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
Nandakishore Santhi, Alexander Vardy · 2006 · arXiv
We consider the problem of estimating the probability of error in multi-hypothesis testing when MAP criterion is used. This probability, which is also known as the Bayes risk is an important measure in many communication and information theory problems. In general, the exact Bayes risk can be difficult to obtain. Many upper and lower bounds are known in literature. One such upper bound is the equivocation bound due to Rényi which is of great philosophical interest because it connects the Bayes risk to conditional entropy. Here we give a simple derivation for an improved equivocation bound. We
Negative / Null Result ReportOpen accessComputer Science
Youlong Wu, Michèle Wigger · 2014 · arXiv
We propose two coding schemes for the two-receiver discrete memoryless broadcast channel (BC) with rate-limited feedback from one or both receivers. They improve over the nofeedback capacity region for a large class of channels, including the class of \emph{strictly essentially less-noisy BCs} that we introduce in this article. Examples of strictly essentially less-noisy BCs are the binary symmetric BC (BSBC) or the binary erasure BC (BEBC) with unequal cross-over or erasure probabilities at the two receivers. When the feedback rates are sufficiently large, our schemes recover all previously k
Negative / Null Result ReportOpen accessComputer Science
T. Gent, S. Huber, K. Mimasu et al. · 2025 · arXiv
We perform a detailed investigation of the viable baryogenesis parameter space of a non-minimal Higgs sector consisting of two Higgs doublets and a singlet pseudoscalar (2HDM$+a$). In such a model, an early Universe period of transient CP violation may occur, driven by a nonvanishing vacuum expectation value of the CP-odd scalar $a$. This naturally avoids the stringent electric dipole moment experimental constraints on beyond-the-Standard-Model sources of CP violation. We provide a state-of-art computation of the baryon asymmetry, providing several important improvements over existing baryogen
Negative / Null Result ReportOpen accessComputer Science
Mehdi S. M. Sajjadi, Morteza Alamgir, Ulrike von Luxburg · 2015 · arXiv
Peer grading is the process of students reviewing each others' work, such as homework submissions, and has lately become a popular mechanism used in massive open online courses (MOOCs). Intrigued by this idea, we used it in a course on algorithms and data structures at the University of Hamburg. Throughout the whole semester, students repeatedly handed in submissions to exercises, which were then evaluated both by teaching assistants and by a peer grading mechanism, yielding a large dataset of teacher and peer grades. We applied different statistical and machine learning methods to aggregate t
Negative / Null Result ReportOpen accessComputer Science
Haoze Wu · 2017 · arXiv
In this project, we aimed to improve the runtime of Minisat, a Conflict-Driven Clause Learning (CDCL) solver that solves the Propositional Boolean Satisfiability (SAT) problem. We first used a logistic regression model to predict the satisfiability of propositional boolean formulae after fixing the values of a certain fraction of the variables in each formula. We then applied the logistic model and added a preprocessing period to Minisat to determine the preferable initial value (either true or false) of each boolean variable using a Monte-Carlo approach. Concretely, for each Monte-Carlo trial
Negative / Null Result Report
Claire M. Douglas, Marnie Newell, Susan Goruk et al. · 2025 · PLOS One
Supplementation of omega-3 (n-3) polyunsaturated fatty acids has been associated with reduced side effects and improved quality of life (QoL) in breast cancer patients receiving chemotherapy. The current study reports secondary outcomes…
View details →DOI: 10.1371/journal.pone.0322178 Negative / Null Result ReportOpen accessComputer Science
Zijian Li, Luzhen Tang, Mengyu Xia et al. · 2025 · arXiv
With generative artificial intelligence driving the growth of dialogic data in education, automated coding is a promising direction for learning analytics to improve efficiency. This surge highlights the need to understand the nuances of student-AI interactions, especially those rare yet crucial. However, automated coding may struggle to capture these rare codes due to imbalanced data, while human coding remains time-consuming and labour-intensive. The current study examined the potential of large language models (LLMs) to approximate or replace humans in deductive, theory-driven coding, while
Negative / Null Result ReportOpen accessEngineering
Maureen van Eijnatten, Leonardo Rundo, K. Joost Batenburg et al. · 2020 · arXiv
This study investigates the use of the unsupervised deep learning framework VoxelMorph for deformable registration of longitudinal abdominopelvic CT images acquired in patients with bone metastases from breast cancer. The CT images were refined prior to registration by automatically removing the CT table and all other extra-corporeal components. To improve the learning capabilities of VoxelMorph when only a limited amount of training data is available, a novel incremental training strategy is proposed based on simulated deformations of consecutive CT images. In a 4-fold cross-validation scheme
Negative / Null Result ReportOpen accessComputer Science
Laura Wenderoth · 2024 · arXiv
This paper investigates the MM dynamics approach proposed by Han et al. (2022) for multi-modal fusion in biomedical classification tasks. The MM dynamics algorithm integrates feature-level and modality-level informativeness to dynamically fuse modalities for improved classification performance. However, our analysis reveals several limitations and challenges in replicating and extending the results of MM dynamics. We found that feature informativeness improves performance and explainability, while modality informativeness does not provide significant advantages and can lead to performance degr
Negative / Null Result ReportOpen accessComputer Science
Yeskendir Koishekenov · 2023 · arXiv
Graph Neural Networks (GNNs) have achieved a lot of success with graph-structured data. However, it is observed that the performance of GNNs does not improve (or even worsen) as the number of layers increases. This effect has known as over-smoothing, which means that the representations of the graph nodes of different classes would become indistinguishable when stacking multiple layers. In this work, we propose a new simple, and efficient method to alleviate the effect of the over-smoothing problem in GNNs by explicitly using relations between node embeddings. Experiments on real-world dataset
Negative / Null Result ReportOpen accessComputer Science
Dawei Ding, Yihui Quek, Peter W. Shor et al. · 2019 · arXiv
We prove that the classical capacity of an arbitrary quantum channel assisted by a free classical feedback channel is bounded from above by the maximum average output entropy of the quantum channel. As a consequence of this bound, we conclude that a classical feedback channel does not improve the classical capacity of a quantum erasure channel, and by taking into account energy constraints, we conclude the same for a pure-loss bosonic channel. The method for establishing the aforementioned entropy bound involves identifying an information measure having two key properties: 1) it does not incre
Negative / Null Result ReportOpen accessMathematics
Rujie Zhu, Xiaodong Xu, Stanisław Radziszowski · 2015 · arXiv
Let $Δ_s=R(K_3,K_s)-R(K_3,K_{s-1})$, where $R(G,H)$ is the Ramsey number of graphs $G$ and $H$ defined as the smallest $n$ such that any edge coloring of $K_n$ with two colors contains $G$ in the first color or $H$ in the second color. In 1980, Erdős and Sós posed some questions about the growth of $Δ_s$. The best known concrete bounds on $Δ_s$ are $3 \le Δ_s \le s$, and they have not improved since the stating of the problem. In this paper we present some constructions, which imply in particular that $R(K_3,K_s) \ge R(K_3,K_{s-1}-e) + 4$. This does not improve the lower bound of 3 on $Δ_s$, b
Negative / Null Result ReportOpen accessComputer Science
D. Hüber, J. L. Friar · 1998 · arXiv
The nucleon-deuteron analyzing power $A_y$ in elastic nucleon-deuteron scattering poses a longstanding puzzle. At energies $E_{lab}$ below approximately 30 MeV $A_y$ cannot be described by any realistic NN force. The inclusion of existing three-nucleon forces does not improve the situation. Because of recent questions about the $^3P_J$ NN phases, we examine whether reasonable changes in the NN force can resolve the puzzle. In order to do this we investigate the effect on the $^3P_J$ waves produced by changes in different parts of the potential (viz., the central force, tensor force, etc.), as
Negative / Null Result ReportOpen accessPhysics
I. Pallecchi, L. Pellegrino, N. Banerjee et al. · 2010 · arXiv
We probe spin transport in Cu_{2}O by measuring spin valve effect in La_{0.7}Sr_{0.3}MnO_{3}/Cu_{2}O/Co and La_{0.7}Sr_{0.3}MnO_{3}/Cu_{2}O/La_{0.7}Sr_{0.3}MnO_{3} epitaxial heterostructures. In La_{0.7}Sr_{0.3}MnO_{3}/Cu_{2}O/Co systems we find that a fraction of out-of-equilibrium spin polarized carrier actually travel across the Cu_{2}O layer up to distances of almost 100 nm at low temperature. The corresponding spin diffusion length dspin is estimated around 40 nm. Furthermore, we find that the insertion of a SrTiO_{3} tunneling barrier does not improve spin injection, likely due to the ma
Negative / Null Result ReportOpen accessComputer Science
Greta Laage, Emma Frejinger, Andrea Lodi et al. · 2021 · arXiv
Airlines and other industries have been making use of sophisticated Revenue Management Systems to maximize revenue for decades. While improving the different components of these systems has been the focus of numerous studies, estimating the impact of such improvements on the revenue has been overlooked in the literature despite its practical importance. Indeed, quantifying the benefit of a change in a system serves as support for investment decisions. This is a challenging problem as it corresponds to the difference between the generated value and the value that would have been generated keepi
Negative / Null Result ReportOpen accessComputer Science
Wataru Masaka, Mitsuki Sakamoto, Kenshi Abe et al. · 2025 · arXiv
We investigate how perturbation does and does not improve the Follow-the-Regularized-Leader (FTRL) algorithm in solving imperfect-information extensive-form games under sampling, where payoffs are estimated from sampled trajectories. While optimistic algorithms are effective under full feedback, they often become unstable in the presence of sampling noise. Payoff perturbation offers a promising alternative for stabilizing learning and achieving \textit{last-iterate convergence}. We present a unified framework for \textit{Perturbed FTRL} algorithms and study two variants: PFTRL-KL (standard KL
Negative / Null Result ReportOpen accessComputer Science
Arwa Arif · 2025 · arXiv
Backtranslation BT is widely used in low resource machine translation MT to generate additional synthetic training data using monolingual corpora. While this approach has shown strong improvements for many language pairs, its effectiveness in high quality, low resource settings remains unclear. In this work, we explore the effectiveness of backtranslation for English Gujarati translation using the multilingual pretrained MBART50 model. Our baseline system, trained on a high quality parallel corpus of approximately 50,000 sentence pairs, achieves a BLEU score of 43.8 on a validation set. We aug
Negative / Null Result ReportOpen accessComputer Science
Lawrence Clegg, John Cartlidge · 2025 · arXiv
Intransitive player dominance, where player A beats B, B beats C, but C beats A, is common in competitive tennis. Yet, there are few known attempts to incorporate it within forecasting methods. We address this problem with a graph neural network approach that explicitly models these intransitive relationships through temporal directed graphs, with players as nodes and their historical match outcomes as directed edges. Our model (65.7% accuracy, 0.214 Brier score) forecasts competitively with established rating systems such as Weighted Elo. Although it does not improve on the baseline in uncond
Negative / Null Result Report
Luh Gd Rahayu Budiarta, I Putu Indra Kusuma · 2024 · Jurnal Pendidikan Bahasa Inggris undiksha
The advent of ChatGPT has surprised many English educators, as theoretically, it has many potentials to support English language teaching. However, the empirical results of how ChatGPT influence English as a foreign language (henceforth,…
View details →DOI: 10.23887/jpbi.v12i3.85769 Negative / Null Result ReportMedicine
Shen, Dai, Hu et al. · 2026 · Quantitative imaging in medicine and surgery
Inguinal lymph node (ILN) metastasis significantly affects prognosis and treatment strategies in patients with gynecological malignancies. Conventional ultrasound (US) provides morphological assessment but has limited sensitivity for…
View details →DOI: 10.21037/qims-2025-1314 Negative / Null Result Report
Jose A. Rivera, Carmen E. Gonzalez, Tonita Bates · 2024 · JCO Oncology Practice
327 Background: Delayed assessment of diagnostic imaging (DI) significant actionable findings (AFs) at an oncological center prompted the creation of a safety net system which used technological advancements to improve communication…
View details →DOI: 10.1200/op.2024.20.10_suppl.327 Negative / Null Result Report
· 2020 · Human Resource Management International Digest
Purpose The author was motivated to focus on the palm oil sector because it is essential to the Indonesian economy. He wanted to discover how to improve performance Design/methodology/approach The author focused on employees of class…
View details →DOI: 10.1108/hrmid-03-2020-0057 Negative / Null Result Report
Zygy Roe-Zurz, Liliana Madrigal, Rahul Sharma et al. · 2025 · Journal of the Endocrine Society
Abstract Disclosure: Z. Roe-Zurz: None. L. Madrigal: None. Graves’ ophthalmopathy (GO) is a serious complication of autoimmune hyperthyroidism, affecting approximately 30% of patients. It is characterized by immune-mediated inflammation of…
View details →DOI: 10.1210/jendso/bvaf149.2207 Negative / Null Result ReportOpen accessComputer Science
Osvaldo Simeone, Haim H. Permuter · 2011 · arXiv
For memoryless sources, delayed side information at the decoder does not improve the rate-distortion function. However, this is not the case for more general sources with memory, as demonstrated by a number of works focusing on the special case of (delayed) feedforward. In this paper, a setting is studied in which the encoder is potentially uncertain about the delay with which measurements of the side information are acquired at the decoder. Assuming a hidden Markov model for the sources, at first, a single-letter characterization is given for the set-up where the side information delay is arb
Negative / Null Result ReportOpen accessComputer Science
Tovly Deutsch, Masoud Jasbi, Stuart Shieber · 2020 · arXiv
Readability assessment aims to automatically classify text by the level appropriate for learning readers. Traditional approaches to this task utilize a variety of linguistically motivated features paired with simple machine learning models. More recent methods have improved performance by discarding these features and utilizing deep learning models. However, it is unknown whether augmenting deep learning models with linguistically motivated features would improve performance further. This paper combines these two approaches with the goal of improving overall model performance and addressing th
Negative / Null Result ReportOpen accessComputer Science
Mitchell A. Gordon, Kevin Duh, Nicholas Andrews · 2020 · arXiv
Pre-trained universal feature extractors, such as BERT for natural language processing and VGG for computer vision, have become effective methods for improving deep learning models without requiring more labeled data. While effective, feature extractors like BERT may be prohibitively large for some deployment scenarios. We explore weight pruning for BERT and ask: how does compression during pre-training affect transfer learning? We find that pruning affects transfer learning in three broad regimes. Low levels of pruning (30-40%) do not affect pre-training loss or transfer to downstream tasks a
Negative / Null Result ReportOpen accessComputer Science
Hyeong Kyu Choi, Xiaojin Zhu, Sharon Li · 2025 · arXiv
Multi-Agent Debate~(MAD) has emerged as a promising paradigm for improving the performance of large language models through collaborative reasoning. Despite recent advances, the key factors driving MAD's effectiveness remain unclear. In this work, we disentangle MAD into two key components--Majority Voting and inter-agent Debate--and assess their respective contributions. Through extensive experiments across seven NLP benchmarks, we find that Majority Voting alone accounts for most of the performance gains typically attributed to MAD. To explain this, we propose a theoretical framework that mo
Negative / Null Result ReportOpen accessComputer Science
Yulin Chen, He He, Chen Zhao · 2026 · arXiv
Reinforcement Learning with Verifiable Reward (RLVR) has proven effective in improving Large Language Model's (LLM) reasoning ability. However, the learning dynamics of RLVR remain underexplored. In this paper, we reveal a counterintuitive phenomenon: among hard examples that the model initially struggles with, a substantial subset remains unlearnable even when correct rollouts are present. To understand the phenomenon, we first demonstrate that existing optimization and sampling techniques fail to resolve unlearnability. With cross-example gradient analysis, we show that unlearnable examples
Negative / Null Result ReportOpen accessAgricultural and Biological Sciences
Louis Jalouzot, Alexis Thual, Yair Lakretz et al. · 2025 · arXiv
We investigate optimal strategies for decoding perceived natural speech from fMRI data acquired from a limited number of participants. Leveraging Lebel et al. (2023)'s dataset of 8 participants, we first demonstrate the effectiveness of training deep neural networks to predict LLM-derived text representations from fMRI activity. Then, in this data regime, we observe that multi-subject training does not improve decoding accuracy compared to single-subject approach. Furthermore, training on similar or different stimuli across subjects has a negligible effect on decoding accuracy. Finally, we fin
