Negative / Null Result ReportOpen accessComputer Science
Andrew Collins, Dominika Tkaczyk, Joeran Beel · 2018 · arXiv
The effectiveness of recommendation algorithms is typically assessed with evaluation metrics such as root mean square error, F1, or click through rates, calculated over entire datasets. The best algorithm is typically chosen based on these overall metrics. However, there is no single-best algorithm for all users, items, and contexts. Choosing a single algorithm based on overall evaluation results is not optimal. In this paper, we propose a meta-learning-based approach to recommendation, which aims to select the best algorithm for each user-item pair. We evaluate our approach using the MovieLen
Negative / Null Result ReportOpen accessComputer Science
Ramin Mohammadi, Sarthak Jain, Stephen Agboola et al. · 2019 · arXiv
Hypertension is a major risk factor for stroke, cardiovascular disease, and end-stage renal disease, and its prevalence is expected to rise dramatically. Effective hypertension management is thus critical. A particular priority is decreasing the incidence of uncontrolled hypertension. Early identification of patients at risk for uncontrolled hypertension would allow targeted use of personalized, proactive treatments. We develop machine learning models (logistic regression and recurrent neural networks) to stratify patients with respect to the risk of exhibiting uncontrolled hypertension within
Negative / Null Result ReportOpen accessComputer Science
Robin Schiewer, Anand Subramoney, Laurenz Wiskott · 2024 · arXiv
Hierarchical model-based reinforcement learning (HMBRL) aims to combine the benefits of better sample efficiency of model based reinforcement learning (MBRL) with the abstraction capability of hierarchical reinforcement learning (HRL) to solve complex tasks efficiently. While HMBRL has great potential, it still lacks wide adoption. In this work we describe a novel HMBRL framework and evaluate it thoroughly. To complement the multi-layered decision making idiom characteristic for HRL, we construct hierarchical world models that simulate environment dynamics at various levels of temporal abstrac
Negative / Null Result ReportOpen accessComputer Science
Michael Wrana, Uzma Maroof, Diogo Barradas · 2025 · arXiv
Website fingerprinting (WF) is a technique that allows an eavesdropper to determine the website a target user is accessing by inspecting the metadata associated with the packets she exchanges via some encrypted tunnel, e.g., Tor. Recent WF attacks built using machine learning (and deep learning) process and summarize trace metadata during their feature extraction phases. This methodology leads to predictions that lack information about the instant at which a given website is detected within a (potentially large) network trace comprised of multiple sequential website accesses -- a setting known
Negative / Null Result ReportOpen accessComputer Science
Rylan Schaeffer, Joshua Kazdan, Yegor Denisov-Blanch · 2025 · arXiv
Sampling from language models impacts the quality and diversity of outputs, affecting both research and real-world applications. Recently, Nguyen et al. 2024's "Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs" introduced a new sampler called min-p, claiming it achieves superior quality and diversity over established samplers such as basic, top-k, and top-p sampling. The significance of these claims was underscored by the paper's recognition as the 18th highest-scoring submission to ICLR 2025 and selection for an Oral presentation. This paper conducts a comprehensive r
Negative / Null Result ReportOpen accessComputer Science
Neil K. R. Sehgal, Manuel Tonneau, Andy Tan et al. · 2025 · arXiv
Large language model (LLM) chatbots show increasing promise in persuasive communication. Yet their real-world utility remains uncertain, particularly in clinical settings where sustained conversations are difficult to scale. In a pre-registered randomized controlled trial, we enrolled 915 U.S. adults (ages 45-75) who had never completed colorectal cancer (CRC) screening. Participants were randomized to: (1) no message control, (2) expert-written patient materials, (3) single AI-generated message, or (4) a motivational interviewing chatbot. All participants were required to remain in their assi
Negative / Null Result ReportMedicine
Joshi, Scheuer, Chew et al. · 2026 · JACC. Heart failure
Heart transplantation (HT) from donation after circulatory death (DCD) donors has successfully expanded the donor pool with excellent short-term survival outcomes, but there is uncertainty regarding long-term outcomes. This study compared…
View details →DOI: 10.1016/j.jchf.2026.103168 Negative / Null Result ReportOpen accessPhysics
Elmeri Rivasto, Katharina-Sophie Isleif, Friederike Januschek et al. · 2025 · arXiv
The Any Light Particle Search II (ALPS II) is a light shining through a wall experiment probing the existence of axions and axion-like particles using a 1064 nm laser source. While ALPS II is already taking data using a heterodyne based detection scheme, cryogenic transition edge sensor (TES) based single-photon detectors are planned to expand the detection system for cross-checking the potential signals, for which a sensitivity on the order of $10^{-24}$ W is required. In order to reach this goal, we have investigated the use of convolutional neural networks (CNN) as binary classifiers to dis
Negative / Null Result ReportOpen accessEngineering
Arjun D. Desai, Francesco Caliva, Claudia Iriondo et al. · 2020 · arXiv
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Simil
Negative / Null Result ReportMedicine
Shi, Qu, Wang et al. · 2026 · Frontiers in microbiology
Human papillomavirus 33 (HPV33) is among the five most prevalent HPV genotypes in China and is commonly involved in co-infections. However, the synergistic effects of specific genotype combinations on cervical carcinogenesis remain…
View details →DOI: 10.3389/fmicb.2026.1787378 Negative / Null Result ReportOpen accessComputer Science
Reza Fotohi, Mehdi Effatparvar, Fateme Sarkohaki et al. · 2019 · arXiv
Multicore is an integrated circuit chip that uses two or more computational engines (cores) places in a single processor. This new approach is used to split the computational work of a threaded application and spread it over multiple execution cores, so that the computer system can benefits from a better performance and better responsiveness of the system. A thread is a unit of execution inside a process that is created and maintained to execute a set of actions/ instructions. Threads can be implemented differently from an operating system to another, but the operating system is in most cases
Negative / Null Result ReportOpen accessComputer Science
Rittick Roy, Urjit A. Yajnik · 2019 · arXiv
Kerr black holes coupled to quantized bosonic fields display a special version of the Hawking effect, governed by the superradiance condition. This leads to rapid growth of boson cloud through spontaneous creation, leading to slowing down of the black hole, and detectable as growth of the black hole shadow. This can be developed into a technique for searching or constraining the existence of ultralight bosons. We study this phenomenon for spin-0 bosons in the shadow of a black hole, with a detailed analysis of Sgr$A^*$, and put estimates on the evolution time scales and subsequent change in th
Negative / Null Result ReportOpen accessComputer Science
Shibdas Roy, Ian R. Petersen, Elanor H. Huntington · 2014 · arXiv
We consider a coherent-classical estimation scheme for a class of linear quantum systems. It comprises an estimator that is a mixed quantum-classical system without involving coherent feedback. The estimator yields a classical estimate of a variable for the quantum plant. We demonstrate that for a passive plant that can be characterized by annihilation operators only, such coherent-classical estimation provides no improvement over purely-classical estimation. An example is also given which shows that if the plant is not assumed to be an annihilation operator only quantum system, it is possible
Negative / Null Result ReportOpen accessComputer Science
Mateusz Praski, Jakub Adamczyk, Wojciech Czech · 2025 · arXiv
Pretrained neural networks have attracted significant interest in chemistry and small molecule drug design. Embeddings from these models are widely used for molecular property prediction, virtual screening, and small data learning in molecular chemistry. This study presents the most extensive comparison of such models to date, evaluating 25 models across 25 datasets. Under a fair comparison framework, we assess models spanning various modalities, architectures, and pretraining strategies. Using a dedicated hierarchical Bayesian statistical testing model, we arrive at a surprising result: nearl
Negative / Null Result ReportOpen accessComputer Science
Cid Reyes-Bustos, Masato Wakayama · 2022 · arXiv
The non-commutative harmonic oscillator (NCHO) is a matrix valued differential operator originally introduced as a generalization of the quantum harmonic oscillator having a weaker $\mathfrak{sl}_2(\mathbb{R})$-symmetry. The spectrum of the NCHO has remarkable properties, including the presence of number theoretical structures such as modular forms, elliptic curves and Eichler cohomology observed in the special values of the associated spectral zeta function. In addition, the Heun ODE picture of the eigenvalue problem of the NCHO reveals a connection with the quantum Rabi model (QRM), a fundam
Negative / Null Result ReportOpen accessComputer Science
Elad Hazan, Tomer Koren, Kfir Y. Levy · 2014 · arXiv
The logistic loss function is often advocated in machine learning and statistics as a smooth and strictly convex surrogate for the 0-1 loss. In this paper we investigate the question of whether these smoothness and convexity properties make the logistic loss preferable to other widely considered options such as the hinge loss. We show that in contrast to known asymptotic bounds, as long as the number of prediction/optimization iterations is sub exponential, the logistic loss provides no improvement over a generic non-smooth loss function such as the hinge loss. In particular we show that the c
Negative / Null Result ReportOpen accessComputer Science
Mai Vu · 2011 · arXiv
We establish the optimal input signaling and the capacity of MIMO channels under per-antenna power constraint. While admitting a linear eigenbeam structure, the optimal input is no longer diagonalizable by the channel right singular vectors as with sum power constraint. We formulate the capacity optimization as an SDP problem and solve in closed-form the optimal input covariance as a function of the dual variable. We then design an efficient algorithm to find this optimal input signaling for all channel sizes. The proposed algorithm allows for straightforward implementation in practical system
Negative / Null Result ReportOpen accessComputer Science
Max Hopkins, Daniel Kane, Shachar Lovett et al. · 2021 · arXiv
The explosive growth of easily-accessible unlabeled data has lead to growing interest in active learning, a paradigm in which data-hungry learning algorithms adaptively select informative examples in order to lower prohibitively expensive labeling costs. Unfortunately, in standard worst-case models of learning, the active setting often provides no improvement over non-adaptive algorithms. To combat this, a series of recent works have considered a model in which the learner may ask enriched queries beyond labels. While such models have seen success in drastically lowering label costs, they tend
Negative / Null Result ReportOpen accessPhysics
King Lun Ng, Maciej Bartłomiej Kruk, Piotr Deuar · 2026 · arXiv
We demonstrate how the beyond-mean-field Lee-Huang-Yang (LHY) corrections and its related physics can be naturally incorporated into the representation of an ultracold Bose gas using the truncated Wigner approach without invoking effective energy terms or local density assumptions. By generating a Bogoliubov ground-state representation with appropriately tailored bare interaction strength $g_0$ and condensate density $n_0$, the expected initial energy and densities are obtained while retaining access to quantum effects beyond the reach of the extended Gross-Pitaevskii equation (EGPE) formulati
Negative / Null Result ReportOpen accessComputer Science
Ravi Shankar Mishra, Kartik Mehta, Nikhil Rasiwasia · 2021 · arXiv
In this paper, we present SANTA, a scalable framework to automatically normalize E-commerce attribute values (e.g. "Win 10 Pro") to a fixed set of pre-defined canonical values (e.g. "Windows 10"). Earlier works on attribute normalization focused on fuzzy string matching (also referred as syntactic matching in this paper). In this work, we first perform an extensive study of nine syntactic matching algorithms and establish that 'cosine' similarity leads to best results, showing 2.7% improvement over commonly used Jaccard index. Next, we argue that string similarity alone is not sufficient for a
Negative / Null Result ReportOpen accessComputer Science
Zohar Feldman, Carmel Domshlak · 2013 · arXiv
Popular Monte-Carlo tree search (MCTS) algorithms for online planning, such as epsilon-greedy tree search and UCT, aim at rapidly identifying a reasonably good action, but provide rather poor worst-case guarantees on performance improvement over time. In contrast, a recently introduced MCTS algorithm BRUE guarantees exponential-rate improvement over time, yet it is not geared towards identifying reasonably good choices right at the go. We take a stand on the individual strengths of these two classes of algorithms, and show how they can be effectively connected. We then rationalize a principle
Negative / Null Result ReportOpen accessPhysics
Surachate Limkumnerd · 2026 · arXiv
Fluctuation relations imply the second-law inequality $\langleΣ_T\rangle\ge0$, but path extrema can also constrain how large the mean entropy production can be. For steady-state processes with entropy-production martingale $M_t=e^{-Σ_t}$, we show that knowing only the positive running maximum of $Σ_t$ gives no improvement over the trivial endpoint bound: rare negative entropy-production excursions can still carry the exponential weight required by the fluctuation relation. Using the running extrema $L_T=\inf M_t$ and $H_T=\sup M_t$, we derive a path-extrema upper envelope $\mathcal{U}_{\rm ext
Negative / Null Result ReportOpen accessComputer Science
Dahlia Shehata, Robin Cohen, Charles Clarke · 2024 · arXiv
Conversational prompt-engineering-based large language models (LLMs) have enabled targeted control over the output creation, enhancing versatility, adaptability and adhoc retrieval. From another perspective, digital misinformation has reached alarming levels. The anonymity, availability and reach of social media offer fertile ground for rumours to propagate. This work proposes to leverage the advancement of prompting-dependent LLMs to combat misinformation by extending the research efforts of the RumourEval task on its Twitter dataset. To the end, we employ two prompting-based LLM variants (GP
Negative / Null Result ReportOpen accessComputer Science
Earl T. Campbell, Joe O'Gorman · 2016 · arXiv
Standard error correction techniques only provide a quantum memory and need extra gadgets to perform computation. Central to quantum algorithms are small angle rotations, which can be fault-tolerantly implemented given a supply of an unconventional species of magic state. We present a low-cost distillation routine for preparing these small angle magic states. Our protocol builds on the work of Duclos-Cianci and Poulin [Phys. Rev. A, 91, 042315 (2015)] by compressing their circuit. Additionally, we present a method of diluting magic states that reduces costs associated with very small angle rot
Negative / Null Result ReportOpen accessEngineering
George Sterpu, Christian Saam, Naomi Harte · 2020 · arXiv
Audio-Visual Speech Recognition (AVSR) seeks to model, and thereby exploit, the dynamic relationship between a human voice and the corresponding mouth movements. A recently proposed multimodal fusion strategy, AV Align, based on state-of-the-art sequence to sequence neural networks, attempts to model this relationship by explicitly aligning the acoustic and visual representations of speech. This study investigates the inner workings of AV Align and visualises the audio-visual alignment patterns. Our experiments are performed on two of the largest publicly available AVSR datasets, TCD-TIMIT and
Negative / Null Result ReportOpen accessComputer Science
Raghav Gupta, Akanksha Jain, Abraham Gonzalez et al. · 2026 · arXiv
Agile hardware design flows are a critically needed force multiplier to meet the exploding demand for compute. Recently, agentic generative AI systems have demonstrated significant advances in algorithm design, improving code efficiency, and enabling discovery across scientific domains. Bridging these worlds, we present ArchAgent, an automated computer architecture discovery system built on AlphaEvolve. We show ArchAgent's ability to automatically design/implement state-of-the-art (SoTA) cache replacement policies (architecting new mechanisms/logic, not only changing parameters), broadly withi
Negative / Null Result ReportOpen accessComputer Science
Adam Karczmarz · 2021 · arXiv
We consider the directed minimum weight cycle problem in the fully dynamic setting. To the best of our knowledge, so far no fully dynamic algorithms have been designed specifically for the minimum weight cycle problem in general digraphs. One can achieve $\tilde{O}(n^2)$ amortized update time by simply invoking the fully dynamic APSP algorithm of Demetrescu and Italiano [J. ACM'04]. This bound, however, yields no improvement over the trivial recompute-from-scratch algorithm for sparse graphs. Our first contribution is a very simple deterministic $(1+ε)$-approximate algorithm supporting vertex
Negative / Null Result ReportOpen accessComputer Science
Amy Zhang, Shagun Sodhani, Khimya Khetarpal et al. · 2020 · arXiv
Many control tasks exhibit similar dynamics that can be modeled as having common latent structure. Hidden-Parameter Markov Decision Processes (HiP-MDPs) explicitly model this structure to improve sample efficiency in multi-task settings. However, this setting makes strong assumptions on the observability of the state that limit its application in real-world scenarios with rich observation spaces. In this work, we leverage ideas of common structure from the HiP-MDP setting, and extend it to enable robust state abstractions inspired by Block MDPs. We derive instantiations of this new framework f
Negative / Null Result Report
Lidia Oktamarina, Wahidatul Khasanah, Yosi Amanda et al. · 2024 · SIGNIFICANT : Journal Of Research And Multidisciplinary
Penelitian Ini Berjudul Pelaksanaan Asesmen Dalam Perkembngan Motorik Halus Di Tk Putra 1 Palembang. Masalah dalam penelitian ini adalah bagaimana Pelaksanaan Asesmen Dalam Perkembngan Motorik Halus Di Tk Putra 1 Palembang. Penelitian ini…
View details →DOI: 10.62668/significant.v3i01.881 Negative / Null Result Report
Rasha S Farag, Aditya S Kalluri, Geetha Iyer et al. · 2025 · BMJ Paediatrics Open
Background Limited evidence exists on the additive risk of bradycardia in children with respiratory syncytial virus (RSV) bronchiolitis receiving dexmedetomidine (DMED). We aim to study the association between RSV bronchiolitis and…
View details →DOI: 10.1136/bmjpo-2025-003625 