Decrease in loss during the training process. by Muhammad Idrees (92773)

Presentation 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

“…We evaluate PLM-derived surprisals, the negative log-probabilities of a word or a sequence of words calculated by a PLM given its preceding context, as a continuous and interpretable measure of treatment-induced language change. …”

Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

“…We evaluate PLM-derived surprisals, the negative log-probabilities of a word or a sequence of words calculated by a PLM given its preceding context, as a continuous and interpretable measure of treatment-induced language change. …”

The prediction result is displayed on the hotel dataset, which includes the training procedure when the loss function is decreased and the model saved with training rounds as a multiple of five. by Hongxia Wang (241142)

“…<p>The prediction result is displayed on the hotel dataset, which includes the training procedure when the loss function is decreased and the model saved with training rounds as a multiple of five.…”

Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

“…Introduction<p>The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. …”

Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

“…<p><b>Objective:</b> To assess the feasibility, accuracy, and reliability of using ChatGPT-4.5 (early-access), a large language model (LLM), for automated scoring of Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) protocols. …”

Loss of MAP4K4 expression causes a decrease in the number of neutrophils. by Guihua Wang (286390)

Training-induced changes in bipedal stance. by Cristina Dolciotti (16317330)

Histogram of number of drugs with (a) increased and (b) decreased usage within 12 months. by Nguyen Thach (20347852)

CS rates increased with protraction and decreased with retraction. by Paul Hage (21071783)

Incorporating a social responsibility goal in the prompt causes overleverage in utilities (A) and decreases risk of low ESG rating (B) (Study 3). by Philipp Winder (21615570)

National (A) Medicare prescription rates per thousand Part D enrollees and (B) Medicare spending for clozapine gradually decreased for 2015-2020. by Luke R. Cavanah (19022435)

The description of balance training. by Sadaf Sepasgozar Sarkhosh (20436143)

“…Additionally, the study aimed to compare the effectiveness of these interventions on clinical and psychological outcomes. This study was a randomized, single-blinded, controlled trial in which participants were assigned to either an exergaming group or a balance training group. …”

MWM training data. by Robert Botelho (20308297)

Parvalbumin-positive interneurons (PV-INs) in perilesional tissue respond to optogenetic stimulation and are involved in voluntary movement, but show a decreased firing rate. by Livia Vignozzi (22430567)

Relation between training-related changes in unipedal and bipedal stance. by Cristina Dolciotti (16317330)

Histograms of the extent of the (a) increase and (b) decrease by the difference in categorical usage within 12 months for all drugs. by Nguyen Thach (20347852)

Time course of Path Length, LFS and Velocity during postural training. by Cristina Dolciotti (16317330)

Time course of 95% Area, X and Y Amplitude Sway during postural training. by Cristina Dolciotti (16317330)

AdaBoost training flowchart. by Yang Guo (399435)

“…HINGO enhances the uniformity of the initial population distribution by means of refraction opposition-based learning strategy in initialization, and improves the local and global search ability of the algorithm by means of sine and cosine strategy, Lévy flight and nonlinear decreasing factor in the search stage. The HINGO-LSSVM-IAdaBoost model is trained and validated on the typical air conditioning fault samples of ASHRAE RP-1043. …”