and outerplanar graphs that admit ($2,kn$)-differential co
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Tobias Fissler, Yannick Hoga · 2024 · arXiv
This paper lays out a principled approach to compare copula forecasts via strictly consistent scores. We first establish the negative result that, in general, copulas fail to be elicitable, implying that copula predictions cannot sensibly be compared on their own. A notable exception is on Fréchet classes, that is, when the marginal distribution structure is given and fixed, in which case we give suitable scores for the copula forecast comparison. As a remedy for the general non-elicitability of copulas, we establish novel multi-objective scores for copula forecast along with marginal forecast
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Vidya Muthukumar, Soham Phade, Anant Sahai · 2020 · arXiv
We study the limiting behavior of the mixed strategies that result from optimal no-regret learning strategies in a repeated game setting where the stage game is any 2 by 2 competitive game. We consider optimal no-regret algorithms that are mean-based and monotonic in their argument. We show that for any such algorithm, the limiting mixed strategies of the players cannot converge almost surely to any Nash equilibrium. This negative result is also shown to hold under a broad relaxation of these assumptions, including popular variants of Online-Mirror-Descent with optimism and/or adaptive step-si
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Kirill Zhirkov, Robert H. Brehm · 2025 · Research & Politics
Activists and politicians often express a belief that the language used to describe immigrant populations, such as “illegal” vs. “undocumented,” can impact policy opinions. Existing experimental studies, however, have found relatively…
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Shrikant Utagi, Subhashish Banerjee, R. Srikanth · 2022 · arXiv
The eternally non-Markovian Pauli channel is an example of a unital channel characterized by a negative decay rate for all time $t>0$. Here we consider the problem of constructing an analogous non-unital channel, and show in particular that a $d$-dimensional generalized amplitude damping (GAD) channel cannot be eternally non-Markovian when the non-Markovianity originates solely from the non-unital part of the channel. We study specific ramifications of this result for qubit GAD. Specifically, we construct a quasi-eternally non-Markovian qubit GAD channel, characterized by a time $t^\ast > 0$,
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Rickmer Schulte, David Rügamer · 2026 · arXiv
The analysis of neural representation has become an integral part of research aiming to better understand the inner workings of neural networks. While there are many different approaches to investigate neural representations, an important line of research has focused on doing so through the lens of intrinsic dimensions (IDs). Although this perspective has provided valuable insights and stimulated substantial follow-up research, important limitations of this approach have remained largely unaddressed. In this paper, we highlight a crucial discrepancy between theory and practice of IDs in neural
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Metin Gurses, Tahsin Cagri Sisman, Bayram Tekin · 2023 · arXiv
We are interested in the charged dust solutions of the Einstein field equations in stationary and axially symmetric spacetimes; and inquire if the naked singularities of the Israel-Wilson-Perjes (IWP) metrics can be removed. The answer is negative in four dimensions. We examine whether this negative result can be avoided by adding scalar or dilaton fields. We show that IWP metrics also arise as solutions of the Einstein-Maxwell system with a stealth dilaton field. We determine the IWP metrics completely in terms of one complex function satisfying the Laplace equation. With the inclusion of the
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MaryBeth Defrance, Tijl De Bie · 2023 · arXiv
Fairness in AI has garnered quite some attention in research, and increasingly also in society. The so-called "Impossibility Theorem" has been one of the more striking research results with both theoretical and practical consequences, as it states that satisfying a certain combination of fairness measures is impossible. To date, this negative result has not yet been complemented with a positive one: a characterization of which combinations of fairness notions are possible. This work aims to fill this gap by identifying maximal sets of commonly used fairness measures that can be simultaneously
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Stephanie Schoch, Yangfeng Ji · 2025 · arXiv
Prior works have shown that in-context learning is brittle to presentation factors such as the order, number, and choice of selected examples. However, ablation-based guidance on selecting the number of examples may ignore the interplay between different presentation factors. In this work we develop a Monte Carlo sampling-based method to study the impact of number of examples while explicitly accounting for effects from order and selected examples. We find that previous guidance on how many in-context examples to select does not always generalize across different sets of selected examples and
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Mohammed Barhoush, Tomoyuki Morimae, Ryo Nishimaki et al. · 2026 · arXiv
Mahadev [SIAM J. Comput. 2022] introduced the first protocol for classical verification of quantum computation based on the Learning-with-Errors (LWE) assumption, achieving a 4-message interactive scheme. This breakthrough naturally raised the question of whether fewer messages are possible in the plain model. Despite its importance, this question has remained unresolved. In this work, we prove that there is no quantum black-box reduction of non-interactive classical verification of quantum computation of $\textsf{QMA}$ to any falsifiable assumption. Here, "non-interactive" means that after an
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Kai Kim, Howard Tsai, Rajat Sen et al. · 2024 · arXiv
Current forecasting approaches are largely unimodal and ignore the rich textual data that often accompany the time series due to lack of well-curated multimodal benchmark dataset. In this work, we develop TimeText Corpus (TTC), a carefully curated, time-aligned text and time dataset for multimodal forecasting. Our dataset is composed of sequences of numbers and text aligned to timestamps, and includes data from two different domains: climate science and healthcare. Our data is a significant contribution to the rare selection of available multimodal datasets. We also propose the Hybrid Multi-Mo
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Francisco Javier Gallego, Miguel Gonzalez, Bangere P. Purnaprajna · 2015 · arXiv
In this paper, we show that if $X$ is a smooth variety of general type of dimension $m \geq 2$, for which its canonical map induces a double cover onto $Y$, where $Y$ is a projective bundle over $\mathbf P^1$, or onto a projective space or onto a quadric hypersurface, embedded by a complete linear series, then the general deformation of the canonical morphism of $X$ again is canonical and again induces a double cover. The second part of the article deals with the existence or non existence of canonical double structures on rational varieties. The negative result in this article has consequence
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Amadeo Irigoyen · 2006 · arXiv
In the theory of approximation there are some problems on approximation of compacts in functional spaces by nonlinear families : first we deal with the polynomial case, and then we consider the analytic case. We demonstrate a negative result in which we claim that an analytic familie of functions with $N$ parameters can not approach the compact $Λ_l(I^s)$ closer than of order $(N\log N)^{\frac{l}{s}}$, when $N$ increases. As applied to an inverse problem in Sturm-Liouville theory, this assertion provides an answer to a question about the best possible reconstruction of the negative potential $
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Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler et al. · 2018 · arXiv
Coresets are one of the central methods to facilitate the analysis of large data sets. We continue a recent line of research applying the theory of coresets to logistic regression. First, we show a negative result, namely, that no strongly sublinear sized coresets exist for logistic regression. To deal with intractable worst-case instances we introduce a complexity measure $μ(X)$, which quantifies the hardness of compressing a data set for logistic regression. $μ(X)$ has an intuitive statistical interpretation that may be of independent interest. For data sets with bounded $μ(X)$-complexity, w
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Marius Bozga, Radu Iosif, Joseph Sifakis · 2021 · arXiv
We study a sound verification method for parametric component-based systems. The method uses a resource logic, a new formal specification language for distributed systems consisting of a finite yet unbounded number of components. The logic allows the description of architecture configurations coordinating instances of a finite number of types of components, by means of inductive definitions similar to the ones used to describe algebraic data types or recursive data structures. For parametric systems specified in this logic, we show that decision problems such as reaching deadlock or violating
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Laurent Bienvenu, Santiago Figueira, Benoit Monin et al. · 2014 · arXiv
Suppose that we are given an infinite binary sequence which is random for a Bernoulli measure of parameter $p$. By the law of large numbers, the frequency of zeros in the sequence tends to~$p$, and thus we can get better and better approximations of $p$ as we read the sequence. We study in this paper a similar question, but from the viewpoint of inductive inference. We suppose now that $p$ is a computable real, but one asks for more: as we are reading more and more bits of our random sequence, we have to eventually guess the exact parameter $p$ (in the form of a Turing code). Can one do such a
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Aldar C-F. Chan · 2007 · arXiv
Any secured system can be modeled as a capability-based access control system in which each user is given a set of secret keys of the resources he is granted access to. In some large systems with resource-constrained devices, such as sensor networks and RFID systems, the design is sensitive to memory or key storage cost. With a goal to minimize the maximum users' key storage, key compression based on key linking, that is, deriving one key from another without compromising security, is studied. A lower bound on key storage needed for a general access structure with key derivation is derived. Th
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Abbas Moameni, Brendan Pass · 2015 · arXiv
We study solutions to the multi-marginal Monge-Kantorovich problem which are concentrated on several graphs over the first marginal. We first present two general conditions on the cost function which ensure, respectively, that any solution must concentrate on either finitely many or countably many graphs. We show that local differential conditions on the cost, known to imply local $d$-rectifiability of the solution, are sufficient to imply a local version of the first of our conditions. We exhibit two examples of cost functions satisfying our conditions, including the Coulomb cost from density
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