Negative / Null Result ReportOpen accessComputer Science
Lukas Graf, Tobias Harks, Julian Schwarz · 2025 · arXiv
A seminal result of [Fleischer et al. and Karakostas and Kolliopulos, both FOCS 2004] states that system optimal multi-commodity static network flows are always implementable as tolled Wardrop equilibrium flows even if users have heterogeneous value-of-time sensitivities. Their proof uses LP-duality to characterize the general implementability of network flows by tolls. For the much more complex setting of $\textit{dynamic flows}$, [Graf et al., SODA 2025] identified necessary and sufficient conditions for a dynamic $s$-$d$ flow to be implementable as a tolled dynamic equilibrium. They used th
View details →Negative / Null Result ReportOpen accessComputer Science
Calvin McCarter, Nicholas Dronen · 2022 · arXiv
Fast approximations to matrix multiplication have the potential to dramatically reduce the cost of neural network inference. Recent work on approximate matrix multiplication proposed to replace costly multiplications with table-lookups by fitting a fast hash function from training data. In this work, we propose improvements to this previous work, targeted to the deep learning inference setting, where one has access to both training data and fixed (already learned) model weight matrices. We further propose a fine-tuning procedure for accelerating entire neural networks while minimizing loss in
View details →Negative / Null Result ReportOpen accessComputer Science
Xinyan Hu, Meena Jagadeesan, Michael I. Jordan et al. · 2023 · arXiv
In content recommender systems such as TikTok and YouTube, the platform's recommendation algorithm shapes content producer incentives. Many platforms employ online learning, which generates intertemporal incentives, since content produced today affects recommendations of future content. We study the game between producers and analyze the content created at equilibrium. We show that standard online learning algorithms, such as Hedge and EXP3, unfortunately incentivize producers to create low-quality content, where producers' effort approaches zero in the long run for typical learning rate sched
View details →Negative / Null Result ReportOpen accessComputer Science
Anqi Mao, Mehryar Mohri, Yutao Zhong · 2024 · arXiv
We present a detailed study of $H$-consistency bounds for regression. We first present new theorems that generalize the tools previously given to establish $H$-consistency bounds. This generalization proves essential for analyzing $H$-consistency bounds specific to regression. Next, we prove a series of novel $H$-consistency bounds for surrogate loss functions of the squared loss, under the assumption of a symmetric distribution and a bounded hypothesis set. This includes positive results for the Huber loss, all $\ell_p$ losses, $p \geq 1$, the squared $ε$-insensitive loss, as well as a negati
View details →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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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 $t^\ast > 0$,
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
Luiz Carlos Ryff · 2010 · arXiv
It follows from Bell's theorem and quantum mechanics that the detection of a particle of an entangled pair can (somehow) "force" the other distant particle of the pair into a well-defined state (which is equivalente to a reduction of the state vector): no property previously shared by the particles can explain the predicted correlations. This result has been corroborated by experiment. However, it has not been experimantally proved-and it is far from obvious-that the absence of detection, as in null-result (NR) experiments could have the very same effect. In this paper a way to try to bridge t
View details →Negative / Null Result ReportOpen accessComputer Science
Bibhabasu De · 2025 · arXiv
The Direct Detection~(DD) experiments are vital for probing the particle nature of Dark Matter~(DM). However, in the absence of a scattering event, DD searches result in stringent bounds on the corresponding parameter space. The paper has considered a $U(1)_{L_μ-L_τ}$-extension of the Standard Model~(SM) and augmented the particle spectrum with $SU(2)_L$-singlet vector-like leptons and scalars. A discrete $Z_2$ symmetry stabilizes the lightest SM-singlet vector-like lepton as the viable DM candidate. In the proposed model, amplitude-level cancellation can be achieved for both DM-electron and D
View details →Negative / Null Result ReportOpen accessComputer Science
Sungwoo Kang · 2026 · arXiv
Honey bee colony losses threaten global pollination services, yet current monitoring systems treat each hive as an isolated unit, ignoring the spatial pathways through which diseases spread across apiaries. This paper introduces the Spatio-Temporal Apiary Graph Convolutional Network (STAG-CN), a graph neural network that models inter-hive relationships for disease onset prediction. STAG-CN operates on a dual adjacency graph combining physical co-location and climatic sensor correlation among hive sessions, and processes multivariate IoT sensor streams through a temporal--spatial--temporal sand
View details →Negative / Null Result ReportOpen accessComputer Science
Leonardo Hsu, Jong-Ping Hsu · 2014 · arXiv
Thim measured the transverse Doppler shift using a system consisting of a stationary antenna and pickup, in addition to a number of intermediate antennas mounted on the rim of a rotating disk. No such shift was detected, although the experiment should have had enough sensitivity to measure it, as predicted by the Lorentz transformations. However, using the Lorentz transformations to analyze the results of experiments involving circular motion, while commonly done, is inappropriate because such an analysis involves non-inertial frames, which are outside the range of validity of special relativi
View details →Negative / Null Result ReportOpen accessComputer Science
J. Luo, Y. X. Nie, Y. Z. Zhang et al. · 2001 · arXiv
The differential acceleration between a rotating mechanical gyroscope and a non-rotating one is directly measured by using a double free-fall interferometer, and no apparent differential acceleration has been observed at the relative level of 2x10{-6}. It means that the equivalence principle is still valid for rotating extended bodies, i.e., the spin-gravity interaction between the extended bodies has not been observed at this level. Also, to the limit of our experimental sensitivity, there is no observed asymmetrical effect or anti-gravity of the rotating gyroscopes as reported by hayasaka et
View details →