Using Machine Learning Methods to Predict the ß-Poly (L-Malic Acid) Production by Different Substrates Addition and Secondary Indexes in Strain <i>Aureobasidium melanogenum</i> by Genan Wang, Jiaqian Li, Shuxian Wang, Yutong Li, Shiwei Chen, Lina Zhang, Tingbin Zhao, Haisong Yin, Shiru Jia, Changsheng Qiao

“…In this study, we directly added potassium acetate, corn steep liquor, MgSO₄, MnSO₄, vitamin B1, vitamin B2, and nicotinamide as the fermentation substrate to the basic fermentation medium based on a generated random matrix that represented the added value. The PMLA production and four secondary indexes, pH, biomass, osmotic pressure, and viscosity were measured after 144 h fermentation. …”
Get full text

Singularity of discrete random matrices by Jain, Vishesh, Sah, Ashwin, Sawhney, Mehtaab

“…Abstract Let $$\xi $$ ξ be a non-constant real-valued random variable with finite support and let $$M_{n}(\xi )$$ M n ( ξ ) denote an $$n\times n$$ n × n random matrix with entries that are independent copies of $$\xi $$ ξ . …”
Get full text

Reducibility and Statistical-Computational Gaps from Secret Leakage by Brennan, Matthew S.

“…We introduce a number of new average-case reduction techniques that also reveal novel connections to combinatorial designs based on the incidence geometry of Fᵗᵣ and to random matrix theory. In particular, we show a convergence result between Wishart and inverse Wishart matrices that may be of independent interest. …”
Get full text

Soil amendment with cow dung modifies the soil nutrition and microbiota to reduce the ginseng replanting problem by Setu Bazie Tagele, Setu Bazie Tagele, Ryeong-Hui Kim, Minsoo Jeong, Kyeongmo Lim, Da-Ryung Jung, Dokyung Lee, Wanro Kim, Jae-Ho Shin, Jae-Ho Shin, Jae-Ho Shin

“…Co-occurrence network analysis based on random matrix theory (RMT) revealed that cow dung transformed the soil microbial network into a highly connected and complex network. …”
Get full text

Studies of and methods for electronic properties of large chemical systems by Welborn, Matthew Gregory

Get full text

On the computation of probabilities and eigenvalues for random and non-random matrices by Peruvamba Sundaresh, Vignesh

Get full text

Algorithms and Algorithmic Barriers in High-Dimensional Statistics and Random Combinatorial Structures by Kizildag, Eren C.

“…By leveraging a certain semicircle law from random matrix theory, we show that a deterministic initialization suffices, provided that the network is sufficiently overparameterized. …”
Get full text

On random embeddings and their application to optimisation by Shao, Z

“…We propose a general random-subspace first-order framework for unconstrained non-convex optimisation that requires a weak probabilistic assump- tion on the subspace gradient, which we show to be satisfied by various random matrix ensembles, such as Gaussian and hashing sketching. …”

Dimensionality reduction in immunology : from viruses to cells by Karthik Shekhar

Get full text

SINGULARITY OF RANDOM SYMMETRIC MATRICES—A COMBINATORIAL APPROACH TO IMPROVED BOUNDS

“…The proof utilizes and extends a novel combinatorial approach to discrete random matrix theory, which has been recently introduced by the authors together with Luh and Samotij.…”
Get full text

Robust vector sensor array processing and performance analysis by Poulsen, Andrew Joseph

Get full text

SINGULARITY OF RANDOM SYMMETRIC MATRICES—A COMBINATORIAL APPROACH TO IMPROVED BOUNDS by Ferber, Asaf, Jain, Vishesh

“…The proof utilizes and extends a novel combinatorial approach to discrete random matrix theory, which has been recently introduced by the authors together with Luh and Samotij.…”
Get full text

Computational Properties of General Indices on Random Networks by R. Aguilar-Sánchez, I. F. Herrera-González, J. A. Méndez-Bermúdez, José M. Sigarreta

“…Within a statistical random matrix theory approach, we show that the average values of the indices normalized to the network size scale with the average degree <inline-formula><math display="inline"><semantics><mfenced open="〈" close="〉"><mi>k</mi></mfenced></semantics></math></inline-formula> of the corresponding random network models, where <inline-formula><math display="inline"><semantics><mrow><mfenced separators="" open="〈" close="〉"><msub><mi>k</mi><mrow><mi>ER</mi></mrow></msub></mfenced><mo>=</mo><mrow><mo>(</mo><msub><mi>n</mi><mi>ER</mi></msub><mo>−</mo><mn>1</mn><mo>)</mo></mrow><mi>p</mi></mrow></semantics></math></inline-formula> and <inline-formula><math display="inline"><semantics><mrow><mfenced separators="" open="〈" close="〉"><msub><mi>k</mi><mrow><mi>RG</mi></mrow></msub></mfenced><mo>=</mo><mrow><mo>(</mo><msub><mi>n</mi><mi>RG</mi></msub><mo>−</mo><mn>1</mn><mo>)</mo></mrow><mrow><mo>(</mo><mi>π</mi><msup><mi>r</mi><mn>2</mn></msup><mo>−</mo><mn>8</mn><msup><mi>r</mi><mn>3</mn></msup><mo>/</mo><mn>3</mn><mo>+</mo><msup><mi>r</mi><mn>4</mn></msup><mo>/</mo><mn>2</mn><mo>)</mo></mrow></mrow></semantics></math></inline-formula>. …”
Get full text

Tight Lower Bound for Linear Sketches of Moments by Andoni, Alexandr, Nguyen, Huy L., Polyanskiy, Yury, Wu, Yihong

“…In this paper, we show a tight lower bound of Ω(n [superscript 1 − 2/p] logn) words for the class of algorithms based on linear sketches, which store only a sketch Ax of input vector x and some (possibly randomized) matrix A. We note that all known algorithms for this problem are linear sketches.…”
Get full text
Get full text